A game-theoretic approach to assess peer-to-peer rooftop solar PV electricity trading under constrained power supply

Abstract

The integration of distributed energy resources and the transition to smart cities are shifting the urban energy sector to a decentralized operating system. Blockchain-based microgrids, where small-scale operators trade electricity among each others, have gained remarkable attention recently. However, most of the proposed schemes study smart grids in prosperous cities. In this study, the performance of a solar-based power trading scheme is investigated in a shortage-prone context, Beirut City. Thus, we resort to a game-theoretic approach to model power trading as a repeated game between buildings at the urban scale. Results show that solar energy can cover up to 25% of the city electricity needs, depending on the rooftops area coverage. On the other hand, we found that deploying a peer-to-peer trading scheme has marginal impact since the energy demand in the city exceeds the supply and most buildings would prioritize self-consumption.

Self-sufficient energy communities using cooperative games over blockchain and smart contracts

“…The prosumers are clustered based on their location and energy trading cost and utility models are defined for renewable systems that consider load demand uncertainty and a social welfare scheme for prosumers. Authors of [24] propose a P2P energy trading schema based on game theory that aims to maximize the usage distributed solar energy. To manage the game schema, an algorithm is proposed that calculates the demand/surplus of renewable energy for each consumer/producer and rank producer-consumer pairs so that the number of transactions is minimized.…”

“…We use cooperative games to define a governance model for creating coalitions of prosumers in an energy community with the goal of balancing the total energy generation and demand. The common interests of the prosumers are the operate the community in a self-sufficient manner fully powered by the renewable energy produced inside the community and with minimal energy exchanges with the main grid: 𝐸 𝐸𝐶,𝑏𝑎𝑙𝑎𝑛𝑐𝑒 (𝑇) = min 𝑇 𝐸 𝐸𝐶,𝑠𝑢𝑟𝑝𝑢𝑙𝑠 (𝑇) − 𝐸 𝐸𝐶,𝑑𝑒𝑓𝑖𝑐𝑖𝑡 (𝑇) ≅ 0 (24) In formula (24), the total surplus of energy in an interval 𝑇 at the local community level is: The achieve this goal each prosumer that are energy sellers will try to form coalition of prosumers to balance its extra generation with the help of energy buyers that have extra demand or by convincing the self-sufficient prosumers to transit in the consumption state. For this it will use the blockchain tamperproof data sharing features and will publish a transactional offer on the chain that is willing to sell its energy surplus to other prosumers interested.…”

“…If the constraints are met the buy order is accepted in the coalition (lines [15][16][17][18][19]. If the prosumer is in a self-sufficient state, flexibility is used to increase its consumption to complete the remaining energy surplus (23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36). The state of the coalition is evaluated using the buy orders that satisfy the constraints stored in the coalition registry.…”

A classification scheme for local energy trading

“…Different matching approaches are presented in Khorasany et al (2021) and Krayem et al (2021). In Khorasany et al (2021), the matching is done via a priority list which is based on the economic profit of a trade between two prosumers.…”

“…The negotiation between two matched prosumers is executed as an iterative approach and the final solution is shown to be a NE. In Krayem et al (2021) the matching is based on the outcome of the Galey-Shapley algorithm, where the input is a distance measure of the difference between surplus and demand between each pair of prosumers. In this approach, there is no negotiation process as the internal trading price is fixed to a certain fraction of the trading price with the external grid.…”