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Boosting progressive optimality algorithm Performance in Optimizing Complex Large-Scale Multi-Reservoir System Operations by Using Discrepant Optimization Windows and Disturbance-Response Strategy

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WATER RESOURCES MANAGEMENT 2024年第11期38卷 4269-4285页

作者： Xu, Nan Chen, Jia Zheguang Middle Sch Keqiao Dist Shaoxing City Shaoxing 312025 Peoples R China Zhuji Municipal Water Conservancy Bur Shaoxing 311800 Peoples R China

The progressive optimality algorithm (POA) is a powerful technique widely used for optimizing multi-reservoir operations;however, two crucial downsides cumber its application to complex large-scale multi-reservoir systems, which are insufficient search directions and the dimensionality problem-the former limits the POA's precision, while the latter reduces its efficiency. Although several POA variants have been developed to overcome these downsides, a further balance between the precision and efficiency of the algorithm is required to boost the POA's capability of optimizing the operation of complex large-scale multi-reservoir systems. In view of this, we made modifications to the original algorithm and developed a new POA variant, referred to as the Direct Search algorithm Based on Disturbance-Response Strategy (DRDSA). On one hand, we changed the POA's uniform optimization window for all reservoirs to discrepant optimization windows for varying reservoirs to enrich the search direction set of the algorithm. On the other hand, we introduced a disturbance-response strategy into the solution of sub-problems to handle the POA's dimensionality problem. Two multi-reservoir operation optimization problems were employed to test the performance of the DRDSA, and seven advanced alternatives including two existing POA variants were used for comparison. The results showed the improved precision and efficiency of the DRDSA. Thus, a new technique is available for optimizing the operation of complex large-scale multi-reservoir systems.

关键词： Multi-reservoir operation optimization Direct search Disturbance-response strategy Discrepant optimization windows progressive optimality algorithm

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Long-Term Joint Operation of Cascade Reservoirs Using Enhanced progressive optimality algorithm and Dynamic Programming Hybrid Approach

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WATER RESOURCES MANAGEMENT 2021年第7期35卷 2265-2279页

作者： Chen, Jia Zhuji Municipal Water Conservancy Bur Shaoxing 311800 Peoples R China

Dynamic Programming (DP) is one of the most classical methods adopted for reservoir operation. It reduces the computational efforts of complex high-dimensional problems by piecewise dimensionality reduction and provides the global optimums of the problems, but it suffers the "curse of dimensionality". progressive optimality algorithm (POA) has been used repeatedly in reservoir operation studies during last decades because it alleviates the "curse of dimensionality" of DP and has good convergence and extensive applicability. Nonetheless, the POA encounters two difficulties in multireservoir operation applications. One is the transfer interrupt problem that makes the search procedure hard to achieve free allocation of water between two non-adjacent stages, and another is the dimensionality problem that leads to a low convergence rate. In order to overcome these deficiencies, this study made some enhancements to the POA and proposed a hybrid approach combining the Enhanced POA and DP (EPOA-DP) for long-term operation of cascade reservoir systems. In EPOA-DP, the EPOA is employed to improve the quality of the solutions and DP is used to reduce the computational effort of the two-stage problem solution. The proposed approach was tested using a real-world four-reservoir cascade system and a ten-reservoir benchmark test example. The results demonstrated that it outperforms the POA both in computational time and quality of the solution.

关键词： Cascade Reservoir Hydropower station Optimal operation Dynamic programming progressive optimality algorithm

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Artificial intelligence-based response surface progressive optimality algorithm for operation optimization of multiple hydropower reservoirs

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ENERGY 2024年 291卷

作者： Niu, Wen-Jing Luo, Tao Yao, Xin-Ru Huang, Qing-Qing Gao, Hao-Yu Feng, Zhong-Kai ChangJiang Water Resources Commiss Bur Hydrol Wuhan 430010 Peoples R China Hohai Univ Coll Hydrol & Water Resources Nanjing 210098 Peoples R China Hohai Univ Natl Key Lab Water Disaster Prevent Nanjing 210098 Peoples R China

Hydropower reservoir operation is critical to ensuring reliable water and energy supply, supporting sustainable economic and social development. Although the progressive optimality algorithm (POA) is a famous modified dynamic programming technique for resolving multistage decision-making problems, its standard method struggles with poor performance in large-scale multireservoir operation problems due to the dimensionality issue. The computation burden grows exponentially with the increase of state variables, making it challenging to find optimal solutions. To overcome this challenge and improve POA's performance, an effective response surface-based progressive optimality algorithm (RSPOA) is proposed for multireservoir system operation optimization. RSPOA decomposes the original multistage problem into numerous easy-to-solve two-stage subproblems. Additionally, an artificial intelligence-based response surface model is integrated to reduce the huge computation required in determining a modified solution for each subproblem. The simulations show that compared to the standard POA method, RSPOA can make obvious improvements in execution efficiency in various operation scenarios. For instance, in the 4-reservoir system in Wu River with 19 discrete states and dry runoff, RSPOA-LSTM achieves about 79.2 % reductions in the computation time of POA. Thus, RSPOA proves to be an effective tool to solve the complex operation optimization challenges of multireservoir systems.

关键词： Multireservoir operation progressive optimality algorithm Artificial intelligence Response surface

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Linking Nelder-Mead Simplex Direct Search Method into Two-Stage progressive optimality algorithm for Optimal Operation of Cascade Hydropower Reservoirs

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JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT 2020年第5期146卷

作者： Feng, Zhong-kai Niu, Wen-jing Zhou, Jian-zhong Cheng, Chun-tian Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China Changjiang Water Resources Commiss Bur Hydrol Wuhan 430010 Peoples R China Dalian Univ Technol Key Lab Ocean Energy Utilizat & Energy Conservat Minist Educ Dalian 116024 Peoples R China

To satisfy growing energy demand, the hydropower industry of China is experiencing unprecedented development, and the total power generation and installed capacity of hydropower in China rank first in the world. The system scale and rate of development have posed computational modeling challenges, because the computational burden in hydropower optimization modeling using classical dynamic programming methods grows exponentially as the number of reservoirs increases. One method designed to reduce this burden, the progressive optimality algorithm (POA), still suffers from the dimensionality problem and the need for iterative computations to address large-scale hydropower systems. To enhance the performance of POA, this work develops a new method referred to as the simplex progressive optimality algorithm (SPOA). In SPOA, the complex multistage problem is divided into several easy-to-solve two-stage subproblems, and then the Nelder-Mead simplex direct search method is adopted to search for the improved solution to each subproblem, enhancing the solution's quality via iterative computation. Experimental results indicate that the proposed SPOA method can significantly reduce execution time and memory usage under different cases, demonstrating its applicability for large-scale hydropower system operation problems.

关键词： Cascade hydropower reservoirs Optimal operation Computable modeling challenge Dimensionality reduction progressive optimality algorithm Nelder-Mead simplex direct search

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Optimizing Hydropower Reservoirs Operation via an Orthogonal progressive optimality algorithm

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JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT 2018年第3期144卷

作者： Feng, Zhong-kai Niu, Wen-jing Cheng, Chun-tian Lund, Jay R. Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Hubei Peoples R China Chang Jiang Water Resources Commiss Bur Hydrol Huangpu Rd 1063 Wuhan 430010 Hubei Peoples R China Dalian Univ Technol Dept Inst Hydropower & Hydroinformat Dalian 116024 Peoples R China Univ Calif Davis Dept Civil & Environm Engn Davis CA 95616 USA

The progressive optimality algorithm (POA) is commonly used to identify optimal hydropower operation schedules in China. However, POA may not converge within a reasonable time for large and complex problems because its computational burden grows exponentially with the expansion of system scale. In order to effectively alleviate the dimensionality problem of POA, an improved POA variant called orthogonal progressive optimality algorithm (OPOA) is introduced in this paper. In the OPOA, an orthogonal experimental design is used to replace the exhaustive combinatorial evaluation at each POA two-stage subproblem. The theoretical analysis shows that POA and OPOA have exponential and approximately polynomial growth in computational complexity, respectively. The proposed method is applied to a large-scale multireservoir system located on the Wu River in China. The results indicate that, compared with POA, OPOA can remarkably enhance the computing efficiency in different cases, showing its practicability and feasibility for multireservoir system operation. (C) 2018 American Society of Civil Engineers.

关键词： Multireservoir system operation progressive optimality algorithm Curse of dimensionality Orthogonal experiment design Dimensionality reduction

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Peak operation of hydropower system with parallel technique and progressive optimality algorithm

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2018年 94卷 267-275页

作者： Feng, Zhong-kai Niu, Wen-jing Cheng, Chun-tian Wu, Xin-yu Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Hubei Peoples R China Changfiang Water Resources Commiss Bur Hydrol Wuhan 430010 Hubei Peoples R China Dalian Univ Technol Inst Hydropower & Hydroinformat Dalian 116024 Peoples R China

With the rapid economic growth in recent years, the peak operation of hydropower system (POHS) is becoming one of the most important optimization problems in power system. However, the rapid expansion of system scale, refined management and operational constraints has greatly increased the optimization difficult of POHS. As a result, it is of great importance to develop effective methods that can ensure the computational efficiency of POHS. The progressive optimality algorithm (POA) is a commonly used technique for solving hydropower operation problem, but its execution time still grows sharply with the increasing number of hydropower plants, making it difficult to satisfy the efficiency requirement of POHS. To address this problem, a novel efficient method called parallel progressive optimality algorithm (PPOA) is presented in this paper. In PPOA, the complex problem is firstly divided into several two-stage optimization subproblems, and then the classical Fork/Join framework is used to realize parallel computation of subproblems, making a significant improvement on execution efficiency. The simulations in a real-world hydropower system demonstrate that as compared with the standard POA, PPOA can use abundant multi-core resources to reduce execution time while keeping the quality of solution, providing a new alternative to solve the complex hydropower peak operation problem. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Hydropower reservoirs Peak operation progressive optimality algorithm Fork/Join framework Parallel computing Curse of dimensionality

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Optimizing electrical power production of hydropower system by uniform progressive optimality algorithm based on two-stage search mechanism and uniform design

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JOURNAL OF CLEANER PRODUCTION 2018年第Jul.20期190卷 432-442页

作者： Feng, Zhong-kai Niu, Wen-jing Cheng, Chun-tian Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Hubei Peoples R China ChangJiang Water Resources Commiss Bur Hydrol Wuhan 430010 Hubei Peoples R China Dalian Univ Technol Inst Hydropower & Hydroinformat Dalian 116024 Peoples R China

As one of the important renewable energy, hydropower is experiencing a booming development period throughout the world in recent years. By the end of 2016, hydropower has occupied 20.1% installed capacity and 19.5% generation in China. Thus, it is of great importance to develop some effective methods to guarantee the overall generation benefit of hydropower system. As a famous optimization tool to solve this problem, the progressive optimality algorithm cannot effectively handle with large-scale hydropower system because its computational burden grows exponentially with the increasing number of hydroplants. Thus, in order to effectively alleviate the dimensionality problem, a novel method called uniform progressive optimality algorithm is introduced here. In the presented method, the complex multistage problem is firstly divided into several two-stage optimization subproblems, and then the uniform design is adopted to sample a small subset from all the possible state vectors at each subproblem, while the successive approximation strategy is adopted to gradually improve the quality of solution. The results from a real-world hydropower system of China indicate that compared with progressive optimality algorithm, the proposed method has superior performance in execution efficiency and convergence speed, which is an effective alternative method for the complex hydropower system operation problem. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Hydropower system operation progressive optimality algorithm Dimensionality reduction Curse of dimensionality Uniform design Successive approximation

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Short-Term Hydroscheduling with Discrepant Objectives Using Multi-Step progressive optimality algorithm

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JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION 2012年第3期48卷 464-479页

作者： Cheng, Chuntian Shen, Jianjian Wu, Xinyu Chau, Kwok-wing Dalian Univ Technol Inst Hydropower & Hydroinformat Dalian 116024 Peoples R China Hong Kong Polytech Univ Dept Civil & Struct Engn Hong Kong Hong Kong Peoples R China

Cheng, Chuntian, Jianjian Shen, Xinyu Wu, and Kwok-wing Chau, 2012. Short-Term Hydroscheduling with Discrepant Objectives Using Multi-step progressive optimality algorithm. Journal of the American Water Resources Association (JAWRA) 48(3): 464-479. DOI: 10.1111/j.1752-1688.2011.00628.x Abstract: With increase in the number and total capacity of hydropower plants in power systems, optimality algorithms with a single objective are not suitable for optimizing the operation of complex hydropower systems to meet complex demands. Hydropower plants should prioritize discrepant objectives, such as peak regulation and maximizing generation during solving of optimal operation problems of hydropower systems. In this article, we present a multi-step progressive optimality algorithm (MSPOA) for the short-term hydroscheduling (STHS) problem to improve the quality of optimal solutions and enhance the convergence speed of progressive optimality algorithm (POA). In MSPOA, the original problem is first decomposed into a sequence of problems with the longer time steps. Next, the problem with the longest time step is solved, and the optimal solution is used as the initial solution for the problem with the second longest time step. This process proceeds until the original problem with the shortest time step is solved. The proposed discrepant-objective method and solution technique are tested for two types of hydroelectric systems. The results show that MSPOA can give better solutions and cost less time than POA due to enlarging feasible range of decision variables and reducing the number of computational stages. Discrepant objectives among hydropower plants can express the operation characteristics of complex hydropower systems more accurately than unique objective or multiple objectives.

关键词： short-term hydroscheduling discrepant objectives progressive optimality algorithm multi-step progressive optimality algorithm

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Stochastic Programming with a Joint Chance Constraint Model for Reservoir Refill Operation Considering Flood Risk

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JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT 2017年第1期143卷 1-11页

作者： Xu, Bin Boyce, Scott E. Zhang, Yu Liu, Qiang Guo, Le Zhong, Ping-An Hohai Univ State Key Lab Hydrol Water Resources & Hydraul En Coll Hydrol & Water Resources 1 Xikang Rd Nanjing 210098 Jiangsu Peoples R China US Geol Survey Calif Water Sci Ctr 4165 Spruance RdSuite 200 San Diego CA 92101 USA Hohai Univ Coll Hydrol & Water Resources 1 Xikang Rd Nanjing 210098 Jiangsu Peoples R China China Yangtze Power Co Ltd 19 Jinrong St Beijing 100032 Peoples R China Hohai Univ Natl Engn Res Ctr Water Resources Efficient Utili Coll Hydrol & Water Resources 1 Xikang Rd Nanjing 210098 Jiangsu Peoples R China

Reservoir refill operation modeling attempts to maximize a set of benefits while minimizing risks. The benefits and risks can be in opposition to each other, such as having enough water for hydropower generation while leaving enough room for flood protection. In addition to multiple objects, the uncertainty of streamflow can make decision making difficult. This paper develops a stochastic optimization model for reservoir refill operation with the objective of maximizing the expected synthesized energy production for a cascade system of hydropower stations while considering flood risk. Streamflow uncertainty is addressed by discretized streamflow scenarios and flood risk is controlled by a joint chance constraint restricting the occurrence probability. With the variability of flood risk level, two advancing refill scenarios for exploring operation benefit are presented. Scenario I loosens the current stagewise storage bounds conditions and allows advancing reservoir refills but keeps the flood risk level the same as the refill policies obtained under the current storage bounds. Scenario II keeps the current storage bounds unchanged but allows increases in flood risk level. The proposed methodology is applied to the Xiluodu cascade system of reservoirs in China and investigates the optimal refill policies obtained by both scenarios. Compared with the benchmark obtained under the current storage bounds and lowest flood risk level, the results show (1) the synthesized energy production can be improved by 2.13% without changing the flood risk level under Scenario I, and (2) the synthesized energy production can also be increased by 0.21% at the expense of increasing the flood risk level by 4.4% when Scenario II is employed. As Scenario I produces higher benefit and lower risk than Scenario II, it is recommended to loosen the current stagewise storage bounds but to keep the flood risk level unchanged during refill operations. (C) 2016 American Society of Civil Engineers.

关键词： Reservoir refill operation Flood risk Stochastic programming Joint chance constraint progressive optimality algorithm

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Derivation of water and power operating rules for multi-reservoirs

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HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES 2016年第2期61卷 359-370页

作者： Zhou, Yanlai Guo, Shenglian Liu, Pan Xu, Chong-yu Zhao, Xiaofeng Wuhan Univ State Key Lab Water Resources & Hydropower Engn S Wuhan 430072 Peoples R China Changjiang River Sci Res Inst Wuhan Peoples R China Univ Oslo Dept Geosci Oslo Norway Hubei Prov Water Resources & Hydropower Planning Wuhan Peoples R China

Water operating rules have been universally used to operate single reservoirs because of their practicability, but the efficiency of operating rules for multi-reservoir systems is unsatisfactory in practice. For better performance, the combination of water and power operating rules is proposed and developed in this paper. The framework of deriving operating rules for multi-reservoirs consists of three modules. First, a deterministic optimal operation module is used to determine the optimal reservoir storage strategies. Second, a fitting module is used to identify and estimate the operating rules using a multiple linear regression analysis (MLR) and artificial neural networks (ANN) approach. Last, a testing module is used to test the fitting operating rules with observed inflows. The Three Gorges and Qing River cascade reservoirs in the Changjiang River basin, China, are selected for a case study. It is shown that the combination of water and power operating rules can improve not only the assurance probability of output power, but also annual average hydropower generation when compared with designed operating rules. It is indicated that the characteristics of flood and non-flood seasons, as well as sample input (water or power), should be considered if the operating rules are developed for multi-reservoirs. [GRAPHICS]

关键词： operating rule genetic algorithm progressive optimality algorithm multi-reservoirs aggregation-decomposition

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