Balance Strategy of Multi Objective Optimization Algorithm in Unified Planning and Integrated Operation Planning of Distribution Network

The operation of the distribution network is affected by various factors, including load changes, equipment failures, and fluctuations in renewable energy. Uncertainty factors increase the complexity of modeling and optimization, leading to a decrease in the reliability of optimization results. Based on this, this article studies the unified planning and scheduling of distribution networks and operations through multi-objective optimization algorithms, with the objective function of minimizing the total lifecycle cost during the planning period, takes node power balance, voltage upper and lower limits, and distribution network connectivity as constraint functions to seek the optimal distribution planning, aiming to optimize the power output allocation of the distribution network through simulated annealing algorithm, and maintain the balance between operation and planning. This study takes historical data of power planning as the parameter input of the algorithm, and uses the function of minimizing various cost factors as the final temperature parameter indicator of the algorithm. Experimental studies have shown that the simulated annealing (SA) algorithm can achieve a power supply capacity of around 90%, with a power loss of only about 10%. Compared to other multi-objective optimization algorithms, SA algorithm is more excellent.