Assessing the Reliability of a Peer-to-Peer Trading Market Model Across Diverse Datasets

This paper presents a novel peer-to-peer (P2P) energy trading framework, employing game theory and agent-based modeling (ABM), to facilitate the exchange of electricity generated by photovoltaic (PV) systems among local stakeholders, including neighbors, and the grid operators. In this innovative ap...

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Bibliographic Details
Published in2024 4th International Conference on Smart Grid and Renewable Energy (SGRE) pp. 1 - 6
Main Authors Mohandes, Nassma, Bayhan, Sertac, Sanfilippo, Antonio
Format Conference Proceeding
LanguageEnglish
Published IEEE 08.01.2024
Subjects
Adaptation models
Agent-based modeling
Electricity market
Energy consumption
Evolutionary game
Game theoretical models
M-leader
N-follower Stackelberg game
Peer-to-peer computing
Peer-to-peer trading(P2P)
Scalability
Smart grids
Stakeholders
Urban areas
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Summary:This paper presents a novel peer-to-peer (P2P) energy trading framework, employing game theory and agent-based modeling (ABM), to facilitate the exchange of electricity generated by photovoltaic (PV) systems among local stakeholders, including neighbors, and the grid operators. In this innovative approach, energy transactions are governed by dynamically changing prices that reflect the evolving balance between energy generation and demand throughout the day. Sellers list surplus energy in the market, specifying generation type, price, and location. The universality of this market framework is confirmed through comprehensive validation exercises involving data from Qatar and Europe (Germany), representing distinct regions. Multiple scenarios were examined, encompassing the integration of battery storage and the implementation of a carbon tax. The study's findings indicate that energy consumption in Germany is notably lower than in Qatar, attributable to less harsh weather conditions and a heightened awareness of energy efficiency among German users. The relatively high cost of grid electricity in Germany serves as a strong incentive for the adoption of PV systems and the reduction of grid-dependent energy consumption. Moreover, the proposed model exhibits scalability, offering the potential to extend its application to entire neighborhoods or even entire cities in various geographical locations. This adaptable framework has the capability to address diverse energy trading needs and preferences across different regions.
DOI:10.1109/SGRE59715.2024.10428913