An Effective Approach to Long-Term Optimal Operation of Large-Scale Reservoir Systems: Case Study of the Three Gorges System

Li, Fang‐Fang; Fu, Xudong; Wan, Xin-Yu

doi:10.1007/s11269-012-0131-0

Cited by 36 publications

(15 citation statements)

References 23 publications

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“…The reason for this is the premature convergence of GA resulting from the high dimensionality of the decision variables and large feasible domain of each variable. A good quality search space should not be too large but cover the global optimum, if possible [35]. In this study, the search space was constrained within a certain range of the historical data.…”

Section: Resultsmentioning

confidence: 99%

Multi-Objective Reservoir Optimization Balancing Energy Generation and Firm Power

Qiu

2015

Energies

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Abstract:To maximize annual power generation and to improve firm power are important but competing goals for hydropower stations. The firm power output is decisive for the installed capacity in design, and represents the reliability of the power generation when the power plant is put into operation. To improve the firm power, the whole generation process needs to be as stable as possible, while the maximization of power generation requires a rapid rise of the water level at the beginning of the storage period. Taking the minimal power output as the firm power, both the total amount and the reliability of the hydropower generation are considered simultaneously in this study. A multi-objective model to improve the comprehensive benefits of hydropower stations are established, which is optimized by Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The Three Gorges Cascade Hydropower System (TGCHS) is taken as the study case, and the Pareto Fronts in different search spaces are obtained. The results not only prove the effectiveness of the proposed method, but also provide operational references for the TGCHS, indicating that there is room of improvement for both the annual power generation and the firm power.

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Section: Resultsmentioning

confidence: 99%

Multi-Objective Reservoir Optimization Balancing Energy Generation and Firm Power

Qiu

2015

Energies

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“…genetic algorithms) can take an unfeasibly long time to find answers to continuous variable problems with many decision variables (Chang et al, 2010;Li et al, 2012). The main reason is that even if one element on the dimensions mutates into an inappropriate value, the whole solution becomes infeasible.…”

Section: Design Thought Of the Hierarchical Modelmentioning

confidence: 99%

Hierarchical multi-reservoir optimization modeling for real-world complexity with application to the Three Gorges system

Shoemaker

Qiu

et al. 2015

Environmental Modelling & Software

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a b s t r a c tHigh dimensionality in real-world multi-reservoir systems greatly hinders the application and popularity of evolutionary algorithms, especially for systems with heterogeneous units. An efficient hierarchical optimization framework is presented for search space reduction, determining the best water distributions, not only between cascade reservoirs, but also among different types of hydropower units. The framework is applied to the Three Gorges Project (TGP) system and the results demonstrate that the difficulties of multi-reservoir optimization caused by high dimensionality can be effectively solved by the proposed hierarchical method. For the day studied, power output could be increased by 6.79 GWh using an optimal decision with the same amount of water actually used; while the same amount of power could be generated with 2.59 Â 10 7 m 3 less water compared to the historical policy. The methodology proposed is general in that it can be used for other reservoir systems and other types of heterogeneous unit generators.

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“…The IGA-SA can help to overcome premature convergence faster than the earlier GA-SA algorithm and can escape the local optima. A hybrid approach, incorporating Incremental DP (IDP) and GA was proposed by Li et al (2012). IDP was used to provide a rough optimal solution which was used for generating the initial population for GA.…”

Section: Introductionmentioning

confidence: 99%

Optimal operation of Mula reservoir with combined use of dynamic programming and genetic algorithm

Rani

Srivastava

2015

Sustain. Water Resour. Manag.

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Genetic algorithm (GA) has been used repeatedly in reservoir operation studies during last two decades. GAs require trying different alternatives, different GA parameter values, and select those which perform best for a particular application. Besides this, there are chances of getting trapped into local optima, since GA starts with randomly generated initial population within the entire search space. Therefore, GA's search process is slow and time-consuming. GA's process may be speeded up if initial population is generated in a narrowed search space. This process may save time spent on sensitivity analysis of parameters. Discrete dynamic programming (DP) provides global optimal solutions, but discreteness and dimensionality are the main disadvantages in reservoir operation applications. To overcome these deficiencies a hybrid approach combining DP and GA (DP-GA) is proposed to study a single reservoir operation problem. Where DP provides the narrowed search space within which the optimal solution for the problem is expected. GA then search to achieve the possible optimal solution within this space avoiding any discreteness of variables. Proposed DP-GA approach was found to outperform both GA and DP in terms of less computational requirement and quality of the solution, respectively.

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An Effective Approach to Long-Term Optimal Operation of Large-Scale Reservoir Systems: Case Study of the Three Gorges System

Cited by 36 publications

References 23 publications

Multi-Objective Reservoir Optimization Balancing Energy Generation and Firm Power

Multi-Objective Reservoir Optimization Balancing Energy Generation and Firm Power

Hierarchical multi-reservoir optimization modeling for real-world complexity with application to the Three Gorges system

Optimal operation of Mula reservoir with combined use of dynamic programming and genetic algorithm

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