Optimization of New Load Connection Points in Distribution Networks Considering Electricity–Carbon Trading and Demand Response

With the increasing integration of wind and photovoltaic generation, demand response, and electricity–carbon trading, distribution network operation faces growing uncertainties, making it difficult for traditional power industry expansion practices to balance economic performance and operational requirements when selecting new load connection points. To address this issue, this study proposes an optimization method for new load connection point selection based on joint source–load scenario generation. The main contribution is the integration of electricity–carbon trading and demand response mechanisms into the load connection point selection process, moving beyond the traditional approach that primarily minimizes local network loss costs and providing a new perspective for the coordinated optimization of distribution network economics and voltage magnitude dispersion. First, medium- and long-term scenarios are generated using historical data and probabilistic modeling. A multidimensional evaluation framework is then established to assess the quality of the generated scenarios. Subsequently, a load connection point selection model incorporating electricity–carbon trading and demand response is developed and solved using a non-dominated sorting genetic algorithm. An improved knee-point method is then employed to select the optimal solution from the Pareto-optimal set, balancing economic performance and voltage magnitude dispersion. Finally, simulations are conducted on the IEEE 33-bus system. The results show that, compared with the scheme without demand response and carbon trading, the proposed method increases annual operating revenue by 5.21% and reduces nodal voltage magnitude dispersion by 14.97%.