摘要
伴随可再生能源渗透率的提升，大规模储能系统（Energy Storage System, ESS）于电力系统动态经济调度（Dynamic Economic Dispatch, DED）中的作用愈发凸显。然而，储能所引入的时序耦合约束致使传统DED问题更具非线性且求解难度增大，传统启发式算法通常难以在收敛速度与寻优精度间达成平衡。为在满足复杂约束的条件下降低发电成本，本文提出一种基于PID搜索算法（PID-based Search Algorithm, PSA）融合比例再分配约束修复机制的储能型动态经济调度方法。该方法借助PID控制器在误差消除方面的卓越特性，从比例、积分、微分三个维度引导种群向全局最优解收敛，有效克服了传统算法易陷入局部最优的弊端。5机组测试系统的仿真结果显示，与粒子群算法（Particle Swarm Optimization, PSO）和遗传算法（Genetic Algorithm, GA）相比，PSA算法在24小时调度周期内可获取最低的燃料成本19380.53美元，较PSO和GA分别降低了约4.5%和7.4%。在收敛性能层面，PSA在200代内即可趋于稳定，迭代过程中适应度值持续下降，这证实了其具备更强的稳定性。表明PSA能够精准规划储能系统的充放电行为，有效实现“削峰填谷”，显著提升了电力系统的运行经济性。
关键词： 动态经济调度；储能系统；PID搜索算法；时序耦合约束；非线性优化
Abstract
With the increasing penetration of renewable energy, large-scale energy storage systems (ESS) have become increasingly vital in dynamic economic dispatch (DED) of power systems. However, the time-coupled constraints introduced by energy storage make traditional DED problems more nonlinear and challenging to solve, as conventional heuristic algorithms often struggle to balance convergence speed and optimization accuracy. To reduce generation costs while satisfying complex constraints, this paper proposes an energy storage-based dynamic economic dispatch method based on the PID-based search algorithm (PSA) integrated with a proportional redistribution constraint repair mechanism. Leveraging the error elimination advantages of PID controllers, this method guides the population toward global optimal solutions through proportional, integral, and differential dimensions, effectively overcoming the tendency of traditional algorithms to get trapped in local optima. MATLAB simulations on a 5-unit test system demonstrate that compared to particle swarm optimization (PSO) and genetic algorithms (GA), the PSA algorithm obtains a minimum fuel cost of 19380.53 USD within the 24-hour dispatch cycle, which is approximately 4.5% and 7.4% lower than that of the PSO and GA, respectively. For convergence performance, PSA stabilizes within 200 generations, with the fitness value decreasing continuously during iterations, which confirms its superior stability. The results show that PSA can precisely plan energy storage system charging and discharging behaviors, effectively realizing “peak shaving and valley filling,” and significantly improving the operational economic efficiency of power systems.
Key words： Dynamic economic scheduling; Energy storage system; PID-based search algorithm; Time-coupled constraints; Nonlinear optimization
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