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This article focuses on the implementation of peer-to-peer (P2P) energy trading and planning of a grid-connected multi-microgrid system based on an advanced optimization approach. The proposed architecture is comprised of three microgrids (MGs) with combinations of distributed energy resources (DERs), including wind turbines, photovoltaic panels, and storage batteries, to meet the load requirement. A game theory technique, Nash equilibrium, is used to structure the proposed model for multiobjective optimization, where the main objectives are used to determine the correct sizing of DERs and optimum payoff values. Due to the variability of DERs, to maintain lower energy costs, the reliability index ( IR ) and levelized cost of energy are the benchmarks considered for optimization. The proposed model is analyzed, and rigorous comparison is carried out for both peer-to-grid and P2P energy trading schemes, considering the Australian profiles for wind speed, solar irradiation, and residential load. The simulation model is built in MATLAB software, and the particle swarm optimization algorithm is exploited for the optimization. The results illustrate that P2P energy trading reduces the payoff value of the multiobjective function (MOF) to 36%. The robustness of MOF is validated and analyzed with different combinations of constant coefficients K 1 and K 2 . In the end, the most economical and suitable models with DERs are proposed for each MG, and the results are verified with sensitivity analysis.
Ali et al. (2022) studied this question.
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