Research on adaptive dispatching of power system considering reserve energy storage and cost

Abstract

The power system (PS) has the problem of grid connection of energy storage (ES) system. When the ES of the communication base station (BS) is associated with the power grid, relevant control strategies are formulated to schedule the base station energy storage (BSES). The total cost required during the scheduling period is determined using the lease income model. In the dispatching process, the BSES is applied to the peak load shifting (PLS) dispatching and economic dispatching of the PS. It is optimized by particle swarm optimization (PSO) algorithm and improved bare bone particle swarm optimization (BBPSO) algorithm. The constructed rental income model is used to calculate the total cost required during the scheduling period. In the dispatching, the BSES is applied to the PLS dispatching and economic dispatching of the PS. This model is optimized by PSO algorithm and improved BBPSO algorithm. The findings indicate that the BSES has good PLS capability. The larger the BS is, the more obvious the charging and discharging situation is. When the time is 4 h, the output load of 150,000 BSES is 486.67 MW, 341.14 MW more than that of 100,000 BSs. The discharge depth affects the lease cost, and the best discharge depth is 0.4. At this discharge depth, the larger the BS scale is, the greater the costs. In improving the performance of BBPSO algorithm, the model has the minimum convergence iteration of 15, with the best convergence effect. In the economic dispatching of PS, the total cost of accessing 200,000 BSs to store energy is 846.4658 million per year, which saves 367.4591 million. The suggested approach can effectively lower PS costs and increase stability.