P2P Energy Trading Based on Multi Objective NSGA-II for Pollution Reduction and Social Welfare Enhancing Considering RES Uncertainty Model

Xiao Cao

Abstract


This research presents an advanced peer-to-peer (P2P) energy trading framework that integrates renewable energy source uncertainty modeling, demand response (DR), and multi-objective optimization using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The proposed approach aims to minimize carbon emissions while maximizing social welfare within a decentralized microgrid network. Uncertainties in photovoltaic irradiance, wind speed, and load demand are modeled using lognormal, Weibull, and normal probability density functions, respectively. A recurrent neural network is employed to forecast stochastic renewable generation and load profiles, providing accurate temporal data for optimization. The simulated system comprises sixty participants, including twenty prosumers and forty consumers, equipped with photovoltaic units, wind turbines, battery energy storage systems, and electric vehicles. Two operational scenarios are examined: a baseline case without DR and an enhanced case including DR. In the baseline scenario, the optimal solution achieves a social welfare index of 6721.449 and total emissions of 3255.172 units. When DR is implemented, emissions are reduced by approximately 17.3 percent, while welfare decreases by about 14.6 percent, revealing a clear environmental-economic trade-off. Analysis of the Pareto fronts confirms that DR participation effectively reduces demand peaks, improves renewable energy utilization, and lowers reliance on diesel generators. Overall, the NSGA-II-based P2P trading and DR coordination framework enhances sustainability, flexibility, and robustness under renewable uncertainty. The proposed model provides a scalable decision-support tool for emission-conscious and welfare-optimized smart grid communities, supporting efficient integration of distributed energy resources in low-carbon electricity markets worldwide globally.

Keywords


Peer-to-Peer energy trading; Uncertainty modelling; Coordinated demand response; Non-dominated Sorting Genetic Algorithm II; Recurrent Neural Network; Social welfare

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References


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DOI (PDF): https://doi.org/10.20508/ijrer.v16i2.16961.g9225

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