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In this paper, multiple flexible loads such as photovoltaic cells, air conditioners, charging piles and batteries are comprehensively considered in special transformer users, and the collaborative optimization strategy of multiple loads is studied to improve the dynamic peak shaving capability of special transformer users. First, a evaluation method for complementarity of multiple loads in special transformer users is introduced to quantify the complementarity among different loads. Second, a collaborative optimization strategy based on the penalty function method and the primal-dual interior point method is developed. By applying the penalty function method, the mixed integer linear programming model is transformed into a continuous nonlinear programming model that can be efficiently solved using the primal-dual interior point method. Integer variables are then re-optimized to ensure feasibility. Finally, the complementarity of 11 kinds of load groups before and after optimization is analyzed through a case. Among them, the complementarity of the load group composed of air conditioners, charging piles and batteries is increased most obviously after optimization, and its comprehensive complementarity reaches 14.9 times of that before optimization. These results validate the effectiveness of the proposed collaborative optimization model and algorithm for multiple loads in special transformer users.

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Research on Collaborative Optimization Technology for Multiple Loads of a Special Transformer

  • 会议论文

  • 发表日期：

    2026年

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  • Zhiwen Yu,
  • Yubo Liang,
  • Jieni Zhang,
  • Qiong Cui,
  • Zetao Ma,
  • Jing Zhang

摘要

In this paper, multiple flexible loads such as photovoltaic cells, air conditioners, charging piles and batteries are comprehensively considered in special transformer users, and the collaborative optimization strategy of multiple loads is studied to improve the dynamic peak shaving capability of special transformer users. First, a evaluation method for complementarity of multiple loads in special transformer users is introduced to quantify the complementarity among different loads. Second, a collaborative optimization strategy based on the penalty function method and the primal-dual interior point method is developed. By applying the penalty function method, the mixed integer linear programming model is transformed into a continuous nonlinear programming model that can be efficiently solved using the primal-dual interior point method. Integer variables are then re-optimized to ensure feasibility. Finally, the complementarity of 11 kinds of load groups before and after optimization is analyzed through a case. Among them, the complementarity of the load group composed of air conditioners, charging piles and batteries is increased most obviously after optimization, and its comprehensive complementarity reaches 14.9 times of that before optimization. These results validate the effectiveness of the proposed collaborative optimization model and algorithm for multiple loads in special transformer users.