Improved genetic algorithms for optimal power flow under both normal and contingent operation states

Lai, Loi Lei; Ma, Jia; Yokoyama, Ryuichi; Zhao, Min

doi:10.1016/s0142-0615(96)00051-8

Cited by 326 publications

(125 citation statements)

References 4 publications

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“…Various stochastic optimization techniques have been suggested and utilized to deal with OPF problems, like the genetic algorithm [11][12][13], particle swarm optimization (PSO) [2], differential evolution (DE) [14], harmony search (HS) algorithm [15], artificial bee colony algorithm [4,16], gravitational search algorithm (GSA) [17], distributed algorithm (DA) [18], and biogeography-based optimization (BBO) [19,20]. A survey of the non-deterministic search (stochastic search) algorithms utilized to solve variants of OPF is presented in [21].…”

Section: Introductionmentioning

confidence: 99%

Optimal Power Flow Using the Jaya Algorithm

et al. 2016

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This paper presents application of a new effective metaheuristic optimization method namely, the Jaya algorithm to deal with different optimum power flow (OPF) problems. Unlike other population-based optimization methods, no algorithm-particular controlling parameters are required for this algorithm. In this work, three goal functions are considered for the OPF solution: generation cost minimization, real power loss reduction, and voltage stability improvement. In addition, the effect of distributed generation (DG) is incorporated into the OPF problem using a modified formulation. For best allocation of DG unit(s), a sensitivity-based procedure is introduced. Simulations are carried out on the modified IEEE 30-bus and IEEE 118-bus networks to determine the effectiveness of the Jaya algorithm. The single objective optimization cases are performed both with and without DG. For all considered cases, results demonstrate that Jaya algorithm can produce an optimum solution with rapid convergence. Statistical analysis is also carried out to check the reliability of the Jaya algorithm. The optimal solution obtained by the Jaya algorithm is compared with different stochastic algorithms, and demonstrably outperforms them in terms of solution optimality and solution feasibility, proving its effectiveness and potential. Notably, optimal placement of DGs results in even better solutions.

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Section: Introductionmentioning

confidence: 99%

Optimal Power Flow Using the Jaya Algorithm

et al. 2016

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“…The heuristic and probabilistic search methods [42] can overcome the limitations of non-convex and discrete control variables, and they have advantages such as simplicity, easier implementation, fewer parameters, and faster convergence. The Genetic Algorithm has some unique abilities including fast convergence speed, robustness, and capability of searching the global optimal solution [43].…”

Section: Constraintsmentioning

confidence: 99%

A Power System Optimal Dispatch Strategy Considering the Flow of Carbon Emissions and Large Consumers

et al. 2015

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Abstract:The carbon emissions trading market and direct power purchases by large consumers are two promising directions of power system development. To trace the carbon emission flow in the power grid, the theory of carbon emission flow is improved by allocating power loss to the load side. Based on the improved carbon emission flow theory, an optimal dispatch model is proposed to optimize the cost of both large consumers and the power grid, which will benefit from the carbon emissions trading market. Moreover, to better simulate reality, the direct purchase of power by large consumers is also considered in this paper. The OPF (optimal power flow) method is applied to solve the problem. To evaluate our proposed optimal dispatch strategy, an IEEE 30-bus system is used to test the performance. The effects of the price of carbon emissions and the price of electricity from normal generators and low-carbon generators with regards to the optimal dispatch are analyzed. The simulation results indicate that the proposed strategy can significantly reduce both the operation cost of the power grid and the power utilization cost of large consumers.

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“…It has been verified on practical 51-bus and 224-bus systems to indicate its feasibility and capability. An improved genetic algorithm (IGA) with the dynamical hierarchy of the coding system was developed to solve the OPF problem [23]. The IGA demonstrate the ability to code a large number of control variables in a practical system.…”

Section: Genetic Algorithm Based Approachmentioning

confidence: 99%

A review on applications of heuristic optimization algorithms for optimal power flow in modern power systems

Ming

Wan

2014

J. Mod. Power Syst. Clean Energy

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Optimal power flow (OPF) is one of the key tools for optimal operation and planning of modern power systems. Due to the high complexity with continuous and discrete control variables, modern heuristic optimization algorithms (HOAs) have been widely employed for the solution of OPF. This paper provides an overview of the latest applications of advanced HOAs in OPF problems. The most frequently applied HOAs for solving the OPF problem in recent years are covered and briefly introduced, including genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), and evolutionary programming (EP), etc.

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Improved genetic algorithms for optimal power flow under both normal and contingent operation states

Cited by 326 publications

References 4 publications

Optimal Power Flow Using the Jaya Algorithm

Optimal Power Flow Using the Jaya Algorithm

A Power System Optimal Dispatch Strategy Considering the Flow of Carbon Emissions and Large Consumers

A review on applications of heuristic optimization algorithms for optimal power flow in modern power systems

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