Currently, most of the power systems are being integrated with flexible AC transmission system devices and renewable energy sources for operating with enhanced security margins and balancing the increasing demand cost-effectively. On the other side, the trend of increasing global warming and extremely changing weather conditions is continuing across the world. Under this scenario, it is essential to realize their effect on various power system components and its economic operation. In this paper, the parameters namely resistance of the transmission line/transformer, load and solar photovoltaic generation are modeled considering ambient temperature effect. Later, economic schedule under changing weather conditions is proposed for attaining multi-objectives simultaneously like total operating cost of conventional energy, real power loss, average voltage collapse point indicator index and average voltage deviation index. Also, the dispatchable problems in the transmission system and various practical operating constraints are handled via optimally setting the parameters of optimal unified power flow controller. The optimization problem is solved using adaptive cuckoo search algorithm (ACSA), in which a dynamically increasing switching parameter in a power of three is adopted for adjusting the random walk between local optima and global optima. The superiority of the proposed ACSA in solving the multiobjective, nonlinear complex optimization problem over basic CSA and particle swarm optimization, chicken swarm optimization and flower pollination algorithm is presented by illustrating various case studies on standard IEEE 14, 30 and 118–bus test systems.

中文翻译：

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考虑天气变化的现代电力系统多目标经济运行自适应布谷鸟搜索算法

目前，大多数电力系统正在与灵活的交流输电系统设备和可再生能源集成，以提高安全性并以经济高效的方式平衡不断增长的需求。另一方面，全球变暖加剧和天气条件极度变化的趋势正在世界范围内持续。在这种情况下，必须意识到它们对各种电力系统组件及其经济运行的影响。在本文中，考虑环境温度效应对传输线/变压器电阻、负载和太阳能光伏发电等参数进行建模。后来，提出了在变化的天气条件下的经济计划，以同时实现常规能源的总运行成本、实际功率损耗、平均电压崩溃点指标指标和平均电压偏差指标。此外，通过优化设置最优统一潮流控制器的参数，处理输电系统中的可调度问题和各种实际运行约束。优化问题是使用自适应布谷鸟搜索算法（ACSA）解决的，其中采用动态增加的 3 次幂的切换参数来调整局部最优和全局最优之间的随机游走。通过对标准 IEEE 14、30 和 118 总线测试系统的各种案例研究，展示了所提出的 ACSA 在解决多目标、非线性复杂优化问题方面优于基本 CSA 和粒子群优化、鸡群优化和花授粉算法的优势。