Dynamic economic dispatch problem or DED is an extension of static economic dispatch problem or SED which is used to determine the generation schedule of the committed units so as to meet the predicted load demand over a time horizon at minimum operating cost under ramp rate constraints and other constraints. This work presents an efficient hybrid method based on particle swarm optimization (PSO) and termite colony optimization (TCO) for solving DED problem. The hybrid method employs PSO for global search and TCO for local search in an interleaved mode towards finding the optimal solution. After the first round iteration of local search by TCO, the best local solutions are considered by PSO to update the schedules globally. In the next round, TCO performs local search around each solution found by PSO. This paper reports the methodology and result of application of PSO–TCO hybrid to 5-unit, 10-unit and 30-unit power dispatch problems; the result shows that the PSO–TCO (HPSTCO) gives improved solution compared to PSO or TCO (when applied separately) and also other hybrid methods.

中文翻译：

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使用混合元启发式的动态经济调度

动态经济调度问题或 DED 是静态经济调度问题或 SED 的扩展，用于确定承诺机组的发电计划，以便在爬坡率约束和其他条件下以最低运营成本满足一段时间内的预测负荷需求约束。这项工作提出了一种基于粒子群优化 (PSO) 和白蚁群优化 (TCO) 的有效混合方法，用于解决 DED 问题。混合方法采用 PSO 进行全局搜索，TCO 用于以交错模式进行局部搜索，以寻找最佳解决方案。在 TCO 对本地搜索进行第一轮迭代后，PSO 会考虑最佳本地解决方案以更新全局调度。在下一轮中，TCO 围绕 PSO 找到的每个解决方案执行本地搜索。本文报告了将 PSO-TCO 混合应用于 5 单元、10 单元和 30 单元电力调度问题的方法和结果；结果表明，与 PSO 或 TCO（单独应用时）以及其他混合方法相比，PSO-TCO (HPSTCO) 提供了改进的解决方案。