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Swarm based mean-variance mapping optimization for convex and non-convex economic dispatch problems

MEMETIC COMPUTING 2017年第2期9卷 91-108页

作者： Khoa, T. H. Vasant, P. M. Singh, M. S. Balbir Dieu, V. N. Univ Teknol PETRONAS Dept Fundamental & Appl Sci Tronoh Malaysia HCMC Univ Technol Dept Power Syst Ho Chi Minh City Vietnam

In power system generation, the economic dispatch (ED) is used to allocate the real power output of thermal generating units to meet the required load demand so as the total cost of thermal generating units is minimized. This paper proposes a swarm based mean-variance mapping optimization for solving the ED problems with convex and nonconvex objective functions. The proposed method is the extension of the original single particle mean-variance mapping optimization by initializing a set of particles. The special feature of the proposed method is a mapping function applied for the mutation based on the mean and variance of n-best population. The proposed is tested on various systems and the obtained results are compared to those from many other optimization methods in the literature. Test results have shown that the proposed method can obtain better solution quality than the other methods. Therefore, the proposed is a potential method for efficiently solving the convex and nonconvex ED problems in power systems.

关键词： mean-variance mapping optimization Economic dispatch Valve-point effects Prohibited operating zone Cubic fuel cost function Quadratic fuel cost function

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Swarm Based mean-variance mapping optimization (MVMO^S) for Economic Dispatch Problem with Valve - Point Effects

Swarm Based Mean-variance Mapping Optimization (MVMO<SUP>S</...

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IEEE International Conference on Industrial Engineering and Engineering Management

作者： Khoa, T. H. Vasant, P. M. Singh, M. S. Balbir Dieu, V. N. Univ Teknol Petronas Dept Fundamental & Appl Sci Seri Iskandar Malaysia HCMC Univ Technol Dept Power Syst Ho Chi Minh City Vietnam

ISBN: (纸本)9781479964109

mean-variance mapping optimization (MVMO) is a novel population-based meta-heuristic technique which has been successfully applied for different power system optimization problems. The special feature of MVMO is the mapping function applied for the mutation based on the mean and variance of n-best population. Recently, the modified version of MVMO has been developed to get more powerful, named as Swarm based mean-variance mapping optimization (MVMOS). This paper proposes MVMOS as a new approach for solving the economic dispatch problem considering valve-point effects. To demonstrate the performance of the proposed method, the proposed MVMOS has been tested on two systems including 3 and 13 thermal generating units with valve-point effects and the obtained results from MVMOS have been compared to those from other existing methods in the literature. It is indicated that the proposed MVMOS is efficient for solving the economic dispatch with valve-point effects.

关键词： mean-variance mapping optimization economic dispatch valve-point effects meta-heuristic

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Optimal Transmission Expansion Planning using mean-variance mapping optimization

Optimal Transmission Expansion Planning using Mean-Variance ...

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6th IEEE/PES Transmission and Distribution - Latin America Conference and Exposition (TandD-LA)

作者： Pringles, Rolando M. Rueda, Jose L. Instituto de Energ&#x00ED a El&#x00E9

ISBN: (纸本)9781467326711

The optimization problem in transmission system expansion planning (TSEP) is a mixed-integer nonlinear programming problem of combinatorial nature that leads to an extremely large number of alternative solutions for medium and large size electric power systems. Due to its complex characteristics, heuristic optimization has become an effective solver. In this paper, a novel heuristic optimization algorithm, namely the mean-variance mapping optimization (MVMO), is adapted to handle the TSEP. Additionally, a variant of MVMO termed as collaborative MVMO (CMVMO) is introduced. CMVMO exploits multicore technology of modern computers as well as distributed computing to enhance the performance of the former MVMO. Several tests were performed on three benchmark systems with different mesh complexity in their topologies in order to compare the performance of both, MVMO and CMVMO, with other evolutionary algorithms. Simulation results show that CMVMO constitutes a powerful algorithm and should earn more attention.

关键词： Collaborative optimization heuristic optimization mean-variance mapping optimization transmission expansion planning

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Evaluation of the mean-variance mapping optimization for Solving Multimodal Problems

Evaluation of the Mean-Variance Mapping Optimization for Sol...

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IEEE Symposium on Swarm Intelligence (SIS)

作者： Rueda, Jose L. Erlich, Istvan Univ Duisburg Essen Inst Elect Power Syst Duisburg Germany

ISBN: (纸本)9781467360043

Based on swarm intelligence principles and an enhanced mapping scheme, the extension of the original single-particle mean-variance mapping optimization (MVMO) to its swarm variant (MVMOS) is investigated in this paper. Numerical experiments and comparisons with other heuristic optimization methods, which were conducted on several composition test functions, demonstrate the feasibility and effectiveness of MVMOS when solving multimodal optimization problems. Sensitivity analysis of the algorithm parameters highlights its robust performance.

关键词： Composition benchmark functions heuristic optimization mean-variance mapping optimization swarm intelligence

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Hybrid mean-variance mapping optimization for Economic Dispatch with Multiple Fuels Considering Valve-Point Effects 1

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2nd International Conference on Nature of Computation and Communication (ICTCC)

作者： Truong, Khoa H. Vasant, Pandian Singh, M. S. Balbir Vo, Dieu N. Univ Teknol Petronas Dept Fundamental & Appl Sci Tronoh Malaysia HCMC Univ Technol Dept Power Syst Ho Chi Minh City Vietnam

ISBN: (数字)9783319469096

ISBN: (纸本)9783319469096;9783319469089

Many thermal generating units of an electric power system are supplied with multi-fuel sources such as coal, natural gas and oil. These fuels represent irreplaceable natural resources and conservation is used as a way to increase energy efficiency. Economic dispatch (ED) is one of the significance optimization problems in power system operation for fuel cost savings. This paper proposes a new approach which is hybrid variant of mean-variance mapping optimization (MVMO-SH) for solving this problem. The MVMO-SH is the improvement of original mean-variance mapping optimization algorithm (MVMO). This method adopts a swarm scheme of MVMO and incorporates local search and multi-parent crossover strategies to enhance its global search ability and improve solution quality for optimization problems. The proposed MVMOSH is tested on 10-unit and large-scale systems with multiple fuels and valvepoint effects. The obtained results are compared to those from other optimization methods available in the literature. The comparisons show that the proposed method provides higher quality solutions than the others. Therefore, the MVMOSH is a promising method for solving the complex ED problems in electric power system.

关键词： Economic dispatch Multiple fuels Valve-point effects mean-variance mapping optimization MVMO MVMO-SH

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Evaluating the mean-variance mapping optimization on the IEEE-CEC 2014 Test Suite

Evaluating the Mean-Variance Mapping Optimization on the IEE...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Erlich, Istvan Rueda, Jose L. Wildenhues, Sebastian Shewarega, Fekadu Univ Duisburg Essen Inst Elect Power Syst Duisburg Germany Delft Univ Technol Dept Elect Sustainable Energy Delft Netherlands

ISBN: (纸本)9781479914883

This paper provides a survey on the performance of the hybrid variant of the mean-variance mapping optimization (MVMO-SH) when applied for solving the IEEE-CEC 2014 competition test suite on Single Objective Real-Parameter Numerical optimization. MVMO-SH adopts a swarm intelligence scheme, where each particle is characterized by its own solution archive and mapping function. Besides, multi-parent crossover is incorporated into the offspring creation stage in order to force the particles with worst fitness to explore other sub-regions of the search space. In addition, MVMO-SH can be customized to perform with an embedded local search strategy. Experimental results demonstrate the search ability of MVMO-SH for effectively tackling a variety of problems with different dimensions and mathematical properties.

关键词： Heuristic optimization mean-variance mapping optimization single objective optimization swarm intelligence

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Swarm Based mean-variance mapping optimization for Solving Economic Dispatch with Cubic Fuel Cost Function 7

Swarm Based Mean-Variance Mapping Optimization for Solving E...

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7th Asian Conference on Intelligent Information and Database Systems (ACIIDS)

作者： Truong, Khoa H. Vasant, Pandian Sing, M. S. Balbir Vo, Dieu N. Univ Teknol PETRONAS Dept Fundament & Appl Sci Tronoh Malaysia HCMC Univ Technol Dept Power Syst Ho Chi Minh City Vietnam

ISBN: (纸本)9783319157054;9783319157047

In power generation system, the economic dispatch (ED) is used to allocate the real power output of thermal generating units to meet required load demand so as their total operating cost is minimized while satisfying all units and system constraints. This paper proposes a novel swarm based mean-variance mapping optimization (MVMOS) for solving the ED problem with the cubic fuel cost function. The special feature of the proposed algorithm is a mapping function applied for the mutation based on the mean and variance of n-best population. This method has been tested on 3, 5 and 26 units and the obtained results are compared to those from genetic algorithm (GA), particle swarm optimization (PSO) and firefly algorithm (FA). Test results have indicated that the proposed method is efficient for solving the ED problem with cubic fuel cost function.

关键词： Economic dispatch Cubic fuel cost function mean-variance mapping optimization Swarm based mean-variance mapping optimization

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Hybrid mean-variance mapping optimization to Determine the Number of Clusters in Network Partitioning of Hydro Quebec Power Grid 1

Hybrid Mean-Variance Mapping Optimization to Determine the N...

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1st International Conference on Networks & Soft Computing (ICNSC)

作者： Sahoo, Subham Sahoo, Soumyashree R. VSSUT Dept Elect & Elect Engn Burla 768018 India

ISBN: (纸本)9781479934867

Finding out the number of clusters for a particular problem statement is quite an important aspect in clustering. Many clustering techniques have been experimented in the recent past providing somewhat satisfactory results. User supplied information as well as cluster validity indices are some of the expensive techniques regarding to the computation time. A novel technique has been proposed using mean-variance mapping optimization, an emerging heuristic optimization algorithm for determination of the cluster tendency for network partitioning in Hydro Quebec Power Grid, Canada. It has been shown that no user supplied information is required and the change in value of control parameters of the algorithm doesn't even change the result thus making it a fine algorithm for determination of clusters for any data set with higher efficiency.

关键词： Cluster Analysis Visual Assessment mean-variance mapping optimization Heuristic optimization

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Hybrid mean-variance mapping optimization for Solving the IEEE-CEC 2013 Competition Problems

Hybrid Mean-Variance Mapping Optimization for Solving the IE...

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IEEE Congress on Evolutionary Computation

作者： Rueda, Jose L. Erlich, Istvan Univ Duisburg Essen Inst Elect Power Syst Duisburg Germany

ISBN: (纸本)9781479904549;9781479904532

mean-variance mapping optimization (MVMO) is a recent addition to the emerging field of heuristic optimization algorithms, which has been quite successful in solving a variety of power system optimization problems. This paper introduces a hybrid variant of MVMO (MVMO-SH) for solving the IEEE-CEC 2013 competition test suite. MVMO-SH is based on a swarm scheme of MVMO with embedded local search and multi-parent crossover strategies to increase search diversity and solution quality. Numerical results attest to the promising prospect of MVMO-SH to become a general purpose optimization algorithm.

关键词： Heuristic optimization mean-variance mapping optimization single objective optimization swarm intelligence

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Dynamic security enhancement of power systems using mean-variance mapping optimization

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2017年第4期25卷 3188-3200页

作者： Kucuktezcan, Cavit Fatih Genc, Veysel Murat Istemihan Istanbul Tech Univ Fac Elect & Elect Dept Elect Engn Istanbul Turkey

In this paper, a preventive control action that involves both generation rescheduling and load curtailment is proposed for enhancing the dynamic security of large interconnected power systems. The control action is formulated as a security-constrained optimization problem that is solved by mean-variance mapping optimization (MVMO) integrated with a self-adaptive penalization technique and artificial neural networks to develop a fast and effective methodology. The proposed methodology is applied to a 16-generator 68-bus test system to solve the security-constrained optimization problem with both continuous and discrete decision variables. To find a proper and cost-effective solution for the control actions within an acceptable time, dynamic security assessment methodology based on artificial neural networks is integrated into the optimization process for predicting the violations of security constraints brought about by the candidate solutions. The proposed method effectively integrates a variety of popular heuristic optimization algorithms, including MVMO, differential evolution, particle swarm optimization, genetic algorithms, big bang-big crunch, and artificial bee colony. MVMO outperforms all the others in various aspects such as reliability and robustness.

关键词： Power system security preventive control generation rescheduling load curtailment heuristic optimization mean-variance mapping optimization dynamic security assessment

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