Collaborative Control Algorithm for Multiple Flexible Loads in Demand Side Energy Management System

Load energy management occupies a core position in the demand side response mechanism of smart grids, and its essence lies in ensuring the stable operation of the power system (EPS) and optimizing the cost structure through refined load energy regulation. In this context, the scheduling role of flexible loads is becoming increasingly prominent. It can optimize the load curve by peak shaving and valley filling, balance the supply and demand of the power grid, effectively reduce operating costs, and improve overall energy efficiency. In order to promote the active role of flexible loads in demand response for residential users, this paper innovatively proposes a multi-dimensional flexible load collaborative control algorithm. This algorithm combines Particle Swarm Optimization (PSO) with Deep Learning (DL) technology, aiming to achieve intelligent and precise scheduling of load energy. PSO, with its excellent global search and fast convergence capabilities, provides an efficient solution for load allocation; The addition of DL technology further enhances the algorithm's accuracy in identifying and predicting dynamic load changes, making scheduling strategies more precise and flexible. Experimental verification shows that the algorithm can significantly improve the response speed and scheduling efficiency of flexible loads.