Conference Papers Year : 2024

Forecast Trading as a Means to Reach Social Optimum on a Peer-to-Peer Market

Abstract

This paper investigates the coupling between a peer-to-peer (P2P) electricity market and a forecast market to alleviate the uncertainty faced by prosumers regarding their renewable energy sources (RES) generation. The work generalizes the analysis from Gaussian-distributed RES production to arbitrary distributions. The P2P trading is modeled as a generalized Nash equilibrium problem, where prosumers trade energy in a decentralized manner. Each agent has the option to purchase a forecast on the forecast market before trading on the electricity market. We establish conditions on arbitrary probability density functions (pdfs) under which the prosumers have incentives to purchase forecasts on the forecast market. Connected with the previous results, this allows us to prove the economic efficiency of the P2P electricity market, i.e., that a social optimum can be reached among the prosumers.

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hal-04866031 , version 1 (06-01-2025)

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  • HAL Id : hal-04866031 , version 1

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Ilia Shilov, Hélène Le Cadre, Ana Bušić, Anibal Sanjab, Pierre Pinson. Forecast Trading as a Means to Reach Social Optimum on a Peer-to-Peer Market. Netgcoop 2024 - 11th International Conference on Network Games, Control and Optimization, Oct 2024, Lille, France. ⟨hal-04866031⟩
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