P2P as a Smarter Way of Energy Trading: A Game Theoretic Case Study for Multi-microgrids

This paper focuses on performing peer-to-peer (P2P) energy trading in a grid-tied multi-microgrid system (MMS). To do so, three microgrids, each consisting of distributed energy resources (DERs) such as wind turbines (WTs), solar photovoltaic (PV) systems, and battery storage systems, are considered. A game theory-based structure, supported by the Nash equilibrium, is then formulated to derive, and solve the multi-objective function (MoF) with the intention of allocating the correct sizing of each DER and finding out the optimum payoff values. The proposed optimisation model is also integrated with reliability index (IR) and levelised cost of energy (LCOE) in order to cut down the energy costs during intermittent periods of DERs. The developed framework is analysed and compared with other benchmark techniques on particle swarm optimisation (PSO) algorithm-facilitated MATLAB environment to conduct both P2P and peer-to-grid (P2G) energy trading. The simulation results: 1) verify the proposed MOF against various constant coefficients' combination, and 2) determine the most financially viable model for each DER through sensitivity analysis.

I. Introduction

The patterns and technologies are advancing swiftly throughout the world to guarantee adequate energy supply in line with the increased amount of demand [1]–[2]. Peer-to-peer (P2P) energy trading is one of such technologies that is supported by the distributed energy resources (DERs) and facilitates the clean energy supply into the electricity grid through mutual negotiations [3]–[4]. It is characteristically different from the peer-to-grid (P2G) mechanism as participants receive unprecedented flexibility to integrate their preference while managing their energy [5] using a sustainable technology such as blockchain [6]. This has demonstrated its suitability both in local energy markets (LEMs) [7] and multi-microgrid systems (MMSs) [8]. In [9]–[11], feasibility study is performed to design and optimise DERs on real word-based scenarios.