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A Multiobjective evolutionary programming Algorithm and Its Applications to Power Generation Expansion Planning

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS 2009年第5期39卷 1086-1096页

作者： Ceciliano Meza, Jose L. Yildirim, Mehmet Bayram Masud, Abu S. M. Inst Invest Elect Cuernavaca 62490 Morelos Mexico Wichita State Univ Dept Ind & Mfg Engn Wichita KS 67260 USA

The generation expansion planning (GEP) problem is defined as the problem of determining WHAT, WHEN, and WHERE new generation units should be installed over a planning horizon to satisfy the expected energy demand. This paper presents a framework to determine the number of new generating units (e.g., conventional steam units, coal units, combined cycle modules, nuclear plants, gas turbines, wind farms, and geothermal and hydro units), power generation capacity for those units, number of new circuits on the network, the voltage phase angle at each node, and the amount of required imported fuel for a single-period generation expansion plan. The resulting mathematical program is a mixed-integer bilinear multiobjective GEP model. The proposed framework includes a multiobjective evolutionary programming algorithm to obtain an approximation of the Pareto front for the multiobjective optimization problem and analytical hierarchy process to select the best alternative. A Mexican power system case study is utilized to illustrate the proposed framework. Results show coherent decisions given the objectives and scenarios considered. Some sensitivity analysis is presented when considering different fuel price scenarios.

关键词： Analytical hierarchy process (AHP) evolutionary programming generation expansion planning (GEP) multicriteria optimization operations research optimization methods power generation planning transmission expansion planning

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Cost benefit analysis on SVC and UPFC in a dynamic economic dispatch problem

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INTERNATIONAL JOURNAL OF ENERGY SECTOR MANAGEMENT 2014年第3期8卷 395-428页

作者： Sreejith, Sekharan Simon, Sishaj P. Vellore Inst Technol Sch Elect Engn Vellore Tamil Nadu India Natl Inst Technol Dept Elect & Elect Engn Tiruchirappalli India

Purpose - The aim of this paper is to compare the performance of static VAR compensator (SVC) and unified power flow controller (UPFC) in dynamic economic dispatch (DED) problem. DED schedules the online generator outputs with the predicted load demands over a certain period so that the electric power system is operated most economically. During last decade, flexible alternating current transmission systems (FACTS) devices are broadly used for maximizing the loadability of existing power system transmission networks. However, based on the literature survey, the performance of SVC and UPFC incorporated in the DED problem and its cost-benefit analysis are not discussed earlier in any of the literature. Design/methodology/approach - Here, the DED problem is solved applying ABC algorithm incorporating SVC and UPFC. The following conditions are investigated with the incorporation of SVC and UPFC into DED problem: the role of SVC and UPFC for improving the power flow and voltage profile and the approximate analysis on cost recovery and payback period with SVC and UPFC in DED problem. Findings - The incorporation of FACTS devices reduces the generation cost and improves the stability of the system. The percentage cost recovered with FACTS devices is estimated approximately using equated monthly installment (EMI) and non-EMI scheme. It is clear from the illustrations that the installation of FACTS devices is profitable after a certain period. Research limitations/implications - In this research work, the generation cost with FACTS devices is only taken into account while calculating the profit. The other benefits like congestion management, cost gained due to land and cost due to stability issues are not considered. For future work, these things can be considered while calculating the benefit. Originality/value - The originality of the work is incorporation of FACTS devices in DED problem and approximate estimation of recovery cost with FACTS devices in DED problem.

关键词： Modeling Energy sector EMI Power flow Optimisation evolutionary programming Transmission Dynamic dispatch Cost recovery Installation cost

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A DNR and DG Sizing Simultaneously by Using EPSO 5

A DNR and DG Sizing Simultaneously by Using EPSO

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5th International Conference on Intelligent Systems, Modelling and Simulation (ISMS)

作者： Sulaima, Mohamad Fani Shamsudin, Nur Hazahsha Jaafar, Hazrik Izzuan Dahalan, Wardiah M. Mokhlis, Hazlie Univ Tekn Malaysia Melaka Fac Elect Engn Malacca Malaysia Univ Malaya Dept Elect Engn Kuala Lumpur Malaysia

ISBN: (纸本)9781479938575

Distribution network planning and operation require the identification of the best topological configuration that is able to fulfill the power demand with minimum power loss. This paper presents an efficient hybridization of both Particle Swarm Optimization (PSO) and evolutionary programming (EP) methods which is called the evolutionary Particle Swarm Optimization (EPSO). The proposed method is used to find the optimal network reconfiguration and optimal size of Distribution Generation (DG) simultaneously. The main objective of this paper is to gain the lowest result of real power losses in the distribution network while improve the computational time as well as satisfying other operating constraints. A comprehensive performance analysis is carried out on IEEE 33 bus distribution system. The proposed method is applied and its impact on the network reconfiguration for real power loss is investigated. The results are presented and compared with the strategy of separated DG sizing and network reconfiguration.

关键词： evolutionary particle swarm optimization particle swarm optimization evolutionary programming distribution generation

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Optimal allocation of Multi-type FACTS Controllers for Total Transfer Capability Enhancement Using Hybrid Particle Swarm Optimization

Optimal allocation of Multi-type FACTS Controllers for Total...

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11th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology

作者： Chansareewittaya, Suppakarn Jirapong, Peerapol Chiang Mai Univ Fac Engn Dept Elect Engn Chiang Mai 50000 Thailand

ISBN: (纸本)9781479929931

In this study, the hybrid particle swarm optimization (hybrid PSO) based on particle swarm optimization (PSO), evolutionary programming (EP), and tabu search (TS) is proposed to determine the optimal allocation of multi-type flexible AC transmission system (FACTS) controllers. Simultaneous-maximized power transfer capability of power transactions between generators and loads without violating system constraints could be obtained. This particular optimal allocation includes optimal types, locations, and parameter settings. Four types of FACTS controllers consist of thyristor-controlled series capacitor (TCSC), thyristor-controlled phase shifter (TCPS), unified power flow controller (UPFC), and static var compensator (SVC). Power transfer capability are calculated based on the optimal power flow (OPF) technique. All test results on IEEE RTS 24-bus system and IEEE 30-bus system indicate that optimally placed OPF with FACTS controllers using the hybrid PSO can enhance higher power transfer capability compared to those from only EP.

关键词： FACTS controller Particle Swarm Optimization evolutionary programming Optimal Power Flow Hybrid Method

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A Novel Communication Technique for Nanorobots Swarms Based on evolutionary Strategies 16

A Novel Communication Technique for Nanorobots Swarms Based ...

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16th UKSim-AMSS International Conference on Computer Modelling and Simulation (UKSim)

作者： Ahmed, Sara Yousef Serry Elsayed ElAraif, Taha Amin, Safaa Elsayed Ain Shams Univ FCIS Dept Comp Sci Cairo Egypt

ISBN: (纸本)9781479949236

This paper is concerned with the issue of modeling the behaviors of a swarm of nanorobots. The proposed algorithm identifies communication based coordination between nanorobots in the swarm in order to survey the predetermined target area. Also, controls the movements of the nanorobots with a self trajectory plan to enable them to make decision. The algorithm uses the (1+1) Evolution Strategy (ES) with One-Fifth success rule which is one of the first adaptive search algorithms proposed for optimization. A simulation platform is implemented and generates nanorobots swarm behaviors through evolutionary programming. When a nanorobot runs the optimization algorithm to mutate its movement plan, the simulator creates three mutation strategies that are randomly applied (Straight strategy, Swap Strategy and High Strategy). The paper includes also a comparative study among these strategies in order to investigate the most efficient one which will take a less average time to reach the predetermined target area. As a consequence, the simulation results show that the high mutation strategy makes the swarm of nanorobots communication to each other more efficient than the other mutation strategies.

关键词： nanotechnology nanorobots swarm communication evolutionary programming evolutionary strategies one-fifth success rule optimization

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Optimized Artificial Neural Network for the Detection of Incipient Faults in Power Transformer

Optimized Artificial Neural Network for the Detection of Inc...

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8th IEEE International Power Engineering and Optimization Conference (PEOCO)

作者： Zakaria, Fathiah Johari, Dalina Musirin, Ismail Univ Teknol Mara Dept Elect Engn Shah Alam Selangor Malaysia

ISBN: (纸本)9781479924226

This paper presents optimized Artificial Neural Network to identify and detect incipient faults in power transformer. This study involved the development of Artificial Neural Network (ANN) models and embedding evolutionary programming (EP) as the computational technique to optimize the built ANN. The optimized ANN is namely as EPANN. As one of the most important equipment in electrical power system, the condition of the equipment need to be monitored closely to avoid any disturbances since its operating status directly influences reliability and stability of the overall power system. Historical industrial data of Dissolved Gas Analysis (DGA) were used and the analysis works are based on IEC 60599 (2007) standard. Based on the acquired findings, the EPANN is proven yields a very satisfactory result compared to non optimized ANN.

关键词： Artificial Neural Network Dissolved Gas Analysis MATLAB Power Transformer evolutionary programming

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A DNR by Using Rank evolutionary Particle Swarm Optimization for Power Loss Minimization 5

A DNR by Using Rank Evolutionary Particle Swarm Optimization...

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5th International Conference on Intelligent Systems, Modelling and Simulation (ISMS)

作者： Sulaima, Mohamad Fani Othman, Siti Noratika Jamri, Mohd Saifuzam Omar, Rosli Sulaiman, Marizan Univ Tekn Malaysia Melaka Fac Elect Engn Malacca Malaysia

ISBN: (纸本)9781479938575

Distribution Network Reconfiguration (DNR) is required to identify the best topology network in order to fulfill the power demand with minimum power losses. This paper proposes a new method which is called as Rank evolutionary Particle Swarm Optimization (REPSO). The proposed method is a combination of the Particle Swarm Optimization (PSO) and the traditional evolutionary programming (EP) algorithm with a rejuvenation of the additional of ranking element. The main objective of this paper is to reduce the power losses while improving the convergence time. The proposed method will be implemented and the real power losses in the IEEE 33-bus test system will be investigated and analyzed accordingly. The results are compared to the conventional PSO and hybridization EPSO method and it is hoped to help the power system engineer in securing the network with the less power loss in the future.

关键词： distribution network reconfiguration rank evolutionary particle swarm optimization particle swarm optimization evolutionary programming

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Hybrid evolutionary Particle Swarm Optimization for the Coordination of Wind and Thermal Generation dispatch

Hybrid Evolutionary Particle Swarm Optimization for the Coor...

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1st International Conference on Advancements in Automation and Control (ICAAC)

作者： Dhayalini, K. Sathiyamoorthy, S. Rajan, Christober Asir C. Anna Univ Dept EEE Madras 600025 Tamil Nadu India JJ Coll Engn & Technol Tiruchirappalli India Pondicherry Engn Coll Dept EEE Pondicherry India

ISBN: (纸本)9783038351245

Development of better wind and thermal coordination dispatch is necessary to determine the optimal dispatch scheme that can integrate wind power reliably and efficiently. In this paper hybrid evolutionary programming (EP) and Particle Swarm Optimization(PSO) approach is utilized to coordinate the wind and thermal generation dispatch and to minimize the total production cost considering wind power generation and valve effect of thermal units. Numerical studies have been performed for three different test systems, i.e., six, thirteen and forty generating unit systems. The simulation results demonstrate the effectiveness of the proposed approach and shows the effect of wind power generation in reducing the total fuel.

关键词： evolutionary programming Wind and Thermal Generation Dispatch Particle Swarm Optimization Fuel cost

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Artificial Intelligence Technique Based Reactive Power Planning Incorporating FACTS Controllers in Real Time Power Transmission System 2

Artificial Intelligence Technique Based Reactive Power Plann...

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IEEE 2nd International Conference on Electrical Energy Systems (ICEES)

作者： Vadivelu, Kallipatti Ramsamy Marutheswar, G. Venkata Annamacharya Inst Technol & Sci Elect & Elect Engn Tirupati Andhra Pradesh India SV Univ Coll Engn Elect & Elect Engn Tirupati Andhra Pradesh India

ISBN: (纸本)9781479937394

Reactive Power Planning is a major concern in the operation and control of power systems This paper compares the effectiveness of evolutionary programming (EP) and Differential Evolution to solve Reactive Power Planning (RPP) problem incorporating FACTS Controllers like Static VAR Compensator (SVC), Thyristor Controlled Series Capacitor (TCSC) and Unified power flow controller (UPFC) considering voltage stability. With help of Fast Voltage Stability Index (FVSI), the critical lines and buses are identified to install the FACTS controllers. The optimal settings of the control variables of the generator voltages, transformer tap settings and allocation and parameter settings of the SVC,TCSC,UPFC are considered for reactive power planning. The test and Validation of the proposed algorithm are conducted on IEEE 30 bus system and 72-bus Indian *** results shows that the UPFC gives better results than SVC and TCSC and the FACTS controllers reduce the system losses.

关键词： FACTS Devices SVC TCSC UPFC Reactive power planning Fast Voltage Stability Index evolutionary programming Differential Evolution

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Emission constrained unit commitment problem solution using invasive weed optimization Algorithm

Emission constrained unit commitment problem solution using ...

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International Conference on Advances in Electrical Engineering (ICAEE)

作者： Saravanan, B. Vasudevan, E. R. Vellore Inst Technol Univ Vellore Tamil Nadu India

ISBN: (纸本)9781479935420

With an increase in environmental considerations over the years, it has become extremely significant to reduce the emission levels in all areas of technology. Likewise, the problem of Unit Commitment (UC), which has always been thought of as a problem to reduce generation cost also involves an added objective of reducing emission levels. This paper presents a new methodology to solve this multi-objective problem. The problem of unit commitment keeping in mind the emission levels has been solved using the evolutionary algorithm of Invasive Weed Optimization (IWO). This proposed algorithm has been tested for two cases, eleven unit twenty four hour systems and eleven unit one sixty eight hour system. Both the results of unit commitment and emission levels have been obtained individually using IWO and the best solution has been achieved by compromising between emission levels and generation cost. This balance has been achieved using trade-off between the two separate solutions. The advantage of this new method gives much more accurate results for both emission and fuel cost which helps in achieving a better optimal solution using trade-offs.

关键词： unit commitment evolutionary programming emission dispatch economic dispatch IWO seeds fitness

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