Day-Ahead Electricity Price Forecasting for Sustainable Electricity Markets: A Multi-Objective Optimization Approach Combining Improved NSGA-II and RBF Neural Networks

Chunlong Li, Zhenghan Liu, Guifan Zhang, Yumiao Sun, Shuang Qiu, Shiwei Song and Donglai Wang ()
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Chunlong Li: Key Laboratory of Regional Multi-Energy System Integration and Control of Liaoning Province, Shenyang Institute of Engineering, Shenyang 110136, China
Zhenghan Liu: State Grid Zhangjiakou Electric Power Supply Company, State Grid Jibei Electric Power Co., Ltd., Zhangjiakou 075000, China
Guifan Zhang: State Grid Zhangjiakou Electric Power Supply Company, State Grid Jibei Electric Power Co., Ltd., Zhangjiakou 075000, China
Yumiao Sun: Shenyang Institute of Engineering University Science Park, Shenyang 110136, China
Shuang Qiu: Key Laboratory of Regional Multi-Energy System Integration and Control of Liaoning Province, Shenyang Institute of Engineering, Shenyang 110136, China
Shiwei Song: Key Laboratory of Regional Multi-Energy System Integration and Control of Liaoning Province, Shenyang Institute of Engineering, Shenyang 110136, China
Donglai Wang: Key Laboratory of Regional Multi-Energy System Integration and Control of Liaoning Province, Shenyang Institute of Engineering, Shenyang 110136, China

Sustainability, 2025, vol. 17, issue 10, 1-31

Abstract: The large-scale integration of renewable energy into power grids introduces substantial stochasticity in generation profiles and operational complexities due to electricity’s non-storable nature. These factors cause significant fluctuations in day-ahead market prices. Accurate price forecasting is crucial for market participants to optimize bidding strategies, mitigate renewable curtailment, and enhance grid sustainability. However, conventional methods struggle to address the nonlinearity, high-frequency dynamics, and multivariate dependencies inherent in electricity prices. This study proposes a novel multi-objective optimization framework combining an improved non-dominated sorting genetic algorithm II (NSGA-II) with a radial basis function (RBF) neural network. The improved NSGA-II algorithm mitigates issues of population diversity loss, slow convergence, and parameter adaptability by incorporating dynamic crowding distance calculations, adaptive crossover and mutation probabilities, and a refined elite retention strategy. Simultaneously, the RBF neural network balances prediction accuracy and model complexity through structural optimization. It is verified by the data of Singapore power market and compared with other forecasting models and error calculation methods. These results highlight the ability of the model to track the peak price of electricity and adapt to seasonal changes, indicating that the improved NSGA-II and RBF (NSGA-II-RBF) model has superior performance and provides a reliable decision support tool for sustainable operation of the power market.

Keywords: electricity price forecasting; multi-objective optimization; improved NSGA-II; RBF neural network; sustainable electricity markets (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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