Co-optimization of system configurations and energy scheduling of multiple community integrated energy systems to improve photovoltaic self-consumption

Abstract

Given the increasing integration of building photovoltaics and the accelerated transformation of power systems, the supply-demand imbalance in photovoltaic communities has become a pressing issue. This study presents a novel optimization model for collaborative planning and scheduling. The alternating direction multiplier method is utilized to derive an optimal economic scheduling strategy for the community cooperative alliance, while ensuring private information protection. Additionally, the genetic algorithms are employed to ascertain the optimal configuration. The optimization model prioritizes the common good over individual interests in community cooperation alliances, meaning some members may need to compromise their self-interest to attain optimized overall benefits. Therefore, a compensation mechanism based on the Nash negotiation principle is established to ensure fair distribution of benefits. The simulation results demonstrate that the primary energy saving rate, annual cost saving rate, and carbon dioxide emission reduction rate of the photovoltaic community alliance are 42.91%, 32.50%, and 41.81%, respectively, and the self-consumption level of photovoltaic is vastly improved to 98.83% on a typical winter day. The sensitivity analysis illustrates that the photovoltaic panels in buildings contribute to a decrease in energy consumption and carbon emission by 25.57% and 26.15%, respectively. In contrast, they result in a cost increase of 11.05%.