Limited adaptive genetic algorithm for inner-plant economical operation of hydropower station

Zheng, Jinhai; Yang, Kun; Lu, Xiuyuan

doi:10.2166/nh.2012.198

Cited by 13 publications

(5 citation statements)

References 16 publications

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“…The load balance can also be strictly observed by using the method proposed by Zheng et al [2013]. As the Genetic Algorithm uses randomly generated chromosomes to start a search, these initial chromosomes may violate the storage constraints (equation (11)) due to improper assignment of power output.…”

Section: Constraint Handlingmentioning

confidence: 99%

A multiobjective short‐term optimal operation model for a cascade system of reservoirs considering the impact on long‐term energy production

Zhong

Stanko

et al. 2015

Water Resources Research

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This paper examines the impact of short-term operation on long-term energy production. We propose a multiobjective optimization model for the short-term, daily operation of a system of cascade reservoirs. The two objectives considered in the daily model are: (1) minimizing the total amount of water released and (2) maximizing the stored energy in the system. Optimizing short-term operation without considering its impact on long-term energy production does not guarantee maximum energy production in the system. Therefore, a major goal of this paper is to identify desirable short-term operation strategies that, at the same time, optimize long-term energy production. First, we solve the daily model for 1 month (30 days) using a nondominated genetic algorithm (NSGAII). We then use the nondominated solutions obtained by NSGAII to assess the impact on long-term energy production using a monthly model. We use historical monthly inflows to characterize the inflow variability. We apply the proposed methodology to the Qingjiang cascade system of reservoirs in China. The results show: (1) in average hydrology scenarios, the solution maximizing stored energy produces the most overall long-term energy production; (2) in moderately wet hydrology scenarios, the solution minimizing water released outperforms the maximizing stored energy solution; and (3) when extremely wet hydrology scenarios are expected, a compromise solution is the best strategy.

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Section: Constraint Handlingmentioning

confidence: 99%

A multiobjective short‐term optimal operation model for a cascade system of reservoirs considering the impact on long‐term energy production

Zhong

Stanko

et al. 2015

Water Resources Research

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“…The basic goal of unit commitment is to effectively schedule the on/off state of hydropower units at the cost of minimum water which is consumed by power generation and switch process of startup and shutdown. The ascertained upstream and downstream water level, load demand, and various constraints are also taken into account [1,30] (Zheng et al 2013;. Normally, the objective function can be signified as follows.…”

Section: Unit Commitment Problemmentioning

confidence: 99%

“…The STHGS problem can be decomposed into two subproblems: the space optimization, namely, economic load dispatch (ELD), and the time optimization, the hydropower unit commitment (UC). The ELD subproblem attempts to reasonably determine the power output of each unit so as to minimize water consumption [1]. Efficiency, under different operation conditions, corresponding to each type of units is discrepant especially the large capacity unit.…”

Section: Introductionmentioning

confidence: 99%

The Improved Binary-Real Coded Shuffled Frog Leaping Algorithm for Solving Short-Term Hydropower Generation Scheduling Problem in Large Hydropower Station

Yang

et al. 2018

Mathematical Problems in Engineering

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The short-term hydro generation scheduling (STHGS) decomposed into unit commitment (UC) and economic load dispatch (ELD) subproblems is complicated problem with integer optimization, which has characteristics of high dimension, nonlinear and complex hydraulic and electrical constraints. In this study, the improved binary-real coded shuffled frog leaping algorithm (IBR-SFLA) is proposed to effectively solve UC and ELD subproblems, respectively. For IB-SFLA, the new grouping strategy is applied to overcome the grouping shortage of SFLA, and modified search strategies for each type of frog subpopulation based on normal cloud model (NCM) and chaotic theory are introduced to enhance search performance. The initialization strategy with chaos theory and adaptive frog activation mechanism are presented to strengthen performance of IR-SFLA on ELD subproblem. Furthermore, to solve ELD subproblem, the optimal economic operation table is formed using IR-SFLA and invoked from database. Moreover, reserve capacity supplement and repair, and minimum on and off time repairing strategies are applied to handle complex constraints in STHGS. Finally, the coupled external and internal model corresponding to UC and ELD subproblems is established and applied to solve STHGS problem in Three Gorges hydropower station. Simulation results obtained from IBR-SFLA are better than other compared algorithms with less water consumption. In conclusion, to solve STHGS optimization problem, the proposed IBR-SFLA presents outstanding performance on solution precision and convergence speed compared to traditional SFLA effectively and outperforms the rivals to get higher precision solution with improving the utilization rate of waterpower resources.

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“…Then, the initial population is encoded, and evaluated by computing the value of the objective function [13]. Next, chosen better fitness individuals and abandoned the bad ones.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Analysis of the influence of loads distribution of hydropower stations on the results of optimization algorithms

Zhang¹,

Wang²,

Wang³

2017

Proceedings of the 2016 International Conference on Engineering and Advanced Technology

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Aiming to solve the problem: the conventional loads distribution of hydropower stations calculated by single model corresponding to multiple optimal algorithms, the way that various models corresponding to multiple optimal algorithms are proposed in the paper. According to characteristics and tasks of hydropower station, loads distribution models should be chosen reasonably. Genetic Algorithms, Simulated Annealing Algorithm, Particle Swarm Optimization Algorithm are introduced different loads distribution models respectively to calculate examples that undertaking different tasks of hydropower stations. When the Particle Swarm Optimization Algorithm is applied to calculate the minimum flow consumption model, because of its fast convergence and strong optimization ability and other characteristics, it is regarded as the best one among the three algorithms; Simulated Annealing Algorithm is in the calculation of the maximum power output model reflecting the greater advantage. BACKGROUNDThe hydropower station achieves economic scheduling, which will increase economic efficiency about 1%~3% [1] and decrease the energy consumption. The speed and accuracy of optimization algorithms calculating the loads distribution of the hydropower stations is the key to ensure economic operation of hydropower station. However, calculation speed and accuracy of optimization algorithms depends on the algorithms themselves, but also rely heavily on models. Therefore, studying the effects of loads distribution model of hydropower stations on the optimization results is significant for operating the hydropower station economically. In this respect, domestic and foreign researchers formed a series of theories gradually after decades of hard working and practicing [3][4][5][6][7][8][9][10][11] , such as nonlinear programming method, dynamic programming method. Although Nonlinear Programming and Dynamic Programming method have their own advantages, the problems should not be ignored including dimension of disaster, computation complex and slow. In addition, the type that a single model corresponds to various optimization algorithms and choosing a priority algorithm is used frequently in previous research in this area. In this paper, two basic models of loads distribution are introduced. One of the two models named minimum flow consumption model is used consuming the minimum water as the target function, and the other is the maximum power output model that generating maximum power is established for the objective function. Introducing Genetic algorithms, Simulated Annealing Algorithms and Particle Swarm Optimization in the two models respectively, and applying to examples. Analysis the results and explore loads distribution model effects on algorithms. LOADS DISTRIBUTION MODELS OF HYDROPOWER STATIONSActually minimum flow consumption model has the "electric definite water" criteria and maximum power output model is the "water definite electric" criteria. Minimum flow consumption model(MFCM) Objective functionwhere k=unit's number; Q ...

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Limited adaptive genetic algorithm for inner-plant economical operation of hydropower station

Cited by 13 publications

References 16 publications

A multiobjective short‐term optimal operation model for a cascade system of reservoirs considering the impact on long‐term energy production

A multiobjective short‐term optimal operation model for a cascade system of reservoirs considering the impact on long‐term energy production

The Improved Binary-Real Coded Shuffled Frog Leaping Algorithm for Solving Short-Term Hydropower Generation Scheduling Problem in Large Hydropower Station

Analysis of the influence of loads distribution of hydropower stations on the results of optimization algorithms

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