Policy Assistance for Adoption of Residential Solar PV in India: A Stakeholder-centric Approach for Welfare Optimization

13 Background: Domestic solar PV installations in India are yet to become a valuable proposition for both the prosumers and utility 14 because of the deficiencies in the formulation of the policy parameters. This paper presents a comprehensive analysis of the 15 consumer-centric business model for rooftop solar PV installations in India. We explore areas where potential policy interventions 16 may be introduced to improve collective stakeholder benefits and incentivize more domestic consumers to install rooftop solar 17 panels in their premises. We propose a policy framework that seeks optimal Feed-in Tariff (FiT) rates, PV capacities and Average 18 Billing Rates (ABRs) towards maximizing stakeholder benefits. The stakeholders considered are the consumers/prosumers and the 19 utility. Results: Case studies with three residential prosumers of different demand and generation profiles (extracted from data provided 21 by Indian utilities) are presented. A multi-objective problem is formulated with the FiT, generation capacity (as a function of 22 demand) and ABR as decision variables, exploring the various welfare trade-offs. The pareto-optimal front is identified for 23 prosumer and utility benefits and suitable points with reasonable tradeoff are selected based on sensitivity analysis of the impact 24 of the decision variables on collective welfare. Conclusions: The paper provides a workflow to fix tariff, FiT and installation capacities for prosumers based on their load demand so as to encourage the adoption of roof-top solar without affecting 29 collective benefits. This provides policymakers and prosumers an effective decision-making tool.


Background 32
Roof-Top solar PV installations are one of the major approaches proposed to promote distributed generation 33 in India [1]. The market potential for roof-top solar generation is 352GW out of which 124GW is technically feasible.

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The Ministry of New and Renewable Energy, Government of India has set a target of 40,000MW of roof-top solar 35 generation by 2022. This accounts for 40% of the total solar power generation capacity envisaged by 2022 [2]- [3].

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Successful adoption of rooftop PV is vital from the perspective of reducing carbon emissions for which a commitment 37 of 33-35 % reduction by 2030 has been given by India in the Paris Climate Conference in [4]- [5]. It also calls for

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The paper is organized as follows: Section 2 describes the methodology adopted for modeling the stakeholder

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In the RESCO based model, the RESCO sets-up the PV system on the rooftop of the customer and signs a PPA (Power

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Purchase Agreement) with the consumer. The investment is recovered through the solar energy tariff. In the utility 99 facilitated model, the installation is owned and managed by the utility with the prosumer compensated either through 100 bill discounts or other incentives.

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In this paper, two types of residential premises are considered. The ones with installed rooftop solar PV panels 102 are called prosumers. Those without local self-generation are referred to as consumers. The case study is confined to 114

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The consumer-centric business model shown in Fig.1 is analysed in this paper. In this model, the potential 116 prosumer has to bear the complete capital and operational expenditure of the rooftop solar PV installation. The

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consumer contacts an EPC firm to set up the installation at his/her premises. Based on the metering regulation, the 118 financial and energy settlements are done through net or gross-metering arrangements [31]. The prosumer consumes 119 the locally generated solar energy, and the surplus energy is exported to the utility at Feed-in-Tariff. In case, the local 120 generation is not sufficient, the prosumer imports energy from the utility at retail tariff rate. The equations describing the components of the utility profit and prosumer savings are given in Table 1.

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Revenue and expense of the utility are modelled by equations (1) and (2) respectively. The terms of equations (1) 125 are described in rows 1-3 of Table 1. The terms of equation (2) are specified in Rows 5 and 6 respectively. The term 126 k is defined as shown in equation (3). Its value depends on whether the prosumer has an excess or deficit of energy.

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The profit of utility is shown in equation (4). The monthly electricity bill that the prosumer would have incurred in the absence of rooftop solar PV installation is given by equation (5), whose individual terms are defined in rows 10 129 and 11 of Table 1. Equation (6) computes the prosumer's electricity bill considering local generation from solar PV.

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Its terms are explained in rows 13, 14, 15 and 17 of Table 1. EMI is calculated using equation (7) where p is the 131 total installation cost, r is the rate of interest and n is the payback period. The savings of prosumers is modelled by 132 equation (8). 133

FC × ( + )
Fixed monthly charge from the prosumers and consumers Purchase of remaining energy to meet the demand of the system from the upstream grid at ACoS From equations (4) and (8), it is evident that the utility profit and prosumer savings depend on the following

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Among these parameters, FiT and ABR are fixed by the utility whereas PV capacity and are decided by the 138 prosumers. The ABR values are derived from the category-wise revenue numbers of the utility [32]. ACoS is defined 139 as the ratio of the total cost for providing power supply to the end consumer to total electricity input/purchased for 140 the total number of consumers [33].

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The objective functions are utility profit and prosumer savings given respectively in equations (4) and (8).

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They are found to be conflicting in nature thereby necessitating a multi-objective optimization approach to explore 144 and identify reasonable trade-offs from the pareto front shown in Figure. 3.

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The proposed methodology is applied to evaluate a distribution test system having two prosumers and three

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The pareto-optimal points for different PV panel sizes are shown in Fig.4 in which absolute values of 172 prosumer saving are plotted against the utility profit. Fig.4 suggests that the best trade-offs for absolute values of 173 utility profit and prosumer savings are in the range of 5-8 kW PV capacities. However, 1-3 kW PV capacities yield 174 reasonable trade-offs in terms of percentages (see Table 6).

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Higher the PV size, more will be the export so that the utility is able to procure energy at FiT. Since the FiT is 193 lower than ACoS, this results in a reduction in cost of procurement. The energy procured from the prosumers is sold 194 at ABR which is higher than the FiT, thereby yielding a better profit for the utility. For 1-3 kW PV capacities, the 195 EMI outgo is compensated by the receipts from the sale of energy only at FiTs greater than or equal to 2.2 Rs/kWh.

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This results in an increasing trend in utility profit with increasing PV sizes. For capacities in the ranges 3-5 kW and 204 Table 6 summarizes the optimal ranges of FiTs, , PV capacities and the corresponding prosumers savings and 205 utility profits. It is found that for existing ABR & ACoS values, the ranges from 50% to 90% of the local solar In Table 7, the analysis is repeated with a higher ABR set to 120 % of the prevailing ACoS. As a result, the utility  hand, in absolute terms, a win-win situation occurs at PV capacities in the range 5-8 kW. From Table 8, it is found that 223 for the prevailing ABR & ACoS values, the optimal ranges from 50% to 60% of . Further, the optimal FiT 224 ranges from Rs 1 to 1.3 per kWh for lowest PV capacities 1-3 kW and from Rs 1 to 1.5 per kWh for higher PV capacities 225 of 3-8 kW. From Table 9, it is found that if the value of ABR is set to 120% of ACoS, then the profit is significantly 226 improved from 8-13 % to 20-48 % across PV capacities. The rise in profits is due to the higher revenue for the utility 227 on account of the increase in ABR. The prevailing ABR is 4.96 Rs/kWh and ACoS is 7.31 Rs/kWh for TATA power.

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In this case, the utility profits and prosumer savings are less compared to MSEDCL, as the prevailing ABR is much 229 less than ACoS for TATA Power.

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The pareto optimal points for different PV ranges are shown in Fig.6. From   India is carried out to identify optimal policy parameters yielding reasonable trade-offs in stakeholder benefits.

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To this end, a multi-objective optimization problem is formulated, with utility profit and prosumer savings as 252 objectives.

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• The results indicate that for prevailing ABR and ACoS values, the local rooftop PV capacity should be chosen 254 such that there is net export of power by the prosumer. This is due to lower prevailing ABR compared to ACoS.

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On the other hand, when prosumer demand exceeds generation, the utility incurs losses due to lower ABR.

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• It is observed that the maximum PV panel capacity yields better trade-offs for all the three ranges considered.

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For lower capacity installations, both prosumer savings and utility profits are affected with the prevailing values 258 of FiT and ACoS.

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• When ABR is set to 120% of ACoS, the pareto front yields a wider range of FiT and with reasonable trade-

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offs. This is because the major share in prosumer savings is from solar energy self-consumption.

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• The recommendations of the study are made with a minimum category-wise (residential consumers here)

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guaranteed profit for the utility. However, Indian utilities resort to cross-subsidizing low-income groups by 277 charging higher tariffs for industrial and commercial consumers and thereby offsetting the losses incurred for 278 subsidized power. Other consumer categories will be included in the study as part of future work.

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• In this work only the consumer-centric business model has been explored. The other business models involving 280 third party agencies (RESCO based models) will be taken up as future work.  All data generated or analysed during this study are included in this published article.

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The authors declare that they have no competing interests