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

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

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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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Constrained mean-variance mapping optimization for truss optimization problems

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STRUCTURAL DESIGN OF TALL AND SPECIAL BUILDINGS 2018年第6期27卷 e1449-e1449页

作者： Aslani, Mohamad Ghasemi, Parnian Gandomi, Amir H. Iowa State Univ Dept Aerosp & Mech Engn Ames IA USA Iowa State Univ Dept Civil Engn Ames IA USA Stevens Inst Technol Sch Business Hoboken NJ 07030 USA

Truss optimization is a complex structural problem that involves geometric and mechanical constraints. In the present study, constrained mean-variance mapping optimization (MVMO) algorithms have been introduced for solving truss optimization problems. Single-solution and population-based variants of MVMO are coupled with an adaptive exterior penalty scheme to handle geometric and mechanical constraints. These tools are explained and tuned for weight minimization of trusses with 10 to 200 members and up to 1,200 nonlinear constraints. The results are compared with those obtained from the literature and classical genetic algorithm. The results show that a MVMO algorithm has a rapid rate of convergence and its final solution can obviously outperform those of other algorithms described in the literature. The observed results suggest that a constrained MVMO is an attractive tool for engineering-based optimization, particularly for computationally expensive problems in which the rate of convergence and global convergence are important.

关键词： exterior penalty mean-variance mapping optimization stochastic search truss weight

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Hybrid mean-variance mapping optimization for Non-Convex Economic Dispatch Problems

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INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2017年第4期8卷 34-59页

作者： Khoa, Truong H. Vasant, Pandian M. Singh, Balbir Mahinder Singh Dieu, V. N. Univ Teknol PETRONAS Dept Elect & Elect Engn Seri Iskandar Malaysia Ton Duc Thang Univ Fac Elect & Elect Engn Ho Chi Minh City Vietnam Univ Teknol PETRONAS Dept Fundamental & Appl Sci Seri Iskandar 32610 Perak Malaysia HCMC Univ Technol Dept Power Syst Ho Chi Minh City Vietnam

The economic dispatch (ED) is one of the important optimization problems in power system generation for fuel cost saving. This paper proposes a hybrid variant of mean-variance mapping optimization (MVMO-SH) for solving such problem considering the non-convex objective functions. The new proposed method is a hybrid variant of the original mean-variance mapping optimization algorithm (MVMO) with the embedded local search and multi-parent crossover to enhance its global search ability and improve solution quality for optimization problems. The proposed MVMO-SH is tested on different non-convex ED problem including valve point effects, multiple fuels and prohibited operating zones characteristics. The result comparisons from the proposed method with other methods in the literature have indicated that the proposed method is more robust and provides better solution quality than the others. Therefore, the proposed MVMO-SH is a promising method for solving the complex ED problems in power systems.

关键词： Economic Dispatch mean-variance mapping optimization Multiple Fuels Prohibited Operating Zone ValvePoint Effects

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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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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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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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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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Solving economic dispatch problem with valve-point effects using swarm-based mean-variance mapping optimization (MVMOS)

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COGENT ENGINEERING 2015年第1期2卷 Article: 1076983页

作者： Khoa, T. H. Vasant, P. M. Singh, M. S. Balbir Dieu, V. N. Univ Teknol PETRONAS Dept Fundamental & Appl Sci Tronoh 31750 Perak Malaysia HCMC Univ Technol Dept Power Syst Ho Chi Minh City Vietnam

mean-variance mapping optimization (MVMO) is a new population-based metaheuristic technique which is 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 become more powerful, named as swarm-based mean-variance mapping optimization (MVMOS). This paper proposes MVMOS as a new approach for solving the economic dispatch (ED) problem considering valve-point effects. To validate the performance of the proposed method, the MVMOS is tested on three systems including 3, 13, and 40 thermal generating units with valve-point effects and the obtained results from MVMOS are compared to those from other existing methods in the literature. Test results have indicated that the proposed MVMOS is more robust and produces better solution quality than many other methods. Therefore, the MVMOS is efficient for solving the ED with valve-point effects.

关键词： mean-variance mapping optimization economic dispatch valve-point effects metaheuristic nonconvex objective function swarm-based mean-variance mapping optimization

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