This paper presents a comparative study for five artificial intelligent (AI) techniques to the dynamic economic dispatch problem: differential evolution, particle swarm optimization, evolutionary programming, genetic algorithm, and simulated annealing. Here, the optimal hourly generation schedule is determined. Dynamic economic dispatch determines the optimal scheduling of online generator outputs with predicted load demands over a certain period of time taking into consideration the ramp rate limits of the generators. The AI techniques for dynamic economic dispatch are evaluated against a ten-unit system with nonsmooth fuel cost function as a common testbed and the results are compared against each other.

中文翻译：

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动态经济调度：差分进化、粒子群优化、进化规划、遗传算法和模拟退火的比较研究

本文对动态经济调度问题的五种人工智能 (AI) 技术进行了比较研究：差分进化、粒子群优化、进化规划、遗传算法和模拟退火。在此，确定最佳每小时发电计划。考虑到发电机的爬坡率限制，动态经济调度决定了在线发电机输出的最佳调度，并在一定时间段内预测负载需求。动态经济调度的 AI 技术针对具有非平滑燃料成本函数的十单元系统作为通用测试平台进行评估，并将结果相互比较。