Multiobjective Stochastic Economic Dispatch With Variable Wind Generation Using Scenario-Based Decomposition and Asynchronous Block Iteration

Fu, Yimu; Liu, Mingbo; Li, Licheng

doi:10.1109/tste.2015.2478488

Cited by 68 publications

(44 citation statements)

References 34 publications

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“…The large-scale combinatorial nature of UC in the NCUC reduces the possibility of using deterministic methods, due to the difficulties of incorporating the various constraints, by opening the possibility of using metaheuristic methods. Various traditional programming algorithms are used, in particular dynamic programming [38], the interior point method [39,40], and further scenario-based decomposition methods that are applied to stochastic ED, e.g., using the asynchronous block iteration method to reduce the computational burden by using multicore computational architectures [41]. Metaheuristic algorithms, mainly genetic algorithms [36,42] and particle swarm optimization [43], have been used in the last decades to solve the ED problem, addressing specific challenges due to the non-convexity of the domain of definition of the variables.…”

Section: Economic Dispatch (Ed)mentioning

confidence: 99%

Metaheuristic Optimization of Power and Energy Systems: Underlying Principles and Main Issues of the ‘Rush to Heuristics’

Chicco

Mazza

2020

Energies

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In the power and energy systems area, a progressive increase of literature contributions that contain applications of metaheuristic algorithms is occurring. In many cases, these applications are merely aimed at proposing the testing of an existing metaheuristic algorithm on a specific problem, claiming that the proposed method is better than other methods that are based on weak comparisons. This ‘rush to heuristics’ does not happen in the evolutionary computation domain, where the rules for setting up rigorous comparisons are stricter but are typical of the domains of application of the metaheuristics. This paper considers the applications to power and energy systems and aims at providing a comprehensive view of the main issues that concern the use of metaheuristics for global optimization problems. A set of underlying principles that characterize the metaheuristic algorithms is presented. The customization of metaheuristic algorithms to fit the constraints of specific problems is discussed. Some weaknesses and pitfalls that are found in literature contributions are identified, and specific guidelines are provided regarding how to prepare sound contributions on the application of metaheuristic algorithms to specific problems.

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Section: Economic Dispatch (Ed)mentioning

confidence: 99%

Metaheuristic Optimization of Power and Energy Systems: Underlying Principles and Main Issues of the ‘Rush to Heuristics’

Chicco

Mazza

2020

Energies

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“…At the same time, Shunjiang Lin proposed a normalized normal constraints algorithm to be applied on multi-objectives problem in large scale power system with wind and pumped storage [16]. Yimu Fu has minimized the power purchase cost and pollutant gas emission on stochastic wind thermal generation [9]. Interior point method has been applied to solve IEEE-39 bus and Real 1534-bus power system with added of wind plant.…”

Section: Ceed With Renewable Energymentioning

confidence: 99%

“…These great innovation are accepted and many researcher are started to include these green technologies into power dispatch problem [5][6][7]. By involving the green technologies, the complexity CEED formulation and constraints are also increased and attract the researcher to focus in this research [7][8][9]. This paper presents the fundamental concept of power dispatch including Economic and Emission Dispatch.…”

Section: Introductionmentioning

confidence: 99%

A review of combined economic emission dispatch for optimal power dispatch with renewable energy

Aswan

Abdullah

Bakar

2019

IJEECS

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This paper provides comprehensive review of recent formulation and solution of combined economic emission dispatch problem considering renewable energy. Generally, power dispatch plays an important role in power generation system that mainly depends on the fossil fuels. Rapid depletion of these sources cause a limitation and increase the fuel price. At the same time, the poisonous gases emit by the thermal generator causes a long term environmental pollution. An extensive researches have been conducted for last few decades by considering Renewable Energy (RE) in power generation system as an alternative source. Therefore, Combined Economic Emission Dispatch (CEED) of thermal and renewable energy resources currently formulated in power dispatch problem. The optimization methods that commonly used to solve the CEED problem are categorized as traditional method, non-conventional method and hybrid method. It can be concluded that non-conventional and hybrid method have great and superior solution for solving multiobjective economic emission dispatch problem. The recent research related to CEED with RE are summarized in terms of methods, test cases, conditions, types of RE and constraints. Thus, it can be useful references for the research in this area.

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“…Although the fuzzy approach is easy to implement, it might be subjected to the dispatchers' attitude and trapped in selecting suitable membership functions [10,11]. In terms of the probabilistic approach, it can be generally divided into scenario-based programming [12][13][14][15][16], robust optimization [17,18], chance-constrained optimization [19,20] and risk-based optimization [21,22].…”

Section: Introductionmentioning

confidence: 99%

Stochastic optimization of cost-risk for integrated energy system considering wind and solar power correlated

Zheng

Kou

2019

J. Mod. Power Syst. Clean Energy

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Due to the growing penetration of renewable energies (REs) in integrated energy system (IES), it is imperative to assess and reduce the negative impacts caused by the uncertain REs. In this paper, an unscented transformation-based mean-standard (UT-MS) deviation model is proposed for the stochastic optimization of cost-risk for IES operation considering wind and solar power correlated. The unscented transformation (UT) sampling method is adopted to characterize the uncertainties of wind and solar power considering the correlated relationship between them. Based on the UT, a mean-standard (MS) deviation model is formulated to depict the trade-off between the cost and risk of stochastic optimization for the IES optimal operation problem. Then the UT-MS model is tackled by a multi-objective group search optimizer with adaptive covariance and Lévy flights embedded with a multiple constraints handling technique (MGSO-ACL-CHT) to ensure the feasibility of Peratooptimal solutions. Furthermore, a decision making method, improve entropy weight (IEW), is developed to select a final operation point from the set of Perato-optimal solutions. In order to verify the feasibility and efficiency of the proposed UT-MS model in dealing with the uncertainties of correlative wind and solar power, simulation studies are conducted on a test IES. Simulation results show that the UT-MS model is capable of handling the uncertainties of correlative wind and solar power within much less samples and less computational burden. Moreover, the MGSO-ACL-CHT and IEW are also demonstrated to be effective in solving the multi-objective UT-MS model of the IES optimal operation problem.

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Multiobjective Stochastic Economic Dispatch With Variable Wind Generation Using Scenario-Based Decomposition and Asynchronous Block Iteration

Cited by 68 publications

References 34 publications

Metaheuristic Optimization of Power and Energy Systems: Underlying Principles and Main Issues of the ‘Rush to Heuristics’

Metaheuristic Optimization of Power and Energy Systems: Underlying Principles and Main Issues of the ‘Rush to Heuristics’

A review of combined economic emission dispatch for optimal power dispatch with renewable energy

Stochastic optimization of cost-risk for integrated energy system considering wind and solar power correlated

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