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Hydropower system operation optimization by discrete differential dynamic programming based on orthogonal experiment design

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ENERGY 2017年 126卷 720-732页

作者： Feng, Zhong-kai Niu, Wen-jing Cheng, Chun-tian Liao, Sheng-li Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China Dalian Univ Technol Inst Hydropower & Hydroinformat Dalian 116024 Peoples R China

With the fast development of hydropower in China, a group of hydropower stations has been put into operation in the past few decades and the hydropower system scale is experiencing a booming period. Hence, the "curse of dimensionality" is posing a great challenge to the optimal operation of hydropower system (OOHS) because the computational cost grows exponentially with the increasing number of plants. discrete differential dynamic programming (DDDP) is a classical method to alleviate the dimensionality problem of dynamic programming for the OOHS, but its memory requirement and computational time still grows exponentially with the increasing number of plants. In order to improve the DDDP performance, a novel method called orthogonal discrete differential dynamic programming (ODDDP) is introduced to solve the OOHS problem. In ODDDP, orthogonal experimental design is employed to select some small but representative state combinations when constructing the corridor around the current trajectory, and then dynamic programming recursion equation is used to find an improved trajectory for the next iteration. The proposed method is applied to the optimal operation of a large-scale hydropower system in China. The results indicate that compared to the standard DDDP, ODDDP only needs about 037% of computing time to obtain the results with about 99.75% of generation in the hydropower system with 7 plants and 3 states per plant, providing a new effective tool for large-scale OOHS problem. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Multi-reservoir system operation discrete differential dynamic programming Orthogonal experiment design Dimensionality reduction Curse of dimensionality

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Improved decomposition-coordination and discrete differential dynamic programming for optimization of large-scale hydropower system

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ENERGY CONVERSION AND MANAGEMENT 2014年 84卷 363-373页

作者： Li, Chunlong Zhou, Jianzhong Ouyang, Shuo Ding, Xiaoling Chen, Lu Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Hubei Key Lab Digital Valley Sci & Technol Wuhan 430074 Peoples R China

With the construction of major hydro plants, more and more large-scale hydropower systems are taking shape gradually, which brings up a challenge to optimize these systems. Optimization of large-scale hydropower system (OLHS), which is to determine water discharges or water levels of overall hydro plants for maximizing total power generation when subjecting to lots of constrains, is a high dimensional, nonlinear and coupling complex problem. In order to solve the OLHS problem effectively, an improved decomposition-coordination and discrete differential dynamic programming (IDC-DDDP) method is proposed in this paper. A strategy that initial solution is generated randomly is adopted to reduce generation time. Meanwhile, a relative coefficient based on maximum output capacity is proposed for more power generation. Moreover, an adaptive bias corridor technology is proposed to enhance convergence speed. The proposed method is applied to long-term optimal dispatches of large-scale hydropower system (LHS) in the Yangtze River basin. Compared to other methods, IDC-DDDP has competitive performances in not only total power generation but also convergence speed, which provides a new method to solve the OLHS problem. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Large-scale hydropower system Long-term optimization Decomposition-coordination discrete differential dynamic programming Improvement strategies The Yangtze River basin

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Parallel discrete differential dynamic programming for multireservoir operation

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ENVIRONMENTAL MODELLING & SOFTWARE 2014年 57卷 152-164页

作者： Cheng, Chuntian Wang, Sen Chau, Kwok-Wing Wu, Xinyu Dalian Univ Technol Inst Hydropower & Hydroinformat Dalian 116024 Peoples R China Hong Kong Polytech Univ Dept Civil & Environm Engn Hong Kong Hong Kong Peoples R China

The curse of dimensionality and computational time cost are a great challenge to operation of large-scale hydropower systems (LSHSs) in China because computer memory and computational time increase exponentially with increasing number of reservoirs. discrete differential dynamic programming (DDDP) is one of the most classical algorithms for alleviating the dimensionality problem for operation of LSHSs. However, the computational time performed on DDDP still increases exponentially with increasing number of reservoirs. Therefore, a fine-grained parallel DDDP (PDDDP) algorithm, which is based on Fork/Join parallel framework in multi-core environment, is proposed to improve the computing efficiency for long-term operation of multireservoir hydropower systems. The proposed algorithm is tested using a huge cascaded hydropower system located on the Lancang River in China. The results demonstrate that the PDDDP algorithm enhances the computing efficiency significantly and takes full advantage of multi-core resources, showing its potential practicability and validity for operation of LSHSs in future. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Hydropower systems discrete differential dynamic programming Parallel Long-term operation Optimization

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Large-scale hydropower system optimization using dynamic programming and object-oriented programming: the case of the Northeast China Power Grid

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WATER SCIENCE AND TECHNOLOGY 2013年第11期68卷 2458-2467页

作者： Li, Ji-Qing Zhang, Yu-Shan Ji, Chang-Ming Wang, Ai-Jing Lund, Jay R. North China Elect Power Univ Renewable Energy Sch Natl Engn Lab Biomass Power Generat Equipment Beijing 102206 Peoples R China Project Commiss State Council Off South To North Water Div Beijing 100053 Peoples R China Univ Calif Davis Davis CA 95616 USA

This paper examines long-term optimal operation using dynamic programming for a large hydropower system of 10 reservoirs in Northeast China. Besides considering flow and hydraulic head, the optimization explicitly includes time-varying electricity market prices to maximize benefit. Two techniques are used to reduce the 'curse of dimensionality' of dynamic programming with many reservoirs. discrete differential dynamic programming (DDDP) reduces the search space and computer memory needed. Object-oriented programming (OOP) and the ability to dynamically allocate and release memory with the C++ language greatly reduces the cumulative effect of computer memory for solving multi-dimensional dynamic programming models. The case study shows that the model can reduce the 'curse of dimensionality' and achieve satisfactory results.

关键词： discrete differential dynamic programming long-term optimal scheduling model object-oriented programming parallel and serial hydroelectric power stations

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Long-Term Optimization of Large-Scale Hydropower System Operations Based on Decomposition-Coordination

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JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT 2020年第10期146卷

作者： Yan, Zhiyu Liao, Shengli Cheng, Chuntian Zhou, Lingan Chen, Fu Dalian Univ Technol Inst Hydropower & Hydroinformat Dalian 116024 Peoples R China Global Energy Interconnect Dev & Cooperat Org 8 Xuanwumennei St Beijing 100031 Peoples R China

The basic objective of long-term optimization of large-scale hydropower system operations (LOLHSO) is to increase water resource use efficiency and construct a clean and low-carbon-intensity energy system. However, limited by rather stochastic inflows, large-scale hydropower systems, and complex objectives and constraints, LOLHSO is characterized by uncertainty, high dimensionality, nonlinearity, and nonconvexity, which pose great challenges in modeling. This paper presents an improved hybrid decomposition-coordination and discrete differential dynamic programming model (IDC-DDDP) for solving the LOLHSO problem. In IDC-DDDP, a decomposition strategy considering the adjustment potential of hydropower plants is designed to reduce the system size. Meanwhile, owing to the sensitivity of DDDP to initial trajectories, a data mining-based strategy is developed as a means of generating superior initial trajectories. A corridor generation strategy is presented to determine the discrete steps and enhance global search abilities. Case studies in a hydropower system in southwestwern China demonstrate the practicability and robustness of the proposed model.

关键词： Large-scale hydropower system Decomposition-coordination discrete differential dynamic programming Optimization

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Optimal nonpoint source pollution control strategies for a reservoir watershed in Taiwan

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2007年第4期85卷 908-917页

作者： Hsieh, Cheng-Daw Yang, Wan-Fa Natl Taiwan Univ Inst Environm Engn Taipei Taiwan Minist Econ Affairs Water Resources Agcy Taipei 231 Taiwan

The purpose of this study is to develop a model for optimal nonpoint source pollution control for the Fei-Tsui Reservoir watershed in Northern Taiwan. Several structural best management practices (BMPs) are selected to treat stormwater runoff. The complete model consists of two interacting components: an optimization model based on discrete differential dynamic programming (DDDP) and a zero-dimensional reservoir water quality model. A predefined procedure is used to locate suitable sites for construction of various selected BMPs in the watershed. In the optimization model, the objective function is to find the best combination of BMP type and placement, which minimizes the total construction and operation, maintenance, and repair (OMR) costs of the BMPs. The constraints are the water quality standards for total phosphorus (TP) and total suspended solids (TSS) concentrations in the reservoir. A zero-dimensional reservoir water quality model of the Vollenweider type is embedded in the optimization framework to simulate pollutant concentrations in Fei-Tsui Reservoir. The resulting optimal cost and benefit of water quality improvement are depicted by the model-derived trade-off curves. The modeling framework developed in the present study could be used as an efficient tool for planning a watershed-wide implementation of BMPs for mitigating stormwater pollution impact on the receiving water bodies. (C) 2006 Elsevier Ltd. All rights reserved.

关键词： best management practices discrete differential dynamic programming cost-benefit analyses water quality model reservoir

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Daily Generation Scheduling of Cascade Hydro Plants Considering Peak Shaving Constraints

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JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT 2016年第4期142卷 4015072.1-4015072.1页

作者： Xie, Mengfei Zhou, Jianzhong Li, Chunlong Lu, Peng Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Hubei Peoples R China

This paper considers the operation of hydro plants, tackles the problem of daily hydro-generation scheduling (DHGS), and obtains the optimal hourly operation of cascade hydropower reservoirs. To meet the practical operational demands of power grid, an improved benefit-maximization model in which the peak shaving demands are taken as constraints is proposed for DHGS, and the joint peak load regulation (JPLR) method is applied to the proposed model. The model can enhance the power-generation efficiency and reduce the water spillage significantly under the condition of peak load regulation. Meanwhile, a hybrid method that combines discrete differential dynamic programming with progressive optimality algorithm is proposed to solve the JPLR problem. The complicated constraints can be handled by the proposed algorithm effectively. Moreover, the hydro-unit-commitment problem is solved by a rapid searching algorithm based on equal incremental principle and empirical methods. With this unit-commitment strategy, the computation speed can be accelerated and approximate optimal solutions can be obtained in a reasonable time frame. The proposed model and methods are applied to the DHGS of cascade hydro plants in Zagunao River in China and achieve good performance.

关键词： Daily hydro-generation scheduling Peak shaving demands Joint peak load regulation discrete differential dynamic programming Progressive optimality algorithm Hydro-unit commitment

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Modified dynamic programming algorithms for GLOSA systems with stochastic signal switching times

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2023年 157卷

作者： Typaldos, Panagiotis Papageorgiou, Markos Tech Univ Crete Sch Prod Engn & Management Dynam Syst & Simulat Lab Khania 73100 Greece Ningbo Univ Fac Maritime & Transportat Ningbo Peoples R China

A discrete-time stochastic optimal control problem was recently proposed to address the GLOSA (Green Light Optimal Speed Advisory) problem in cases where the next signal switching time is decided in real time and is therefore uncertain in advance. The corresponding numerical solution via SDP (Stochastic dynamic programming) calls for substantial computation time, which excludes problem solution in the vehicle's on-board computer in real time. In this context, the present paper concentrates on the challenge of developing numerical algorithms to solve efficiently the stochastic GLOSA problem. As a first attempt to overcome the computation time bottleneck, a modified version of dynamic programming, known as discrete differential dynamic programming (DDDP) was recently employed for the numerical solution of the stochastic optimal control problem and was demonstrated to achieve results equivalent to those obtained with the ordinary SDP algorithm, albeit with significantly reduced computation times. After an outline of the DDDP approach, the present work considers a second modified version of dynamic programming, known as differential dynamic programming (DDP). For the stochastic GLOSA problem, it is demonstrated that DDP achieves quasi-instantaneous (extremely fast) solutions in terms of CPU times, which allows for the proposed approach to be readily executable online, in an MPC (Model Predictive Control) framework, in the vehicle's on-board computer. The novel numerical approach is tested and demonstrated by use of realistic examples and is compared to the SDP and DDDP solutions. It should be noted that DDP does not require discretization of variables, hence the obtained solutions may be slightly superior to the standard SDP solutions.

关键词： GLOSA Stochastic dynamic programming discrete differential dynamic programming (DDDP) differential dynamic programming (DDP) Traffic light advisory

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Optimization of large-scale hydropower system peak operation with hybrid dynamic programming and domain knowledge

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JOURNAL OF CLEANER PRODUCTION 2018年 171卷 390-402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 ChangJiang 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 power demands in China keep growing and the need for reducing peak loads is becoming more prominent. With the merits of fast startup and shutdown, hydropower is often used to respond the peak load. In order to meet the practical requirement of peak operation in electrical power system, a novel min-max dynamic programming model is formulated for the peak operation of hydropower system. Then, the hybrid dynamic programming method is presented to alleviate the dimensionality problem in large-scale hydropower system, where the dynamic programming successive approximation is employed to divide the complex multi-dimensional problem into a series of small subproblems, and then the discrete differential dynamic programming is adopted to sequentially solve these subproblems. In addition, inspired by domain knowledge, the initial solution generation method and feasible space identification method are designed to promote the convergence speed of algorithm. The proposed method is used to solve the peak operation problem of a large-scale hydropower system in China. The simulations with different load demands indicate that the hybrid dynamic programming can achieve satisfactory performance in reducing peak loads of power system. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Hydropower system Peak operation Domain knowledge Curse of dimensionality discrete differential dynamic programming dynamic programming successive approximation

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Research on Optimal Operation of Cascade Reservoirs under Complex Water-Level Flow Output Constraints

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WATER 2024年第20期16卷 2963页

作者： Wu, Chengjun Wang, Zhongmei Yue, Peng Lai, Zhiqiang Wang, Yanyun Yellow River Conservancy Commiss Yellow River Inst Hydraul Res Zhengzhou 450003 Peoples R China Minist Water Resources Key Lab Lower Yellow River Channel & Estuary Regul Zhengzhou 450003 Peoples R China Qinghai Sanjiang Hydropower Dev Co Ltd Xining 810008 Peoples R China China Inst Water Resources & Hydropower Res Beijing 100038 Peoples R China

To enhance the efficiency of solving the optimal operation model for cascade reservoirs, this paper first constructed an optimal operation model of cascade reservoirs. The model comprehensively considered the ecological flow, the guaranteed output of hydroelectric power plants, and the relaxation constraints of the water level at the end of water supply and storage period. The relaxation constraints refer to two relaxation variable constraints, which are used to ensure that the water levels decline in the water supply period and rise in the water storage periods. At the same time, to avoid the challenges of "dimension disaster" and susceptibility to local optima commonly encountered in existing optimization algorithms when resolving the above model, a novel optimization algorithm, M-IWO-ODDDP, derived from the optimization principles of the Invasive Weed Optimization (IWO) and discrete differential dynamic programming (DDDP) algorithms, was proposed in this paper. The 11 cascade hydropower stations in the Wujiang River basin were used as a case study, and the results showed that the water-level dispatching process exhibited a high degree of conformity with the actual dispatching process during both the water supply and storage periods. Furthermore, the output calculation results based on the M-IWO-ODDDP algorithm were 3.94% and 0.30% higher than the actual output and ODDDP calculation results, respectively, while reducing water abandonment by 21.58% and 4.07%.

关键词： cascade hydropower stations complex constraints optimal operation model discrete differential dynamic programming orthogonal test

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