A genetic algorithm-based approach to economic dispatch of power systems

Ma, H.; El-Keib, A.A.; Smith, Robert E.

doi:10.1109/secon.1994.324300

Cited by 11 publications

(6 citation statements)

References 5 publications

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“…However, under practical circumstances, the ramp rate limit restrains the operating range of all the online units for tuning the generator operation between two operating periods [44][45]. The generation may increase or decrease with corresponding upper and lower ramp rate limits.…”

Section: 11mentioning

confidence: 99%

Solutions of Non-smooth Economic Dispatch Problems by Swarm Intelligence

Hosseini

Yang

Gandomi

et al. 2015

Adaptation, Learning, and Optimization

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Abstract. The increasing costs of fuels and operations of power generating units necessitate the development of optimization methods for economic dispatch (ED) problems. Classical optimization techniques such as direct search and gradient methods often fail to find global optimum solutions. Modern optimization techniques are often meta-heuristic, and they are very promising in solving nonlinear programming problems. This chapter presents a novel method to determine the feasible optimal solutions of the ED problems utilizing the newly developed Bat Algorithm (BA). The proposed BA is based on the echolocation behavior of bats. This technique is adapted to solve non-convex ED problems under different nonlinear constraints such as transmission losses, ramp rate limits, multi-fuel options and prohibited operating zones. Parameters are tuned to give the best results for these problems. To describe the efficiency and applicability of the proposed algorithm, we will use four ED test systems with non-convexity. We will compare our results with some of the most recently published ED solution methods. Comparing with the other existing techniques, the proposed approach can find better solutions than other methods. This method can be deemed to be a promising alternative for solving the ED problems in real systems.

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Section: 11mentioning

confidence: 99%

Solutions of Non-smooth Economic Dispatch Problems by Swarm Intelligence

Hosseini

Yang

Gandomi

et al. 2015

Adaptation, Learning, and Optimization

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“…This rule calls for an agent located on state r at the current stage to move to state s at the next stage along a shorter path with a high amount of pheromone rs . This is achieved by a state transition rule that utilizes both (8) with being a parameter which determines the relative importance of pheromone versus path length ( > 0).…”

Section: A State Transition Rulementioning

confidence: 99%

“…Recently, meta-heuristic approaches became popular in the effort to overcome shortcomings of traditional optimization techniques. Techniques such as genetic algorithms [7], [8], seeded memetic algorithms [9], evolutionary programming [10], simulated annealing [11], and tabu search [12] have been widely investigated to solve the UC problem. These methods can accommodate more complicated constraints and are claimed to produce solutions of improved quality.…”

Section: Introductionmentioning

confidence: 99%

An Improved Ant Colony Search Algorithm for Unit Commitment Application

El-Sharkh¹,

Sisworahardjo²,

Rahman³

et al. 2006

2006 IEEE PES Power Systems Conference and Exposition

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This paper presents an improved ant colony search algorithm that is suitable for solving unit commitment (UC) problems. Ant colony search algorithm (ACSA) is a metaheuristic technique for solving hard combinatorial optimization problems. It is a population-based approach that uses exploitation of positive feedback, distributed computation as well as constructive greedy heuristic. Positive feedback is for fast discovery of good solutions, while the greedy heuristic helps find adequate solutions in the early stages of the search process, and finally distributed computation avoids early convergence. The ACSA was inspired by the behavior of real ants that are capable of finding the shortest path from food sources to the nest without using visual cues. The constraints used in the solution of the UC problem using this approach are: real power balance, real power operating limits of generating units, spinning reserve, start up cost, and minimum up and down time constraints. The approach determines the units schedule followed by the consideration of unit transition related constraints. The proposed approach is expected to yield a better operational cost for the UC problem and use less computational resources compared to the traditional ACSA.

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“…Moreover, evolutionary and behavioural random search algorithms such as the genetic algorithm (GA; Ma et al, 1994;Sheble and Brittig, 1994;Walters and Sheble, 1993), particle swarm optimization (PSO; Gaing, 2003;Park et al, 2005) etc. have previously been implemented on the ELD problem at hand.…”

Section: Introductionmentioning

confidence: 99%

Economic load dispatch using population-variance harmony search algorithm

Panigrahi

Pandi

Das

et al. 2011

Transactions of the Institute of Measurement and Control

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This paper presents a novel stochastic optimization approach to solve the constrained economic load dispatch (ELD) problem using the harmonic search algorithm (HS). HS is a recently developed derivative-free, meta-heuristic optimization algorithm, which draws inspiration from the musical process of searching for a perfect state of harmony. This work analyses the evolution of the population-variance over successive generations in HS and thereby draws some important conclusions regarding the explorative power of HS. The proposed methodology easily takes care of solving non-convex ELD problems along with different constraints like transmission losses, ramp rate limits of the generators and prohibited operating zones. Simulations were performed over various standard test systems with different number of generating units and a comparative study is carried out with other existing relevant approaches. The findings affirmed the robustness and proficiency of the proposed methodology over other existing techniques.

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A genetic algorithm-based approach to economic dispatch of power systems

Cited by 11 publications

References 5 publications

Solutions of Non-smooth Economic Dispatch Problems by Swarm Intelligence

Solutions of Non-smooth Economic Dispatch Problems by Swarm Intelligence

An Improved Ant Colony Search Algorithm for Unit Commitment Application

Economic load dispatch using population-variance harmony search algorithm

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