d 4.4 energy services case study...business models in a world characterised by distributed...

Business Models in a World Characterised by Distributed Generation

NNE5/2001/256

D 4.4 Energy Services Case Study

Iñaki Laresgoiti, Carlos Madina, Luis Pedrosa, Ángel Díaz

Labein

Identifier: LAB_ENS_WP04_002_04.doc

Date: 2004-03-11

Class: Deliverable

Responsible Partner: Labein

Annexes: None

Distribution: Public

Overview: Specification of the case study related with the provision ofenergy services

D 4.4: Energy Services Case Study EESD Project NNE5/2001/256 BUSMOD

BUSMOD: Business Models in a World Characterised by Distributed Generation

The BUSMOD Consortium consists of:

IBERDROLA Principal Contractor & Coordinator SpainLABEIN Principal Contractor SpainVUA Principal Contractor The NetherlandsECN Principal Contractor The NetherlandsSINTEF Principal Contractor NorwayUMIST Principal Contractor United KingdomEnerSearch Principal Contractor Sweden



Control Versions:

Version Date Author Description of Changes

01 2003-06-24 Iñaki Laresgoiti Creation of the document

02 2003-12-17 Iñaki Laresgoiti Inclusion of modificationssuggested in the workshopwith partners of the project

03 2004-02-23 Iñaki Laresgoiti Inclusion of modificationssuggested in the firstinterview with the testgroup

04 2004-03-11 Iñaki Laresgoiti Inclusion of modificationssuggested in the secondinterview with the testgroup



PREFACE

This case study is a part of the work package 4 "Distributed Generation Case StudyScenarios".

The objective of this workpackage is the analysis of four distributed generation businessscenarios based on the methodology designed in task D3.1. The simulation of the businessscenarios will provide a validation of the methodology and a measurement of the feasibilityof the analysed scenarios. The following four scenarios in this workpackage are analysed.

• 4.1 Distributed Balancing Services (ECN)

• 4.2 Active Management of Distribution Networks related to DG (UMIST)

• 4.3 Consumers' and Suppliers' Alliances in a Deregulated Power Market (SINTEF)

• 4.4 Energy Services (LABEIN)

This document describes the results of the financial analysis of the case study “EnergyServices”.

ACKNOWLEDGEMENTS

The authors acknowledge the valuable contributions to this work by Mr J Arceluz and Mrs AAlonso of Iberdrola, Spain and Mr G Dorronsoro of Millenium Energy, Spain.



ACRONYMS AND ABBREVIATIONS

CHP Combined Heat and Power

DG Distributed Generation

DSM Demand Side Mangement

DSO Distribution System Operator

ESCO Energy Services COmpany

ICT Information and Communication Technology

IRR Internal Rate of Return

LAN Local Area Network

NEC National Energy Commission

NPV Net Present Value

O&M Operational and Maintenance

PV Photovoltaic

RES Renewable Energy Sources

SSM Supply Side Mangement

T&D Transmission and Distribution

TSO Transmission System Operator

VAT Value Added Tax



Table of contents1 OVERVIEW.......................................................................................................................10

1.1 INTRODUCTION................................................................................................................101.2 GENERAL BUSINESS IDEA................................................................................................11

1.2.1 Aggregator .............................................................................................................111.2.2 Customers .............................................................................................................12

1.3 REGULATORY INCENTIVES INVOLVED...............................................................................131.4 BUSINESS PROCESS .......................................................................................................131.5 OWNERSHIP, ACTORS INVOLVED .....................................................................................131.6 SCOPE............................................................................................................................141.7 TARGET AUDIENCE..........................................................................................................15

2 BASE SCENARIO............................................................................................................16

2.1 STEP 1: BUSINESS CASE DESCRIPTION............................................................................162.2 STEP 2: GOAL SELECTION ...............................................................................................16

2.2.1 Strategic goals .......................................................................................................162.2.2 Operational goals...................................................................................................17

2.3 STEP 3: TECHNOLOGY SELECTION ..................................................................................182.3.1 Select technology characteristics..........................................................................182.3.2 Estimate required characteristics for the technology ............................................192.3.3 Select technological solution .................................................................................212.3.4 Additional constraints ............................................................................................21

2.4 STEP 4: VALUE ACTIVITY SELECTION ...............................................................................222.5 STEP 5: VALUE INTERFACE SELECTION............................................................................22

2.5.1 General value interfaces........................................................................................222.5.2 Optional value interfaces .......................................................................................232.5.3 Construct all goal-specific value interfaces...........................................................242.5.4 Draw the selected value activities .........................................................................25

2.6 STEP 6: PORTS CONNECTION ..........................................................................................302.7 STEP 7: ADDING ACTORS TO THE VALUE MODEL ..............................................................332.8 STEP 8: SCENARIO PATH IDENTIFICATION .......................................................................352.9 STEP 9: INFORMATION SYSTEM MODEL CONSTRUCTION .................................................372.10 STEP 10: BASE-LINE PROFITABILITY SHEETS CALCULATION ........................................40

2.10.1 Microhydro scenario...........................................................................................412.10.2 PV Scenario .......................................................................................................422.10.3 Microturbine Scenario........................................................................................422.10.4 Comparison........................................................................................................43

2.11 STEP 11: SENSITIVITY ANALYSIS .................................................................................442.11.1 Number of retail customers ...............................................................................442.11.2 Number of tariff customers ................................................................................452.11.3 Over-price for retail ............................................................................................452.11.4 Scheduling price, or control price ......................................................................452.11.5 DG electricity price.............................................................................................462.11.6 Premium.............................................................................................................472.11.7 Fuel Price...........................................................................................................472.11.8 Efficiency............................................................................................................47

2.12 STEP 12: INVESTMENT ANALYSIS ................................................................................482.12.1 Micro hydro scenario..........................................................................................492.12.2 PV scenario........................................................................................................53



2.12.3 Microturbine scenario.........................................................................................552.12.4 Conclusions .......................................................................................................57

3 REFERENCES .................................................................................................................60

4 ANNEX I – VALUATION FUNCTIONS ...........................................................................61

4.1.1 Value Exchange 1..................................................................................................614.1.2 Value Exchange 2..................................................................................................614.1.3 Value Exchange 3..................................................................................................624.1.4 Value Exchange 4..................................................................................................624.1.5 Value Exchange 5..................................................................................................624.1.6 Value Exchange 6..................................................................................................624.1.7 Value Exchange 7..................................................................................................624.1.8 Value Exchange 8..................................................................................................634.1.9 Value Exchange 9..................................................................................................634.1.10 Value Exchange 10............................................................................................634.1.11 Value Exchange 11............................................................................................644.1.12 Value Exchange 12............................................................................................644.1.13 Value Exchange 13............................................................................................644.1.14 Value Exchange 14............................................................................................644.1.15 Value Exchange 15............................................................................................654.1.16 Value Exchange 16............................................................................................654.1.17 Value Exchange 17............................................................................................654.1.18 Value Exchange 18............................................................................................654.1.19 Value Exchange 19............................................................................................664.1.20 Value Exchange 20............................................................................................664.1.21 Value Exchange 21............................................................................................664.1.22 Value Exchange 22............................................................................................664.1.23 Value Exchange 23............................................................................................674.1.24 Value Exchange 24............................................................................................674.1.25 Value Exchange 25............................................................................................674.1.26 O&M costs .........................................................................................................67

5 ANNEX II – PROFITABILITY SHEETS...........................................................................68

6 ANNEX III – DEMAND RESPONSE PROCESS.............................................................73

6.1 EQUIPMENT.....................................................................................................................736.2 TIME-OF-USE ..................................................................................................................746.3 DEMAND SHIFTING...........................................................................................................766.4 DISTRIBUTED GENERATION .............................................................................................77



List of TablesTABLE 1. STRATEGIC GOALS ......................................................................................................16TABLE 2. OPERATIONAL GOALS ..................................................................................................17TABLE 3. REQUIRED TECHNOLOGY CHARACTERISTICS TO REACH GOALS ....................................18TABLE 4. MINIMUM REQUIRED CHARACTERISTICS FOR THE TECHNOLOGY...................................20TABLE 5. TECHNOLOGY CHARACTERISTICS ................................................................................21TABLE 6. ALL THE VALUE-ACTIVITIES IN THE SCENARIO ...............................................................22TABLE 7. GENERAL VALUE INTERFACES AND THEIR CORRESPONDING INTERFACE IN THE MODEL.23TABLE 8. OPTIONAL VALUE INTERFACES AND THEIR CORRESPONDING INTERFACE IN THE MODEL 23TABLE 9. GOAL-SPECIFIC VALUE INTERFACES.............................................................................24TABLE 10. ASSIGNMENT OF ACTORS TO VALUE ACTIVITIES ........................................................33TABLE 11. ACTOR-VALUE ACTIVITY LIST...................................................................................33TABLE 12. VALUE EXCHANGES BETWEEN ACTORS INCLUDED IN THE MODEL ..............................40TABLE 13. ASSUMPTIONS FOR THE BUSINESS MODEL................................................................41TABLE 14. ASSUMPTIONS FOR TECHNOLOGIES .........................................................................41TABLE 15. ACTORS’ PROFITABILITIES IN THE MICRO HYDRO SCENARIO......................................41TABLE 16. ACTORS’ PROFITABILITIES IN THE PV SCENARIO.......................................................42TABLE 17. ACTORS’ PROFITABILITIES IN THE MICROTURBINE SCENARIO ....................................42TABLE 18. PARAMETERS TO CHANGE IN SENSITIVITY ANALYSIS ................................................44TABLE 19. INFLUENCE OF THE NUMBER OF RETAIL CUSTOMERS ................................................44TABLE 20. INFLUENCE OF THE NUMBER OF TARIFF CUSTOMERS ................................................45TABLE 21. INFLUENCE OF THE OVER-PRICE FOR RETAIL ............................................................45TABLE 22. INFLUENCE OF SCHEDULING PRICE, OR CONTROL PRICE...........................................46TABLE 23. INFLUENCE OF DG ELECTRICITY PRICE ....................................................................46TABLE 24. INFLUENCE OF PREMIUM..........................................................................................47TABLE 25. INFLUENCE OF FUEL PRICE.......................................................................................47TABLE 26. NPV IN THE BASE SCENARIO (MICRO HYDRO)...........................................................50TABLE 27. NPV WHEN CUSTOMERS CONTROL APPLIANCES (MICRO HYDRO)..............................51TABLE 28. NPV WITH CUSTOMERS’ CONTROL AND REDUCED GENERATOR CAPACITY (MICRO

HYDRO) ..............................................................................................................................52TABLE 29. NPV WITH CUSTOMERS’ CONTROL AND SALES TO THE MARKET (MICRO HYDRO).......52TABLE 30. NPV IN THE BASE SCENARIO (PV)............................................................................53TABLE 31. NPV WHEN CUSTOMERS CONTROL APPLIANCES (PV) ..............................................54TABLE 32. NPV WITH CUSTOMERS’ CONTROL AND REDUCED GENERATOR CAPACITY (PV) ........54TABLE 33. NPV WITH CUSTOMERS’ CONTROL AND SALES TO THE MARKET (PV)........................55TABLE 34. NPV IN THE BASE SCENARIO (MICROTURBINE) .........................................................55TABLE 35. NPV WHEN CUSTOMERS CONTROL APPLIANCES (MICROTURBINE) ............................56TABLE 36. NPV WITH CUSTOMERS’ CONTROL AND SALES TO THE MARKET (MICROTURBINE) .....57TABLE 37. PROFITABILITY SHEETS FOR MICRO HYDRO SCENARIO .............................................68TABLE 38. PROFITABILITY SHEETS FOR PV SCENARIO...............................................................69TABLE 39. PROFITABILITY SHEETS FOR MICROTURBINE SCENARIO............................................71TABLE 40. AVERAGE HOUSEHOLD DEMAND IN EVERY HOUR ......................................................74TABLE 41. TIME-OF-USE OF HOUSEHOLD EQUIPMENT................................................................75TABLE 42. HOURLY DEMAND PER CUSTOMER............................................................................75TABLE 43. HOURLY MARKET PRICES FOR A JANUARY DAY.........................................................75TABLE 44. HOURLY DEMAND AFTER DEMAND SHIFTING .............................................................77



List of FiguresFIGURE 1. OBTAINMENT OF PURCHASE AND GENERATION CURVES............................................12FIGURE 2. ELECTRIC SYSTEM REGULATION ..............................................................................25FIGURE 3. TRADE.....................................................................................................................25FIGURE 4. GENERATION ...........................................................................................................26FIGURE 5. DISTRIBUTION ..........................................................................................................26FIGURE 6. SUPPLY...................................................................................................................27FIGURE 7. CONSUMPTION.........................................................................................................27FIGURE 8. BALANCING..............................................................................................................28FIGURE 9. ENERGY EFFICIENCY ...............................................................................................28FIGURE 10. AGGREGATION .........................................................................................................28FIGURE 11. METERING...............................................................................................................29FIGURE 12. FUEL SUPPLY..........................................................................................................29FIGURE 13. MARKET MANAGEMENT............................................................................................29FIGURE 14. STRAIGHTFORWARD PORTS CONNECTION ................................................................30FIGURE 15. ALL ACTIVITIES CONNECTED.....................................................................................31FIGURE 16. ACTIVITIES DIAGRAM................................................................................................32FIGURE 17. ACTORS...................................................................................................................34FIGURE 18. SCENARIO PATH ......................................................................................................35FIGURE 19. INFORMATION SYSTEM MODEL.................................................................................39FIGURE 20. COMPARISON OF PROFITABILITIES FOR THE THREE SCENARIOS UNDER STUDY..........43FIGURE 21. SUMMARY OF SENSITIVITY ANALYSIS .......................................................................48FIGURE 22. VALUE MODEL WHEN CUSTOMERS CONTROL APPLIANCES ........................................51FIGURE 23. COMPARISON OF PROFITABLE SCENARIOS................................................................59



1 Overview

1.1 Introduction

During the last few years, Spain has faced a huge increase in the demand for electricity, withdemand increases from year to year between 5,5% and 6,6% in the period covering from1997 to 2001. The accumulated demand increase from 1996 to 2002 was 34,6%.

This big increase has lead the Spanish electric system to operate with a hedging index (theratio between the available capacity and the peak demand) of nearly 1. This means that if amajor event happens, such as a failure of one of the big central power plants, in peakdemand periods, the system will not be able to serve all the electricity demand, leading topower shortages. In December 2001, the hedging index felt below 1, because that was a drywinter, and the Transmission System Operator had to leave without service all the curtailableloads, and even some residential loads.

More recently, in June 2003, there were very high temperatures, and the electricity demandfor air conditioning set a new record in the summer peak demand. As a result, some powershortages happened in the south and southeast of the Spanish system.

Investors realised that new generating capacity was needed in Spain, and made requests formore than 40 GW in Combined Cycle power stations, and more than 50 GW in wind farms(currently, the capacity in the whole Spanish electric system is about 50 GW). At a firstglance, if those requests were accepted, Spain would not have electricity supply problems.

However, wind is an intermittent, non-dispatchable energy source, and one of the mainproblems in the recent power shortages was the lack of wind power generation, becausewind did not blow. Besides, the Combined Cycle power plants would put Spain in acompletely dependent-upon-gas position, and if gas price increased, electricity price wouldincrease too. Last, the transmission grid is not designed to transmit big amounts of electricitythrough several hundreds of kilometers, and a lack in transmission capacity would rise (infact, many of the new power plants requested are projected to be placed in the northwest ofSpain, while the place where demand increases more rapidly is the southeast).

Taking all those issues into account, one way to reduce the problems derived from theincrease of demand for electricity is reducing the electricity consumption, or shifting part ofthe demand from high-demand periods to low-demand periods. This way, the customer willnot face shortages (both because grid constraints and because demand would be higherthan generation) and will pay a cheaper price for electricity.

Industrial customers usually have strict consumption patterns, so that demand shifting is notpossible most of the times. They can perform other measures, such as energy efficiency ordistributed generation, in order to reduce their electricity consumption.

Commercial customers have strict patterns too. Nevertheless, they can shift air-conditioningloads, accumulating cold during the night and using it throughout the day.



On the other hand, household customers have three kinds of consumption:

• Consumptions that are made throughout the day (e.g. freezer, refrigerator, boiler,…)

• Consumptions that have to be performed before a certain time and are made for alimited period (e.g. washing machine, dishwasher,…)

• Consumptions that have to be done on a certain point of time (e.g. television, lights,…)

The electricity demand pattern of the first category can be altered by automatically shiftingthe moment on which the motor of the apparatus switches on or off. The demand pattern ofthe second category can be shifted by moving the starting time to another point in whichelectricity will be cheaper for the whole consumption period. The electricity demand patternof the third category cannot be shifted.

As a result, household customers may get benefits from demand shifting and distributedgeneration. Nevertheless, they may find it difficult to manage their demand and generation,and thereby, they can hire an independent load and generation manager. This manager canaggregate different customers, so as to obtain more economic benefits.

1.2 General business idea

The business idea will be to achieve the reduction of the electricity bill of each customer, byshifting loads to low-price periods, by buying electricity in wholesale market, and by usingown generation during peak times.

This case study needs an aggregator and some final customers, so it can be developed.

1.2.1 Aggregator

In the case study that we are going to develop, an aggregator will change the consumptionpattern of some household customers, in order to obtain cheaper electricity. In addition, theaggregator will operate several generators in those customers’ sites.

The aggregator has to know the demand of the customer for each of the consumption kindslisted above. If the customer has a good knowledge of his/her consumption, the aggregatorwill have all the information from this actor. If not, the aggregator has to perform a tracking ofthe customer’s consumption, to determine the consumption pattern.

Once the aggregator knows customer’s needs, he/she will obtain a minimum demand curveand a demand curve throughout the day. The minimum demand curve shows the non-shiftable load in every hour of the day, and the difference between the demand curve andthe minimum demand curve will be the shiftable load for each hour of the day. Theaggregator obtains those curves for all the customers in his/her portfolio, and aggregatesthem to obtain an aggregated minimum demand curve, and an aggregated demand curve.

The aggregator distributes the shiftable load during the day, and, since the electricity pricevaries throughout the day, the aggregator will distribute load to low-price periods, obtainingan aggregated demand curve after demand shifing.



The aggregator will perform the demand shifting without having real data, because he/shedoes not know the actual distribution and value of the market clearing price throughout theday, neither the actual demand of the customer (due to weather conditions,etc.), but he/shewill foresee which the most probable values will be.

If electricity price goes above the DG generation price offered by customers, the aggregatorwill schedule the operation of generators, until the electricity price goes down. As a result,the aggregator obtains a demand curve, a generation curve and a purchase curve thecustomers he/she represents, as Figure 1 shows:

Figure 1. Obtainment of purchase and generation curves

Once these curves are obtained for every customer, the aggregator will distribute costs andbenefits between customers, depending on their consumption and generation.

The aggregator will purchase the electricity on behalf of the customers, usually through long-or medium-term bilateral contracts or in the day-ahead market. As a result, every customerwill know in advance how much electricity he/she will consume and generate throughout theday. The aggregator will turn appliances and the generator on/off, in order to follow theschedule, by using a remote communication system, so that the customer has not to worryabout turning them on/off.

1.2.2 Customers

The customer can switch electric appliances on or off in exceptional circumstances, notfollowing the scheduled pattern. This may lead the aggregator to imbalances (except ifanother customer performs the opposite action), so the aggregator will demand thatcustomer to pay for all the costs the aggregator will have to pay for to the transmissionsystem operator as a result of that imbalance.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

DG GenerationMinimum DemandActual DemandPurchase

Electricity PriceDG Generation Cost



The communication system consists of a gateway located at the customer’s premises, aLAN connecting the gateway and every appliance to be controlled, and an Internetcommunication between the gateway and the aggregator.

1.3 Regulatory incentives involved

Night tariff (both for integrated tariff and T&D fee) is the only regulatory incentive offered fordemand shifting. Besides, if the generator is a CHP unit, or if it uses renewable energysources, the electricity produced receives a premium.

1.4 Business process

Many options exist for the case study, such as:

1. The customer pays a fixed monthly fee for the management of the load and thegeneration, the maintenance of the equipment in the customer’s home, electricitymetering and part of the costs incurred by the aggregator when installing all the requireditems to perform this scenario. The aggregator also acts as supplier, and supplieselectricity at market price. The bill includes a fixed term and a consumption-dependantterm.

Another possibility is that the customer paid the cost at the moment of the installation. Inthis case, the fixed term will be slightly lower.

2. The aggregator increases the supply price a fixed percentage over the market price. Thispercentage includes management, metering and maintenance. If the customer did notpay the installation costs, he/she will be billed a fixed term.

In this case study, we have chosen to consider a hybrid option, which means that thecustomers pays a fixed monthly bill for scheduling and control, and pays an overprice for theelectricity purchased in the market. Nevertheless, the aggregator will charge the electricitypurchased from distributed generators at the price he/she has paid for it.

1.5 Ownership, actors involved

The aggregator owns the software and hardware tools to determine consumption andgeneration curves for each customer, and the part of the communication system he/sheneeds to enter in the Internet. The customer will own the LAN, the gateway, the controldevices in the appliances, and the communication system needed to enter in the Internet.

The only varying ownership refers to the distributed generator, which can be owned by bothof them. If the aggregator owns the generator, he/she will demand to the customer a rentingpayment, and if not, the customer would have faced a higher investment cost. That rentingpayment may be included into a fixed payment, or as an increase in the variable term,increasing the percentage to be received by the aggregator.



In this case study, we have chosen the customer ownership, so that this actor can obtainhigher benefits. The customer pays for the installation of the generation unit, sells theelectricity to the aggregator, and obtains the subsidies if the generator complies the rules tobe subsidised.

1.6 Scope

This scenario will be analysed under the current regulatory conditions in the Spanishelectricity market. This scenario does not take into account the advantages of producingheat at the same time of electricity, considering it as a wasted energy. Nevertheless, it isimportant to stress the importance CHP has in this case study, since, under current Spanishregulation, subsidies are paid to generators using RES or CHP, so fossil-fuel-firedgenerators must be used in CHP, although we do not analyse the efficiency advantages ofusing such generation kind. The conditions under which this scenario can be implementedare:

• Liberalised markets: This way, the customer can generate and the aggregator canpurchase electricity in the market and supply electricity to customers. In addition, incountries where the market is not open, electricity tariffs usually do not discriminate morethan a couple of pricing periods, which makes this scenario less profitable. Thereby, thehigher the difference between prices in peak and non-peak periods, the better suitabilityfor this business.

• Customers able to change their load profile: This scenario is aimed to customers withshiftable loads.

Ø Industrial customers may perform this scenario if they can shift some load, which isnot usual.

Ø Commercial customer have very strict load patterns and load cannot be moved fromhigh-demand periods to low-demand periods. They can install the distributedgeneration to reduce their load, but they do not perform this scenario as a whole. Onthe other hand, if the use air-conditioning, they will obtain high benefits if theyimplement this scenario.

Ø Household customers are the most likely to implement the scenario, because theycan easily shift load and need an aggregator to see really important benefits.

• Customers that need to form coalitions to enter into the market: a single householdcustomer have some transaction costs related to his/her entry to the electricity market,but if several customers form a coalition, market transaction costs are made only oncefor all the customers, so they can share those costs between all of them.

• Distributed Generators using Renewable Energy Sources, or Combined Heat andPower: DG must use RES or CHP to improve the economics of the case study, sincethose technologies receive a premium for the electricity generated, under the currentSpanish regulation. It is important to stress here that CHP is considered only for thepremium and that we do not care about how generated heat is used, nor about thebenefits that use could bring to any actor involved in the business idea. On the otherhand, waste reduction technologies are included in the renewable category in this report.



1.7 Target audience

This scenario is expected to be used by suppliers who want to enter in the electricitybusiness by offering this value-added service to customers.

Customer coalitions are also expected to implement this business scenario.

Regulatory authorities might also be interested in this business scenario, since it clearlyshows cash flows for all the actors involved in it, so that the Regulator can see which actorobtains the highest profit, as well as regulatory actions to be taken in order to foster a certainkind of generation.

In order to validate the scenario, as well as to perform its dissemination, Labein has heldsome interviews with interested parties: a person working in regulatory issues, an electricitysupplier and an ESCO.



2 Base scenario

We will follow the BUSMOD methodology developed in Work Package 3 of this project. [1]

2.1 Step 1: Business case description

Final customers want to manage their energy consumption to reduce their electricity bills. Anaggregator takes those customers’ loads as a whole, and performs the role of electricitysupplier too. Customers install in their premises distributed generators, whose output is alsomanaged by the aggregator to reduce the energy purchases. The aggregator covers thedemand not satisfied by distributed generators purchasing in the market. Customers benefitfrom price differences between high-demand and low-demand periods, and those customerswith distributed generators using renewable energy sources or combined heat and power,obtain government subsidies. Customers may be commercial or household customers, whilegenerators can be both based on renewable energy sources or fossil-fuel-fired.

2.2 Step 2: Goal selection

The initiator of the business is the aggregator, which tries to increase his market share as asupplier by offering services to final customers willing to smoothen the electricity price.

2.2.1 Strategic goals

Table 1. Strategic goals

Goal hierarchy Type Apply Stakeholder

Market development M

S1.1 Enter new business M

S1.2 Increase market share M X Aggregator

S1.3 Long-term sustainable development M X Government

S1.4 Minimize costs of infrastructure investments M

S1

S1.5 Smoothen the electricity price fluctuations M X Customer

Environmental goals E

S2.1 Reduce environmental emissions E X Government

S2.2 Reduce use of primary fuel/dependence on primary fuel E

S2.3 Promote use of renewable energy E X Government

S2

S2.4 Maximize output of DG environmental benefits E X Government

Quality and efficiency QE

S3.1 Improve security of supply QES3

S3.2 Improve network management QE



2.2.2 Operational goals

Table 2. Operational goals

Goal hierarchy Type Value Activity Apply StakeholderO1. Make profit MG. Generate electricityG1 Increase generation efficiency MG2 Benefit from generating subsidized RES electricity E X CustomerG3 Reduce emissions of generation E X GovernmentG4 Provide network management services QE

Generation

S. Supply electricityS1 Sell reserved electricity in peaking hours MS2 Benefit from green electricity incentives ES3 Avoid purchases in peaking hours M

Supply

X AggregatorD. Transmit/Distribute electricity M

Reduce expenses MD1.1 Reduce investments expenses MD1D1.2 Reduce operational expenses MImprove distribution service quality QED2.1 Reduce the need for peak reserved capacity QED2.2 Subcontract producer to avoid grid upgrade QE

D2

D2.3 Reduce network losses QE

Transmission/Distribution

T. Trade electricity M TradeES. Supply DG equipment M

Increase utilization of DG MES1.1 Increase sales RES generators EES1.2 Increase sales New efficient technologies QE

ES1

ES1.3 Increase sales of ICT equipment for DG QE

Manufacturing

EL. Lease DG equipment MEL1 Lease RES EEL2 Lease New generators QE

Leasing

M. Provide metering services M Metering X AggregatorO2. Efficient system functioning QE

NM. Provide network management services for transmission grid QE NetworkManagement

Provide ancillary services QENM1.1 Provide voltage control QENM1.2 Provide frequency control QE

NM1

NM1.3 Provide black start QE

Transmission

NM2 Provide active management of distribution grid QE Distribution/Transmission

NM2.1 Provide demand side management (DSM) QE Balancing X AggregatorNM2.2 Provide supply side management (SSM) QE Balancing X AggregatorNM2.3 Balancing services M Balancing

A. Represent groups of customers/suppliers/generators M Aggregation X AggregatorEE. Provide Energy efficiency QEEE1 Provide on-site load management services QE X AggregatorEE2 Provide other energy efficiency services for customers QE

Energyefficiency

MM. Provide market management services QE MarketManagement

O3. Efficient market functioningR. Guarantee a fair operation of the systemR1 Oblige distribution companies to connect RES ER2 Oblige distribution companies to connect DG M

Regulation



Goal hierarchy Type Value Activity Apply StakeholderR3 Oblige suppliers to accept RES ER4 Oblige MO to give priority to RES EK. Fulfil Kyoto obligations EK1 Increase government investments in RES/DG EK2 Develop RES promotion schemes E

K2.1 Tax exemption (Netherlands, Spain) E X GovernmentK2.2 Premiums system (Spain) X GovernmentK2.3 Investment subsidies E X Government

K3 Organize “green” market EK3.1 ROC certificate market E

RegulationPolicy making

O4. Consume electricityC. Reduce costs MC1 Reduce consumption in peaking hours QE X CustomerC2 Efficient use of heat QEC3 Avoid transmission and distribution costs MC4 Perform on-site generation activity M X CustomerC5 Use energy efficiency services QE

Consumption

X CustomerQ. Improve electricity service quality QEQ1 Provide power in remote/isolated area QEQ2 Provide back-up power within short timeframe QEQ3 Provide back-up power with continuous output QEQ4 Provide continuous reliable power QE

Consumption

Every operational goal of one stakeholder contributes to the strategic goal of thatstakeholder, and no goal prevent any other strategic or operational goal.

2.3 Step 3: Technology selection

2.3.1 Select technology characteristics

The general goal-characteristic table is presented in Deliverable 3.1 [1]. By using that tableas a guideline, and taking into account the specific characteristics of this case study, Table 3includes the required technology characteristics to reach the goals identified in Step 2.

Table 3. Required technology characteristics to reach goals

Strategic and Operational Goals (selected on Step 2)

Hig

h E

ffici

ency

Low

Em

issi

ons

Qui

ck S

tart-

up T

ime

Hig

h P

redi

ctab

ility

The

rmal

Out

put

Cap

acity

con

stra

ints

Grid

con

nect

ion

Low

Cap

ital E

xpen

ses

Low

Fix

ed E

xpen

ses

Low

Var

iabl

e E

xpen

ses

Customer’s goalsS1.5 Smoothen the electricity price fluctuations G G G G GO1-G2 Benefit from generating subsidized RES electricity G F G G F G G

O4-C1 Reduce consumption in peaking hours G G G G G G G GO4-C4 Perform on-site generation activity F G G G G G G

O4-C5 Use energy efficiency services F F G G G G



Strategic and Operational Goals (selected on Step 2)

Hig

h E

ffici

ency

Low

Em

issi

ons

Qui

ck S

tart-

up T

ime

Hig

h P

redi

ctab

ility

The

rmal

Out

put

Cap

acity

con

stra

ints

Grid

con

nect

ion

Low

Cap

ital E

xpen

ses

Low

Fix

ed E

xpen

ses

Low

Var

iabl

e E

xpen

ses

Aggregator’s goalsS1.2 Increase market share G G G G G

O1-S3 Avoid purchases in peaking hours G G G GO2-NM2.1 Provide demand side management G G G G

O2-NM2.2 Provide supply side management F G G F G F F F GO2-A Represent groups of customers/suppliers/generatorsO2-EE1 Provide on-site load management services G G G G G

O2-M Provide metering servicesGovernment’s goals

S1.3 Long-term sustainable development G G GS2.1 Reduce environmental emissions G F GS2.3 Promote use of renewable energy G F GS2.4 Maximize output of DG environmental benefits G F G G FO1-G3 Reduce emissions of generation G F GO3-K2.1 Tax exemption G F G F GO3-K2.2 Premiums system F F G F FO3-K2.3 Investment subsidies G F F GTechnology characteristics for final customers F G G F F F F F

Technology characteristics for the aggregator G F F G F G G FTechnology characteristics for the government F F G F F

LegendF Important characteristic G Moderately Important/ Important in

certain applicationsRelatively Unimportant

2.3.2 Estimate required characteristics for the technology

By analysing Table 3, the most important characteristics for main actors can be obtained:

• Customer: Grid connection, efficiency, thermal output, capital expenses and O&Mexpenses (both fixed and variable) have big importance. Emissions and predictabilityhave less importance.

• Aggregator: Grid connection, start-up time, predictability and O&M variable expenseshave big importance. Efficiency, capacity constraints, O&M fixed expenses and capitalexpenses have moderate importance.

• Government: Emissions, capacity constraints, efficiency and grid connection have bigimportance. Thermal output has moderate importance.

Thermal output refers to the need of using fossil-fuel-fired technologies in CHP.



The customer will own the generator and, thus, this actor will be the responsible for choosingthe technology to be used. Nevertheless, the customer has to follow the aggregator’sinstructions when using the generator, so the aggregator could influence the technologyselection process. The government can promote low-emissions technologies, but cannotforce customers or aggregators to use a certain technology, so government will have lessimportance than any of the other two actors.

Taking this hierarchy into account and conclusions from Table 3, the characteristics can beclassified in:

A. Very important: Grid connection, efficiency, fixed expenses, capital expenses andvariable expenses.

B. Important: Thermal output, predictability, emissions and start-up time.

C. Not very important: Capacity constraints.

For ‘A’ category characteristics, the technology must have the best range available; for ‘B’characteristics, it can have medium range performance; and for ‘C’ characteristic, thetechnology can have a worst range.

In Table 4, minimum required characteristics for the technology are summarised.

Table 4. Minimum required characteristics for the technology

Characteristic Possible Range RequiredRange

1 Performance characteristics1.1 Efficiency High/Good/Low High1.2 CO2 Emissions High/Middle/Null Middle1.3 Start-up Time Very Quick/Quick/Slow Quick1.4 Thermal Output Possible/Not Possible Possible2 Economic characteristics2.1 Capital Expenses Low/Middle/High Low2.2 Installation Expenses Low/Middle/High Low2.3 Fixed O&M Expenses Low/Middle/High Low2.4 Variable O&M Expenses Low/Middle/High Low3 Size and availability characteristics3.1 Capacity Micro/Mini/Very Small/Small/Medium/Large Small3.2 Grid Connection High Voltage/Middle Voltage/Low Voltage/Not Required LV3.3 Predictability High/Low High

Table 4 limits the qualitative characteristics for the technology, but those limits must beconverted to number in order to obtain the technology:

1. Efficiency: High efficiency (>60%), Good (40-60%), Low (<40%)

2. CO2 Emissions: Null (0 lb/MWh), Middle (About 1000 lb/MWh), High (>1000 lb/MWh)

3. Start-up time: Very Quick (< 10 sec), Quick (10-60 sec), Slow (Hours)

4. Capital Expenses: Low (<1000 €/kW), Middle (1000-2000 €/kW), High (>2000 €/kW)

5. Installation Expenses: Low (<500 €/kW), Middle (500-1000 €/kW), High (>1000 €/kW)



6. Fixed O&M Expenses: Low (<10 €/kW), Middle (10-15 €/kW), High (>15 €/kW)

7. Variable O&M Expenses: Low (<0,005 €/kWh), Middle (0,005-0,01 €/kWh), High (>0,01€/kWh)

8. Capacity: Micro (5-10 kW), Mini (<100 kW), Very Small (<1 MW), Small (<10 MW),Medium (<50 MW), Large (>50 MW)

9. Connection: Low Voltage (<1 kV), Middle Voltage (1-30 kV), High Voltage (> 30 kV)

2.3.3 Select technological solution

In Table 5, the characteristics for different fossil-fuel-fired and renewable technologies aresummarised.

Table 5. Technology characteristics

Technology

Effi

cien

cy

CO

2E

mis

sion

s

Sta

rt-u

p T

ime

The

rmal

Out

put

Tot

alE

xpen

ses

O&

M

Fix

edE

xpen

ses

O&

M

Var

iabl

eE

xpen

ses

Pre

dict

abili

ty

NOT RENEWABLEReciprocating engines High Very High Very quick Yes Low High Low HighMicroturbines Good High Quick Yes Low Low Low HighFuel cells Good Low Quick-Slow Yes High Low High HighRENEWABLEWind turbines N/A Null Quick No High Low High LowSolar N/A Null Slow No High Low Low LowMicro hydro N/A Null Quick-Slow No Medium High Low HighBiomass N/A Middle Quick Yes Medium High High HighWaste reduction N/A Middle Quick Yes High High High HighGeothermal N/A Null - Yes High High High HighWave N/A Null - No High High High HighTidal N/A Null - No High High High High

Fuel cells, wind, biomass, waste, geothermal, wave and tidal do not comply with two or moremain (A) characteristics, so these technologies will not be used. Engines will not be usedbecause of their big emissions, so customers will not want to have this technology in theirhomes. Besides, this technology has high fixed expenses (A characteristic).

As a result, only microturbines, solar and micro hydro will be considered.

2.3.4 Additional constraints

Spanish regulation states that generation units below 50 MW using RES or CHP have anspecial regulation. Any of those RES technologies and CHP below 25 MW receive apremium, depending on the energy source and capacity. Nevertheless, both for CHP andmicro hydro, the premium is higher for installations with a capacity below 10 MW, and insolar PV facilities, the premium is much higher for installations below 5 kW. As a result, inour scenario, microturbines will be used in a CHP plant below 10 MW, micro hydro capacitywill be below 10 MW and PV capacity below 5 kW.



Capital expenses also have importance when selecting the technology to be used.Nevertheless, investment costs can be partially recovered in some RES technologies due togovernment subsidies and tax exemptions. Both solar and micro hydro receive governmentsubsidies. Predictability must be high, in order to perform the scheduling in advance, but itmay be slightly lower when using RES, since storage technologies can be used to regulatethe output from RES (batteries with PV, and a small dam with micro hydro). On the otherhand, RES technologies can only be used in places where the source is available, which canconstraint the use of one or other technology.

2.4 Step 4: Value activity selection

Table 2 shows the activities performed by main actors, such as regulation, generation,supply, consumption,… but in this scenario, some other activities must be performed in orderfor the scenario to work, such as distribution, market management or fuel supply.

Table 6. All the value-activities in the scenario

# Value activity Included in the model?1 Electric System Regulation Yes2 Policy Making3 Trade Yes4 Network Management5 Generation Yes6 Transmission7 Distribution Yes8 Supply Yes9 Consumption Yes10 Manufacturing11 Leasing12 Balancing1 Yes13 Energy Efficiency Yes14 Aggregation Yes15 Metering Yes16 Fuel Supply Yes17 Heat Supply18 Market Management Yes

2.5 Step 5: Value interface selection

2.5.1 General value interfaces

Taking into account the value activities identifies in Table 6, those activities’ general andoptional interfaces can be obtained in D3.1 [1].

1The idea of Balancing activity is not the same as in D3.1 [1]. In that document, someone performs load andgeneration balancing on behalf of the DSO and, thus, this actor receives money from the DSO for providingbalancing, while pays to customers and producers to manage their demand and generation. In our scenario,balancing is performed on behalf of customers and producers, so that they will spend less money.



Table 7 shows the general interface and the specific interface for our scenario for everyactivity identified in Step 4.

Table 7. General value interfaces and their corresponding interface in the model

General Interface Specific Interface in the modelActivityValue In Value Out Value In Value Out

Trade Electricity Fee Electricity Electricity market feeTrade Fee Electricity Electricity market fee Electricity

DG electricity fee ElectricityGeneration Fee ElectricityElectricity market fee Electricity

Distribution Fee T&D services T&D fee T&D servicesElectricity Electricity market feeSupply Electricity Electricity feeElectricity DG electricity feeElectricity retail fee ElectricitySupply Electricity retail

feeElectricity

Electricity tariff ElectricitySupply T&D services T&D fee T&D services T&D fee

Electricity Electricity retail feeConsumption Electricity Electricity retail feeElectricity Electricity tariffControl fee Consumption controlBalancing Fee Balancing serviceControl fee Generation controlScheduling fee Best consumption patternEnergy

Efficiency% energysavings

Energy efficiencyservices Scheduling fee Best generation pattern

Aggregation Non-aggregatedneeds

Benefits Non-aggregated load Benefits

Aggregation Benefits Aggregated needs Benefits Aggregated loadsMetering Fee Metering services Metering fee Metering servicesFuel Supply Fee Fuel Fuel fee FuelMarketManagement

Marketmanagement fee

Market managementservices

Costs of the MarketOperator

Market management

2.5.2 Optional value interfaces

Table 8. Optional value interfaces and their corresponding interface in the model

Optional Interface Specific Interface in the modelActivityValue In Value Out Value In Value Out

ElectricSystemRegulation

Ecologicalbenefits

Subsidy “Special Rules”generation

“Special Rules” subsidies

“Special Rules” taxes ObligationElectricSystemRegulation

Fullfilment Regulation

NEC cost Regulation

Generation Regulation Economic &ecological benfits

“Special Rules”subsidies

“Special Rules”generation

Obligation “Special Rules” taxesDistribution Regulation Economic &ecological benfits Regulation NEC cost

Supply Regulation Economic &ecological benfits

“Special Rules”subsidies


Consumption Energy efficiencyservices

% energy savings Best consumptionpattern

Scheduling fee



2.5.3 Construct all goal-specific value interfaces

Table 9. Goal-specific value interfaces

InterfaceGoal Goal Name Activity Libraryreference Value In Value Out

S1.2 Increase market share Supply 8.2 Electricity retail fee ElectricityS1.3 Long-term sustainable

developmentElectric SystemRegulation

1.1 “Special Rules”taxes

Obligation

S1.5 Smoothen theelectricity pricefluctuations

Consumption 9.1 Electricity Electricity retailfee

S2.1 Reduce environmentalemissions

Electric SystemRegulation

1.2 “Special Rules”generation

“Special Rules”subsidy

S2.3 Promote use ofrenewable energy




S2.4 Maximise output of DGenvironmental benefits




O1-G2 Benefit from generatingsubsidised RESelectricity

Generation 5.2 “Special Rules”subsidy


O1-G3 Reduce environmentalemissions

Generation 5.2 “Special Rules”subsidy


O1-S3 Avoid purchases inpeaking hours

Supply 8.1 Electricity Electricitymarket fee

O1-M Provide meteringservices

Metering 15.1 Metering fee Meteringservices

O2-NM2.1 Provide DSM Balancing 12.3 Control fee Consumptioncontrol

O2-NM2.2 Provide SSM Balancing 12.3 Control fee Generationcontrol

14.1 Non-aggregatedload

BenefitsO2-A Represent groups ofcustomers

Aggregation

14.2 Benefits Aggregatedloads

Scheduling fee Bestconsumptionpattern

O2-EE1 Provide on-site loadmanagement services

EnergyEfficiency

13.1

Scheduling fee Best generationpattern

O3-K2.1 Tax exemption Electric SystemRegulation



O3-K2.2 Premium system Electric SystemRegulation



O3-K2.3 Investment subsidies Electric SystemRegulation



O4-C1 Reduce consumption inpeaking hours

Consumption 9.2 Best consumptionpattern

Scheduling fee

O4-C4 Perform on-sitegeneration activity

Generation 5.1 DG electricity fee Electricity

O4-C5 Use energy efficiencyservices

Consumption 9.2 Best consumptionpattern

Scheduling fee



2.5.4 Draw the selected value activities

Figure 2. Electric System Regulation

Figure 3. Trade



4-a. Distributed generation 4-b. Central generation

Figure 4. Generation

Figure 5. Distribution



6-a. Supply at retail price

6-b. Supply at tariff price

Figure 6. Supply

7-a. Consumption at retail price 7-b. Consumption at tariff price

Figure 7. Consumption



Figure 8. Balancing

Figure 9. Energy Efficiency

Figure 10. Aggregation



11-a. Metering (Aggregator) 11-b. Metering (DSO)

Figure 11. Metering

Figure 12. Fuel Supply

Figure 13. Market Management



2.6 Step 6: Ports connection

This step will be performed through several substeps. First of all, equal value exchanges indifferent actors will be connected, as Figure 14 shows:

Figure 14. Straightforward ports connection



In Figure 14, some activities are lacking, since their value exchanges do not appear in otheractivities, but they can be easily included in the model, taking into account which activitiesneed the services offered by lacking activities (See section 2.5.4), as Figure 15 shows.

Figure 15. All activities connected



Last, the link between Energy Efficiency and Balancing must be included in the model, sothat the scenario will work, even though it has not been identified in Step 5. Figure 16 showsthe complete activities diagram.

Figure 16. Activities diagram



2.7 Step 7: Adding actors to the value model

The first task to do is assigning actors to the activities performed in the value model, asTable 10 shows.

Table 10. Assignment of actors to value activities

Value Activity General Actor Name Actor Name in the ModelElectric System Regulation Regulatory Authorities RegulatorTrade Market Operator Market Operator

Producer Central ProducerGenerationAutoproducer Retail Customer

Distribution Distribution System Operator Distribution System OperatorSupplier Distribution System OperatorSupplyAggregator AggregatorFinal Customer Tariff CustomerConsumptionAutoproducer Retail Customer

Balancing Aggregator AggregatorEnergy Efficiency Energy Service Company AggregatorAggregation Aggregator AggregatorMetering Distribution System Operator Distribution System Operator

Aggregator AggregatorFuel Supply Fuel Supplier Fuel SupplierMarket Management Market Operator Market Operator

As a result, the actors listed in Table 11 should be included in the value model.

Table 11. Actor-Value Activity list

Actor Value Activities DescriptionSupplyBalancingEnergy EfficiencyAggregation

1 Aggregator

Metering

Aggregator supplies electricity to the Retail Customer andalso balancing, energy efficiency, aggregation and meteringservices, both for consumption and generation; obtainssubsidies from the Regulator on behalf of the RetailCustomer.

Generation2 Retail CustomerConsumption

Retail Customer consumes and produces electricity, sellingthe output to the Aggregator (no autoconsumption).

DistributionSupply

3 DistributionSystem Operator

Metering

DSO offers T&D services to the Aggregator and marketactors, and supplies electricity and metering services (tariff)to the Tariff Customer. Obtains subsidies from T&D and tariff

Trade4 Market OperatorMarket Management

Market Operator trades electricity and obtains payment as apart of T&D services (and tariff) from the DSO.

5 Regulator Regulation Regulator collects subsidies from the DSO and gives themto the Aggregator (who acts on behalf of Retail Customer).

6 Central Producer Generation Central Producer produces electricity to sell it in the market7 Tariff Customer Consumption Tariff Customer obtains electricity and services from DSO.8 Fuel Supplier Fuel Supply Fuel Supplier provides fuel for the Retail Customer.

Both Retail Customer and Tariff Customer must be modelled as market segments, sinceseveral of them are required to implement this scenario. Besides, actors in the model canchoose between different Central Producers and Fuel Suppliers, so these actors will also bemodelled as market segments. On the other hand, the aggregator, the DSO, the MarketOperator and the Regulator will be unique, so they are not modelled as market segments.



In Figure 17 the value model including participanting actors is depicted.

Figure 17. Actors



2.8 Step 8: Scenario Path Identification

Figure 18. Scenario Path



Following the scenario path in Figure 18, the business process can be described as follows:

First of all, the Retail Customer wants to consume electricity and, to that end he/she needselectricity from Supply (1), the benefits of aggregating from Aggregation (2), the best patternof consumption from Energy Efficiency (3), the real-time control of consumption fromBalancing (4) and metering of consumption from Metering (5). All those needs are satisfiedby the Aggregator with his/her different roles. Aggregation (2) only needs to obtain benefitsfrom supply, which does not need anything to provide that service, so the scenario path endshere. On the other hand, Metering (5) is a well-known profitable business, so the scenariosubpath ends here.

The Retail Customer obtains electricity (1-a) from Supply, but also grid connection (1-b),since it is needed to obtain electricity from the market, and a fixed term is imposed, whetherelectricity is consumed or not. So, scenario subpath (1) splits in two subpaths:

a) Electricity is needed, which can be obtained in two ways: from the distributed generator(1-a-I) or from the market (1-a-II), so scenario subpath 1-a splits in two new subpaths.

I. The distributed generator needs some things to produce electricity, such as Fuel,from Fuel Supply (7), which is a profitable activity, so this scenario subpath endshere. Besides, it needs “Special Rules” subsidies from Supply (6), Best generationpattern from Energy Efficiency (8), Generation control from Balancing (9) andMetering (10), which is a profitable activity, so this scenario subpath ends here.

Subpaths (4) and (9) are linked with and AND joint, because they need theconsumption & generation schedule from Energy Efficiency (11). On the other hand,Energy Efficiency does not need anything to obtain that schedule, so subpaths (3),(8) and (11) are linked with an AND joint and the scenario subpath ends here.

“Special Rules” subsidies (6) are obtained from the Regulator, which pays themregardless of the subsidies obtained, so this scenario subpath ends in Regulationactivity.

II. Electricity obtained from the market is provided by Trade, which performed by theMarket Operator. Trade obtains electricity from Production, performed by the CentralProducer (12)

b) T&D services are required from Distribution activity. T&D services are used to pay for:

I. “Special Rules” taxes, to Regulation. The scenario path ends here.

II. NEC costs, to Regulation. The scenario path ends here.

III. Costs of the Market Operator, to Market Management (performed by the MarketOperator) . The scenario path ends here.

There is another subscenario linked to the main subscenario. Here, the start stimulus isplaced in the market segment of non-eligible customers, or customers that prefer to remainpurchasing electricity at tariff price. They obtain electricity (13) and metering (14) from theDSO, which acts as a supplier in this case. Supply activity from the DSO has to obtainelectricity from the market (13-a), and T&D services from distribution activity (13-b). Thesecustomers are necessary to the good functioning of this scenario, since their consumptionwill pay for “Special Rules” subsidies.



2.9 Step 9: Information System Model Construction

Some activities do not have new requirements derived from the implementation of thisscenario, such as Electric System Regulation, Trade, Distribution, Aggregation, Fuel Supplyand Market Management.

Other activities do not have new requirements when they are performed by some actors, butthey do have when they are performed by others, such as Generation (when it is performedby the Central Producer), Supply (when it is performed by the DSO), Metering (when it isperformed by the DSO) and Consumption (when it is performed by Tariff Customers).

As a result, only the Aggregator and Retail Customers will have new requirements.Requirements for different activities are listed below:

1) Software needs:

a) Aggregator:

Ø Supply: It needs no significant software requirements. The only software needed isthe one required by the Market Operator to enter in the market.

Ø Balancing: The software needed to convert the output of the optimised consumptionand generation schedule, into signals to send to each customer’s gateway in real-time operation.

Ø Energy Efficiency: The software needed to optimise generation and consumption forall the customers as a whole. Besides, it needs an historical database, to predict thetimes when electricity will be cheaper, as well as their value, since scheduling is donethe day before the actual electricity consumption. The prediction will also be donewith some software. (Database ? Prediction ? Optimisation).

Ø Metering: Database, in which actual consumption and generation profiles are stored,in order to determine imbalances.

b) Retail Customer:

Ø Generation: The software to program every customers’ gateways, so that eachgateway can start/stop its generator when needed.

Ø Consumption: The software to program every customers’ gateways, so that eachgateway can turn its customer’s appliances on/off when needed.

2) Quality requirements for the software:

a) Aggregator:

Ø Supply: The requirements imposed by the Market Operator.

Ø Balancing: High quality requirements.

Ø Energy Efficiency: No special quality requirements are needed, since scheduling canbe done at any time during the day.



Ø Metering: High quality requirements.

b) Retail Customer:

Ø Generation: No special quality requirements are needed, since communication canbe done at any time during the day.

Ø Consumption: No special quality requirements are needed, since communication canbe done at any time during the day.

3) Hardware needs:

a) Aggregator:

Ø Supply: A PC, with no special requirements.

Ø Balancing: A PC. Its requirements depend on the number of customers to control.

Ø Energy Efficiency: A strong computer able to deal with the software required in thisactivity.

Ø Metering: Metering devices and a gateway in which store the database.

b) Retail Customer:

Ø Generation: The gateway and the control device located in the distributed generator.

Ø Consumption: The gateway and the control device located in every appliance to becontrolled.

4) Interaction between software systems:

• The output of the software of the Energy Efficiency activity will be used as input to theBalancing software

• The Balancing activity software has to communicate with the software which controlsgateways, both in generation and consumption.

• The output of the Metering activity software must go to the software of the Supplyactivity, in order to prepare the invoices.

5) Hardware connections:

• Energy Efficiency and Balancing are performed by the same actor, so they can be linkedby a Local Area Network (LAN). Bandwith has to be broad, since data-flow will be high.Latency does not need to be very high (no need for real-time). No need for highreliability. Only two PC’s are connected.

• The Balancing activity and customers will be connected through an Internet connection.Bandwith does not need to be broad, since data-flow will be low. Latency can also below, since real-time operation is not needed. No need for high reliability. The number ofsites to connect depends on the number of customers, all of which will be connected toonly one balancing PC.



• Metering activity sends it results through an Internet connection to the aggregator’s site.Bandwith has to be broad (at least at the reception point). Latency can be low, sincereal-time operation is not needed. No need for high reliability. The number of sites toconnect depends on the number of customers, all of which will be connected to only oneaggregator’s PC.

• Supply activity will be linked to the Market Operator’s information system. Thisinformation system will not be modelled, since the Market Operator will have the samesystem whether the scenario is implemented or not.

Taking the above requirements into account, the final value model is shown in Figure 19:

Figure 19. Information System Model

CUSTOMER

Software demanded by theMarket Operator AGGREGATOR

Strong PC

Price database Forecasting software Optimisation software

PC

Scheduling software

LAN

PC

Accounting software

Gateway

Control software

INTERNET

Consumption&generation database

INTERNET

Control device

LAN (Real-time)

Metering device

Count On/Off

LAN (Real-time)



2.10 Step 10: Base-line Profitability Sheets Calculation

The profitability sheet for each actor is obtained going through the scenario path (Figure 18)and accounting the monetary value objects each actor exchanges with his/her environment.To that end, several value exchanges have been identified in previous steps. Nevertheless,not all the actors to be taken into account have been modelled, since otherwise, the modelcould be more complicated, and even difficult to read. In fact, actors can be classified inthree main categories:

• Core actors: These actors need to be examined in order to say whether they areprofitable or not. In this case, the only core actor is the Retail Customer.

• Direct environmental actors: These actors are profitable, so for the business case we donot analyse their profitability, but we do fill in the existing value exchanges in the modeland within profitability sheets. These are the rest of the actors identified up to now.

• In-direct environmental actors: These actors are needed by the direct-environmentalactors only to operate or exist. In addition, these in-direct actors do not have a directconnection to a core actor. These are the TSO and the Government (to include taxes inthe profitability analysis).

In this step, we must take into account all money flows going in and out of all the actors.Table 12 shows value exchanges between actors. Non-monetary value objects arepresented in brackets.

Table 12. Value exchanges between actors included in the model

1 Aggregator (Electricity ) Retail Customer Electricity retail fee2 Aggregator (Best consumption pattern) Retail Customer Scheduling fee3 Aggregator (Consumption control) Retail Customer Control fee4 Aggregator (Metering services) Retail Customer Metering fee5 Aggregator (Best generation pattern) Retail Customer Scheduling fee6 Aggregator (Generation control) Retail Customer Control fee7 Aggregator (Metering services) Retail Customer Metering fee8 Aggregator DG electricity fee Retail Customer (Electricity)9 Aggregator “Special Rules” subsidies Retail Customer (“Special Rules” generation)10 Aggregator T&D fee DSO (T&D services)11 Aggregator Electricity market fee Market Operator (Electricity)12 Aggregator (“Special Rules” generation) Regulator “Special Rules” subsidies13 Retail Customer Fuel fee Fuel Supplier (Fuel)14 DSO Electricity market fee Market Operator (Electricity)15 DSO Costs of the Market Operator Market Operator (Market management)16 DSO “Special Rules” taxes Regulator (Obligation)17 DSO NEC cost Regulator (Regulation)18 DSO (Electricity) Tariff Customer Electricity tariff fee19 DSO (Metering services) Tariff Customer Metering fee20 Market Operator Electricity market fee Central Producer (Electricity)21 Aggregator Taxes Government (Obligation)22 DSO TSO part TSO (Transmission services)23 DSO Other taxes Government (Obligation)24 DSO Taxes Government (Obligation)25 Fuel Supplier Taxes Government (Obligation)



Once value exchanges have been identified, next step is obtaining valuation functions formonetary objects and gathering data to enter in those functions (See Annex I). Besides,O&M costs must be taken into account, such as generator’s O&M for Retail Customer, ICTcosts for the Aggregator,…

Even when all available data have been introduced in valuation functions, some values mustbe assumed, since multiple possibilities can be used (generator capacity, number of tariffcustomers, number of retail customers…). Those assumptions define part of the basescenario and are listed in Table 13.

Table 13. Assumptions for the business model

Number of tariff customers 100.000Number of retail customers 10.000Average capacity 3,3 kWAverage consumption 10,606 kWh/dayOver-price of retail (%) 10%Scheduling price 1 €/monthControl price 1 €/monthCost of metering database 1 €/monthO&M ICT 1 €/month

Besides, different generation technologies can be used for this scenario (See Step 3:Technology selection): micro hydro, solar and microturbines. The assumptions for thesetechnologies, which complete the base scenario, are shown in Table 14.

Table 14. Assumptions for technologies

Scenario Hydro PV MicroturbineCapacity (kW) 10 5 25Availability (%) 40% 21% 94%Premium (€/kWh) 0,029464 0,360610 0,021276Efficiency (%) - - 30%O&M costs (€/kWh) 0,013327 0,050064 0,002

By using assumptions from Table 13 and Table 14, the base-line for each scenario can beperformed, obtaining profitability sheets, which can be seen in Annex II – Profitability Sheets.

2.10.1 Microhydro scenario

The profitability sheets for microhydro scenario are presented in Table 37.

Table 15 summarises the profitability for each actor in this scenario, obtained fromprofitability sheets.

Table 15. Actors’ profitabilities in the Micro hydro scenario

PROFITABILITY (€/year)Aggregator Retail

CustomerDSO Market

OperatorRegulator Central

ProducerTariff

CustomersFuel

Supplier1.240.788 -2.838.479 9.391.682 23.736 1.849.078 13.467.886 -47.198.712 0

Every actor has a positive profitability, except Retail and Tariff Customers, who obtainelectricity as a valuable good for them. The actors obtaining the highest profit are the CentralProducer, and the DSO.



The Regulator obtains enough money to pay for “Special Rules” subsidies and obtain aprofit, so a higher premium could be paid, or a reduction in electricity tariff or T&D fee couldbe assumed. The Aggregator obtains a good profit (more than a million euro per year), andeach Retail Customer pays 283,85 €/year. Although profitability for Retail Customers isnegative, their feasibility must be analysed (See Step 12), since the savings obtainedthrough demand shifting might make the business profitable.

2.10.2 PV Scenario

The profitability sheets for PV scenario are presented in Table 38. Table 16 summarises theprofitability for each actor in this scenario, obtained from profitability sheets.

Table 16. Actors’ profitabilities in the PV scenario


CustomerDSO Market


ProducerTariff

CustomersFuel

Supplier1.334.856 -1.452.279 9.391.682 23.736 -813.525 14.150.560 -47.198.712 0

Every actor has a positive profitability, except Retail and Tariff Customers, who obtainelectricity as a valuable good for them. Besides, the Regulator has a negative profitability.The negative profitability for the Regulator means that a change in regulation must be done,either increasing tariff and/or T&D fee, or reducing the premium to be paid to PV generation.The actors obtaining the highest profit are the Central Producer, and the DSO. TheAggregator obtains a good profit (more than a million euro per year), and each RetailCustomer pays 191,11 €/year. Although profitability for Retail Customers is negative, theirfeasibility must be analysed (See Step 12) since the investment in the generator might turnthe business into profitable.

2.10.3 Microturbine Scenario

This scenario is somewhat special, since it needs fuel to operate. In valuation functions (SeeAnnex I – Valuation Functions, Value Exchange 8), DG price has been set up to O&M costs.Nevertheless, as microturbines use fuel, DG price must be increased.

Even if all the demand is satisfied by DG, fuel requirements will be below 50.000 kWht, thatis, tariff 3.2 must be paid (See Value Exchange 13). As a result, fuel is paid at 3,82cent/kWht (including fixed term and meter renting), and, as efficiency is 30% (See Table 14),generation cost is 12,73 cent/kWh, higher than market price for any hour (See Annex III –Demand Response Process). Consequently, there will be no DG generation.

The profitability sheets for microturbine scenario are presented in Table 39. Table 17summarises the profitability for each actor in this scenario, obtained from profitability sheets.

Table 17. Actors’ profitabilities in the Microturbine scenario


CustomerDSO Market


ProducerTariff

CustomersFuel

Supplier1.405.008 -5.552.634 9.391.682 23.736 2.989.738 14.755.469 -47.198.712 0

Every actor has a positive profitability, except Retail and Tariff Customers, who obtainelectricity as a valuable good for them.



The actors obtaining the highest profit are the Central Producer, and the DSO. TheAggregator obtains a good profit (more than a million euro per year), and each RetailCustomer pay 555,26 €/year. Although profitability for Retail Customers is negative, theirfeasibility must be analysed (See Step 12), since the savings obtained through demandshifting might make the business profitable.

2.10.4 Comparison

The DSO and the Market Operator have the same profitability for the three scenarios. This isconsistent, because the total electricity amount to be consumed is equal in all of them andthe money they receive depends on it, and not on the characteristics of distributedgenerators. Tariff Customers have the same profitability too, because they consume thesame amount of electricity in the three scenarios.

On the other hand, the less electricity is generated from DG, the more electricity that has tobe purchased in the market and, thus, the Central Producer will obtain more profit, but alsothe Aggregator, since he obtains revenues from selling electricity. The most electricity isgenerated in the micro hydro scenario, because it can generate all the day and sale price islower than in microturbine (higher O&M costs due to fuel purchases), while the leastelectricity is generated in the PV scenario, because it cannot generate during the night.

The higher the premium (PV > micro hydro > microturbine), the more profit for RetailCustomers. On the other hand, the Regulator has to pay for higher subsidies, so this actor’sprofitability will be lower.

The Fuel Supplier does not obtain profit, even in the microturbine scenario, since there is noDG generation in this scenario and the other technologies do not need fuel.

Figure 20 presents the different cash flows some actors face in the three scenarios. Actorsnot depicted are those having the same cash flow in the three scenarios and, thus, theirprofitability do not depend on the technology of DG to be used.

Figure 20. Comparison of profitabilities for the three scenarios under study

-10.000.000

-5.000.000

0

5.000.000

10.000.000

15.000.000

Microhydro PV Microturbine

Retail CustomersAggregatorRegulatorCentral Producer



2.11 Step 11: Sensitivity Analysis

The sensitivity analysis is aimed towards offering an overview of the impact that someparameters may have on the yearly profitability of different actors.

In the scenarios proposed for the business case under study, the parameters to be changedare shown in Table 18.

Table 18. Parameters to change in Sensitivity Analysis

Parameter Base scenario VariationsNumber of retail customers 10.000 10 5.000 20.000Number of tariff customers 100.000 0 50.000 200.000Over-price for retail (%) 10% 0 5% 20%Scheduling price (€/month) 1,0 0,0 0,5 2,0Control price (€/month) 1,0 0,0 0,5 2,0DG electricity price (€/kWh) Generation cost 0 Half DoublePremium (€/kWh) Present price 0 Half DoubleFuel Price (€/kWhT) Present price 0 Half DoubleEfficiency 30% 1% 15% 60%

2.11.1 Number of retail customers

This parameter has a lineal relationship with actors’ profitabilities, so any profitability can beeasily determined for a given number of retail customers.

Table 19. Influence of the number of retail customers

Scenario Micro hydro PV MicroturbineAggregator 124,08 133,49 140,50DSO 48,04 48,04 48,04Market Operator 0,25 0,25 0,25Regulator -82,86 -349,12 31,20Central Producer 0,00 68,27 128,76

• Every actor obtains a higher profit when more retail customers are included in the model,except the Regulator, because it pays for subsidies. As a result, the higher the premium,the higher the sensitivity (-349,12 €/customer in PV, while it is only -82,86 €/customer inmicro hydro). In microturbine scenario, the Regulator obtains profit, because there is noDG generation.

• The highest influence of this parameter is in the Aggregator. The Aggregator obtainsabout 130 € from each customer implementing this business. The more electricitypurchased in the market (microturbine), the more sensitivity to this parameter, since, theAggregator charges an overprice to electricity purchased in the market.

• The DSO and the Market Operator are sensitive to the parameter, but not to thescenario, since they receive money from electricity consumption, which is the same in allthe scenario under study.

The influence in those actors’ profitabilities depends on the percentage each actorsreceives from T&D fee and, thus, it is higher in the DSO (48,04 €/customer), than in theMarket Operator (0,25 €/customer).



• The Central Producer is influenced because of the electricity the Aggregator purchasesin the market, so the more retail customers, the more electricity to be purchased and,thus, the more profit for the Central Producer. There are no purchases in the micro hydroscenario, so Central Producer’s profitability is not influenced by this parameter.

2.11.2 Number of tariff customers

This parameter has a lineal relationship with actors’ profitabilities, so any profitability can beeasily determined for a given number of tariff customers.

Table 20. Influence of the number of tariff customers

Scenario Micro hydro PV MicroturbineDSO 89,11 89,11 89,11Market Operator 0,21 0,21 0,21Regulator 26,78 26,78 26,78Central Producer 134,68 134,68 134,68

Every actor obtains a higher profit when more tariff customers are included in the model, andit does not depend on the scenario. This is due to the fact that tariff customers purchase thesame electricity amount in any scenario and, thereby, the money they pay is the same. Theinfluence in each actor depends on the part of the tariff for each of them. The main part ofthe tariff goes for the Central Producer, and the DSO, which are the most influenced.

2.11.3 Over-price for retail

This parameter has a lineal relationship with actors’ profitabilities, so any profitability can beeasily determined for a given over-price for retail.

Table 21. Influence of the over-price for retail

Scenario Micro hydro PV MicroturbineAggregator 0,00 9.406,83 16.422,00Retail Customer 0,00 -11.469,81 -20.023,46

• This parameter does not affect micro hydro scenario since there are no electricitypurchases in the market.

• The Aggregator is very sensitive to the over-price for retail and a change of 1% in thisparameter leads to a profit increase of more than 16.000 € in microturbine scenario, andmore than 9.000 € in PV, because in that scenario less electricity is purchased in themarket.

• Each Retail Customer will not face a big profit loss for an increase of 1% (2,00 €/year inmicroturbine, or 1,15 €/year in PV).

2.11.4 Scheduling price, or control price

These two parameters have the same influence in profitability, so they are included in thesame section. These parameters have lineal relationships with actors’ profitabilities, so anyprofitability can be easily determined for a given scheduling price, or control price.



Table 22. Influence of scheduling price, or control price

Scenario Micro hydro PV MicroturbineAggregator 240.000,00 240.000,00 240.000,00Retail Customer -278.400,00 -278.400,00 -278.400,00

• The Aggregator is very sensitive to scheduling and control prices and a change of 1€/month in any of these parameters leads to a profit increase of 240.000 € in anyscenario.

• Retail Customers are also very sensitive, since an increase of 1 €/month implies a profitloss of 27,84 €/year. On the other hand, if scheduling price and control price are set tozero, each customer will save more than 55 €/year, which may have influence in theprofitability for the business (See Step 12: Investment Analysis).

2.11.5 DG electricity price

This parameter does not have a lineal relationship with profitabilities. Changes in DGelectricity price vary the electricity amount to be purchased in the market on behalf of retailcustomers, but in a non-linear way. For example, in both micro hydro and microturbinescenario, profitabilities are equal for any DG electricity price below 1,65 cent/kWh, since thatis the lowest market price (See Annex III – Demand Response Process). In PV scenario thatbounding price will be 3,66 cent/kWh (the lowest electricity price while there is sunlight).

Besides, when DG electricity is increased in a 10% above the limit price, profitability doesnot change a 10%, since electricity to be purchased in the market is not a 10% lower,because market price is non-linear and electricity consumption is not homogenous for eachhour of the day.

As a result, Table 23 shows the profitabilities for each actor when DG electricity price ismodified:

Table 23. Influence of DG electricity price

Scenario Electricityprice (€/kWh)

Aggregator RetailCustomer

Regulator CentralProducer

Fuel Supplier

0,000000 1.240.788 -2.725.331 1.849.078 13.467.886 00,006664 1.240.788 -2.781.905 1.849.078 13.467.886 00,013327 1.240.788 -2.838.480 1.849.078 13.467.886 0

Micro hydro

0,026654 1.261.748 -3.349.629 2.183.691 13.573.459 00,000000 1.311.013 843.392 -2.973.466 13.966.988 00,025032 1.311.013 752.612 -2.973.466 13.966.988 00,050064 1.334.856 -1.452.279 -813.525 14.150.560 0

PV

0,100128 1.385.244 -4.722.326 2.283.451 14.575.768 00,000000 1.240.788 -8.321.304 2.166.066 13.467.886 4.928.876

0,020850 1.357.562 -6.173.807 2.866.530 14.334.055 1.109.530

0,041700 1.405.008 -5.552.634 2.989.738 14.755.469 0

Microturbine

0,083400 1.405.008 -5.552.634 2.989.738 14.755.469 0

Only affected actors have been shown in Table 23.



2.11.6 Premium

This parameter has a lineal relationship with actors’ profitabilities, so any profitability can beeasily determined for a given premium.

Table 24. Influence of premium

Scenario Micro hydro PV MicroturbineRetail Customer 38.713.659,38 10.546.748,01 0,00Regulator -38.713.659,38 -10.546.748,01 0,00

The profit or the loss for Retail Customers is balanced by the Regulator, since the Regulatorpays for subsidies, which go through the Aggregator into Retail Customers. The effect of thepremium depends on the electricity generated from DG.

As a result, the premium has less influence in the PV scenario than in micro hydro, and ithas no influence in microturbine scenario. An increase of 1 €/kWh in the value of premiumimplies a profit increase for each customer of 3.871,37 €/year in micro hydro scenario. Thisis unlikely to happen, but an increase of 1 cent/kWh is possible and it leads to a profitincrease of 38,71 €/customer.

2.11.7 Fuel Price

Fuel price is of importance only in microturbune scenario, since the other two do not usefuel. This parameter does not have a lineal relationship with actors’ profitabilities, as in DGelectricity price case.

Table 25 shows the profitabilities for each actor when fuel price is modified, but only affectedactors are presented:

Table 25. Influence of fuel price

Fuel price(€/kWh)

Aggregator Retail Customer Regulator CentralProducer

Fuel Supplier

0,000000 1.372.804,85 -6.054.077,15 2.913.660,60 14.466.191,13 816.387,28

0,016457 1.385.243,83 -5.989.055,89 2.948.066,71 14.575.768,03 602.386,74

0,032913 1.405.007,97 -5.552.633,69 2.989.737,67 14.755.468,66 0,000,065826 1.405.007,97 -5.552.633,69 2.989.737,67 14.755.468,66 0,00

If fuel price is higher than 2,8 cent/kWht (excluding fixed term and meter renting), and takinginto account that efficiency is 30%, DG electricity price must be higher than 10,2 cent/kWhe,which is the highest market price, i.e., no DG generation will be performed.

2.11.8 Efficiency

Efficiency could be of importance only in microturbune scenario, since the other two do notuse fuel. Nevertheless, since no DG generation is performed, it has no influence underconditions of the base scenario.



2.11.9 Summary

The influence of different parameters in Retail Customers’ profitability can be seen in Figure21. In the figure, the effect of overprice for retail (blue), scheduling or control price (pink), DGprice (yellow), premium (red) and fuel price (brown) are compared for the three technologiesunder analysis. Besides, a comparison of Retail Customers’ cash flows is performed.

Figure 21. Summary of Sensitivity Analysis

2.12 Step 12: Investment Analysis

In this business idea, the focus will be put in the investment requirements for the core actor,i.e., the Retail Customer.

Retail Customers' Cash Flows

-6.000.000,00

-5.000.000,00

-4.000.000,00

-3.000.000,00

-2.000.000,00

-1.000.000,00

0,00Micro Hydro PV Microturbine

Micro Hydro scenario

-4.000.000,00

-3.500.000,00

-3.000.000,00

-2.500.000,00

-2.000.000,00

-1.500.000,00

-1.000.000,000 50% Base 200%

Eur

o / y

ear

PV Scenario

-6.000.000,00

-5.000.000,00

-4.000.000,00

-3.000.000,00

-2.000.000,00

-1.000.000,00

0,00

1.000.000,00

2.000.000,00

3.000.000,00

0 50% Base 200%

Eur

o / y

ear

Microturbine Scenario

-8.500.000,00

-8.000.000,00

-7.500.000,00

-7.000.000,00

-6.500.000,00

-6.000.000,00

-5.500.000,00

-5.000.000,000 50% Base 200%

Eur

o / y

ear



When analysing the feasiblity of the investment needed to develop the business idea athand, it must be taken into account the income obtained in profitability analysis but also thesavings obtained as a result of implementing the scenario.

2.12.1 Micro hydro scenario

For micro hydro, the investment cost is 1.373,93 €/kW and O&M costs 0,013327, theseprojects are usually financed up to an 80% of the total investment, and build-up time is 1,5years [3]2. Loans are usually financed by the IDAE, a public institution supported by theMinistry of Economy.

In the case of micro hydro, the loan is offered at the Euribor interest. As a result, totalinvestment is 137,393 million €, 109,9144 of which will be financed by the IDAE. If we take aloan time horizon of 20 years, and a loan rate of 2,5%, the yearly payment will be7.050.693,17 €.

The electricity bill before shifting load included:

• Electricity: 10.000 customers * 191,11 €/customer = 1.911.140 € (See Annex III –Demand Response Process)

• T&D services: 1.567.057,89 € (See Value Exchange 10 in Annex I – ValuationFunctions, since its value will be the same after and before implementing the business,because no hourly discrimination is taken into account)

• Electricity tax: 1,0115*4,864%*(Electricity+T&D) = 177.829,70€ (See Value Exchange 1)

• Metering: 12 months * (0,54 €/active power meter + 0,72 €/reactive power meter) *Number of customers = 151.200 €/year (See Value Exchange 4, but before theimplementation, the customer did not need the clock commutation service)

• VAT: 16% * (Electricity+T&D+Electricity Tax+Metering) = 609.156,41 € (See ValueExchange 1)

• Total electricity bill = 4.416.384,00 €

After the implementation of the scenario, electricity bill includes (See Value Exchange 1):

• Electricity: DG electricity fee + T&D fee + Market fee * Overprice = 515.936,94 € (SeeValue Exchange 8) + 1.567.057,89 € (See Value Exchange 10) + 0,00 € (See ValueExchange 11) * (1+10%) = 2.082.994,83 €

• Electricity tax: 1,0115*4,864%*Electricity = 106.497,20 €

• VAT: 16% * (Electricity+Electricity Tax) = 350.318,73 €

• Total electricity bill = 2.539.810,76 €

2 Costs are increased by a 14% inflation (between 1999 and 2003)



Metering is included in the payment to be done for the Demand-Side Management (See fromValue Exchange 2 to Value Exchange 7), that is, scheduling + control + metering, whichcosts 1.439.328 €/year (See Annex II – Profitability Sheets).

Taking into account loan costs, the savings, an inflation rate of 5% and a discount rate of7%, the investment analysis for the first 5 years is shown in Table 26.

Table 26. NPV in the base scenario (micro hydro)

Year 0 1 2 3 4 5Loan costs -34.529.293 -7.050.693 -7.050.693 -7.050.693 -7.050.693 -7.050.693Electricity retail fee -4.416.384 -3.652.002 -2.800.141 -2.940.148 -3.087.156 -3.241.514DSM payment 0 -755.647 -1.586.859 -1.666.202 -1.749.512 -1.836.988O&M costs 0 -270.867 -568.820 -597.261 -627.125 -658.481Fuel fee 0 0 0 0 0 0Former electricity bill 4.416.384 4.637.203 4.869.063 5.112.517 5.368.142 5.636.549DG electricity fee 0 270.867 568.820 597.261 627.125 658.481Premium 0 598.846 1.257.577 1.320.456 1.386.478 1.455.802SUM -34.529.293 -6.222.293 -5.311.053 -5.224.071 -5.132.740 -5.036.843NPV -34.529.293 -5.815.227 -4.638.880 -4.264.398 -3.915.743 -3.591.199Accumulated NPV -34.529.293 -40.344.521 -44.983.400 -49.247.799 -53.163.542 -56.754.741

During the first year, there is no generation, so no O&M cost, and during the second year,generation and O&M costs are halved, since the generator will be in operation only in thesecond half of the year (construction time = 1,5 years).

Besides, we assume that no demand shifting is performed until the generator is available, soelectricity retail fee and former electricity bill are equal during the first 1,5 years.

Expected generator lifetime is 30 years, so, feasibility is shown by the accumulated NPVafter year 30, which is -74.191.979 €.

As a result, some parameters must be changed, in order for the business to be profitable.The Aggregator is interested in developing this scenario, so he/she can reduce the burdenimposed on the customer. In fact, customers themselves can schedule and control theirappliances, since all shitable loads are moved from daytime to low-price periods (between 4and 6 a.m. and 3 and 4 p.m., which result in the lowest average price periods).

Thereby, scheduling price and control price will be set to zero. This leads to a change in thevalue model obtained in steps 5-8, which can be seen in Figure 22.



Figure 22. Value model when customers control appliances

Taking this new model into account, the new investment analysis is shown in Table 27.

Table 27. NPV when customers control appliances (micro hydro)

Year 0 1 2 3 4 5Loan costs -34.529.293 -7.050.693 -7.050.693 -7.050.693 -7.050.693 -7.050.693Electricity retail fee -4.416.384 -3.652.002 -2.800.141 -2.940.148 -3.087.156 -3.241.514DSM payment 0 -463.327 -972.987 -1.021.636 -1.072.718 -1.126.354O&M costs 0 -270.867 -568.820 -597.261 -627.125 -658.481Fuel fee 0 0 0 0 0 0Former electricity bill 4.416.384 4.637.203 4.869.063 5.112.517 5.368.142 5.636.549DG electricity fee 0 270.867 568.820 597.261 627.125 658.481Premium 0 598.846 1.257.577 1.320.456 1.386.478 1.455.802SUM -34.529.293 -5.929.973 -4.697.181 -4.579.506 -4.455.946 -4.326.209NPV -34.529.293 -5.542.031 -4.102.700 -3.738.241 -3.399.420 -3.084.527Accumulated NPV -34.529.293 -40.071.324 -44.174.024 -47.912.265 -51.311.686 -54.396.213

NPV has risen, but not enough to recover the investment, since accumulated NPV in year 30is -61.829.968 €. When customers control appliances, demand shifting process is profitable(former electricity bill > electricity retail fee + DSM payment).



On the other hand, micro hydro generators are known to be profitable businesses if theygenerate electricity whenever they can. In principle, there are two possibilities to improve theeconomics of the business: reducing generator’s capacity to better match customers’electricity needs, or selling surplus electricity to the market.

1. Reduce generator’s capacity:

The generator is highly under-utilised, since maximum generation is 40.000 kWh per hour,and maximum hourly demand is 15.984 kWh. The highest profitability is obtained for agenerator capacity of 0,97 kW, even all the demand is not satisfied by DG.

Table 28. NPV with customers’ control and reduced generator capacity (micro hydro)

Year 0 1 2 3 4 5Loan costs -3.349.341 -683.917 -683.917 -683.917 -683.917 -683.917Electricity retail fee -4.416.384 -3.850.588 -3.217.171 -3.378.030 -3.546.931 -3.724.278DSM payment 0 -463.327 -972.987 -1.021.636 -1.072.718 -1.126.354O&M costs 0 -185.635 -389.834 -409.326 -429.792 -451.282Fuel fee 0 0 0 0 0 0Former electricity bill 4.416.384 4.637.203 4.869.063 5.112.517 5.368.142 5.636.549DG electricity fee 0 185.635 389.834 409.326 429.792 451.282Premium 0 410.412 861.866 904.959 950.207 997.717SUM -3.349.341 49.783 856.854 933.892 1.014.783 1.099.718NPV -3.349.341 46.526 748.409 762.334 774.173 784.083Accumulated NPV -3.349.341 -3.302.815 -2.554.406 -1.792.072 -1.017.899 -233.816

This possibility is profitable, since accumulated NPV is 20.432.912 € after 30 years(accumulated NPV starts to be positive in year 6). Accumulated NPV per customer is2.043,29 € after 30 years. Internal Rate of Return (IRR), i.e. the discount rate that makesprofitability zero after the expected lifetime, of this first option is 28,09%.

2. Generate the maximum electricity available and sell the surplus electricity to the market:

Now, DG electricity bill includes electricity sold to the market. Electricity generation per hourwill be 40.000 kWh, which increases O&M costs, but also DG electricity fee and premium.

Table 29. NPV with customers’ control and sales to the market (micro hydro)

Year 0 1 2 3 4 5Loan costs -34.529.293 -7.050.693 -7.050.693 -7.050.693 -7.050.693 -7.050.693Electricity retail fee -4.416.384 -3.652.002 -2.800.141 -2.940.148 -3.087.156 -3.241.514DSM payment 0 -463.327 -972.987 -1.021.636 -1.072.718 -1.126.354O&M costs 0 -2.451.635 -5.148.433 -5.405.855 -5.676.148 -5.959.955Fuel fee 0 0 0 0 0 0Former electricity bill 4.416.384 4.637.203 4.869.063 5.112.517 5.368.142 5.636.549DG electricity fee 0 7.493.371 15.736.079 16.522.883 17.349.027 18.216.478Premium 0 5.420.197 11.382.415 11.951.535 12.549.112 13.176.568SUM -34.529.293 3.933.114 16.015.302 17.168.601 18.379.566 19.651.079NPV -34.529.293 3.675.807 13.988.385 14.014.693 14.021.683 14.010.948Accumulated NPV -34.529.293 -30.853.486 -16.865.101 -2.850.408 11.171.275 25.182.223

Expected lifetime is refered to a yearly operation of 3.100 hours [3], while the turbine isoperating 8.760 hours/year in this scenario, so only 9 years must be taken as lifetime.Accumulated NPV in year 9 is 80.813.923 € (8.081,39 €/customer), and the IRR 38,86%.



Consequently, this second option is more profitable for retail customers, but not all actorshave a positive profitability (the Regulator loses more than 7 million €/year), so regulationmust be changed, or the number of tariff customers increased.

At a first glance, the expected lifetime for generators in option 1 (reduce generator’scapacity) should be reduced, as in the case of market sales, since they are in operationevery time.

Nevertheless, it is likely to happen that some big generators are used (with a total capacityof 9.700 kW), instead of having each customer a small generator. In this case, retailcustomers share costs and benefits, and generators are used alternatively, so that each ofthem has a maximum yearly operation of 3.100 hours.

2.12.2 PV scenario

Investment cost is 6.600 €/kW and O&M costs 0,050064, these projects are usually financedup to an 70% of the total investment, 40% is subsidised by the government (it can be up tothe 50%) and build-up time is 0,5 years [3]. In this case, the loan from the IDAE is at Euribor– 3%, so it is a 0% interest rate.

As a result, total investment is 330 million €, 132 of which will be subsidised by theGovernment and another 138,6 million financed by the IDAE.

The electricity bill before the implementation of the business model was 4.416.384,00 €/year,while the new bill is 3.816.213,75 €/year (See section 2.12.1, taking into account thatelectricity is purchased in the market now, since DG price is higher and there is nogeneration during the night).

If we take a loan time horizon of 20 years, and a loan rate of 7%, the yearly payment will be6.930.000,00 €. Taking into account loan costs, the savings, an inflation rate of 5% and adiscount rate of 7%, the investment analysis for the first 5 years is shown in Table 30.

Table 30. NPV in the base scenario (PV)

Year 0 1 2 3 4 5Loan costs -66.330.000 -6.930.000 -6.930.000 -6.930.000 -6.930.000 -6.930.000Electricity retail fee -4.116.299 -4.007.024 -4.207.376 -4.417.744 -4.638.632 -4.870.563DSM payment -719.664 -1.511.294 -1.586.859 -1.666.202 -1.749.512 -1.836.988O&M costs -264.006 -554.413 -582.134 -611.240 -641.802 -673.892Fuel fee 0 0 0 0 0 0Former electricity bill 4.416.384 4.637.203 4.869.063 5.112.517 5.368.142 5.636.549DG electricity fee 264.006 554.413 582.134 611.240 641.802 673.892Premium 1.901.631 3.993.426 4.193.097 4.402.752 4.622.890 4.854.034SUM -64.847.947 -3.817.690 -3.662.074 -3.498.678 -3.327.112 -3.146.967NPV -64.847.947 -3.567.934 -3.198.597 -2.855.963 -2.538.238 -2.243.744Accumulated NPV -64.847.947 -68.415.882 -71.614.479 -74.470.443 -77.008.680 -79.252.424

Expected lifetime is 20 years, and the accumulated NPV in that year is -89.348.682 € (-8.934,87 € per customer), so the business is not profitable.

As a result, the model will be changed to reduce the burden on retail customers (See section2.12.1).



Taking this new model into account, the new investment analysis is shown in Table 31.

Table 31. NPV when customers control appliances (PV)

Year 0 1 2 3 4 5Loan costs -66.330.000 -6.930.000 -6.930.000 -6.930.000 -6.930.000 -6.930.000Electricity retail fee -4.116.299 -4.007.024 -4.207.376 -4.417.744 -4.638.632 -4.870.563DSM payment -441.264 -926.654 -972.987 -1.021.636 -1.072.718 -1.126.354O&M costs -264.006 -554.413 -582.134 -611.240 -641.802 -673.892Fuel fee 0 0 0 0 0 0Former electricity bill 4.416.384 4.637.203 4.869.063 5.112.517 5.368.142 5.636.549DG electricity fee 264.006 554.413 582.134 611.240 641.802 673.892Premium 1.901.631 3.993.426 4.193.097 4.402.752 4.622.890 4.854.034SUM -64.569.547 -3.233.050 -3.048.202 -2.854.112 -2.650.318 -2.436.334NPV -64.569.547 -3.021.542 -2.662.418 -2.329.806 -2.021.915 -1.737.072Accumulated NPV -64.569.547 -67.591.089 -70.253.507 -72.583.313 -74.605.228 -76.342.300

NPV has risen, but not enough to recover the investment, since accumulated NPV in year 20is -79.881.569 € (-7.988,16 € per customer). When customers control appliances, demandshifting process is profitable (former electricity bill > electricity retail fee + DSM payment).

In order to make the business feasible, generator’s performance must be enhanced.

1. Reduce generator’s capacity:

The generator is under-utilised, since maximum generation is 10.500 kWh per hour, andmaximum hourly demand is 9.327 kWh while the generator is running. The highestprofitability is obtained for a generator capacity of 1,52 kW, even all the demand is notsatisfied by DG.

Table 32. NPV with customers’ control and reduced generator capacity (PV)

Year 0 1 2 3 4 5Loan costs -20.164.320 -2.106.720 -2.106.720 -2.106.720 -2.106.720 -2.106.720Electricity retail fee -4.157.570 -4.093.695 -4.298.379 -4.513.298 -4.738.963 -4.975.911DSM payment -441.264 -926.654 -972.987 -1.021.636 -1.072.718 -1.126.354O&M costs -220.650 -463.365 -486.533 -510.860 -536.403 -563.223Fuel fee 0 0 0 0 0 0Former electricity bill 4.416.384 4.637.203 4.869.063 5.112.517 5.368.142 5.636.549DG electricity fee 220.650 463.365 486.533 510.860 536.403 563.223Premium 1.589.337 3.337.608 3.504.488 3.679.712 3.863.698 4.056.883SUM -18.757.433 847.742 995.465 1.150.574 1.313.439 1.484.447NPV -18.757.433 792.282 869.478 939.211 1.002.016 1.058.390Accumulated NPV -18.757.433 -17.965.151 -17.095.674 -16.156.463 -15.154.447 -14.096.057

This possibility is profitable, since accumulated NPV is 5.358.844 € after 20 years(accumulated NPV per customer is 535,88 €). On the other hand, the IRR for this option is9,48%.

2. Generate the maximum electricity available and sell the surplus electricity to the market:

Now, DG electricity bill includes electricity sold to the market. Electricity generation per hourwill be 2.352 kWh while there is sunlight, that is, between 8 a.m. and 8 p.m., which increasesO&M costs, but also DG electricity fee and premium, and thus profit, as Table 33 shows.



Table 33. NPV with customers’ control and sales to the market (PV)

Year 0 1 2 3 4 5Loan costs -66.330.000 -6.930.000 -6.930.000 -6.930.000 -6.930.000 -6.930.000Electricity retail fee -4.116.299 -4.007.024 -4.207.376 -4.417.744 -4.638.632 -4.870.563DSM payment -441.264 -926.654 -972.987 -1.021.636 -1.072.718 -1.126.354O&M costs -863.416 -1.813.174 -1.903.833 -1.999.024 -2.098.976 -2.203.925Fuel fee 0 0 0 0 0 0Former electricity bill 4.416.384 4.637.203 4.869.063 5.112.517 5.368.142 5.636.549DG electricity fee 986.884 2.072.457 2.176.080 2.284.884 2.399.128 2.519.084Premium 6.219.170 13.060.257 13.713.270 14.398.934 15.118.881 15.874.825SUM -60.128.541 6.093.065 6.744.218 7.427.929 8.145.825 8.899.616NPV -60.128.541 5.694.453 5.890.661 6.063.402 6.214.411 6.345.303Accumulated NPV -60.128.541 -54.434.088 -48.543.427 -42.480.024 -36.265.613 -29.920.310

In the 20th year, accumulated NPV is 71.136.788 € (positive from year 10 on) and,consequently, the business is feasible. Accumulated NPV per customer is 7.113,68 €, andthe IRR is 16,24%.

As in the microturbine scenario, profitability is not positive for every actor involved in thebusiness, since the Regulator pays more subsidies than the subsidies received.

2.12.3 Microturbine scenario

Investment cost is 1.000 €/kW and O&M costs 0,002 €/kWh, these projects are usuallyfinanced up to an 80% of the total investment and build-up time is 1 year. As a result, totalinvestment is 250 million €, 200 of which will be financed by the IDAE. As in the micro hydroscenario, the loan has the Euribor interest

The electricity bill before the implementation of the business model was 4.416.384,00 €/year,while the new bill is 3.373.477,52 €/year (See section 2.12.1).

If we take a loan time horizon of 20 years, and a loan rate of 2,5%, the yearly payment willbe 12.829.425,75 €. Taking into account loan costs, the savings, an inflation rate of 5% anda discount rate of 7%, the investment analysis for the first 5 years is shown in Table 34.

Table 34. NPV in the base scenario (microturbine)

Year 0 1 2 3 4 5Loan costs -62.829.426 -12.829.426 -12.829.426 -12.829.426 -12.829.426 -12.829.426Electricity retail fee -4.416.384 -4.318.971 -4.534.920 -4.761.665 -4.999.749 -5.249.736DSM payment 0 -1.511.294 -1.586.859 -1.666.202 -1.749.512 -1.836.988O&M costs 0 0 0 0 0 0Fuel fee 0 0 0 0 0 0Former electricity bill 4.416.384 4.637.203 4.869.063 5.112.517 5.368.142 5.636.549DG electricity fee 0 0 0 0 0 0Premium 0 0 0 0 0 0SUM -62.829.426 -14.022.488 -14.082.141 -14.144.777 -14.210.544 -14.279.600NPV -62.829.426 -13.105.129 -12.299.887 -11.546.351 -10.841.156 -10.181.158Accumulated NPV -62.829.426 -75.934.555 -88.234.442 -99.780.793 -110.621.949 -120.803.107

Expected lifetime is 25 years, and the accumulated NPV in that year is -221.178.104 €, sothe business is not profitable (-22.117,81 € per customer).



Consequently, the model will be changed to reduce the burden on retail customers (Seesection 2.12.1). Taking this new model into account, the new investment analysis is shown inTable 35.

Table 35. NPV when customers control appliances (microturbine)

Year 0 1 2 3 4 5Loan costs -62.829.426 -12.829.426 -12.829.426 -12.829.426 -12.829.426 -12.829.426Electricity retail fee -4.416.384 -4.318.971 -4.534.920 -4.761.665 -4.999.749 -5.249.736DSM payment 0 -926.654 -972.987 -1.021.636 -1.072.718 -1.126.354O&M costs 0 0 0 0 0 0Fuel fee 0 0 0 0 0 0Former electricity bill 4.416.384 4.637.203 4.869.063 5.112.517 5.368.142 5.636.549DG electricity fee 0 0 0 0 0 0Premium 0 0 0 0 0 0SUM -62.829.426 -13.437.848 -13.468.269 -13.500.211 -13.533.750 -13.568.967NPV -62.829.426 -12.558.736 -11.763.708 -11.020.194 -10.324.833 -9.674.486Accumulated NPV -62.829.426 -75.388.162 -87.151.870 -98.172.064 -108.496.897 -118.171.383

NPV has risen, but not enough to recover the investment, since accumulated NPV in year 25is -210.184.916 € (-21.018,49 € per customer). In this scenario, demand shifting is notprofitable, even when customers control appliances (former electricity bill < electricity retailfee + DSM payment + Fuel fee).

Up to now, we assumed that CHP was used to generate electricity, regardless of the use ofgenerated heat. In this point, we must valuate the generated heat, since otherwise thebusiness is not profitable.

To that end, we will find out the fuel savings reached due to the implementation of thescenario.

From our interviews with the test group, we assume that average gas consumption for anSpanish household is in the range of 8.000 kWhT/year. In this case, the tariff to be usedbefore demand shifting was 3.2 (See Annex I – Valuation Functions - Value Exchange 13):

• Fixed term = 5,11 €/month/customer * 12 months * 10.000 customers = 613.200 €

• Variable term=0,032913€/kWh*8.000kWh/year/customer*10.000 customers=2.633.040 €

• Meter renting = 0,57 €/month/customer * 12 months * 10.000 customers = 68.400 €

• VAT (16%) = 530.342 €

• Total fuel bill before implementing the case study = 3.844.982 €

Microturbines have an efficiency of 30% (See Table 14), but if thermal uses are taken intoaccount, it rises to about 70%, which means a thermal efficiency of 40%.

On the other hand, there is no heat generation if there is no electricity generation, so DGelectricity price must be lowered in order for electricity generation to be profitable. As aresult, a new contract must be signed between Retail Customers and the Aggregator, so thatthe Aggregator purchases every hour electricity to Retail Customers (even if market price islower) at the same price (not taking into account market fluctuations).



Thereby, if DG electricty price is higher than market price, the Aggregator will be losingmoney, while in the other case, Retail Customers will not take all the advantages of marketprice fluctuations. Besides, the generator will be in operation at full load, to obtain higherrevenues. The Aggregator will demand a price in which he does not lose money. As a result,DG electricity price will be 4,38 €/kWh (the best option for both Retail Customers and theAggregator), so that the Aggregator loses about 40 €/day and the business is profitable forRetail Customers.

Fuel requirements are obtained from DG generation (Annex III – Demand ResponseProcess) and electric efficiency (Table 14). By applying the thermal efficiency to fuelrequirements, heat generation can be obtained (DG generation*Thermal efficiency/Electricefficiency). In this case, heat generation is 1.372.400 kWh/year. The difference betweenheat demand (8.000 kWh/customer) and heat generation is the heat to be obtained byburning fuel in traditional ways (no CHP). That heat is not needed in this case, since heatgeneration is higher than consumption.

Table 36. NPV with customers’ control and sales to the market (microturbine)

Year 0 1 2 3 4 5Loan costs -62.829.426 -12.829.426 -12.829.426 -12.829.426 -12.829.426 -12.829.426Electricity retail fee -4.416.384 -3.772.700 -3.961.335 -4.159.402 -4.367.372 -4.585.741DSM payment -634.334 -666.051 -699.354 -734.321 -771.037O&M costs -2.161.530 -2.269.607 -2.383.087 -2.502.241 -2.627.353Fuel fee -101.851.320 -106.943.885 -112.291.080 -117.905.634 -123.800.915Former electricity bill 4.416.384 4.637.203 4.869.063 5.112.517 5.368.142 5.636.549Former fuel bill 4.037.232 4.239.093 4.451.048 4.673.600 4.907.280DG electricity fee 94.675.014 99.408.765 104.379.203 109.598.163 115.078.071Premium 22.994.356 24.144.074 25.351.278 26.618.842 27.949.784SUM -62.829.426 5.094.495 5.990.691 6.931.697 7.919.753 8.957.212NPV -62.829.426 4.761.210 5.232.501 5.658.329 6.041.941 6.386.368Accumulated NPV -62.829.426 -58.068.216 -52.835.714 -47.177.385 -41.135.444 -34.749.075

This scenario is feasible, since accumulated NPV in year 25 is 138.285.105 €.

Nevertheless, there is a constraint not taken into account until now, which is the hours ofoperation for the microturbine. It is assumed a lifetime of 25 years, but the microturbinemight have a shorter lifetime when used in high operation regimes, such as ours. In [4],operation hours for a microturbine are set as 40.000-75.000, so we will assume 57.500hours (average). In this scenario, microturbine is operating 12 hours a day, 365 days a year,which leads to an expected lifetime of 13,13 years. In year 13, accumulated NPV is24.889.644 € (2.488,96 €/customer), with an IRR of 12,04%.

However, the scenario has a problem, since now more electricity is generated thanconsumed and, so, more tariff customers are needed. Besides, profitability is not positive forevery actor involved in the business idea yet, because the regulator pays more subsidiesthan received, so some regulatory changes are needed.

2.12.4 Conclusions

The business idea seems to be profitable, but deeper analysis must be carried out, in orderto determine its actual profitability. Nevetheless, that is not the aim of the BUSMODmethodology, but to identify potential profitable business, and this is one.



In principle, the business was developed so that the Aggregator schedules and controlscustomers’ appliances and generators. However, that business tended not to be feasible,since savings were not high enough to cover the payment to the Aggregator. One reasonmight be that price difference between high and low demand periods is not as big as in otherelectric markets (some U.S. markets, NordPool…). Another possibility refers to the fact thatonly three appliances (washing machine, dryer, dishwasher) and the water boiler wereconsidered, and that if more electric equipment were included (air conditioning, heating…),savings might increase. Last, the Aggregator charges twice for the same services(scheduling, control and metering), and charges an overprice above the market price, so theAggregator may ask for a smaller part of the savings.

On the other hand, electricity prices have almost the same profile for every day, socustomers can purchase some devices to turn appliances on at a certain time, like thoseused to turn central heating on/off at a desired hour. That way, customers have a small initialinvestment for control devices and might not be taking the full advantage of demand shifting,but they do not have to pay for scheduling or control, and, in the end, they will probably savemore money by controlling appliances themselves, that is the result of the analysisperformed here, at least.

Besides, distributed generators are highly underutilised, because their aggregated capacityis much higher than the maximum hourly demand. The problem is that investment dependson the capacity, while profit depends on generation. As a result, aggregated capacity mustbe lowered, i.e. capacity per customers reduced (to reduce investment cost) or surpluselectricity sold to the market (to increase income) to improve the economics of the business.

In the scenarios under analysis, selling the surplus electricity to the market implies that theprofitability of the Regulator turns into negative, since the Regulator collects “Special Rules”subsidies from tariff customers to pay for them to the Aggregator. If “Special Rules”generation is higher, the Regulator will pay more subsidies, so, if the payment from tariffcustomers is not increased, the Regulator will be losing money. As a result, a change inregulation is needed, or a change in the scope of the scenario, either increasing the numberof tariff customers, or reducing the number of retail customers implementing the scenario.

On the other hand, the reduction in generator’s capacity is profitable in the micro hydro andPV scenarios, but selling surplus electricity to the market is a better option.

Summarising, the most profitable scenario is the microhydro scenario, since it has thehighest accumulated NPV/customer, and the highest IRR, while the worst scenario is themicroturbine scenario. As a result, if location conditions are good for micro hydro, that will bethe technology to be chosen. If not, PV will be used, unless sun is not available (unlikely tohappen in Spain) or generated extra heat could be sold to other customers, or should beneeded because thermal requirements for Retail Customers implementing this scenario arehigher than the average of the country.

Figure 23 presents a comparison of profitable solutions for the different technologies understudy.



Comparison of different technologies

-100.000.000

-50.000.000

0

50.000.000

100.000.000

150.000.000

200.000.000

250.000.000

300.000.000

350.000.000

400.000.000

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Year

Acc

um

ula

ted

NP

V

Hydro Reduced Capacity

Hydro Sales

PV Reduced Capacity

PV Sales

Microturbine

Figure 23. Comparison of profitable scenarios



3 References

[1] Kartseva, Vera; Soetendal, Jasper; Gordijn, Jaap; Akkermans, Hans; Schildwacht,Joost; “D 3.1: Distributed Generation Modelling”; BUSMOD Project; October 2003(available in http://busmod.e3value.com)

[2] Mutale, Joseph; Strbac, Goran; Lambine, André; Morch, Andrei Z.; Laresgoiti, Iñaki;Kester, Josco; “D 3.2: e3-value Methodology Testing”; BUSMOD Project; October 2003

[3] Ministry of Industry and Energy, "Plan de Fomento de las Energías Renovables",Chapter 6-Annex A "Análisis Económico de Proyectos Tipo, por Tecnologías”, 1999.

[4] Comisión Nacional para el Ahorro de Energía (CONAE), “Aplicación de Microturbinaspara Generación Distribuida”, July 2001. (available inhttp://www.conae.gob.mx/work/secciones/443/imágenes/microturbinas.doc)

[5] García-Bosch, Ignacio; Laresgoiti, Iñaki; Madina, Carlos; Zabala, Eduardo; Pedrosa,Luis; Díaz, Angel; Kester, Josco; Morch, Andrei Z.; Iosif, Victor; Sweet, Patrick;Hamilton, Luc; “D 2.1: Arising Scenarios on Distributed Generation Business”,BUSMOD Project, January 2003 (available in http://busmod.e3value.com)



4 Annex I – Valuation Functions

In order to obtain profitability sheets, some valuation functions are needed for several moneyexchanges. The Aggregator takes the load of all the retail customers and uses it as a singleload, so results will be provided for all the retail customers as a whole. Results for tariffcustomers, central producers and fuel suppliers are also presented for all the marketsegment as a whole.

4.1.1 Value Exchange 1

In this value exchange, the Aggregator sells electricity to the Retail Customer. The electricitybill will be formed by three items: the electricity retail fee, the electricity tax and the ValueAdded Tax (VAT).

Electricity retail fee includes the cost of electricity for the Aggregator and his/her profit. Thecost of electricity for the Aggregator includes purchases in the market (See Value Exchange11) and purchases to the distributed generator (See Value Exchange 8), as well as T&Dpayment to the DSO (See Value Exchange 10). Purchases in the market will be chargedwith an overprice, while DG electricity and T&D costs will only be charged at cost price.

On the other hand, electricity tax is 1,05113 times the 4,864% of the electricity retail fee, andthe VAT is the 16% of the sum of electricity retail fee and electricity tax.3

Valuation functions for this value exchange are listed below.

• Electricity retail fee = T&D cost + DG electricity cost + (Market electricity cost *(1+Overprice of retail))

• Electricity tax = 1,0115 * 4,864 % * Electricity retail fee

• VAT = 16% * (Electricity retail fee + Electricity tax)


The Aggregator offers best consumption pattern to the Retail Customer and demandsscheduling fee in return. Scheduling fee will be a monthly fixed fee and will be charged bythe VAT:

• Scheduling fee = Scheduling price * 12 * Number of retail customers

• VAT = Scheduling fee * 16%

Scheduling price and number of retail customers are assumed in Table 13.

3 Ley 66/1997, de 30 de diciembre, de Medidas Fiscales, Administrativas y del Orden Social, Capítulo IX




The Aggregator offers consumption control to the Retail Customer and demands control feein return. Control fee will be a monthly fixed fee and will be charged by the VAT:

• Control fee = Control price * 12 * Number of retail customers

• VAT = Control fee * 16%

Control price and number of retail customers are assumed in Table 13.


The Aggregator offers metering services to the Retail Customer and demands metering feein return. Metering fee will be a monthly fixed fee and will be charged by the VAT. Meteringcost includes the renting of devices and the cost of the metering database.

• Metering fee = (Active energy device renting + Reactive energy device renting +Commutation clock service + Cost of the metering database) * 12 * Number of retailcustomers

• VAT = Metering fee * 16%

Metering devices rentings are 0,54 €/month for active energy, 0,72 €/month for reactiveenergy and 0,91 €/month for commutation clock service4. Cost of the metering database andnumber of retail customers are assumed in Table 13.


The Aggregator offers best generation pattern to the Retail Customer and demandsscheduling fee in return. We assume that this exchange is equal to Value Exchange 2 and,thus, it has the same valuation functions and data.


The Aggregator offers generation control to the Retail Customer and demands control fee inreturn. We assume that this exchange is equal to Value Exchange 3 and, thus, it has thesame valuation functions and data.


The Aggregator offers metering services to the Retail Customer and demands metering feein return. We assume that this exchange is equal to Value Exchange 4 and, thus, it has thesame valuation functions and data.

4 Real Decreto 1802/2003 por el que se establece la tarifa eléctrica para 2004




The Aggregator purchases electricity to the Retail Customer.

• DG electricity fee = DG electricity price * Generation

The amount of electricity generated from DG is a result of the demand response process(See Annex III – Demand Response Process), while DG electricity price is equal togeneration O&M cost (See section 4.1.26).


The Aggregator collects subsidies for “Special Rules”5 generation from the Regulator onRetail Customer’s behalf. The premium is different in each scenario (See Table 14). Thesubsidies to pay to retail customers are obtained with the following valuation function:

• “Special Rules” subsidies = Premium * Generation


The Aggregator pays T&D fee on retail customers’ behalf to the DSO. There are twopossibilities when choosing the T&D tariff to use: the same price for every hour of the day(Tariff 2.0A), or different prices for night and day hours (Tariff 2.0NA) 6:

• Tariff 2.0A:

Ø Capacity term = 17,346456 €/kW.year

Ø Energy term = 0,025693 €/kWh

• Tariff 2.0NA:

Ø Capacity term = 16,416992 €/kW.year

Ø Energy terms = 0,035314 €/kWh (day); 0,023034 €/kWh (night)

Average capacity is assumed in Table 13, while hourly consumption is described in Annex III– Demand Response Process.

• T&D fee = (Average capacity * Capacity term * Number of retail customers) +(Consumption (day) * Energy term (day)) + (Consumption (night) * Energy term (night))

The cheapest solution is the 2.0A tariff (every hour at the same price). This exchange has notax, because they are paid by retail customers.

5 “Special Rules” includes generation units using CHP or RES and with a capacity up to 50 MW





The Aggregator purchases electricity in the market.

• Electricity market fee = ? (Hourly consumption * Hourly market price)

The amount of electricity consumed each hour and its price is a result of the demandresponse process (See Annex III – Demand Response Process). This exchange has no tax,because they are paid by retail customers.


The Regulator pays to the Aggregator the subsidies for the “Special Rules” generation,produced by retail customers. We assume that this exchange is equal to Value Exchange 9and, thus, it has the same valuation functions and data.


The Retail Customer purchases fuel to the Fuel Supplier.

• Fuel requirements = DG Generation / Efficiency

DG Generation is described in Annex III – Demand Response Process, while Efficiency isshown in Table 14.

Fuel price has different tariffs7:

Tariff Consumption (kWht/year) Fixed term (€/month) Variable term (€/kWht)3.1 < 5.000 2,29 0,03973.2 <50.000 5,11 0,0329133.3 <100.000 39,65 0,0246243.4 >100.000 59,17 0,022283

Renting of the metering device is also dependant on maximum hourly consumption:

Maximum consumption (m3/h) 3 6 10 25 40Renting (€/month) 0,57 1,04 1,25%*175,37 1,25%*322,79 1,25%*626

Money exchanges are:

• Fuel fee = (Fuel requirements * Fuel price) + Fixed term + Renting of the metering device

• VAT = 16% * Fuel fee


The DSO purchases electricity for tariff customers in the market.

7 Orden ECO/33/2004, de 15 de enero, por la que se establecen las tarifas de gas natural



• Electricity market fee = Average consumption of tariff customers * Number of tariffcustomers * Average market price

We will take the average price of the day we use for hourly market prices, 0,04395 €/kWh8

(See Table 43). Both the average consumption of tariff customers and their number is inTable 13. The exchange has no taxes, since they are paid by tariff customers.


The DSO pays for Market Operator’s costs, once the DSO collects them (imposed by thegovernment), from tariff customers through the Integrated Tariff (0,057%), and from retailcustomers from T&D fee (0,159%)9. See Value Exchange 10 and Value Exchange 18.

• Costs of the Market Operator = (0,159% * T&D fee) + (0,057% * Electricity tariff fee)


The DSO pays to the Regulator for the “Special Rules” subsidies collected from customers,through percentages imposed by the government, 19,712% from T&D fee and 7,097% ofIntegrated Tariff10. See Value Exchange 10 and Value Exchange 18.

• “Special Rules” taxes = (19,712% * T&D fee) + (7,097% * Electricity tariff fee)


The DSO collects from customers the costs of the Regulator (imposed by the government),from tariff customers through the Integrated Tariff (0,069%), and from retail customers fromT&D fee (0,201%)11. See Value Exchange 10 and Value Exchange 18.

• Costs of the Regulator = (0,201% * T&D fee) + (0,069% * Electricity tariff fee)


Tariff customers purchase electricity to the DSO through the Integrated Tariff. This tariffconsists of a capacity term, an energy term, the electricity tax and the VAT.

• Electricity tariff fee = (Capacity term *12 * Average capacity * Number of tariff customers)+ (Energy term * Average consumption * Number of tariff customers)

8 Yearly average was 0,03582 €/kWh during 2003, but all calculations will be made for that day, so the mostconsistent criterium is taking the same day for everything.






• Electricity tax = 1,05113 * 4,864% * Electricity tariff fee

• VAT = 16% * (Electricity tariff fee + Electricity tax)

Average capacity and consumption, as well as the number of tariff customers are assumedin Table 13. On the other hand, capacity term and energy term are 1,436140 €/kW.monthand 0,081587 €/kWh respectively12.


The DSO offers metering services to tariff customers.

• Metering fee = (Active energy device renting + Reactive energy device renting) * 12 *Number of tariff customers

• VAT = Metering fee * 16%

Metering devices rentings are 0,54 €/month for active energy and 0,72 €/month for reactiveenergy13. Number of retail customers are assumed in Table 13.


The Market Operator has to purchase electricity for the Aggregator and the DSO fromcentral producers. See Value Exchange 11 and Value Exchange 14.

• Electricity market fee = Purchases from the Aggregator + Purchases from the DSO


The Aggregator has to pay to the Government for the taxes collected from retail customers(See Value Exchange 1).

• Taxes = Electricity tax + VAT


The DSO has to pay to the TSO for transmission services, as a part of the money collectedfrom customers, through percentages imposed by the government, 14,120% from T&D feeand 6,416% of Integrated Tariff14. See Value Exchange 10 and Value Exchange 18.

• TSO part = (14,120% * T&D fee) + (6,416% * Electricity tariff fee)







The DSO pays to the Government for other taxes collected from customers, from tariffcustomers through the Integrated Tariff (9,211%), and from retail customers from T&D fee(9,935%)15. See Value Exchange 10 and Value Exchange 18.

• Costs of the Regulator = (9,935% * T&D fee) + (9,211% * Electricity tariff fee)


The DSO has to pay for electricity tax and VAT collected from tariff customers to theGovernment (See Value Exchange 18 and Value Exchange 19):

• Taxes = Electricity tax + VAT


The Fuel Supplier has to pay for VAT collected from retail customers to the Government(See Value Exchange 13):

• Taxes = VAT

4.1.26 O&M costs

• O&M Costs for DG: In Table 14, O&M costs values for the three scenarios are listed.Generation from DG is obtained in Annex III – Demand Response Process.

• O&M Costs for Aggregator: O&M costs of ICT are assumed in Table 13.

• O&M Costs for DSO = 0,0105 €/kWh16. Distributed electricity is the sum of electricityconsumed by retail customers and tariff customers (average consumption * number ofcustomers – See Table 13).

• O&M Costs for Central Producer = 0,00916 €/kWh17. Generation is all the electricityconsumed, minus the electricity generated by DG.

• O&M Costs for TSO = 0,013327 €/kWh18. Transmitted electricity is assumed to be thesame as distributed electricity.


16 Costs for an Spanish DSO in 2003

17 Marcos Fano, J.Mª., “Costes de la generación eléctrica en algunas tecnologías” (http://www.energuia.com)

18 Costs for the Spanish TSO in 2003



5 Annex II – Profitability Sheets

In this Annex, profitability sheets for the three scenarios developed are presented.

Table 37 shows the profitability sheet for micro hydro scenario.

Table 37. Profitability sheets for Micro hydro scenario

Value Object In Value In Value Object Out Value OutAggregator

Retail Customer Electricity retail fee 2.539.810,76 (Electricity)Scheduling fee 139.200,00 (Best consumption pattern)Control fee 139.200,00 (Consumption control)Metering fee 441.264,00 (Metering services)Scheduling fee 139.200,00 (Best generation pattern)Control fee 139.200,00 (Generation control)Metering fee 441.264,00 (Metering services)(Electricity) DG Electricity fee 515.936,94("Special Rules" generation) "Special Rules" subsidies 1.140.659,26

DSO (T&D services) T&D fee 1.567.057,89Market Operator (Electricity) Electricity market fee 0,00Regulator "Special Rules" subsidies 1.140.659,26 ("Special Rules" generation)Government (Obligation) Taxes 655.343,93O&M ICT (O&M) O&M Costs 12,00TOTAL 5.119.798,02 3.879.010,02

Retail CustomersAggregator (Electricity) Electricity retail fee 2.539.810,76

(Best consumption pattern) Scheduling fee 139.200,00(Consumption control) Control fee 139.200,00(Metering services) Metering fee 441.264,00(Best generation pattern) Scheduling fee 139.200,00(Generation control) Control fee 139.200,00(Metering services) Metering fee 441.264,00DG Electricity fee 515.936,94 (Electricity)"Special Rules" subsidies 1.140.659,26 ("Special Rules" generation)

Fuel Supplier (Fuel) Fuel fee 0,00O&M Generator (O&M) O&M Costs 515.936,94TOTAL 1.656.596,20 4.495.075,69

DSOAggregator T&D fee 1.567.057,89 (T&D services)Market Operator (Electricity) Electricity market fee 17.013.880,05

(Market management) Costs of the Market Operator 23.736,09Regulator (Obligation) "Special Rules" taxes 2.954.020,77

(Regulation) NEC cost 35.716,90Tariff Customer Electricity tariff fee 45.444.792,09 (Electricity)

Metering fee 1.753.920,00 (Metering services)TSO (Transmission services) TSO part 2.612.575,44

(Obligation) Other Taxes 3.847.214,78Government (Obligation) Taxes 8.415.719,83O&M Grid (O&M) O&M Costs 4.471.224,45TOTAL 48.765.769,98 39.374.088,31



Value Object In Value In Value Object Out Value OutMarket Operator

Aggregator Electricity market fee 0,00 (Electricity)DSO Electricity market fee 17.013.880,05 (Electricity)

Costs of the Market Operator 23.736,09 (Market management)Central Producer (Electricity) Electricity market fee 17.013.880,05TOTAL 17.037.616,14 17.013.880,05

RegulatorAggregator ("Special Rules" generation) "Special Rules" subsidies 1.140.659,26DSO "Special Rules" taxes 2.954.020,77 (Obligation)

NEC cost 35.716,90 (Regulation)TOTAL 2.989.737,67 1.140.659,26

Central ProducerMarket Operator Electricity market fee 17.237.825,89 (Electricity)O&M Generator (O&M) O&M Costs 3.654.677,56TOTAL 17.237.825,89 3.654.677,56

Tariff CustomerDSO (Electricity) Electricity tariff fee 45.444.792,09

(Metering services) Metering fee 1.753.920,00TOTAL 0,00 47.198.712,09

Fuel SupplierRetail Customer Fuel fee 0,00 (Fuel)Government (Obligation) Taxes 0,00TOTAL 0,00 0,00

TSODSO TSO part 2.612.575,44 (Transmission services)O&M Grid (O&M) O&M Costs 395.596,91TOTAL 2.612.575,44 395.596,91

GovernmentAggregator Taxes 655.343,93 (Obligation)DSO Other Taxes 3.847.214,78 (Obligation)

Taxes 8.415.719,83 (Obligation)TOTAL 12.918.278,54 0,00

Table 38 presents the profitability sheets for PV scenario.

Table 38. Profitability sheets for PV scenario


Retail Customer Electricity retail fee 3.816.213,75 (Electricity)Scheduling fee 139.200,00 (Best consumption pattern)Control fee 139.200,00 (Consumption control)Metering fee 441.264,00 (Metering services)Scheduling fee 139.200,00 (Best generation pattern)Control fee 139.200,00 (Generation control)Metering fee 441.264,00 (Metering services)(Electricity) DG Electricity fee 528.012,39("Special Rules" generation) "Special Rules" subsidies 3.803.262,80

DSO (T&D services) T&D fee 1.567.057,89Market Operator (Electricity) Electricity market fee 940.682,64Regulator "Special Rules" subsidies 3.803.262,80 ("Special Rules" generation)Government (Obligation) Taxes 884.920,56O&M ICT (O&M) O&M Costs 12,00TOTAL 9.058.804,55 7.723.948,28



Value Object In Value In Value Object Out Value OutRetail Customers

Aggregator (Electricity) Electricity retail fee 3.816.213,75(Best consumption pattern) Scheduling fee 139.200,00(Consumption control) Control fee 139.200,00(Metering services) Metering fee 441.264,00(Best generation pattern) Scheduling fee 139.200,00(Generation control) Control fee 139.200,00(Metering services) Metering fee 441.264,00DG Electricity fee 528.012,39 (Electricity)"Special Rules" subsidies 3.803.262,80 ("Special Rules" generation)

Fuel Supplier (Fuel) Fuel fee 0,00O&M Generator (O&M) O&M Costs 528.012,39TOTAL 4.331.275,19 5.783.554,14





(Obligation) Other taxes 3.847.214,78Government (Obligation) Taxes 8.415.719,83O&M Grid (O&M) O&M Costs 4.471.224,45TOTAL 48.765.769,98 39.374.088,31

Market OperatorAggregator Electricity market fee 940.682,64 (Electricity)DSO Electricity market fee 17.013.880,05 (Electricity)


RegulatorAggregator ("Special Rules" generation) "Special Rules" subsidies 3.803.262,80DSO "Special Rules" taxes 2.954.020,77 (Obligation)

NEC cost 35.716,90 (Regulation)TOTAL 2.989.737,67 3.803.262,80








Value Object In Value In Value Object Out Value OutGovernment

Aggregator Taxes 884.920,56 (Obligation)DSO Other taxes 3.847.214,78 (Obligation)

Taxes 8.415.719,83 (Obligation)TOTAL 13.147.855,18 0,00

The profitability sheet for the last scenario, microturbine scenario, is presented below, inTable 39.

Table 39. Profitability sheets for Microturbine scenario


Retail Customer Electricity retail fee 4.113.305,69 (Electricity)Scheduling fee 139.200,00 (Best consumption pattern)Control fee 139.200,00 (Consumption control)Metering fee 441.264,00 (Metering services)Scheduling fee 139.200,00 (Best generation pattern)Control fee 139.200,00 (Generation control)Metering fee 441.264,00 (Metering services)(Electricity) DG Electricity fee 0,00("Special Rules" generation) "Special Rules" subsidies 0,00

DSO (T&D services) T&D fee 1.567.057,89Market Operator (Electricity) Electricity market fee 1.642.199,66Regulator "Special Rules" subsidies 367.437,54 ("Special Rules" generation)Government (Obligation) Taxes 938.356,17O&M ICT (O&M) O&M Costs 12,00TOTAL 5.552.633,69 4.147.625,72

Retail CustomersAggregator (Electricity) Electricity retail fee 4.113.305,69

(Best consumption pattern) Scheduling fee 139.200,00(Consumption control) Control fee 139.200,00(Metering services) Metering fee 441.264,00(Best generation pattern) Scheduling fee 139.200,00(Generation control) Control fee 139.200,00(Metering services) Metering fee 441.264,00DG Electricity fee 0,00 (Electricity)"Special Rules" subsidies 0,00 ("Special Rules" generation)

Fuel Supplier (Fuel) Fuel fee 0,00O&M Generator (O&M) O&M Costs 0,00TOTAL 0,00 5.552.633,69





(Obligation) Other taxes 3.847.214,78Government (Obligation) Taxes 8.415.719,83O&M Grid (O&M) O&M Costs 4.471.224,45TOTAL 48.765.769,98 39.374.088,31



Value Object In Value In Value Object Out Value OutMarket Operator

Aggregator Electricity market fee 1.642.199,66 (Electricity)DSO Electricity market fee 17.013.880,05 (Electricity)


RegulatorAggregator ("Special Rules" generation) "Special Rules" subsidies 0,0DSO "Special Rules" taxes 2.954.020,77 (Obligation)

NEC cost 35.716,90 (Regulation)TOTAL 2.989.737,67 0,00






GovernmentAggregator Taxes 938.356,17 (Obligation)DSO Other taxes 3.847.214,78 (Obligation)

Taxes 8.415.719,83 (Obligation)Fuel Supplier Taxes 0,00 (Obligation)TOTAL 13.201.290,79 0,00



6 Annex III – Demand Response Process

The main focus of the business under analysis is changing demand patterns, to obtain acheaper electricity bill. Thereby, some distributed generators are used, but also load shifting.Each household has a different demand pattern, so averages must be used.

In 1998, the Spanish TSO, with the assistance of most Spanish DSOs, carried out a projectto map out different demand pattern in Spanish final customers, the INDEL project19.

Although the project was published in 1998, data were taken in 1996, so for the analysisdescribed here, we have taken into account the demand increase in these last years (34,6%between 1996 and 2002).

6.1 Equipment

The project describes patterns for several appliances for household use, but in the businessmodel described in this document, only washing machines, dryers, dishwashers and boilerswere considered:

• Washing machine: In 1996, the 95% of Spanish households had washing machine, withan average capacity of 2,1 kW. The washing program took about 90 minutes, with anaverage load of 0,75 kW (hot water, 0,33 kW with cold water).

• Dryer: The 5% of Spanish households had dryer in 1996, whose average capacity was2,1 kW. The program took 45 minutes on average and used 1,5 kW.

• Dishwasher: The 25% of Spanish households had dishwasher in 1996, with an averagecapacity of 2,1 kW. The program took an hour with an average load of 1,5 kW.

• Boiler: In 1996, the 12,5% of the Spanish households had electric boiler, with an averagecapacity of 1 kW. The average daily consumption of boilers was 3 kWh. The 25% ofthem were accumulators with a capacity of 75 l, and the remainding 75% of traditionalboilers had 50 l. It consumed the maximum capacity during about 15 minutes.

For simplification, we assume that the washing machine, the dryer and the dishwasher areonly put in operation once a day, and that their program takes an hour.

On the other hand, boilers only operate for a quarter of hour, with a capacity of 1 kW, butthey consume 3 kWh, so they must be put into operation 12 times/day.

Taking the market share of each appliance, different customer types were identified:

19 Red Eléctrica de España, “Proyecto INDEL – Atlas de la Demanda Eléctrica Española”,http://www.ree.es/cap07/pdf/indel/Atlas_INDEL_REE.pdf



A. Customers having washing machine only (59,23%)

B. Customers having washing machine and dishwasher (19,74%)

C. Customers having washing machine and boiler (8,46%)

D. Customers having no equipment (3,12%)

E. Customers having washing machine and dryer (3,12%)

F. Customers having washing machine, dishwasher and boiler (2,82%)

G. Customers having dishwasher only (1,04%)

H. Customers having washing machine, dryer and dishwasher (1,04%)

I. Customers having boiler only (0,45%)

J. Customers having washing machine, dryer and boiler (0,45%)

K. Customers having dryer only (0,16%)

L. Customers having dishwasher and boiler (0,16%)

M. Customers having washing machine, dryer, dishwasher and boiler (0,15%)

N. Customers having dryer and dishwasher (0,05%)

O. Customers having dryer and boiler (0,02%)

P. Customers having dryer, dishwasher and boiler (0,01%)

6.2 Time-of-use

For the business case at hand, it is important to know the time-of-use of each load to beshifted.

First of all, the average load demanded by each customer every hour must be known. Table40 shows the demand in 1996, and the estimated increase, taking into account that averageincrease has been 34,6%.

Table 40. Average household demand in every hour

W/customer 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 241996 330 225 165 150 125 130 150 195 250 275 290 300 325 365 400 410 400 390 425 475 525 565 550 4652002 (+34%) 444 303 222 202 168 175 202 263 337 370 390 404 438 491 538 552 538 525 572 639 707 761 740 626

As a result, the average daily consumption for a household customer is 10,606 kWh (SeeTable 13).



Besides, the INDEL project provided, for each appliance, the percentage of appliancesworking in each hour of the day, as Table 41 shows.

Table 41. Time-of-use of household equipment

% 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24Washing machine 0 0 0 0 0 0 0 2 6 14 18 14 9 6 2 2 3 4 4 4 4 4 3 1Dryer 0 0 0 0 0 0 0 1 2 12 15 17 12 10 2 2 2 3 4 4 5 4 3 2Dishwasher 1 0 0 0 0 0 0 1 2 3 3 3 4 6 12 12 10 4 2 3 6 10 10 8Boiler 3 2 1 1 1 1 3 7 11 10 5 3 2 2 3 6 9 8 6 4 3 3 3 3

Next step is determining the shiftable load in each hour of the day. To that end, we musttake into account the market share of each appliance, its average load during an hour, thepercentage of use of that appliance in each hour, and the number of customers under study(N).

For example, at 11 a.m., there are working 18% of washing machines, 15% of dryers, 3% ofdish washers and 5% of boilers.

• Load of washing machines = 18% * 95% * 0,75 kW * N = 0,1283*N

• Load of dryers = 15% * 5% * 1,5 kW * N = 0,01125*N

• Load of dishwashers = 3% * 25% * 1,5 kW * N = 0,01125*N

• Load of boilers = 5% * 12,5% * 1 kW * 12 operation times * N = 0,075*N

As a result, at 11 a.m., there are 0,2258*N kW shiftable, and 0,390*N kW total.

Following the same process for the whole day, Table 42 is obtained, in which hourly demandper customer is presented.

Table 42. Hourly demand per customer

W/cust 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24Total 444 303 222 202 168 175 202 263 337 370 391 404 437 491 539 552 538 525 572 639 707 760 740 626

Shiftable 49 30 15 15 15 15 45 124 217 270 226 169 118 103 106 151 195 166 129 103 100 114 106 84

Non-shiftable

395 273 207 187 153 160 157 139 120 100 165 235 319 388 433 401 343 359 443 536 607 646 634 542

Last data required are market prices for every hour. These data have been obtained fromthe Spanish Market Operator’s website20. Averages cannot be taken, since they do notreflect real price peaks, because peaks do not occur always at the same hour. Table 43shows hourly market prices for the 15th of January of 2003 in cent/kWh.

Table 43. Hourly market prices for a January day

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24Price 3,06 2,34 2,09 1,97 1,88 1,65 2,37 2,82 3,66 5,04 5,60 6,42 5,40 5,11 3,74 4,43 5,11 5,57 7,69 10,1 6,88 5,36 3,82 3,37

As a result, the average daily electricity cost for a household customer can be obtained fromTable 40 and Table 43: 52,36 cent/customer, that is, a yearly cost of 191,11 €/customer.

20 http://www.omel.com



6.3 Demand shifting

As Table 43 shows, demand should be shifted from hours about 8 p.m. to 5 a.m. Theproblem is that not all the load can be moved to the same hour.

As stated in section 6.1, the capacity of boilers is 1 kW, while the other three applianceshave 2,1 kW. Average capacity in household customers is 3,3 kW (See Table 13), so boilerscan be used jointly with another appliance, but no other appliances can be used at the sametime. As a result, customers with more than one appliance (except boilers) will shift part ofthe demand to the cheapest electricity price hour, and the remainding of shiftable load toclose hours.

Dryers and dishwashers have a higher consumption, so they will be shifted before theshifting of washing machines. Consequently, all the dryers will be moved to 6 a.m., and alsothe 95% of dishwashers (the other 5% has dryer) and the 71,25% of washing machines (theother 28,75% has dryer or dishwasher). The remainding 5% of dishwasher will be shifted to5 a.m. (next cheapest hour), as well as the 27,5% of washing machines (the other 1,25%have dryer and dishwasher, and will be shifted to 4 a.m.).

Boilers will not be replaced, so they will heat water in long periods and will not replace thewater they used with new cold water until their load cycle is close to start. Daily boilerconsumption is 3 kWh. Taking into account that the specific heat for water is 1 cal/ºC.g,density 1000 g/l, and that 1 kWh = 860,425 kcal, the daily hot water consumption is about100 l. Thus, boilers will have two cycles, one in night hours and another one during thecheapest day hours.

Accumulators will heat water for two hours (67 l) between 5 and 7 a.m., and one hour (33 l)at 3 p.m.; while traditional boilers will heat an hour and a half (50 l) between 5:30 a.m. and 7a.m., and another hour and a half between 3 p.m. and 4:30 p.m.

As a result, the demand for hot water, 25% for accumulators and 75% for traditional boilers,will be shifted to four hours:

• 5 a.m.: A 33,33% of the demand for accumulators plus a 16,67% of the demand fortraditional boilers (20,8035% of the demand for hot water)

• 6 a.m.: A 33,33% of the demand for accumulators plus a 33,33% of the demand fortraditional boilers (33,33% of the demand for hot water)

• 3 p.m.: A 33,33% of the demand for accumulators plus a 33% of the demand fortraditional boilers (33,33% of the demand for hot water)

• 4 p.m.: A 16,67% of the demand for traditional boilers (12,505% of the demand for hotwater)

Once demand shifting is performed, the resulting hourly demand per customer is presentedin Table 44.



Table 44. Hourly demand after demand shifting

W/cust 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24Total 395 273 207 196 680 1599 157 139 120 100 165 235 319 389 933 589 343 359 443 537 607 647 634 542

The new daily electricity cost will be 44,99 cent/customer, that is, a yearly cost of 164,20€/customer. The yearly saving per customer is, therefore, 26,91 €/year (14,08%).

6.4 Distributed Generation

In order to reduce even more the electricity bill, retail customers install in their premisesdistributed generators. In principle, each customer has a small-scale distributed generator,but it is also possible to have bigger generators, sharing costs and benefits between all thecustomers. In this business model we assume that all the customers have a distributedgenerator.

Distributed generators will be put into operation when DG electricity price is lower thanmarket price. The difference between demand and DG generation must be purchased in themarket. As a result, the electricity cost for the Aggregator at any time is the sum of marketcost (electricity purchased in the market * market price) and DG cost (electricity purchasedto DG * DG electricity price). For example, if DG price is 0,03 €/kWh, the generator will berunning from 9 a.m. to 1 a.m.

An important issue refers to availability of DG generator. Micro hydro generator has anavailability of 40%, PV generator is available the 21% of daylight hours (8-20) andmicroturbines are available the 94% of time (See Table 14). Availability has to be taken intoaccount when calculating the electricity produced, since actual electricity generation per hourwill be generator’s capacity * availability * number of generators.

Distributed generators will be included in the “Special Rules” and receive a premium for theelectricity produced. The premium depends on the technology used to generate and theamount of electricity produced (See Table 14).

Summarising, the savings anticipated in section 6.3 are increased by the electricity notpurchased in the market and the premium obtained for the “Special Rules” generation.

d 4.4 energy services case study...business models in a world characterised by distributed...

Documents