Multi-Objective Operation Optimization of Park Microgrid Based on Green Power Trading Price Prediction in China

The dual-carbon objective aspires to enhance China’s medium- and long-term green power trading and facilitate the low-carbon economic operation of park microgrids from both medium- and long-term and spot market perspectives. First, the integration of medium- and long-term green power trading with spot trading was meticulously analyzed, leading to the formulation of a power purchase strategy for park microgrid operators. Subsequently, a sophisticated Bayesian fuzzy learning method was employed to simulate the interaction between supply and demand, enabling the prediction of the price for bilaterally negotiated green power trading. Finally, a comprehensive multi-objective optimization model was established for the synergistic operation of park microgrid in the medium- and long-term green power and spot markets. This model astutely considers factors such as green power trading, distributed photovoltaic generation, medium- and long-term thermal power decomposition, energy storage systems, and power market dynamics while evaluating both economic and environmental benefits. The Levy-based improved bird-flocking algorithm was utilized to address the multi-faceted problem. Through rigorous computational analysis and simulation of the park’s operational processes, the results demonstrate the potential to optimize user power consumption structures, reduce power purchase costs, and promote the green and low-carbon transformation of the park.