Urban-Rural Energy Network Load Optimization Scheduling Based on Co-evolutionary Algorithm

With the transformation of the global energy structure, the problem of urban and rural energy dispatch is becoming increasingly prominent, especially in the joint dispatch of power and natural gas systems. It is urgent to solve the optimal allocation of energy, reduce operating costs and improve the stability and sustainability of the system. Traditional optimization methods often face challenges such as low efficiency and unstable solution quality when facing complex coordination problems with multiple objectives and multiple constraints. To this end, this paper proposes a joint dispatch optimization method based on co-evolutionary algorithm (CEA) to solve the coordinated dispatch problem in urban and rural energy systems, especially between power and natural gas systems. The algorithm effectively improves the ability to solve multi-objective optimization problems through cooperation and competition among multiple populations. In the algorithm design, combined with the characteristics of power and natural gas systems, this paper innovatively introduces population initialization strategy, fitness evaluation mechanism, information exchange mechanism and co-evolutionary cooperation strategy. Through comparative experiments with traditional genetic algorithms and particle swarm optimization algorithms, this method has significant advantages in system stability, cost optimization and energy network operation efficiency. This provides new ideas and algorithm support for the efficient and coordinated development of urban and rural energy systems in the future.