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Does P2P Trading Favor Investments in PV-Battery Systems?

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  • Andreolli, Francesca
  • D'Alpaos, Chiara
  • Kort, Peter

Abstract

Due to the deployment of distributed renewable energy sources (e.g., solar), the introduction of communication technologies, and the digitalization of the power system (e.g., smart meters, control devices), energy consumers are switching from passive to active in the management of their energy consumption, production, and storage patterns. In a consumer-centric energy market, Peer-to-Peer (P2P) trading allows consumers and prosumers to directly trade energy without any intermediation by traditional energy suppliers. In this paper, we investigate households’ decisions to invest in domestic PV plants coupled with battery storage, namely PVbattery systems (PVBs), and to participate in a local energy community (EC), in which energy quotas can be exchanged among EC members via P2P trading. Thanks to storage and P2P, households can strategically decide their optimal course of action and their optimal energy production/consumption patterns and can actively offer services that other EC participants bid for. In detail, we examine whether P2P trading can increase the value of investments in PVBs and affect the decision on both the optimal investment timing and size. Following the real options approach, we develop a stochastic optimization model. Our results show that ceteris paribus, thanks to P2P trading opportunities, households accelerate investments and invest in larger plants compared to scenarios in which P2P trading in not permitted. According to our findings, at current market prices, it is never optimal to invest immediately and, as P2P traded energy increases, households invest earlier and in smaller plants.

Suggested Citation

  • Andreolli, Francesca & D'Alpaos, Chiara & Kort, Peter, 2023. "Does P2P Trading Favor Investments in PV-Battery Systems?," FEEM Working Papers 330498, Fondazione Eni Enrico Mattei (FEEM).
  • Handle: RePEc:ags:feemwp:330498
    DOI: 10.22004/ag.econ.330498
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    Keywords

    Research and Development/Tech Change/Emerging Technologies; Resource /Energy Economics and Policy;

    JEL classification:

    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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