Robust Diversity-based Sine-Cosine Algorithm for Optimizing Hydropower Multi-reservoir Systems

被引:20
|
作者
Ahmadianfar, Iman [1 ]
Noshadian, Saeed [1 ]
Elagib, Nadir Ahmed [2 ,3 ]
Salarijazi, Meysam [4 ]
机构
[1] Behbahan Khatam Alanbia Univ Technol, Dept Civil Engn, Behbahan, Iran
[2] TH Koln Univ Appl Sci, Inst Technol & Resources Management Trop & Subtro, Betzdorferstr 2, D-50679 Cologne, Deutz, Germany
[3] Univ Cologne, Fac Math & Nat Sci, Inst Geog, Albertus Magnus Pl, D-50923 Cologne, Germany
[4] Gorgan Univ Agr Sci & Nat Resources, Fac Water & Soil Engn, Dept Water Engn, Gorgan, Golestan, Iran
关键词
Hydropower; Optimization; Operation rule; Multi-reservoir; Adaptive dynamic mechanism; DIFFERENTIAL EVOLUTION ALGORITHM; GENETIC ALGORITHMS; OPTIMIZATION; OPERATION; PERFORMANCE; POLICIES; RULE;
D O I
10.1007/s11269-021-02903-6
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Hydropower energy generation depends on the available water resources. Therefore, planning and operation of the water resource systems are paramount tasks for energy management. Since reservoirs are one of the important components of water resources systems, extracting optimal operating policies for proper management of energy generated from these systems is an imperative step. Optimizing reservoir system operation (ORSO) is a non-linear, large-scale, and non-convex problem with a large number of constraints and decision variables. To solve ORSO problem effectively, a robust diversity-based, sine-cosine algorithm (RDB-SCA) is developed in the present study by introducing several strategies to balance the global exploration and local exploitation ability and to achieve accurate and reliable solutions. An efficient linear operation rule is coupled with the RDB-SCA to maximize the energy generation. The proposed method is then applied to a real-world, multi-reservoir system to extract optimal operational policies and, consequently, maximize the energy production. It is shown that the RDB-SCA is able to generate 24, 14, and 6% more energy than the original SCA, respectively for 2-, 3-, and 4-reservoir systems. The present findings are useful to suggest guidelines for efficient operation of hydropower multi-reservoir systems. This paper is supported by https://imanahmadianfar.com/codes.
引用
收藏
页码:3513 / 3538
页数:26
相关论文
共 50 条
  • [21] A Modified Sine-Cosine Algorithm Based on Neighborhood Search and Greedy Levy Mutation
    Qu, Chiwen
    Zeng, Zhiliu
    Dai, Jun
    Yi, Zhongjun
    He, Wei
    COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2018, 2018
  • [22] Sine-cosine algorithm-based optimization for automatic voltage regulator system
    Hekimoglu, Baran
    TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL, 2019, 41 (06) : 1761 - 1771
  • [23] An improved sine-cosine algorithm based on orthogonal parallel information for global optimization
    Rizk-Allah, Rizk M.
    SOFT COMPUTING, 2019, 23 (16) : 7135 - 7161
  • [24] The Implementation of a New Optimization Method for Hydropower Generation and Multi-Reservoir Systems
    Moghani, Abbas
    Karami, Hojat
    WATER RESOURCES MANAGEMENT, 2024, 38 (05) : 1711 - 1735
  • [25] The Implementation of a New Optimization Method for Hydropower Generation and Multi-Reservoir Systems
    Abbas Moghani
    Hojat Karami
    Water Resources Management, 2024, 38 : 1711 - 1735
  • [26] Optimal power flow solution in power systems using a novel Sine-Cosine algorithm
    Attia, Abdel-Fattah
    El Sehiemy, Ragab A.
    Hasanien, Hany M.
    INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2018, 99 : 331 - 343
  • [27] Joint optimization of multi-reservoir systems based on imperialist competitive algorithm
    Xiang, Jie, 1733, CAFET INNOVA Technical Society, 1-2-18/103, Mohini Mansion, Gagan Mahal Road,, Domalguda, Hyderabad, 500029, India (07):
  • [28] An Improved Block-Matching Algorithm Based on Chaotic Sine-Cosine Algorithm for Motion Estimation
    Dash, Bodhisattva
    Rup, Suvendu
    ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2018, PT III, 2018, 11141 : 759 - 770
  • [29] A simulation - Optimization models for multi-reservoir hydropower systems design at watershed scale
    Hatamkhani, Amir
    Moridi, Ali
    Yazdi, Jafar
    RENEWABLE ENERGY, 2020, 149 : 253 - 263
  • [30] Automatic Data Clustering based on Hybrid Atom Search Optimization and Sine-Cosine Algorithm
    Abd Elaziz, Mohamed
    Neggaz, Nabil
    Ewees, Ahmed A.
    Lu, Songfeng
    2019 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2019, : 2315 - 2322