Background

Unit Commitment (UC) is a complication in the domain of power systems engineering which is integral to the secure, efficient, and economic daily operation of a power system. UC is an optimization problem that aims at scheduling which generating units will be on at what time to meet electricity demand over a given horizon and that horizon it's typically 24–48 hours from now.

Objective

This research paper proposes a hybrid approach which is the extension of hGADE algorithm aims at solving mixed-integer optimization problem known as the Unit Commitment scheduling problem. The Whale Optimization Algorithm has been incorporated for the calculation of total operation cost of power system operation.

Method

The technique has been tested on a 6 unit system by taking into consideration various system and unit constraints. The hybridization of differential evolution, genetic algorithm, and whale optimization algorithm has produced a significant improvement in overall results.

Result

It has been found that the average cost of operation is 142814.9603 INR and it gets reduced to 142809.8944 INR after applying optimization (hGADE). The cost is further reduced to 142790.0 INR after the application of the Whale Optimization Algorithm (WOA).

Conclusion

In this paper, we present a hybrid approach which involves the blending of Differential Evolution, Genetic Algorithm, and Whale Optimization aim at solving mixed-integer optimization problem known as Unit Commitment scheduling problem and has given promising results.

中文翻译：

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基于混合鲸鱼优化-差分进化和遗传算法的单位承诺调度问题解决方法：WODEGA

背景

单位承诺（UC）是电力系统工程领域中的一个复杂问题，对于电力系统的安全，高效和经济的日常运行而言是不可或缺的。UC是一个优化问题，旨在调度给定范围内哪些发电机组将在什么时间满足电力需求，并且通常从现在开始24-48小时。

目的

本文提出了一种混合方法，它是hGADE算法的扩展，旨在解决混合整数优化问题，即单元承诺计划问题。鲸鱼优化算法已被并入，用于计算电力系统运行的总运行成本。

方法

该技术已通过考虑各种系统和单元约束在6单元系统上进行了测试。差分进化，遗传算法和鲸鱼优化算法的混合在整体结果上产生了重大改进。

结果

已经发现，平均运营成本为142814.9603 INR，应用优化（hGADE）后平均成本降低为142809.8944 INR。应用鲸鱼优化算法（WOA）后，成本进一步降低到142790.0 INR。

结论

在本文中，我们提出了一种包含差分进化，遗传算法和鲸鱼优化的混合方法，旨在解决称为单元承诺调度问题的混合整数优化问题，并给出了可喜的结果。