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

PNGD =
nat. gas plants in Delhi
PNGM =
nat. gas plants in Maharashtra
PSD =
solar plants in Delhi
PLHM =
large hydro power plants in
Maharashtra
PNSD =
non-solar renewable energy
plants in Delhi
PSM =
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
WNSM
13
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 ≤ 𝑃 𝑁𝐺𝐷 ≤ 768.69𝑀𝑈𝑠
0 ≤ 𝑃𝑆𝐷 ≤ ∞
0 ≤ 𝑃 𝑁𝑆𝐷 ≤ 849.77𝑀𝑈𝑠
𝑃𝑆𝐷 + 𝑅 𝑆𝐷/1000 ≥ 0.0025 × (𝑃𝐶𝐷 + 𝑃 𝑁𝐺𝐷)
𝑃 𝑁𝑆𝐷 + 𝑅 𝑁𝑆𝐷/1000 ≥ 0.0595 × (𝑃𝐶𝐷 + 𝑃 𝑁𝐺𝐷)
0 ≤ 𝑃𝐶𝑀 ≤ 95920.22𝑀𝑈𝑠
0 ≤ 𝑃 𝑁𝐺𝑀 ≤ 17491.968𝑀𝑈𝑠
0 ≤ 𝑃𝐿𝐻𝑀 ≤ 14771.988𝑀𝑈𝑠
0 ≤ 𝑃𝑆𝑀 ≤ ∞
0 ≤ 𝑃 𝑁𝑆𝑀 ≤ 46095.18𝑀𝑈𝑠

𝑃𝑆𝑀 + 𝑅 𝑆𝑀/1000 ≥ 0.05 × (𝑃𝐶𝑀 + 𝑃 𝑁𝐺𝑀 + 𝑃𝐿𝐻𝑀)
𝑃 𝑁𝑆𝑀 + 𝑅 𝑁𝑆𝑀/1000 ≥ 0.085 × (𝑃𝐶𝑀 + 𝑃 𝑁𝐺𝑀 + 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 ≤ 𝑃 𝑁𝐺𝐷 ≤ 768.69𝑀𝑈𝑠
0 ≤ 𝑃𝑆𝐷 ≤ ∞
0 ≤ 𝑃 𝑁𝑆𝐷 ≤ 849.77𝑀𝑈𝑠
𝑃𝑆𝐷 + 𝑅 𝑆𝐷/1000 ≥ 0.0025 × (𝑃𝐶𝐷 + 𝑃 𝑁𝐺𝐷)
𝑃 𝑁𝑆𝐷 + 𝑅 𝑁𝑆𝐷/1000 ≥ 0.0595 × (𝑃𝐶𝐷 + 𝑃 𝑁𝐺𝐷)
4204𝑀𝑈𝑠 ≤ 𝑃𝐶𝑀 ≤ 95920.22𝑀𝑈𝑠
490.46𝑀𝑈𝑠 ≤ 𝑃 𝑁𝐺𝑀 ≤ 17491.968𝑀𝑈𝑠
1121.07𝑀𝑈𝑠 ≤ 𝑃𝐿𝐻𝑀 ≤ 14771.988𝑀𝑈𝑠
0 ≤ 𝑃𝑆𝑀 ≤ ∞
0 ≤ 𝑃 𝑁𝑆𝑀 ≤ 46095.18𝑀𝑈𝑠
𝑃𝑆𝑀 + 𝑅 𝑆𝑀/1000 ≥ 0.005 × (𝑃𝐶𝑀 + 𝑃 𝑁𝐺𝑀 + 𝑃𝐿𝐻𝑀)
𝑃 𝑁𝑆𝑀 + 𝑅 𝑁𝑆𝑀/1000 ≥ 0.085 × (𝑃𝐶𝑀 + 𝑃 𝑁𝐺𝑀 + 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
34.85
0
0
1000
2000
3000
4000
5000
6000
7000
8000
PCD PNGD PSD PNSD PCM PNGM PLHM PSM PNSM
Electricity(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):
Variabl
e Cells
Final Reduce
d
Objec
tive
Allowa
ble
Allowa
ble
Name Value Cost Coeffi
cient
Increas
e
Decrea
se
Pc 0 970000 37800
00
1E+30 970000
Png 0 460000 32700
00
1E+30 460000
Plh 6971.8
10945
0 28100
00
460000 441400
0
Ps 34.859
05473
0 78700
00
441400
0
493250
0
Pns 0 967960
.199
54300
00
1E+30 967960
.199
Rns 592603
.9303
0 1500 892.47
70642
1500
Rs 0 4392.0
39801
9300 1E+30 4392.0
39801
Pc 5575.5
6
0 37800
00
409000
0
1E+30
Png 768.69 0 32700
00
460000
0
1E+30
Ps 1708.6
1
0 78700
00
1E+30 225000
0
Pns 849.77 0 56200
00
225000
0
1E+30
Rns 0 1500 1500 1E+30 1500
Rs 0 9300 9300 1E+30 9300

Constra
ints
Final Shado
w
Const
raint
Allowa
ble
Allowa
ble
Name Value Price R.H.
Side
Increas
e
Decrea
se
Existing natural gas electricity production
capacity constraint (in Delhi)
768.69 -
460000
0
768.6
9
1688.5
28055
768.69
Total non-solar resource potential in Delhi 849.77 -
225000
0
849.7
7
1692.7
49375
472.28
7125
Minimum solar RPO target compliance
constraint in Delhi
1708.6
1
0 0 1692.7
49375
1E+30
Minimum non-solar RPO target
compliance constraint in Delhi
849.77 0 0 472.28
7125
1E+30
Total energy procurement constraint in
FY2014
7006.6
7
296203
9.801
7006.
67
7839.1
699
7006.6
7
Existing coal electricity production
capacity constraint (in Mumbai)
0 0 95290
.22
1E+30 95290.
22
0 0 17491
.96
1E+30 17491.
96
Existing large hydro production capacity
constraint (in Mumbai)
6971.8
10945
0 14771
.98
1E+30 7800.1
69055
Total non-solar resource potential in
Mumbai
0 0 46095
.18
1E+30 46095.
18
constraint in Mumbai
34.859
05473
490796
0.199
0 7006.6
7
35.033
35
Minimum non-solar RPO target
compliance constraint in Mumbai
592.60
39303
150000
0
0 1E+30 592.60
39303
FY2014
8902.6
3
787000
0
8902.
63
1E+30 1692.7
49375
Existing coal electricity production
5575.5
6
-
409000
0
5575.
56
1688.5
28055
5575.5
6
Objective Cell (Min)
Name Original
Value
Final Value
Z 6256573
6612
62565736612
Variable Cells
Name Original
Value
Final Value Integer

Pc 0 0 Contin
Png 0 0 Contin
Plh 6971.810
945
6971.810945 Contin
Ps 34.85905
473
34.85905473 Contin
Pns 0 0 Contin
Rns 592603.9
303
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
Existing natural gas
electricity production
capacity constraint (in
Delhi)
768.69 $AA$19<=768.6
9
Binding 0
Total non-solar resource
potential in Delhi
849.77 $AC$19<=849.7
7
Binding 0
Minimum solar RPO
target compliance
constraint in Delhi
1708.61 $AD$19>=0.002
5*$AF$19
Not Binding 1692.749375
Minimum non-solar RPO
target compliance
constraint in Delhi
849.77 $AE$19>=0.059
5*$AF$19
Total energy procurement
constraint in FY2014
7006.67 $Q$19=$T$6 Binding 0
Existing coal electricity
production capacity
0 $R$19<=95290.
22
Existing natural gas
electricity production
capacity constraint (in
Mumbai)
0 $S$19<=17491.9
6
Existing large hydro
production capacity
6971.810
945
$T$19<=14771.9
8

Total non-solar resource
potential in Mumbai
0 $V$19<=46095.
18
Minimum solar RPO
target compliance
34.85905
473
$W$19>=0.005*
$P$19
Binding 0
Minimum non-solar RPO
target compliance
592.6039
303
$X$19>=0.085*$
P$19
Binding 0
Total energy procurement
constraint in FY2014
8902.63 $Y$19=$U$6 Binding 0
Existing coal electricity
production capacity
constraint (in Delhi)
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 2277.342
045
2277.342045 Contin
Ps 34.85905
473
34.85905473 Contin
Pns 0 0 Contin
Rns 592603.9
303
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<=768.6
9
Binding 0

768.69 $AA$19>=$U$8 Not
Binding
145.505
9
Total non-solar resource potential in Delhi 849.77 $AC$19<=849.7
7
Binding 0
constraint in Delhi
1708.61 $AD$19>=0.002
5*$AF$19
Not
Binding
1692.74
9375
Minimum non-solar RPO target compliance
constraint in Delhi
849.77 $AE$19>=0.059
5*$AF$19
Not
Binding
472.287
125
Total energy procurement constraint in FY2014 7006.67 $Q$19=$T$6 Binding 0
Existing coal electricity production capacity
4204.002 $R$19<=95290.
22
Not
Binding
91086.2
18
4204.002 $R$19>=$T$7 Binding 0
490.4669 $S$19<=17491.9
6
Not
Binding
17001.4
931
490.4669 $S$19>=$T$8 Binding 0
2277.342
045
$T$19<=14771.9
8
Not
Binding
12494.6
3795
2277.342
045
$T$19>=$T$9 Not
Binding
1156.27
4845
Total non-solar resource potential in Mumbai 0 $V$19<=46095.
18
Not
Binding
46095.1
8
34.85905
473
$W$19>=0.005*
$P$19
Binding 0
592.6039
303
$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 Reduce
d
Object
ive
Allowa
ble
Allowa
ble
Name Value Cost Coeffic
ient
Increas
e
Decreas
e
Pc 4204.00
2
0 37800
00
1E+30 970000
Png 490.466
9
0 32700
00
1E+30 460000
Plh 2277.34
2045
0 28100
00
460000 441400
0

Ps 34.8590
5473
0 78700
00
441400
0
493250
0
Pns 0 967960.
199
54300
00
1E+30 967960.
199
Rns 592603.
9303
0 1500 892.477
0642
1500
Rs 0 4392.03
9801
9300 1E+30 4392.03
9801
Pc 5575.56 0 37800
00
409000
0
1E+30
Png 768.69 0 32700
00
460000
0
1E+30
Ps 1708.61 0 78700
00
1E+30 225000
0
Pns 849.77 0 56200
00
225000
0
1E+30
Rns 0 1500 1500 1E+30 1500
Rs 0 9300 9300 1E+30 9300
Constraints
Final Shadow Constr
aint
Allowa
ble
Allowa
ble
Name Value Price R.H.
Side
Increas
e
Decreas
e
768.69 -
460000
0
768.69 1688.52
8055
145.505
9
768.69 0 623.18
41
145.505
9
1E+30
Total non-solar resource potential in Delhi 849.77 -
225000
0
849.77 1692.74
9375
472.287
125
constraint in Delhi
1708.61 0 0 1692.74
9375
1E+30
constraint in Delhi
849.77 0 0 472.287
125
1E+30
FY2014
7006.67 296203
9.801
7006.6
7
12557.1
1114
1162.05
622
4204.00
2
0 95290.
22
1E+30 91086.2
18
4204.00
2
970000 4204.0
02
1156.27
4845
4204.00
2
490.466
9
0 17491.
96
1E+30 17001.4
931
490.466
9
460000 490.46
69
1156.27
4845
490.466
9

2277.34
2045
0 14771.
98
1E+30 12494.6
3795
2277.34
2045
0 1121.0
672
1156.27
4845
1E+30
Total non-solar resource potential in Mumbai 0 0 46095.
18
1E+30 46095.1
8
34.8590
5473
490796
0.199
0 1162.05
622
35.0333
5
592.603
9303
150000
0
0 1E+30 592.603
9303
FY2014
8902.63 787000
0
8902.6
3
1E+30 1692.74
9375
5575.56 -
409000
0
5575.5
6
1688.52
8055
233.982
5575.56 0 5341.5
78
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>.
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>.

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