Optimal short-term generation scheduling of hydrothermal systems by implementation of real-coded genetic algorithm based on improved Mühlenbein mutation

Nazari-Heris, Morteza; Mohammadi‐Ivatloo, Behnam; Haghrah, A.

doi:10.1016/j.energy.2017.04.007

Cited by 58 publications

(20 citation statements)

References 36 publications

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“…The optimal unit commitment problem with probabilistic reserve determination preferred a mixed integer programming technique. The scheduling of hydropower plants was not only discussed in the past but also emphasized on scheduling of hydrothermal systems [118][119][120][121][122][123][124][125][126][127][128][129][130]. The addition and restriction in branch and bound algorithm is considered as the central key in the development of integer programming approach [131].…”

Section: Advantages and Disadvantagesmentioning

confidence: 99%

Modeling and Solution Techniques Used for Hydro Generation Scheduling

Parvez

Shen

Khan

et al. 2019

Water

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The hydro generation scheduling problem has a unit commitment sub-problem which deals with start-up/shut-down costs related hydropower units. Hydro power is the only renewable energy source for many countries, so there is a need to find better methods which give optimal hydro scheduling. In this paper, the different optimization techniques like lagrange relaxation, augmented lagrange relaxation, mixed integer programming methods, heuristic methods like genetic algorithm, fuzzy logics, nonlinear approach, stochastic programming and dynamic programming techniques are discussed. The lagrange relaxation approach deals with constraints of pumped storage hydro plants and gives efficient results. Dynamic programming handles simple constraints and it is easily adaptable but its major drawback is curse of dimensionality. However, the mixed integer nonlinear programming, mixed integer linear programming, sequential lagrange and non-linear approach deals with network constraints and head sensitive cascaded hydropower plants. The stochastic programming, fuzzy logics and simulated annealing is helpful in satisfying the ramping rate, spinning reserve and power balance constraints. Genetic algorithm has the ability to obtain the results in a short interval. Fuzzy logic never needs a mathematical formulation but it is very complex. Future work is also suggested.

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Section: Advantages and Disadvantagesmentioning

confidence: 99%

Modeling and Solution Techniques Used for Hydro Generation Scheduling

Parvez

Shen

Khan

et al. 2019

Water

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“…A modified Realcoded Genetic Algorithm approach using mutation based on random transfer vectors (RCGA-RTVM) was presented in Haghrah, et al [10]. A combination of improved Mühlenbein mutation and RCGA is proposed in Nazari-Heris, et al [11] and the Parallel Multi-objective Genetic Algorithm (PMOGA) is proposed in Feng, et al [12]. A modified dynamic neighborhood learning-based particle swarm optimization (MDNLPSO) [13] and multi-objective quantumbehaved particle swarm optimization (MOQBPSO) [14] obtained better cost than most methods, including conventional PSO.…”

Section: Introductionmentioning

confidence: 99%

Environmental Economic Hydrothermal System Dispatch by Using a Novel Differential Evolution

Dinh

Nguyen

2018

J. Eng. Technol. Sci.

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This paper proposes the Novel Differential Evolution (NDE) method for solving the environmental economic hydrothermal system dispatch (EEHTSD) problem with the aim to reduce electricity generation fuel costs and emissions of thermal units. The EEHTSD problem is constrained by limitations on generations, active power balance, and amount of available water. NDE applies two modified techniques. The first one is modified mutation, which is used to balance global and local search. The second one is modified selection, which is used to keep the best solutions. When performing this modified selection, the proposed method completely reduces the impact of crossover by setting it to one. Moreover, the task of tuning this factor can be canceled. Original Differential Evolution (ODE), ODE with the first modification (MMDE), and ODE with the second modification (MSDE), and NDE were tested on two different hydrothermal systems for comparison and evaluation purposes. The performance of NDE was also compared to existing methods. It was indicated that the proposed NDE is a very promising method for solving the EEHTSD problem.

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“…Mathematical models applied to study the STHS include non-linear programming [5], decomposition method [6], Lagrange multiplier [7], Opt Quest/NLP (OQNLP) concept [8], and dynamic programming (DP) [9]. The heuristic approaches are applied to study STHS including genetic algorithm [10], particle swarm optimization [11], differential evolution [12], Cuckoo search method [13], group search optimization [14], artificial bee colony [15], and teaching learning based optimization [16]. The authors have proposed a real coded genetic algorithm approach based on new mutation process to provide the optimal solution of hydrothermal systems in [17].…”

Section: Introductionmentioning

confidence: 99%

Robust stochastic optimal short-term generation scheduling of hydrothermal systems in deregulated environment

Nazari-Heris

Mohammadi‐Ivatloo²,

Asadi

2018

Journal of Energy Systems

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Hydrothermal systems play a significant role in electric energy systems as important power generation units, which have been studied in previous researches considering remarkable efforts. The optimal short-term hydrothermal scheduling (STHS) aims to attain optimal production scheduling of thermal and hydro plants for determining minimum operation cost of providing demand in the determined time interval. Different constraints should be studied in the solution of such issue containing limitations associated with water discharge, water storage, power production of plants, power balance of the system and water balance of hydro plants. In addition, valve impacts of thermal plants and complex hydraulic coupling of hydro plants are the other operational and technical constraints. In this study, the robust stochastic STHS is studied considering market price and demand uncertainties. Accordingly, robust optimization method is employed in this study to model price uncertainties. In addition, the uncertainties of load demand are handled using scenario-based modeling procedure. The scheduling problem of hydrothermal system is studied in a deregulated environment, where the hydrothermal system belongs to the private company and is capable to sell the surplus generated power to the market. Accordingly, the company aims to obtain maximum profit by selling power to market in addition to supplying power demand of the company. The introduced scheme for stochastic robust STHS is simulated and the provided solutions are investigated to verify the effectiveness of the scheme.

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Optimal short-term generation scheduling of hydrothermal systems by implementation of real-coded genetic algorithm based on improved Mühlenbein mutation

Cited by 58 publications

References 36 publications

Modeling and Solution Techniques Used for Hydro Generation Scheduling

Modeling and Solution Techniques Used for Hydro Generation Scheduling

Environmental Economic Hydrothermal System Dispatch by Using a Novel Differential Evolution

Robust stochastic optimal short-term generation scheduling of hydrothermal systems in deregulated environment

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