An optimization based economic operation scheduling method for source network load storage integration technology

Under the drive of energy structure transformation and the "dual carbon" goal, efficient collaborative scheduling of the integrated system of source, grid, load, and storage has become a key technology to improve energy economy and low-carbon performance. This study proposes a source network load storage integrated economic operation scheduling method based on policy optimization to address the problems of lagging policy adjustment and insufficient multi-objective collaboration capability in traditional scheduling methods. Firstly, a multidimensional strategy decision library is constructed, integrating renewable energy output forecasting, load demand response, energy storage charging and discharging optimization, and grid interaction strategies. The strategy priority is quantified through a dynamic weight evaluation model; Secondly, establish a multi-objective optimization model with system operation economy as the core, taking into account power supply reliability, low carbon, and equipment loss, and introduce an improved particle swarm reinforcement learning hybrid algorithm for dynamic solution; Further propose a dynamic scheduling framework based on rolling time domain, combined with real-time data feedback and strategy adaptive correction mechanism, to achieve online collaborative optimization of source network load storage strategy. Simulation experiments show that compared with traditional methods, the proposed method can reduce system operating costs by 12.6%, improve renewable energy consumption rate by 9.8%, and reduce carbon emissions by 14.3% in typical scenarios, verifying its effectiveness and engineering applicability in complex energy interaction scenarios.