A hybrid Univariate Marginal Distribution Algorithm for dynamic economic dispatch of units considering valve-point effects and ramp rates

Gu, Wei; Wu, Yuhou; Zhang, GuoYong

doi:10.1002/etep.1854

Cited by 9 publications

(11 citation statements)

References 38 publications

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“…Recently, several evolutionary programs are investigated for solving ELD problem. The literature documents the most relevant . The evolutionary algorithms in general require limited knowledge of the problem.…”

Section: Introductionmentioning

confidence: 99%

“…The most promising meta‐heuristic approaches for ELD are fast converging evolutionary programming, ant colony, population variant differential evolution, genetic algorithm, Tabu search, differential harmony, bacterial foraging, artificial bee colony, firefly algorithm, simulated annealing, semi‐definite programming, gravitational search algorithm, and hybrid univariate marginal distribution algorithm . It could be concluded based on literature that some evolutionary approaches suffer from serious drawbacks as premature convergence, time consuming, and initial solution sensitivity. Moreover, they differ widely according to the implementation complexity, the computation speed, and the storage requirements …”

Section: Introductionmentioning

confidence: 99%

“…It could be concluded based on literature that some evolutionary approaches suffer from serious drawbacks as premature convergence, time consuming, and initial solution sensitivity. Moreover, they differ widely according to the implementation complexity, the computation speed, and the storage requirements …”

Section: Introductionmentioning

confidence: 99%

“…To remedy the deficiency of a single evolutionary technique and enhance the performance, the merging between two meta‐heuristic methods is advised. The synergy improves the search capabilities and overcomes the limitations of each method.…”

Section: Introductionmentioning

confidence: 99%

“…The approach of combining two evolutionary techniques provides better solution for ELD than that of single optimization method. However, the hybrid scenario is more complicated and requires more computation requirements than the elementary method …”

Section: Introductionmentioning

confidence: 99%

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Ant lion optimizer versus particle swarm and artificial immune system for economical and eco‐friendly power system operation

Hatata

Hafez

2018

Int Trans Electr Energ Syst

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Summary Economic load dispatch (ELD) became recently a mandatory tool for reliable and economic power system operation. ELD improves the security of the power supply while minimizing the generation costs. Daily load curve imposes conflicting requirements on ELD scheme, such as fulfilling demand during peak periods while limiting the cost. Demand response (DR) is an intelligent approach that ensures load demand fulfillment without introducing additional costs. DR allows shifting the electricity consumption to the non‐peak periods. Therefore, DR could be considered as a virtual power plant. ELD is usually solved as nonlinear constraint optimization problem. Three simple, fertile, and robust meta‐heuristic optimizations are competing in this article for solving ELD incorporating DR as virtual power plant. These are ant lion optimizer (ALO), particle swarm optimization (PSO), and artificial immune system (AIS). The objectives are minimizing the overall cost, system losses, and emission levels. Variety of distinctive study systems are considered to test the functionality and feasibility of the proposed algorithms; they are 6‐bus, 30‐bus, and 118‐bus IEEE systems. MATLAB environment is used for coding ALO, PSO, AIS, and the systems under concern. The comprehensive results show that incorporating DR in ELD reduces the costs/losses/emissions without affecting the power system security and reliability. The comparative results verify the robustness and reliability of ALO in minimizing the overall operating cost and emission levels of the power systems under concern while confining with the constraints. Moreover, they show that ALO has the highest accuracy and convergence efficiency while enjoying the reduced computation storage and execution time.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ant lion optimizer versus particle swarm and artificial immune system for economical and eco‐friendly power system operation

Hatata

Hafez

2018

Int Trans Electr Energ Syst

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A robust mean variance optimization model for economic dispatch with wind power integration and evenly distributed Pareto front generation

Ding

Kou

Ma³

et al. 2017

Int. Trans. Electr. Energ. Syst.

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A robust mean variance optimization model is set up for an economic dispatch problem integrating uncertain wind power in a real-time electricity market. The objective of the model is to find a robust optimal solution so as to minimize both the expected total generation cost and hedge the risk resulting from the robustness. Furthermore, the proposed model is transformed into a biobjective second-order cone programming. To generate an evenly distributed Pareto front set that will not overrepresent one region of the design space or neglect others, we propose a normalized-constraint method in this work. The Pareto front, demonstrated in the Institute of Electrical and Electronics Engineers 118-bus case study, shows a trade-off between total generation cost and risk. An important observation during the case study is that the Pareto front of the robust mean variance model has a special shape that, by sacrificing only a little total generation cost, can greatly reduce the risk in the robust real-time economic dispatch.

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Level-Based Analysis of the Univariate Marginal Distribution Algorithm

Dang¹,

Lehre

Nguyen

2018

Algorithmica

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Estimation of Distribution Algorithms (EDAs) are stochastic heuristics that search for optimal solutions by learning and sampling from probabilistic models. Despite their popularity in real-world applications, there is little rigorous understanding of their performance. Even for the Univariate Marginal Distribution Algorithm (UMDA) -a simple population-based EDA assuming independence between decision variables -the optimisation time on the linear problem OneMax was until recently undetermined. The incomplete theoretical understanding of EDAs is mainly due to lack of appropriate analytical tools.We show that the recently developed level-based theorem for non-elitist populations combined with anticoncentration results yield upper bounds on the expected optimisation time of the UMDA. This approach results in the bound O nλ log λ + n 2 on two problems, LeadingOnes and BinVal, for population sizes λ > µ = Ω(log n), where µ and λ are parameters of the algorithm. We also prove that the UMDA with population sizes µ ∈ O ( √ n) ∩ Ω(log n) optimises OneMax in expected time O (λn), and for larger population sizes µ = Ω( √ n log n), in expected time O (λ √ n). The facility and generality of our arguments suggest that this is a promising approach to derive bounds on the expected optimisation time of EDAs.

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A hybrid Univariate Marginal Distribution Algorithm for dynamic economic dispatch of units considering valve-point effects and ramp rates

Cited by 9 publications

References 38 publications

Ant lion optimizer versus particle swarm and artificial immune system for economical and eco‐friendly power system operation

Ant lion optimizer versus particle swarm and artificial immune system for economical and eco‐friendly power system operation

A robust mean variance optimization model for economic dispatch with wind power integration and evenly distributed Pareto front generation

Level-Based Analysis of the Univariate Marginal Distribution Algorithm

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