Solving Fuel-Based Unit Commitment Problem Using Improved Binary Bald Eagle Search

IF 4.9 3区 计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY
Sharaz Ali, Mohammed Azmi Al-Betar, Mohamed Nasor, Mohammed A. Awadallah
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Abstract

The Unit Commitment Problem (UCP) corresponds to the planning of power generation schedules. The objective of the fuel-based unit commitment problem is to determine the optimal schedule of power generators needed to meet the power demand, which also minimizes the total operating cost while adhering to different constraints such as power generation limits, unit startup, and shutdown times. In this paper, four different binary variants of the Bald Eagle Search (BES) algorithm, were introduced, which used two variants using S-shape, U-shape, and V-shape transfer functions. In addition, the best-performing variant (using an S-shape transfer function) was selected and improved further by incorporating two binary operators: swap-window and window-mutation. This variation is labeled Improved Binary Bald Eagle Search (IBBESS2). All five variants of the proposed algorithm were successfully adopted to solve the fuel-based unit commitment problem using seven test cases of 4-, 10-, 20-, 40-, 60-, 80-, and 100-unit. For comparative evaluation, 34 comparative methods from existing literature were compared, in which IBBESS2 achieved competitive scores against other optimization techniques. In other words, the proposed IBBESS2 performs better than all other competitors by achieving the best average scores in 20-, 40-, 60-, 80-, and 100-unit problems. Furthermore, IBBESS2 demonstrated quicker convergence to an optimal solution than other algorithms, especially in large-scale unit commitment problems. The Friedman statistical test further validates the results, where the proposed IBBESS2 is ranked the best. In conclusion, the proposed IBBESS2 can be considered a powerful method for solving large-scale UCP and other related problems.

Abstract Image

利用改进的二元秃鹰搜索解决燃料型机组承诺问题
机组承诺问题(UCP)与发电计划的规划相对应。基于燃料的机组承诺问题的目标是确定满足电力需求所需的最佳发电机计划,在遵守发电限制、机组启动和关闭时间等不同约束条件的同时,使总运营成本最小化。本文介绍了白头鹰搜索(BES)算法的四种不同的二进制变体,其中两种变体使用了 S 型、U 型和 V 型传递函数。此外,我们还选择了性能最好的变体(使用 S 形传递函数),并通过加入两个二进制算子:交换窗口和窗口突变,对其进行了进一步改进。这种变体被称为改进的二进制白头鹰搜索(IBBESS2)。利用 4、10、20、40、60、80 和 100 单位的七个测试案例,成功采用了所提算法的所有五个变体来解决基于燃料的单位承诺问题。为了进行比较评估,对现有文献中的 34 种比较方法进行了比较,其中 IBBESS2 在与其他优化技术的比较中取得了有竞争力的成绩。换句话说,IBBESS2 在 20、40、60、80 和 100 个单位的问题上取得了最佳平均分,表现优于所有其他竞争对手。此外,与其他算法相比,IBBESS2 能更快地收敛到最优解,尤其是在大规模机组承诺问题上。弗里德曼统计检验进一步验证了这一结果,即所提出的 IBBESS2 是最好的。总之,所提出的 IBBESS2 可被视为解决大规模 UCP 及其他相关问题的有力方法。
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来源期刊
Journal of Bionic Engineering
Journal of Bionic Engineering 工程技术-材料科学:生物材料
CiteScore
7.10
自引率
10.00%
发文量
162
审稿时长
10.0 months
期刊介绍: The Journal of Bionic Engineering (JBE) is a peer-reviewed journal that publishes original research papers and reviews that apply the knowledge learned from nature and biological systems to solve concrete engineering problems. The topics that JBE covers include but are not limited to: Mechanisms, kinematical mechanics and control of animal locomotion, development of mobile robots with walking (running and crawling), swimming or flying abilities inspired by animal locomotion. Structures, morphologies, composition and physical properties of natural and biomaterials; fabrication of new materials mimicking the properties and functions of natural and biomaterials. Biomedical materials, artificial organs and tissue engineering for medical applications; rehabilitation equipment and devices. Development of bioinspired computation methods and artificial intelligence for engineering applications.
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