Power Coordination Control of Wind-Solar Farms Cluster Based on Discrete Particle Swarm Algorithm
Ji Shunguo, Zhao Xin, Xu Ying · 2023
With the continuous integration of wind farms and photovoltaic power stations into the grid, the grid infrastructure in regions is gradually lagging behind the rapid growth of new energy generation capacity, posing challenges to the stability and reliability of the power system. In order to address this issue, researchers have proposed a new energy grid dispatch optimization method based on the Discrete Particle Swarm Algorithm (DPSO). This method aims to optimize power dispatching to maximize the integration capacity of new energy sources, reduce the demand for thermal power generation, and enhance the overall security and economic efficiency of the grid. This study first considers steady-state and dynamic active power balance constraints, which are crucial for the normal operation of the power system. Then, researchers employ the Discrete Particle Swarm Algorithm for power dispatch optimization. This algorithm simulates the behavior of particle swarms in nature when searching for the best positions to find optimal solutions. Through the Multi-Objective Particle Swarm Optimization (MPSO) algorithm, they are able to achieve the optimal power dispatch for new energy integration. To validate the effectiveness of this algorithm, simulations were conducted based on real data. The simulation results demonstrate that this algorithm can significantly increase the utilization of new energy, reduce the power demand of thermal power generation, and contribute not only to reducing reliance on traditional energy sources but also to enhancing the overall security of the grid, reducing carbon emissions, and running the power system more economically efficiently. This research provides a strong reference and reference for further research and applications in the field of new energy grid dispatching.