Optimal gamma based fixed head hydrothermal scheduling using genetic algorithm

Sasikala, J.; Ramaswamy, M.

doi:10.1016/j.eswa.2009.10.015

Cited by 45 publications

(28 citation statements)

References 23 publications

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“…Recently, several novel methods based on artificial intelligence techniques have been implemented for solving the HTS problems such as simulated annealing approach (SA) [10], evolutionary programming (EP) [11][12][13][14], genetic algorithm (GA) [15][16][17][18], differential evolution (DE) [19], artificial immune system (AIS) [20], and Hopfield neural network (HNN) [21]. In the SA technique, the appropriate setting of the relevant control parameters is a difficult task and it usually suffers slow speed of convergence when dealing with practical sized power systems.…”

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confidence: 99%

Modified cuckoo search algorithm for short-term hydrothermal scheduling

Nguyen

2015

International Journal of Electrical Power & Energy Systems

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confidence: 99%

Modified cuckoo search algorithm for short-term hydrothermal scheduling

Nguyen

2015

International Journal of Electrical Power & Energy Systems

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“…Scholars have done many mathematical model researches about the hydrothermal power system since Ricard first put forward a mathematical model of optimal coordination of hydrothermal power system in 1940s. These researches mainly focus on the effect of head variation, electrical efficiency [2][3][4], value point of thermal generator [5] and the transmission loss of the active power [6] without consideration of the electricity network structure and effect of the reactive power. Power flow constraint is an important method to consider the effect of the electricity network and reactive power.…”

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confidence: 99%

Short-term optimal hydrothermal scheduling with power flow constraint

Lin

Huang

Zhang

et al. 2015

The 27th Chinese Control and Decision Conference (2015 CCDC)

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Short-term optimal hydrothermal scheduling(STOHS) problem [1] is usually supposed to find a reasonable scheduling scheme in a period of time(mostly 24h) while satisfying various constraints. The scheduling scheme aims to make full use of water resources, reduce the proportion of the thermal generation, minimize the operating cost of the hydrothermal system. Scholars have done many mathematical model researches about the hydrothermal power system since Ricard first put forward a mathematical model of optimal coordination of hydrothermal power system in 1940s. These researches mainly focus on the effect of head variation, electrical efficiency [2][3][4], value point of thermal generator [5] and the transmission loss of the active power [6] without consideration of the electricity network structure and effect of the reactive power. Power flow constraint is an important method to consider the effect of the electricity network and reactive power. The key point of the paper is to introduce the power flow constraint into the hydrothermal scheduling for the advantages to reflect the real working states of the hydrothermal power systems. The STOHS problem is a dynamic optimal power flow(OPF) problem essentially after taking the power flow constraint into account. There are only a few scholars who have added power flow constraint in the optimal hydrothermal scheduling model [7].Although Newton method and interior point method are usually used to solve the OPF problem [8-9] , both of the two methods have difficulties in solving the STOHS problem. One vital point of the difficulties is that the two methods are base on the condition that the objective function is derivable or differentiable. But the objective function about different This work was supported by National Natural Science Foundation of China (No. 61403321) and Knowledge Innovation Program of Shenzhen City (No. JCYJ20130327150859765).purpose of the hydrothermal scheduling is not always derivable and differentiable. As a member of the modern optimization algorithm, particle swarm optimization(PSO)[10] algorithm has no request about the derivability and differentiability of the objective function and it is more flexible and scalable, which gives PSO algorithm advantages to solve the STOHS problem. PSO algorithm has been developed a lot for its fast rate of convergence, simple principle and convenience to be programmed since it was put forward. An improved particle swarm optimization is applied to the STOHS problem in this study. The algorithm is verified by the IEEE nine buses test system, and the results show the feasibility and efficiency of the algorithm. This paper is organized as follows. Section 2 gives the formulation procedure of the STOHS problem. Section 3 takes an overview of the improved PSO algorithm. Section 4 shows the solving procedures of the improved PSO algorithm. Section 5 shows the simulation result of the IEEE nine buses test system. Finally, section 6 makes a conclusion of the paper. PROBLEM FORMULATIONSThe purpose of the STOHS is to make full...

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“…Typical algorithms such as evolutionary programming (EP) [6], genetic algorithm (GA) [7], differential evolution (DE) [8,9] clonal selection (CS) [10] and particle swarm optimization (PSO) [11] have obtained good effect. However, those algorithms are easy to trap into the local optimum and sensitive to initial point which may debase the solution quality as well as effectiveness.…”

Section: Introductionmentioning

confidence: 99%

A Chaotic Quantum Behaved Particle Swarm Optimization Algorithm for Short-term Hydrothermal Scheduling

Chen¹,

Huang²,

Zhi³

2017

TOEEJ

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This study proposes a novel chaotic quantum-behaved particle swarm optimization (CQPSO) algorithm for solving shortterm hydrothermal scheduling problem with a set of equality and inequality constraints. In the proposed method, chaotic local search technique is employed to enhance the local search capability and convergence rate of the algorithm. In addition, a novel constraint handling strategy is presented to deal with the complicated equality constrains and then ensures the feasibility and effectiveness of solution. A system including four hydro plants coupled hydraulically and three thermal plants has been tested by the proposed algorithm. The results are compared with particle swarm optimization (PSO), quantum-behaved particle swarm optimization (QPSO) and other population-based artificial intelligence algorithms considered. Comparison results reveal that the proposed method can cope with short-term hydrothermal scheduling problem and outperforms other evolutionary methods in the literature.

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Optimal gamma based fixed head hydrothermal scheduling using genetic algorithm

Cited by 45 publications

References 23 publications

Modified cuckoo search algorithm for short-term hydrothermal scheduling

Modified cuckoo search algorithm for short-term hydrothermal scheduling

Short-term optimal hydrothermal scheduling with power flow constraint

A Chaotic Quantum Behaved Particle Swarm Optimization Algorithm for Short-term Hydrothermal Scheduling

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