Abstract Single period economic dispatch cannot handle the intertemporal constraints in multi-period environment. To cope with this issue, the extension of economic dispatch over multiple time intervals (i.e., dynamic economic dispatch) has been introduced that considers the intertemporal constraints between different time intervals. Another issue is determining the most economical generation dispatch that could supply the area demand without violating the tie-line capacity, which cannot be solved by conventional economic dispatch problems. However, this study shows that the most economic schedule of power generation cannot satisfy echo-system expectation; therefore, making a compromise between fuel cost and environmental issues, a hot-button subject in industrialized nations, seems to be crucial. To reach the goals a bi-objective multi-area dynamic economic dispatch approach, which can handle intertemporal and multi-area constraints concurrently, is proposed to assist power system operators more and more. Finally, a hybrid algorithm, namely gray wolf optimizer-particle swarm optimization is introduced to solve the proposed problem and also a set of benchmark problems. By implementing the proposed approach on two small (10-unit, three areas) and large (40-unit, four areas) scale test systems, about 3.1% and 3.3% improvement in generation cost is obtained, respectively compare to the best reported results in the literature.

中文翻译：

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一种解决双目标多区域动态经济排放调度问题的高效混合方法

摘要 单期经济调度无法处理多期环境下的跨期约束。为了解决这个问题，引入了经济调度在多个时间间隔上的扩展（即动态经济调度），它考虑了不同时间间隔之间的跨期约束。另一个问题是确定最经济的发电调度，可以在不违反联络线容量的情况下满足区域需求，这是传统经济调度问题无法解决的。然而，这项研究表明，最经济的发电计划不能满足回声系统的期望；因此，在燃料成本和环境问题之间做出妥协，这是工业化国家的热门话题，似乎至关重要。为了实现这些目标，提出了一种双目标多区域动态经济调度方法，该方法可以同时处理跨期和多区域约束，以帮助越来越多的电力系统运营商。最后，引入了一种混合算法，即灰狼优化器-粒子群优化来解决所提出的问题以及一组基准问题。通过在两个小型（10 个单元，三个区域）和大型（40 个单元，四个区域）规模的测试系统上实施所提出的方法，与最佳报告结果相比，发电成本分别提高了 3.1% 和 3.3%在文献中。即引入灰狼优化器-粒子群优化来解决所提出的问题以及一组基准问题。通过在两个小型（10 个单元，三个区域）和大型（40 个单元，四个区域）规模的测试系统上实施所提出的方法，与最佳报告结果相比，发电成本分别提高了 3.1% 和 3.3%在文献中。即引入灰狼优化器-粒子群优化来解决所提出的问题以及一组基准问题。通过在两个小型（10 个单元，三个区域）和大型（40 个单元，四个区域）规模的测试系统上实施所提出的方法，与最佳报告结果相比，发电成本分别提高了 3.1% 和 3.3%在文献中。