going smarter: the case of distributed generation

www.eprg.group.cam.ac.uk

Karim Anaya

Judge Business SchoolUniversity of Cambridge

International Academic Symposium Energy and Environmental Policy

IEB, Barcelona

02 February 2016

Going Smarter: The case of Distributed Generation


• Aim of the study

• About distributed generation (DG)

• Going smarter

• Case study o Introduction

o Methods

o Scenarios

o Variables and assumptions

o Results

• Conclusions

Outline


3

Aim of the study*

• Evaluate and quantify the most relevant benefits from

facilitating earlier and greater quantities of DG by

examining different connection scenarios using smart

solutions.

• Analyse the allocation of these benefits across the

different parties.

• Propose an innovative way that allows a fairer allocation

of these benefits (smart connection incentive).

(*) This is one of the five papers (Anaya and Pollitt) that the EPRG has produced under the context of the Flexible Plug

and Play project (implemented by UK Power Networks).


4

About distributed generation(*)

Definition• Power generator unit connected to the distribution network or to the point

of consumption.

• Can be grid connected or stand alone.

Challenges and opportunities • Distribution networks designed to be passive with minimal levels of control

and network visibility.

• Negative effect: voltage fluctuation and regulation, thermal constraints,

frequency variation and regulation, power factor correction, harmonics. (Ochoa et al.,2011; Passey et al., 2011; Wojszczyk and Brandao; 2011).

• Positive effect: lower electrical losses, demand reduction, network

reinforcement deferral, security of supply, provision of ancillary services.(Gil and Joos, 2006; Mendez et al., 2006; Harrison et al., 2007; Passey et al., 2011; Wang et al., 2009).

(*) Also known as embedded generation, decentralised generation, disperse generation.


5

About distributed generation

Support for DG• Renewable DG: Feed-in Tariff (FIT), Premium FIT, Renewables Obligation.

• Sophistication of these schemes has increased over time (technology

specific digression rates, bonuses and stepped tariffs), Anaya and Pollitt (2015b).

DG is getting important…

Figure 1: Number of DG Enquiries (UK Power Networks)

Source: UK Power Networks

208 376683

1,967

3,596

6,879

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

2008 2009 2010 2011 2012 2013

No of DG enquiries

Type of DG enquiries (2013):

Solar PV (87.8%),

onshore wind (6.2%),

CHP (3.1%)


6

Going Smarter in DG

• It is not only about smart technical solutions…

Going

smarter

Smart technical

solutions

Active Network

Management (ANM),

Dynamic Line

Rating (DLR),

Quadrature-booster (QB)

`

Smart

commercial

arrangements

flexible/interruptible

capacity, lower connection

costs, capacity quota,

principle of access

(pro rata, LIFO)

Smart regulation

Funding schemes

(LCNF, NIC), flexibility project,

auctions for DG, lower

socialisation cost

Figure 2: Going smarter


7

Case Study

Introduction: • Based on Flexible Plug and Play project implemented by UK Power

Networks in the constrained area of March Grid.

• Going smarter vs conventional reinforcement (£4.1m).

• Offers for smart connections with pro rata curtailment and maximum

quota (33.5 MW), Baringa – UK Power Networks (2013).

Figure 3: Constrained Area

March Grid

Constraints (33 and 11kV):

*Reverse power flow limitation

*Thermal line limits

*700 sq. km Trial Area


8

Case Study

Methods: Estimation of net benefits using a Cost Benefit Analysis (CBA):

1. DG Owners

𝑹𝒆𝒗𝒆𝒏𝒖𝒆𝒔 - Costs

Where 𝑹𝒆𝒗𝒆𝒏𝒖𝒆𝒔 = 𝒆𝒍𝒆𝒄𝒕𝒓𝒊𝒄𝒊𝒕𝒚 𝒓𝒆𝒗𝒆𝒏𝒖𝒆𝒔 + 𝒊𝒏𝒄𝒆𝒏𝒕𝒊𝒗𝒆𝒔 + 𝑬𝑩 𝑮𝒆𝒏. +𝒆𝒏𝒆𝒓𝒈𝒚 𝒔𝒂𝒗𝒊𝒏𝒈𝒔 𝒔𝒐𝒍𝒂𝒓 𝑷𝑽

𝑪𝒐𝒔𝒕𝒔 = 𝒈𝒆𝒏𝒆𝒓𝒂𝒕𝒊𝒐𝒏 𝒄𝒐𝒔𝒕𝒔 + 𝒄𝒐𝒏𝒏𝒆𝒄𝒕𝒊𝒐𝒏 𝒄𝒐𝒔𝒕𝒔

2. Wider society

Supplier share of embedded benefits (EB)

3. DNO

DG Incentives (DG connected by March 2015). Incentives already removed, OFGEM (2009,

2013)

* All benefits and costs discounted (2014 prices). For further details about the methodology see Anaya and Pollitt (2015a).


9

Case Study

Scenarios:• Connection scenarios based on the latest list of generators that planned to

connect to the constrained area of March grid (before April 2015).

• Connection options: S1: partial quota (100% wind), S2: partial quota (a mix

of generation), S3: full quota (a mix of generation).

Table 1: Summary of Scenarios

Scenario Installed capacity

(MW) wind solar PV AD CHP

Scenario 1 14.5 100%

Scenario 2 27.6 52.5% 43.9% 3.6%

Scenario 3 33.5 60.8% 36.2% 3.0%

Generation mix (% installed capacity)


10

Case Study

Scenarios:Table 2: List of generators

No Capacity

Estimated

annual

curtailment

Estimated

annual

curtailment

Estimated

annual

curtailment

FPP smarter

connection

costs 1/

Generators (MW) MWh MWh MWh (£ m) S1 S2 S3

1 Wind 1 0.5 0.085% 1 1.63% 21 1.84% 24 0.06 0.15 0.08 0.06

2 wind 2 1 0.085% 2 1.63% 43 1.84% 48 0.13 0.30 0.16 0.13

3 Wind 3 1.5 0.085% 3 1.63% 64 1.84% 73 0.19 0.45 0.23 0.19

4 Wind 4 0.5 0.085% 1 1.63% 21 1.84% 24 0.06 0.15 0.08 0.06

5 Wind 5 10 0.085% 22 1.63% 428 1.84% 484 1.29 2.98 1.57 1.29

6 Wind 6 0.5 0.085% 1 1.63% 21 1.84% 24 0.06 0.15 0.08 0.06

7 Wind 7 0.5 0.085% 1 1.63% 21 1.84% 24 0.06 0.15 0.08 0.06

8 Solar PV 1 4 2.15% 84 2.30% 90 0.52 0.63 0.52

9 Solar PV 2 6.927 2.15% 146 2.30% 156 0.89 1.08 0.89

10 Solar PV 3 1.2 2.15% 25 2.30% 27 0.15 0.19 0.15

11 AD CHP 1 0.5 0.53% 19 0.59% 22 0.06 0.08 0.06

12 AD CHP 2 0.5 0.53% 19 0.59% 22 0.06 0.08 0.06

13 Wind 8 5.873 1.84% 284 0.76 0.761/ The cost of the ANM equipment (estimated in £50,000/generator) has been included in these costs.

Annual

curtailment

limit (%)

Annual

curtailment

limit (%)

Annual

curtailment

limit (%)

Reinforcement costs (£m)

Generators Scenario 1 (S1) Scenario 2 (S2) Scenario 3 (S3) Costs (2014 prices)


11

Case Study

Variables and assumptions:

Technical variables:Capacity factor (wind: 30%, solar PV: 11.16%, AD CHP: 84%), PV module

degradation (0.55% pa), export rate for PV (85%), losses average

transmission (2%), ratio (losses): 45% generator, 55% supplier

Annual curtailment rateVariable. Max. value: wind (1.84%),

solar PV (2.3%), AD CHP (0.6%)

Discount rateTechnology-specific: wind (8.3%),

solar PV (6.2%), AD CHP (13%)

Source: DECC (2011, 2013), Elexon (2013), Pöyry (2013), UK Solar Trade Association, Smarter Grid Solutions.


12

Case Study

Results:Figure 4a: Generators’ benefits Figure 4b: Wider society benefits

Figure 4c: DNO’s benefits


13

Case Study

Results:

• Going smarter is by far the most cost effective solution but…

• Wider society benefits the least.

• A smart connection incentive would help to a better allocation of

the benefits for connecting more DG…

Smart connection incentive (paid by generators to DNO):

• Recreates the benefits from earlier losses incentives @ £48.42 /

MWh.

• Estimation of system losses reduction based on OFGEM (2003).

• Estimated at £13,535/MW (average).

• Represents 8% (S1, t+20) of total savings due to network

investment deferral.


14

Case Study

Results:

Table 3: Allocation of Benefits

Parties Type of benefit (£m) 1/

Unit S1 S2 S3

DG owners

Non-firm connections (going

smarter) £m 19.0 22.7 27.7

Embedded benefits (generators) £m 0.5 0.8 1.0

(-) Smart connection incentive £m -0.2 -0.3 -0.4

DNO DG incentives £m 0.4 0.8 0.9

Smart connection incentive £m 0.2 0.3 0.4

Wider society Embedded benefits (suppliers) £m 0.6 0.7 1.1

(-) DG incentives £m -0.4 -0.8 -0.9

Total £m 20.1 24.2 29.7benefits £m/MW 1.4 0.9 0.9


15

Conclusions• Substantial benefits from smart connection arrangements over

conventional alternative.

• DG owners benefit the most from smarter connections.

• The benefits of faster, smarter connection need to be shared out better,

in a way that all parties clearly benefit.

• DNOs should be allowed to charge DG owners. A smart connection

incentive should be an option.

• The smart connection incentive may contribute to the reduction of

network upgrade or reinforcement costs which usually are borne by

customers.

• Incentives/subsidies paid by wider society are more than their direct

benefits, but reflect cost of achieving the EU renewables target.


16

References• Anaya, K. and Pollitt, M.G. (2015a), “Options for allocating and releasing distribution system capacity: Deciding between interruptible connections and firm

DG connections”, Applied Energy, Vol. 144, pp. 96-105.

• Anaya, Karim L. and Pollitt, M.G. (2015b). “Integrating distributed generation: Regulation and trends in three leading countries,” Energy Policy, Vol. 85,

issue C, p. 475-486. In special issue: “The regional integration of energy markets”.

• Baringa – UK Power Networks (2013), “Flexible Plug and Play: Reinforcement quota calculation for March Grid”, Baringa Partners and UK Power

Networks, London.

• DECC (2011), “Consultation on proposal for the levels of banded support under the Renewables Obligation for the period 2013-17 and the Renewables

Obligation Order 2012”, Department of Energy and Climate Change, London.

• DECC (2013), “Electricity Generation Costs (December 2013)”, Department of Energy and Climate Change, London.

• Elexon (2013), “Transmission Losses. Guidance”, ELEXON, London.

• Gil, H.A. and Joos, G. (2006), “On the quantification of the network capacity deferral value of distributed generation”, IEEE Trans. Power Syst., Vol. 21,

No. 4, pp. 1592-1599.

• Harrison, G.P., Piccolo, A., Siano, P. and Wallace, R. (2007), “Exploring the tradeoffs between incentives for distributed generation developers and

DNOs”, IEEE Trans. Power Syst., Vol. 22, No. 2, pp. 821-828.

• Mendez, V.H., Rivier, J. and Gomez, T. (2006), “Assessment of energy distribution losses for increasing penetration of distributed generation”, IEEE

Trans. Power Syst., Vol. 21, No. 2, pp. 533-540.

• Ochoa, L.F., Keane, A. and Harrison, G.P. (2011), “Minimizing the reactive support for distributed generation: Enhanced passive operation and smart

distribution networks”, IEEE Trans. Power Syst., Vol. 26, No. 4, pp. 2134-2142.

• OFGEM (2003), “Electricity distribution losses. A Consultation document. January 2003”, Office of Gas and Electricity Markets, London.

• OFGEM (2009), “Electricity distribution price control review. Final proposal – Incentives and Obligations”, Office of Gas and Electricity Markets, London.

• OFGEM (2013), “Strategy decision for the RIIO-ED1 electricity distribution price control. Outputs, incentives and innovation. Supplementary annex to

RIIO-ED1 overview paper”, Office of Gas and Electricity Markets, London.

• Passey, R., Spooner, T., MacGill, I., Watt, M. and Syngellakis, K. (2011), ”The potential impacts of grid-connected distributed generation and how to

address them: A review of technical and non-technical factors”, Energy Policy, Vol. 39, No. 10 , pp. 6280-6290.

• Pöyry (2013), “Potential impact of revised Renewables Obligation technology bands: updated modelling”, Pöyry Management Consultancy, Oxford.

• UK Power Networks (2015), “DG Forum Flexible Distributed Generation”, UK Power Networks, London.

• Wang, D.T.C, Ochoa, L.F. and Harrison, G.P. (2009), “Distributed generation and security of supply: Assessing the investment deferral”, Paper presented

at 2009 IEEE Bucharest Power Tech Conference, June 28th-July 2nd, Bucharest, Romania.

• Wojszczyk, B. and Brandao, M. (2011), “High penetration of distributed generation and its impact of electric grid performance – utility perspective”, Paper

presented at Innovative Smart Grid Technologies Asia (ISGT), 2011 IEEE PES, November 2011.


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Thank you!


18

Appendix: Smart connection incentive

Assumptions for estimation of the smart connection incentive:

• Estimated based on benefits from losses incentive to DNOs;

• Accordingly to OFGEM (2003), the share of distribution losses is 19% (132kV), 14%

(33kV), 34% (11 kV) and 34% (LV);

• If a generator is connected at 33kV, electric losses are 19% of the average distribution

losses;

• If a generator is connected at 11kV, electric losses are 33% (19%+14%);

• Average distribution losses: 4.89% (Easter Power Networks, period 2005/06-2009/10);

• Example: wind farm (0.5MW, connected at11kV) generates 1,310 MWh pa, annual losses

would be 64.05 MWh (1,310*4.89%), thus losses reduction are 21.13 MWh pa (64.05*33%)

• Same procedure is applied for the rest of generators (across Scenarios: 1, 2, 3)

• Losses reduction value at £48.42 MWh (OFGEM)


19

Appendix: Smart connection incentive

Example smart connection incentive:

• Smart connection incentive: £15,850/MW (Scenario 1), £12,360/MW (Scenario 2) and

£12,395/MW (Scenario 3). Average: £13,535/MW (based on OFGEM (2003)).

• Looking at how reasonable this is:- DNO’s fee as % of savings due to deferral of investment varies based on the year when the network upgrade

is made (t+1,…., t+20)

- Smart connection incentive (as % of total reinforcement costs) varies from 8% (Scenario 1, t+20) to 178%

(Scenario 3, t+1)

Figure A: Smart connection incentive as percentage of total savings for network investment deferral

going smarter: the case of distributed generation

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