systems analysis for tata power corporation’s (tpc) distribution business in india

NRE 597 Final Term Project Divyesh K., Jiarui C., Matthew G. and Nalin D.

Systems Analysis for Tata Power Corporation’s (TPC) distribution business in India

1 ABSTRACT

This report analyses the electricity distribution business of India’s private sector distribution company –

Tata Power Corporation (TPC) from an environmental systems perspective. The aim is to find an

optimum electricity mix derived from non-renewable and renewable energy sources so that the power

procurement and obligation compliance costs are minimum given existing installed power generation

capacity and RPO* constraints. We compared costs of operation in two scenarios – a) A business as usual

case, where TPC follows the current Power Purchase Agreement (PPA) commitments and b) a

hypothetical case where TPC forgoes current PPAs and is free to access power from any generator. Our

model suggests that TPC with no PPAs will incur a total cost of power procurement and RPO compliance

(Delhi + Mumbai) of 62.56 billion INR† and with PPAs TPC spends 66.86 billion INR. A no PPA

scenario has savings to the tune of 4.3 billion INR. The analysis has applications pertaining to

restructuring of existing distribution business for TPC and also serves as a guide for other private players

looking to explore opportunities in India’s dynamic power distribution business.

2 INTRODUCTION

TPC operates as a distribution company (DISCOM) in North Delhi‡ and Mumbai§, and as a distribution

franchise for Jamshedpur circle1. For the analysis in this report, we are considering operation of TPC as a

DISCOM only and not as a distribution franchise**, hence we focus on TPC’s operation in Delhi and

Mumbai only. TPC as of April 2014, serves a consumer base of 1.4 million in Delhi and 0.5 million2 in

Mumbai. Higher consumer base translates into higher electricity demand in Delhi. In both cases, TPC

currently has long term power purchase agreements (PPAs) with local generators. Other than PPAs a

DISCOM is constrained to meet renewable power purchase obligations. TPC as a DISCOM is obligated

under RPO regulations to procure a minimum percentage of renewable energy of total annual energy

sales. TPC complies with RPO targets on behalf of its consumers. More about RPO Regulations is

detailed below:

In India, government-mandated Renewable Purchase Obligations (RPO) operate in much the same way as

Renewable Portfolio Standards (RPS) do in the United States. These state-specific RPO mandates require

that the obligated entities listed below procure a certain percentage of their total annual energy sales from

renewable sources:

* Renewable Purchase Obligation, similar to US’s RPS. † INR – Indian National Rupees ‡ Delhi is the National Capital of India. § Mumbai is the largest city of State of Maharashtra, it is regarded as the commercial capital of India ** Distribution franchises in India are presently not obligated by RPO


Open Access†† (OA) Consumers - These are those consumers that do not meet their power needs

by procuring from a distribution company, and instead procure power from any generator willing

to sell power at a competitive price.

Distribution Companies - These are in most cases government owned/public utilities tasked to

operate as a distributor of electricity at regional levels. Private utilities run Tata Power, Reliance

Power and Torrent Power only.

Captive Power Plant Owners - These are generators who set up their own power plants to meet

their energy needs. As per Indian Electricity Laws, any entity having 26% ownership and

procuring a minimum of 51% energy from such plants is a captive power plant.

For all of these obligated entities, RPO targets are further distinguished as being either Solar (S)‡‡ or Non-

Solar (NS). This distinction leads to different minimum renewable requirements for both S and NS. RPO

targets are primary drivers for demand in REC markets. REC stands for Renewable Energy Certificates

and is essentially a de-materialized form of renewable energy transaction. Trading in REC markets

happens on the last Wednesday of every month and the market clearing price is determined via a double

sided closed auction system, meaning both the buyers and sellers of RECs remain undisclosed. REC

markets trade separately for non-solar and solar RECs. The Central Electricity Regulatory Commission§§

(CERC) has determined floor and forbearance price for both type of RECs. The prices are:

Particulars Floor Price Forbearance or Ceiling Price

Non Solar RECs 1500 INR per REC 3300 INR per REC

Solar RECs 9300 INR per REC 13400 INR per REC

Table 1: Floor and Forbearance price of Non-Solar and Solar RECs

As 1 REC is equivalent to 1 MWh of renewable electricity, it can be concluded that 1 unit (or kWh) of

non-solar electricity in REC market costs INR 1.5 and that a unit of solar electricity costs INR 9.3. The

floor and forbearance price mentioned above are only determined up to 2017. Prices post 2017 are being

estimated by electricity regulators and will be made available in the near future. Penalty for Non-

compliance of RPO targets has been defined in most states as the product of forbearance price and actual

deficit. However, at present the state government is inefficient in enforcing penalties for non-compliance.

This is also a prime reason that Indian REC markets have been performing poorly for over a year.

For the sake of this report, the focus will be on DISCOMs and since, in practice, it is the DISCOM that is

procuring the electricity to meet these RPO standards on behalf of the consumer, we look at optimization

functions specifically related to the DISCOM.

TPC has two options for complying with RPO standards:

†† Open Access is defined in Electricity Act 2003 as - “Non-discriminatory provision for the use of transmission lines or distribution system or associated facilities with such lines or system by any licensee or consumer or a person engaged in generation in accordance with the regulations specified by the Appropriate Commission”. It allows large users of power — typically having connected load of 1 megawatt (Mw) and above — to buy cheaper power from the open market. ‡‡ Similar RPS solar carve-out mechanism in US. §§ CERC is an apex regulatory body of Power Sector in India.


Purchase Renewable Energy Credits (RECs) from the REC market. An REC is a symbolic

holding of 1 MWh of renewable electricity produced which is able to be sold from the generator

to any buyer willing to purchase in that market.

Procure physical electricity from renewable sources. This means TPC procures physical

renewable electricity from other RE generators within the state.

RPOs are set based on the Indian Financial Year (FY) that runs from April 1st until March 31st of the

following year. Table 2 below shows the RPO standards for both Delhi and Maharashtra currently. While

there are a number of factors involved, differences between states are primarily due to the differential

renewable potential. Delhi is a renewable resource-poor state and Maharashtra has significantly high

renewable resource potential.

Delhi Delhi Maharashtra Maharashtra

Solar Non-Solar Solar Non-Solar

FY 2014-15 0.25% 5.95% 0.50% 8.50%

Table 2: Non-Solar and Solar RPO targets for FY 2014-15 in Delhi and Maharashtra

A schematic diagram of distribution business by Tata Power Company offers more clarity on its

operations.

Figure 1: Schematic of usual power distribution business

For any distribution company (DISCOM), the annual revenue requirement (ARR) is the official document

in which the budget of a full financial year is presented before the state electricity regulator for approval.

After intense scrutiny, the state regulator approves the ARR, which now becomes to be known as Tariff

Order (T.O) for a particular financial year (FY). A mid-year review of all approved data is carried out in a

separate document known as Annual Performance Review (APR), which is just to check if the funds


allocated are adequate for smooth functioning of the DISCOM. Finally after a financial year ends on the

31st of March, another final set of review documents known as True-up is initiated by the regulators. Any

surplus or deficit in funds is then accounted for in the ensuing year’s T.O.

In ARRs, power purchase cost remains the single most dominant factor. Other factors such as operating

and maintenance cost, labour costs etc. have just a marginal influence with respect to power purchase cost

in the overall budget formulation for a financial year. Therefore, it becomes pertinent to minimize this

power purchase cost of any distribution company.

3 METHODS

As discussed earlier, this analysis only takes into account TPC’s distribution business in Delhi and

Mumbai. Mumbai (Maharashtra) and Delhi both have different renewable resource potential which leads

to differential RPO targets for the current year FY 2013-14. Since, Maharashtra has more renewable

potential to be harnessed it has higher RPO targets whereas Delhi’s low RPO targets are attributed to its

low renewable resource potential.

3.1 SYSTEM DESCRIPTION

3.1.1 Objective

TPC needs to comply with a minimum percentage of solar and non-solar RPO targets for both Delhi and

Maharashtra in their total operational considerations. Therefore, the objective of this analysis is to

minimize the cost of physical power (renewable and non-renewable) procurement as well as the cost of

procuring RECs (solar and non-solar) from the market for Delhi and Maharashtra in the context of RPO

and capacity constraints.

3.1.2 System Boundaries

The system is limited to geographical regions of Delhi and Maharashtra, with the following assumptions:

Assumption 1: No electricity transfer from other state.

TPC in both states doesn’t procure power from other states. TPC-Delhi meets all its power procurement

needs by purchasing power within its territory, similarly TPC-Mumbai procures power from within

Maharashtra only.

Assumption 2: Large hydro potential is negligible for Delhi.

In Delhi, power generators produce power only from coal, natural gas, solar energy, non-solar renewable

energy resources.

In Maharashtra, there are coal, natural gas, solar energy, non-solar renewable energy resources as well as

large hydro generators.

Assumption 3: Prices of non-solar and solar RECs are considered to be at their floor.

Subsequent to poor performance in Indian REC markets, it is being assumed that the price of RECs for

both solar and non-solar categories will be at the floor price. The graphs below throw more light on the

present situation:


Figure 2: Non Solar REC price in INR3

Figure 3: Solar REC price in INR4

3.1.3 Decision Variables

The decision variable in this system is the physical power purchased from different types of generators, as

well, the amount of solar and non-solar RECs*** procuring in Delhi and Maharashtra. The physical power

P values are represented in MUs††† and number of RECs are denoted by ‘R’.

PCD = Physical power procured from

coal plants in Delhi PCM =

Physical power procured from

coal plants in Maharashtra

*** 1 REC = 1 MWh = 1000 KWh = 1000 units ††† Million Units – 1000,000 units ( 1 unit = 1 kWh)


PNGD = Physical power procured from

nat. gas plants in Delhi PNGM =


nat. gas plants in Maharashtra

PSD = Physical power procured from

solar plants in Delhi PLHM =


large hydro power plants in

Maharashtra

PNSD =


non-solar renewable energy

plants in Delhi

PSM = Physical power procured from

solar plants in Maharashtra

RSD = Solar RECs procured in Delhi PNSM =


non-solar renewable energy

plants in Maharashtra

RNSD = Non-Solar Renewable RECs

procured in Delhi RSM =

Solar RECs procured in

Maharashtra

RNSM = Non-Solar Renewable RECs

procured in Maharashtra

3.1.4 System Model

Tata Power Company is a traditional electricity distribution company and has made contracts with

electricity generation companies, known as Power Purchase Agreements (PPA). As discussed earlier,

TPC is operating in two scenarios: firstly, in its business as usual scenario, where it abides by current

PPAs; secondly, in a scenario with no PPAs executed, meaning TPC has free access to any generator

producing power from any resource.

For both scenario 1 and scenario 2, the objective is to minimize total cost of physical power procurement

and RECs purchase under capacity limitation and PPA. Hence, one objective function (as defined below)

works for both scenarios:

(Min) Z = [PCDWCD + PNGDWNGD + PSDWSD +PNSDWNSD+ RSD WRSD +RNSD WRNSD] + [PCMWCM +

PNGMWNGM + PLHMWLHM +PSMWSM + PNSMWNSM + RSMWRSM +RNSM WRNSM]

Where ‘P’ values are decision variable and ‘W’ values are constants (“W” being the per unit cost of

electricity from different sources). These per unit cost of electricity are considered as:

Mumbai

Power purchase cost

(in INR Per kWh) Reference

WCM 3.78 WISE Report5

WNGM 3.27 CERC Annual Report 20136

WLHM 2.81 CERC Annual Report 20137

WSM 7.87 CERC Annual Report 20138

WNSM13 5.43 CERC Annual Report 20139


WRNS 1.5

Non Solar REC Floor Price - Assumed that market will continue

performing poorly due to low enforcement of RPO

WRS 9.3

Solar REC Floor Price - Assumed that market will continue


Delhi

Power purchase cost

(in INR Per kWh) Reference

WCD 3.78 WISE Report10

WNGD 3.27 CERC Annual Report 201311

WLHD 2.81 CERC Annual Report 201312

WSD 7.87 CERC Annual Report 201313

WNSD‡‡‡ 5.62 CERC Annual Report 201314

WRNSD 1.5

Non Solar REC Floor Price - Assumed that market will continue


WRSD 9.3

Solar REC Floor Price - Assumed that market will continue


‘W’ values in the case of non-solar resources is different for both Delhi and Mumbai. This is because

Maharashtra and Delhi have different levelized costs of electricity from different non-solar resources.

More details on individual costs for different non-solar resources can be found in the Appendix.

3.1.5 Constraints

For defining the constraints, since the cost and quantity of power in a PPA between a generator and

DISCOM is not available in public domain, reasonable assumptions have be considered.

Assumption 4: TPC procures total electricity equal to total consumer electricity demand in FY2014 in

Delhi and Maharashtra respectively. Which means the sum of PCD, PNGD, PSD, PNSD is equal to consumer

electricity demand in Delhi (=8902.63 MUs), and the sum of PCM, PNGM, PLHM, PSM, PNSM is equal to

consumer electricity demand in Mumbai (=7006.67 MUs).

Assumption 5: A PPA between TPC and electricity generation companies are only made with

conventional generators who generate electricity from nonrenewable energy, and it equals consumer

demand multiplied by India’s national installed capacity ratio. It can be represented in the equation below:

𝑚𝑖𝑛𝑖𝑚𝑢𝑚 𝑝𝑟𝑜𝑐𝑢𝑟𝑒𝑚𝑒𝑛𝑡 𝑓𝑟𝑜𝑚 𝑒𝑎𝑐ℎ 𝑛𝑜𝑛 − 𝑟𝑒𝑛𝑒𝑤𝑎𝑏𝑙𝑒 𝑠𝑜𝑢𝑟𝑐𝑒 𝑜𝑓 𝑒𝑛𝑒𝑟𝑔𝑦

= 𝑐𝑜𝑛𝑠𝑢𝑚𝑒𝑟 𝑑𝑒𝑚𝑎𝑛𝑑 × 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦 𝑟𝑎𝑡𝑖𝑜

In India, the national installed capacity ratios are:

Source of energy Installed capacity ratio

Coal 60%

Natural gas 7%

‡‡‡ ‘W’ values here are averages of per unit cost for all Non-Solar renewable resources which include – Wind, Small hydro (less than 25 MW), Biomass power, non-fossil based cogeneration, biomass gasifier power, biogas based cogeneration.


Large hydro 16%

2014 Mumbai Delhi

Percentage Total power procured 7006.67 8902.63

60% Coal Min 4204.002 5341.578

7% NG Min 490.4669 623.1841

16% LH Min 1121.067

Assumption 6: In each state, the maximum of non-renewable electricity that can be harnessed from

different sources equals installed capacity of plants. So for each non-renewable source of electricity, the

maximum capacity can be obtained by the following equation:

Max. Capacity of non-renewable resources = 𝑖𝑛𝑠𝑡𝑎𝑙𝑙𝑒𝑑 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦 × 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦 𝑓𝑎𝑐𝑡𝑜𝑟 ×

365 𝑑𝑎𝑦𝑠/𝑦𝑟 × 24ℎ𝑟/𝑑𝑎𝑦

However, there is no minimum renewable power procurement requirement consideration in both Delhi

and Maharashtra. Minimum renewable (physical power+RECs) procurement is limited by RPO

regulations.

Assumption 6: Solar potential in India is manifold. There are no reports in public domain which quantify

the total potential of solar energy resource. Hence, there is no maximum value for PSM and PSD. Whereas,

the maximum values for PNSM and PNSD are defined by the respective non-solar resource potential in each

state.

For Scenario 1 (Business as usual with PPA):

Besides RPO constraints, TPC also has to comply with current PPAs, thus the objective function is

subjected to:

Total Electricity Provided by Tata in Delhi: PCD + PNGD + PSD + PNSD = 8,902.63 MU

Coal Capacity in Delhi: 5,341.58 MU ≤ PCD ≤ 5,575.56 MU

Natural Gas Capacity in Delhi: 623.184 MU ≤ PNGD ≤ 768.89 MU

Solar Capacity in Delhi: 0 MU ≤ PSD

Non-Solar Renewable Capacity in Delhi: 0 MU ≤ PNSD ≤ 849.77 MU

Solar RPO in Delhi: (PCD + PNGD) * 0.0025 ≤ RSD + PSD

Non-Solar RPO in Delhi: (PCD + PNGD) * 0.0595 ≤ RNSD + PNSD

Total Electricity Provided by Tata in Maharashtra: PCM + PNGM + PLHM + PSM + PNSM = 7,006.67 MU

Coal Capacity in Maharashtra: 4,204 MU ≤ PCM ≤ 95,290.22 MU

Natural Gas Capacity in Maharashtra: 490.467 MU ≤ PNGM ≤ 17,491.96 MU

Large Hydropower Capacity in Maharashtra: 1,121.07 MU ≤ PLHM ≤ 14,771.98 MU

Solar Capacity in Maharashtra: 0 MU ≤ PSM

Non-Solar Renewable Capactiy in Maharashtra: 0 MU ≤ PNSM ≤ 46,095.18 MU

Solar RPO in Maharashtra: (PCM + PNGM + PLHM) * 0.005 ≤ RSM + PSM

Non-Solar RPO in Maharashtra: (PCM + PNGM + PLHM) * 0.085 ≤ RNSM + PNSM


For Scenario 2 (Non-PPA):

This is a hypothetical scenario which considers no PPAs for electricity from traditional sources of energy.

No PPAs signify that there is no minimum limitations for power procurement. So in this case, the

objective function is subjected to:

Total Electricity Provided by Tata in Delhi: PCD + PNGD + PSD + PNSD = 8,902.63 MU

Coal Capacity in Delhi: 0 MU ≤ PCD ≤ 5,575.56 MU

Natural Gas Capacity in Delhi: 0 MU ≤ PNGD ≤ 768.89 MU

Solar Capacity in Delhi: 0 MU ≤ PSD

Non-Solar Renewable Capacity in Delhi: 0 MU ≤ PNSD ≤ 849.77 MU

Solar REC in Delhi: (PCD + PNGD) * 0.0025 ≤ RSD

Non-Solar REC in Delhi: (PCD + PNGD) * 0.0595 ≤ RNSD

Total Electricity Provided by Tata in Maharashtra: PCM + PNGM + PLHM + PSM + PNSM = 7,006.67 MU

Coal Capacity in Maharashtra: 0 MU ≤ PCM ≤ 95,290.22 MU

Natural Gas Capacity in Maharashtra: 0 MU ≤ PNGM ≤ 17,491.96 MU

Large Hydropower Capacity in Maharashtra: 0 MU ≤ PLHM ≤ 14,771.98 MU

Solar Capacity in Maharashtra: 0 MU ≤ PSM

Non-Solar Renewable Capacity in Maharashtra: 0 MU ≤ PNSM ≤ 46,095.18 MU

Solar REC in Maharashtra: (PCM + PNGM + PLHM) * 0.005 ≤ RSM

Non-Solar REC in Maharashtra: (PCM + PNGM + PLHM) * 0.085 ≤ RNSM

In a nutshell –

Objective function for no-PPA scenario:

(Min) Z = [3.78*PCD + 3.27*PNGD + 7.87*PSD +5.62*PNSD+ 9.3*RSD +1.5*RNSD] + [3.78*PCM + 3.27*PNGM

+ 2.81*PLHM +7.87*PSM + 5.43*PNSM + 9.3*RSM + 1.5*RNSM]

Such that,

0 ≤ 𝑃𝐶𝐷 ≤ 5575.56𝑀𝑈𝑠

0 ≤ 𝑃𝑁𝐺𝐷 ≤ 768.69𝑀𝑈𝑠

0 ≤ 𝑃𝑆𝐷 ≤ ∞

0 ≤ 𝑃𝑁𝑆𝐷 ≤ 849.77𝑀𝑈𝑠

𝑃𝑆𝐷 + 𝑅𝑆𝐷/1000 ≥ 0.0025 × (𝑃𝐶𝐷 + 𝑃𝑁𝐺𝐷)

𝑃𝑁𝑆𝐷 + 𝑅𝑁𝑆𝐷/1000 ≥ 0.0595 × (𝑃𝐶𝐷 + 𝑃𝑁𝐺𝐷)

0 ≤ 𝑃𝐶𝑀 ≤ 95920.22𝑀𝑈𝑠

0 ≤ 𝑃𝑁𝐺𝑀 ≤ 17491.968𝑀𝑈𝑠

0 ≤ 𝑃𝐿𝐻𝑀 ≤ 14771.988𝑀𝑈𝑠

0 ≤ 𝑃𝑆𝑀 ≤ ∞

0 ≤ 𝑃𝑁𝑆𝑀 ≤ 46095.18𝑀𝑈𝑠


𝑃𝑆𝑀 + 𝑅𝑆𝑀/1000 ≥ 0.05 × (𝑃𝐶𝑀 + 𝑃𝑁𝐺𝑀 + 𝑃𝐿𝐻𝑀)

𝑃𝑁𝑆𝑀 + 𝑅𝑁𝑆𝑀/1000 ≥ 0.085 × (𝑃𝐶𝑀 + 𝑃𝑁𝐺𝑀 + 𝑃𝐿𝐻𝑀)

Objective function for PPA scenario:

(Min) Z = [3.78*PCD + 3.27*PNGD + 7.87*PSD +5.62*PNSD+ 9.3*RSD +1.5*RNSD] + [3.78*PCM + 3.27*PNGM

+ 2.81*PLHM +7.87*PSM + 5.43*PNSM + 9.3*RSM + 1.5*RNSM]

Such that,

5341.58𝑀𝑈𝑠 ≤ 𝑃𝐶𝐷 ≤ 5575.56𝑀𝑈𝑠

623.18𝑀𝑈𝑠 ≤ 𝑃𝑁𝐺𝐷 ≤ 768.69𝑀𝑈𝑠

0 ≤ 𝑃𝑆𝐷 ≤ ∞

0 ≤ 𝑃𝑁𝑆𝐷 ≤ 849.77𝑀𝑈𝑠

𝑃𝑆𝐷 + 𝑅𝑆𝐷/1000 ≥ 0.0025 × (𝑃𝐶𝐷 + 𝑃𝑁𝐺𝐷)

𝑃𝑁𝑆𝐷 + 𝑅𝑁𝑆𝐷/1000 ≥ 0.0595 × (𝑃𝐶𝐷 + 𝑃𝑁𝐺𝐷)

4204𝑀𝑈𝑠 ≤ 𝑃𝐶𝑀 ≤ 95920.22𝑀𝑈𝑠

490.46𝑀𝑈𝑠 ≤ 𝑃𝑁𝐺𝑀 ≤ 17491.968𝑀𝑈𝑠

1121.07𝑀𝑈𝑠 ≤ 𝑃𝐿𝐻𝑀 ≤ 14771.988𝑀𝑈𝑠

0 ≤ 𝑃𝑆𝑀 ≤ ∞

0 ≤ 𝑃𝑁𝑆𝑀 ≤ 46095.18𝑀𝑈𝑠

𝑃𝑆𝑀 + 𝑅𝑆𝑀/1000 ≥ 0.005 × (𝑃𝐶𝑀 + 𝑃𝑁𝐺𝑀 + 𝑃𝐿𝐻𝑀)

𝑃𝑁𝑆𝑀 + 𝑅𝑁𝑆𝑀/1000 ≥ 0.085 × (𝑃𝐶𝑀 + 𝑃𝑁𝐺𝑀 + 𝑃𝐿𝐻𝑀)

3.2 ANALYSIS APPROACH

The models of these two scenarios were both analyzed by Simplex optimization techniques, with the goal

of minimizing collective cost on physical power procurement and buying RECs. The model was built in

Excel and analyzed by Solver. The results of the analysis provide insights for TPC on which sources of

power should be procured and what quantity of RECs should be bought. Standard sensitivity analysis

were also performed to find binding and non-binding constraints, as well as to determine shadow prices.


4 RESULTS

4.1 ENERGY PROCUREMENT BY SOURCE

Optimizing each of the two functions (PPA and Non-PPA) produced results distinctive by Indian state

(see Figure 4). In Delhi, all of the decision variables remained the same even after the Power Purchase

Agreements and their consequent constraints had been taken away. However, in Maharashtra, the change

occurred in the non-renewable energy procurement. Whereas in the scenario with PPA the non-renewable

energy mix was split between coal, natural gas, and large hydropower (albeit not evenly), when the PPA

were removed, all non-renewable energy procurement shifted to large hydropower. As in the state of

Delhi, renewable energy procurement remained the same whether there was a PPA in place or not. All

values of optimized energy procurement can be found in Figure 4.

Figure 4. Decision Variables by PPA/Non-PPA Scenario

In assessing the composition of the energy procurement portfolios in each of the two states, there are

some similarities and a few major differences (see Figure 5). In the PPA scenario, both Delhi and

Maharashtra procure similar proportions of their energy from coal and natural gas sources. However,

while the remainder of the energy is procured through renewable sources in Delhi, that same amount is

almost entirely procured through large hydropower in Maharashtra. Then, in the Non-PPA scenario,

Delhi’s procurement (and thus its proportions) remain the same, where Maharashtra’s portfolio converts

almost entirely to large hydropower with only a thin sliver of energy procured from solar.

5575.56

768.69

1708.61

849.77

4204

490.46

2277.34

00 0

6971.80

34.850

0

1000

2000

3000

4000

5000

6000

7000

8000

PCD PNGD PSD PNSD PCM PNGM PLHM PSM PNSM

Elec

tric

ity

(MU

)

Variable

Energy Procurement by Source

PPA Non-PPA


4.2 RENEWABLE ENERGY CREDITS

Delhi Maharashtra

Solar RECs 0 0

Non-Solar RECs 0 592,604 Figure 6. REC Purchasing by State

The results of the optimization function show that for most scenarios, Renewable Energy Credits (RECs)

are not the most cost effective choice for TPC to achieve the Renewable Portfolio Objectives (see Figure

6). In fact, it does not make sense for either solar or non-solar RECs to be purchased in the state of Delhi.

Similarly, it does not minimize cost to purchase solar RECs in Maharashtra, only non-solar RECs.

Decisions to purchase RECs remain unchanged in optimizing both the PPA and the Non-PPA scenarios.

Figure 5. Proportion Energy Procurement by State


4.3 SENSITIVITY ANALYSIS: COMPARING THE SHADOW PRICING.

Shadow Price (INR) Delhi Maharashtra

Total Power 7,870,000 2,962,039

Solar RPO 0 4,907,960

Non-Solar RPO 0 1,500,000 Figure 7. Shadow Price Analysis for Delhi and Maharashtra for non-PPA condition

Without PPA, the resource-poor state of Delhi has a relatively large effect on the net cost to increase its

electricity supply to the consumers than in the case of Maharashtra – a resource rich state. Maharashtra’s

cost has less of an impact on price as this state has the option to buy electricity from the large hydro-

power plants.

Delhi being devoid of any non-solar renewable energy except from municipal waste projects, relies

heavily on the non-renewable generators. Even though the Levelized Cost of Electricity (LCOE) for solar

plants is highest (INR 7.87), the dependency on the scarce resources has obliged New Delhi to procure all

available energy from the fossil-based plants and further from renewable sources of electricity. Therefore,

Delhi does not buy the solar and non-solar RECs (as shown in fig.7).

The LCOE of large hydro plants in Maharashtra, on the other hand, is so cheap (INR 2.81) that the

effective price to procure the large hydro power is lesser even after purchasing the RECs, to meet its RPO

obligation. Since Maharashtra doesn’t buy any non-solar renewables, the state has to meet the RPO

mandates by purchasing the RECs. As shown in Fig. 7, the RPOs (specifically the solar RPO) are binding

in Maharashtra and thus sensitive in pricing.

5 DISCUSSION

5.1 IMPACT OF POWER PURCHASE AGREEMENT

Although the Power Purchase Agreements (PPAs) are often long term contracts spanning from 10-15

years – building a supply and demand trust between the consumers and generators – they have shown loss

for a DISCOM by buying dischargeable sources of energy such as coal and natural gas fuels. By offering

open access to choose electricity by source based on minimizing the cost and abiding to the RPO

standards (that is without PPA), TPC could avoid INR 4.3 billion, a 6.4% savings.

Interestingly, where power supply is limited in the case of Delhi, the role of agreements has no effect at

all. The state of Maharashtra with surplus energy only experiences plummeting price. Resource switching

from non-solar, coal and natural gas to large hydro suggests that a greater energy mix brings a

competitive platform for the generator, and lowers the investment on procuring electricity.


5.2 IMPACT ON POWER MARKET:

In Maharashtra, the major source of electricity, without PPA, is from the large hydro plants which

provides a constant electricity supply unlike intermittent sources such as wind and solar based power

plants. This stabilizes the grid for the state and surplus demand can be met by the other sources (both

renewable and non-renewable sources). In Delhi, renewables are compelled to meet the peak as well as

non-peak demands. The chances of blackout in a resource-poor state such as Delhi will be higher even if

the state has surplus renewable source of energy. Hence, a balance of energy mix and storage are the other

factors which Delhi has to consider and invest in, to be a self-sufficient state.

If electricity transmission and distribution is prohibited from the neighboring states, the scope of

renewables are higher only where the energy sources are in surplus. Maharashtra depends upon the large

hydro-plants which indirectly incentivizes the growth of renewable generators by mandating the RPO

policy.

By changing the structure of agreement and power availability by sources, solar RECs still can’t meet its

objective because of such a high certificate price. Policy to enhance the solar sector through RECs doesn’t

optimize the system in any of the considered conditions. Instead of lowering the prices of solar REC to

avoid market failure, the Indian Government should modify the current policy by providing tax credits to

solar generators.

There are some negative externalities associated with the outcome of the paper’s result. Firstly, the

dependency of power from large hydro plants in Maharashtra neglects the variability in precipitation

which affects the dam’s plant operation factor. Additionally, Delhi with limited resource has to rely on the

fossil fuel based plants which have negative impacts the public health due to emission of greenhouse

gases (GHGs), toxins, and other particulate matter.

5.3 SHORTCOMINGS:

1. The model is considered to be close-loop system where it assumes no electricity transfer from any

neighboring states, violating the open access policy. Presently, Delhi purchases energy from the

neighboring grid and thus avoids self-power production which is not feasible due to urbanization.

In fact, Maharashtra generates huge revenue from the neighboring states by selling its surplus

energy.

2. The paper assumes equal levelized prices (LCOE) for both the states. In reality, the levalized cost

varies geographically even if the technology for power production is similar. Moreover, variable

state taxes are imposed on the prices and also affect variation in prices for the same fuel source.

Furthermore, the transmission and distribution costs of electricity which are closely related to the

plant locations is not considered in the calculations. Interestingly, if recalculated with the actual

cost, Tata Power Corporation will find more benefits from the non-PPA condition.

3. Generally, the generators have a mixed energy portfolio to produce power. TPC procures energy

from generators directly, which doesn’t account for the sources of its energy. This paper is only

concerned with the power sources, not the generator’s profile or its PPA.

6 APPENDIX

Non-Solar and Solar costs (INR) per unit in Delhi and Mumbai:


Non-Solar Resource Non Solar - Delhi Non-Solar - Mumbai

Wind 5.27 3.86

Small Hydro 5.66 4.02

Biomass Power 5.36 6.01

Non-Fossil cogeneration 6.06 5.63

Biomass gasifier 6.67 6.43

Biomass cogeneration 4.74 6.67

Average 5.62 5.43

Solver system analysis (PPA Case):

Variable Cells

Final Reduced

Objective

Allowable

Allowable

Name Value Cost Coefficient

Increase

Decrease

Pc 0 970000 3780000

1E+30 970000

Png 0 460000 3270000

1E+30 460000

Plh 6971.810945

0 2810000

460000 4414000

Ps 34.85905473

0 7870000

4414000

4932500

Pns 0 967960.199

5430000

1E+30 967960.199

Rns 592603.9303

0 1500 892.4770642

1500

Rs 0 4392.039801

9300 1E+30 4392.039801

Pc 5575.56

0 3780000

4090000

1E+30

Png 768.69 0 3270000

4600000

1E+30

Ps 1708.61

0 7870000

1E+30 2250000

Pns 849.77 0 5620000

2250000

1E+30

Rns 0 1500 1500 1E+30 1500

Rs 0 9300 9300 1E+30 9300


Constraints

Final Shadow

Constraint

Allowable

Allowable

Name Value Price R.H. Side

Increase

Decrease

Existing natural gas electricity production capacity constraint (in Delhi)

768.69 -460000

0

768.69

1688.528055

768.69

Total non-solar resource potential in Delhi 849.77 -225000

0

849.77

1692.749375

472.287125

Minimum solar RPO target compliance constraint in Delhi

1708.61

0 0 1692.749375

1E+30

Minimum non-solar RPO target compliance constraint in Delhi

849.77 0 0 472.287125

1E+30

Total energy procurement constraint in FY2014

7006.67

2962039.801

7006.67

7839.1699

7006.67

Existing coal electricity production capacity constraint (in Mumbai)

0 0 95290.22

1E+30 95290.22

Existing natural gas electricity production capacity constraint (in Mumbai)

0 0 17491.96

1E+30 17491.96

Existing large hydro production capacity constraint (in Mumbai)

6971.810945

0 14771.98

1E+30 7800.169055

Total non-solar resource potential in Mumbai

0 0 46095.18

1E+30 46095.18

Minimum solar RPO target compliance constraint in Mumbai

34.85905473

4907960.199

0 7006.67

35.03335

Minimum non-solar RPO target compliance constraint in Mumbai

592.6039303

1500000

0 1E+30 592.6039303


8902.63

7870000

8902.63

1E+30 1692.749375

Existing coal electricity production capacity constraint (in Delhi)

5575.56

-409000

0

5575.56

1688.528055

5575.56

Objective Cell (Min)

Name Original Value

Final Value

Z 62565736612

62565736612

Variable Cells

Name Original Value

Final Value Integer


Pc 0 0 Contin

Png 0 0 Contin

Plh 6971.810945

6971.810945 Contin

Ps 34.85905473

34.85905473 Contin

Pns 0 0 Contin

Rns 592603.9303

592603.9303 Contin

Rs 0 0 Contin

Pc 5575.56 5575.56 Contin

Png 768.69 768.69 Contin

Ps 1708.61 1708.61 Contin

Pns 849.77 849.77 Contin

Rns 0 0 Contin

Rs 0 0 Contin

Constraints

Name Cell Value

Formula Status Slack


768.69 $AA$19<=768.69

Binding 0

Total non-solar resource potential in Delhi

849.77 $AC$19<=849.77

Binding 0


1708.61 $AD$19>=0.0025*$AF$19

Not Binding 1692.749375


849.77 $AE$19>=0.0595*$AF$19



7006.67 $Q$19=$T$6 Binding 0


0 $R$19<=95290.22



0 $S$19<=17491.96



6971.810945

$T$19<=14771.98



Total non-solar resource potential in Mumbai

0 $V$19<=46095.18



34.85905473

$W$19>=0.005*$P$19

Binding 0


592.6039303

$X$19>=0.085*$P$19

Binding 0


8902.63 $Y$19=$U$6 Binding 0


5575.56 $Z$19<=5575.56 Binding 0

Solver system analysis (non-PPA Case):

Variable Cells

Name Original Value

Final Value Integer

Pc 4204.002 4204.002 Contin

Png 490.4669 490.4669 Contin

Plh 2277.342045

2277.342045 Contin

Ps 34.85905473

34.85905473 Contin

Pns 0 0 Contin

Rns 592603.9303

592603.9303 Contin

Rs 0 0 Contin

Pc 5575.56 5575.56 Contin

Png 768.69 768.69 Contin

Ps 1708.61 1708.61 Contin

Pns 849.77 849.77 Contin

Rns 0 0 Contin

Rs 0 0 Contin

Constraints

Name Cell Value

Formula Status Slack


768.69 $AA$19<=768.69

Binding 0



768.69 $AA$19>=$U$8 Not Binding

145.5059

Total non-solar resource potential in Delhi 849.77 $AC$19<=849.77

Binding 0


1708.61 $AD$19>=0.0025*$AF$19

Not Binding

1692.749375


849.77 $AE$19>=0.0595*$AF$19

Not Binding

472.287125

Total energy procurement constraint in FY2014 7006.67 $Q$19=$T$6 Binding 0


4204.002 $R$19<=95290.22

Not Binding

91086.218


4204.002 $R$19>=$T$7 Binding 0


490.4669 $S$19<=17491.96

Not Binding

17001.4931


490.4669 $S$19>=$T$8 Binding 0


2277.342045

$T$19<=14771.98

Not Binding

12494.63795


2277.342045

$T$19>=$T$9 Not Binding

1156.274845

Total non-solar resource potential in Mumbai 0 $V$19<=46095.18

Not Binding

46095.18


34.85905473

$W$19>=0.005*$P$19

Binding 0


592.6039303

$X$19>=0.085*$P$19

Binding 0

Total energy procurement constraint in FY2014 8902.63 $Y$19=$U$6 Binding 0


5575.56 $Z$19<=5575.56 Binding 0


5575.56 $Z$19>=$U$7 Not Binding

233.982

Variable Cells

Final Reduced

Objective

Allowable

Allowable

Name Value Cost Coefficient

Increase

Decrease

Pc 4204.002

0 3780000

1E+30 970000

Png 490.4669

0 3270000

1E+30 460000

Plh 2277.342045

0 2810000

460000 4414000


Ps 34.85905473

0 7870000

4414000

4932500

Pns 0 967960.199

5430000

1E+30 967960.199

Rns 592603.9303

0 1500 892.4770642

1500

Rs 0 4392.039801

9300 1E+30 4392.039801

Pc 5575.56 0 3780000

4090000

1E+30

Png 768.69 0 3270000

4600000

1E+30

Ps 1708.61 0 7870000

1E+30 2250000

Pns 849.77 0 5620000

2250000

1E+30

Rns 0 1500 1500 1E+30 1500

Rs 0 9300 9300 1E+30 9300

Constraints

Final Shadow Constraint

Allowable

Allowable

Name Value Price R.H. Side

Increase

Decrease


768.69 -460000

0

768.69 1688.528055

145.5059


768.69 0 623.1841

145.5059

1E+30

Total non-solar resource potential in Delhi 849.77 -225000

0

849.77 1692.749375

472.287125


1708.61 0 0 1692.749375

1E+30


849.77 0 0 472.287125

1E+30


7006.67 2962039.801

7006.67

12557.11114

1162.05622


4204.002

0 95290.22

1E+30 91086.218


4204.002

970000 4204.002

1156.274845

4204.002


490.4669

0 17491.96

1E+30 17001.4931


490.4669

460000 490.4669

1156.274845

490.4669



2277.342045

0 14771.98

1E+30 12494.63795


2277.342045

0 1121.0672

1156.274845

1E+30

Total non-solar resource potential in Mumbai 0 0 46095.18

1E+30 46095.18


34.85905473

4907960.199

0 1162.05622

35.03335


592.6039303

1500000

0 1E+30 592.6039303


8902.63 7870000

8902.63

1E+30 1692.749375


5575.56 -409000

0

5575.56

1688.528055

233.982


5575.56 0 5341.578

233.982 1E+30

7 REFERENCES

1 “Distribution.” Tata Power. 2014. Accessed 15 December 2014

<http://www.tatapower.com/businesses/distribution.aspx>. 2 “95th Annual Report 2013-14.” Tata Power. 2014. Accessed 15 December 2014 <http://www.tatapower.com/investor-relations/pdf/95Annual-Report-2013-14.pdf>. 3 “REC Trading Report November-2014.” REConnect Energy Solutions. 28 November 2014. Accessed 15 December 2014 <http://reconnectenergy.com/blog/2014/11/rec-trading-report-november-2014/>. 4 “REC Trading Report November-2014.” REConnect Energy Solutions. 28 November 2014. Accessed 15 December 2014 <http://reconnectenergy.com/blog/2014/11/rec-trading-report-november-2014/>. 5 “Future of Coal Electricity in India and Sustainable Alternatives - Summary” World Institute of Sustainable Energy. 2013. Page 45 Accessed 15 December 2014 <http://wisein.org/WISE_Projects/final_coal_report.pdf>. 6 “CERC Annual Report 2013.” Central Electricity Regulatory Commission. 2013. Page 115. Accessed 15 December 2014 <http://www.cercind.gov.in/2013/annual_report/AR1213E.pdf>. 7 “CERC Annual Report 2013.” Central Electricity Regulatory Commission. 2013. Page 117. Accessed 15 December 2014 <http://www.cercind.gov.in/2013/annual_report/AR1213E.pdf>. 8 “CERC Annual Report 2013.” Central Electricity Regulatory Commission. 2013. Page 119. Accessed 15 December 2014 <http://www.cercind.gov.in/2013/annual_report/AR1213E.pdf>. 9 “CERC Annual Report 2013.” Central Electricity Regulatory Commission. 2013. Page 119. Accessed 15 December 2014 <http://www.cercind.gov.in/2013/annual_report/AR1213E.pdf>. 10 “Future of Coal Electricity in India and Sustainable Alternatives - Summary” World Institute of Sustainable Energy. 2013. Page 45 Accessed 15 December 2014 <http://wisein.org/WISE_Projects/final_coal_report.pdf>. 11 “CERC Annual Report 2013.” Central Electricity Regulatory Commission. 2013. Page 115. Accessed 15 December 2014 <http://www.cercind.gov.in/2013/annual_report/AR1213E.pdf>. 12 “CERC Annual Report 2013.” Central Electricity Regulatory Commission. 2013. Page 117. Accessed 15 December 2014 <http://www.cercind.gov.in/2013/annual_report/AR1213E.pdf>. 13 “CERC Annual Report 2013.” Central Electricity Regulatory Commission. 2013. Page 119. Accessed 15 December 2014 <http://www.cercind.gov.in/2013/annual_report/AR1213E.pdf>. 14 “CERC Annual Report 2013.” Central Electricity Regulatory Commission. 2013. Page 119. Accessed 15 December 2014 <http://www.cercind.gov.in/2013/annual_report/AR1213E.pdf>.

systems analysis for tata power corporation’s (tpc) distribution business in india

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existing distribution

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