Pub Date : 2007-11-01DOI: 10.1109/ISAP.2007.4441642
Tsung-Ying Lee, Chun-Lung Chen
This paper investigates the effects of photovoltaic generation system (PVGS) on the selection of contract capacities for time-of-use (TOU) rate industrial users. A benefit cost ratio (BCR) was served to evaluate the economic benefits of PVGS in TOU rate industrial users. Evolutionary programming (EP) is applied to solve the optimal installation capacity of PVGS and the optimal contract capacities for TOU rate industrial users. The impacts of PVGS installation capacity on the selection of contract capacities for TOU rate industrial users were evaluated. To apply the EP to solve the previous problem, an individual which was composed of PVGS installation capacity and TOU rate user contract capacities, was defined. A fitness function evaluates the economic benefits of PVGS was applied to calculate the fitness of individual. After that EP starts to calculate the optimal PVGS installation capacity and TOU rate user contract capacities. Through the cooperation of agents called individuals, the near optimal solution of the previous problem can be effectively reached. Finally, a numerical example was served to demonstrate the feasibility of the new approach, and EP solution quality and computation efficiency were compared to those of other algorithms.
{"title":"Effects of Photovoltaic Generation System on the Contract Capacity Selection of Time-Of-Use Rate Industrial Users","authors":"Tsung-Ying Lee, Chun-Lung Chen","doi":"10.1109/ISAP.2007.4441642","DOIUrl":"https://doi.org/10.1109/ISAP.2007.4441642","url":null,"abstract":"This paper investigates the effects of photovoltaic generation system (PVGS) on the selection of contract capacities for time-of-use (TOU) rate industrial users. A benefit cost ratio (BCR) was served to evaluate the economic benefits of PVGS in TOU rate industrial users. Evolutionary programming (EP) is applied to solve the optimal installation capacity of PVGS and the optimal contract capacities for TOU rate industrial users. The impacts of PVGS installation capacity on the selection of contract capacities for TOU rate industrial users were evaluated. To apply the EP to solve the previous problem, an individual which was composed of PVGS installation capacity and TOU rate user contract capacities, was defined. A fitness function evaluates the economic benefits of PVGS was applied to calculate the fitness of individual. After that EP starts to calculate the optimal PVGS installation capacity and TOU rate user contract capacities. Through the cooperation of agents called individuals, the near optimal solution of the previous problem can be effectively reached. Finally, a numerical example was served to demonstrate the feasibility of the new approach, and EP solution quality and computation efficiency were compared to those of other algorithms.","PeriodicalId":320068,"journal":{"name":"2007 International Conference on Intelligent Systems Applications to Power Systems","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134216136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-11-01DOI: 10.1109/ISAP.2007.4441632
M. Farsangi, H. Nezamabadi-pour, K.Y. Lee
In this paper, Guaranteed Convergence Particle Swarm Optimization (GCPSO) Algorithm is used for VAr planning with the Static Var Compensators (SVC) in a large-scale power system. To enhance voltage stability, the planning problem is formulated as a multiobjective optimization problem for maximizing fuzzy performance indices. The multi-objective VAr planning problem is solved by the fuzzy GCPSO and the results are compared with those obtained by the Particle Swarm Optimization (PSO) and Genetic Algorithm
{"title":"Implementation of GCPSO for Multi-objective VAr Planning with SVC and Its Comparison with GA and PSO","authors":"M. Farsangi, H. Nezamabadi-pour, K.Y. Lee","doi":"10.1109/ISAP.2007.4441632","DOIUrl":"https://doi.org/10.1109/ISAP.2007.4441632","url":null,"abstract":"In this paper, Guaranteed Convergence Particle Swarm Optimization (GCPSO) Algorithm is used for VAr planning with the Static Var Compensators (SVC) in a large-scale power system. To enhance voltage stability, the planning problem is formulated as a multiobjective optimization problem for maximizing fuzzy performance indices. The multi-objective VAr planning problem is solved by the fuzzy GCPSO and the results are compared with those obtained by the Particle Swarm Optimization (PSO) and Genetic Algorithm","PeriodicalId":320068,"journal":{"name":"2007 International Conference on Intelligent Systems Applications to Power Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129113700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-11-01DOI: 10.1109/ISAP.2007.4441624
R. Garduno-Ramirez, K.Y. Lee
Mostly during wide-range operation, load-following capability and efficacy for frequency regulation of fossil-fuel power plants may be affected by interaction among the control loops, caused by the non-linear coupled plant dynamics. This paper introduces a fuzzy gain-scheduling decoupling control scheme to improve plant response under large power excursions throughout the power plant operating space. The control scheme consists of single-loop PID controllers in series with an inverse interaction compensator. Both, the controllers and compensator are gain-scheduled with fuzzy systems to embrace the entire operating space. The proposed control scheme is evaluated through simulation experiments. Results show improved wide-range operation.
{"title":"Fuzzy Gain-Scheduling PID+Decoupling Control for Power Plant Wide-Range Operation","authors":"R. Garduno-Ramirez, K.Y. Lee","doi":"10.1109/ISAP.2007.4441624","DOIUrl":"https://doi.org/10.1109/ISAP.2007.4441624","url":null,"abstract":"Mostly during wide-range operation, load-following capability and efficacy for frequency regulation of fossil-fuel power plants may be affected by interaction among the control loops, caused by the non-linear coupled plant dynamics. This paper introduces a fuzzy gain-scheduling decoupling control scheme to improve plant response under large power excursions throughout the power plant operating space. The control scheme consists of single-loop PID controllers in series with an inverse interaction compensator. Both, the controllers and compensator are gain-scheduled with fuzzy systems to embrace the entire operating space. The proposed control scheme is evaluated through simulation experiments. Results show improved wide-range operation.","PeriodicalId":320068,"journal":{"name":"2007 International Conference on Intelligent Systems Applications to Power Systems","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124620450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-11-01DOI: 10.1109/ISAP.2007.4441626
Y. Chang, C. Yang
After deregulation, power transactions are significantly increased and thus it becomes more urgent to improve system transmission loadability (TL). Utilization of flexible AC transmission systems (FACTS) can be a better choice to accommodate the requirement instead of building new transmission lines. FACTS devices can enhance system dynamic behavior and system reliability. If they are installed at suitable positions and provide system with sufficient capacities, TL may be largely improved. This issue is playing an increasingly vital role in operation and control for the deregulated markets. The objectives of the optimization problem in the paper involve to maximize the benefit from the future fuel expense with proper investment in the allocation of FACTS devices and to improve TL the most. The solution method proposed is based on the particle swarm optimization (PSO) algorithm involving in the computational procedure of the continuation power flow (PFC). Only static VAR compensator (SVC) is used; however, the installation of SVC and the effectiveness of the solution method can be validated.
{"title":"Benefit-Based Optimal Allocation of FACTS: SVC Device for Improvement of Transmission Network Loadability","authors":"Y. Chang, C. Yang","doi":"10.1109/ISAP.2007.4441626","DOIUrl":"https://doi.org/10.1109/ISAP.2007.4441626","url":null,"abstract":"After deregulation, power transactions are significantly increased and thus it becomes more urgent to improve system transmission loadability (TL). Utilization of flexible AC transmission systems (FACTS) can be a better choice to accommodate the requirement instead of building new transmission lines. FACTS devices can enhance system dynamic behavior and system reliability. If they are installed at suitable positions and provide system with sufficient capacities, TL may be largely improved. This issue is playing an increasingly vital role in operation and control for the deregulated markets. The objectives of the optimization problem in the paper involve to maximize the benefit from the future fuel expense with proper investment in the allocation of FACTS devices and to improve TL the most. The solution method proposed is based on the particle swarm optimization (PSO) algorithm involving in the computational procedure of the continuation power flow (PFC). Only static VAR compensator (SVC) is used; however, the installation of SVC and the effectiveness of the solution method can be validated.","PeriodicalId":320068,"journal":{"name":"2007 International Conference on Intelligent Systems Applications to Power Systems","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115243422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-11-01DOI: 10.3182/20080706-5-KR-1001.02028
U. Moon, Seung-chul Lee, K.Y. Lee
This paper proposes an adaptive dynamic matrix control (DMC) using fuzzy inference and its application to boiler-turbine system. In a conventional DMC, object system is described as a step response model (SRM). However, a nonlinear system is not effectively described as a single SRM. In this paper, nine SRMs at various operating points are represented as fuzzy inference rules. On-line fuzzy inference is performed at every sampling step to find the suitable SRM. Therefore, the proposed adaptive DMC can consider the nonlinearity of boiler-turbine system. The simulation results show satisfactory result with wide range operation of boiler-turbine system.
{"title":"An Adaptive Dynamic Matrix Control of a Boiler-Turbine System Using Fuzzy Inference","authors":"U. Moon, Seung-chul Lee, K.Y. Lee","doi":"10.3182/20080706-5-KR-1001.02028","DOIUrl":"https://doi.org/10.3182/20080706-5-KR-1001.02028","url":null,"abstract":"This paper proposes an adaptive dynamic matrix control (DMC) using fuzzy inference and its application to boiler-turbine system. In a conventional DMC, object system is described as a step response model (SRM). However, a nonlinear system is not effectively described as a single SRM. In this paper, nine SRMs at various operating points are represented as fuzzy inference rules. On-line fuzzy inference is performed at every sampling step to find the suitable SRM. Therefore, the proposed adaptive DMC can consider the nonlinearity of boiler-turbine system. The simulation results show satisfactory result with wide range operation of boiler-turbine system.","PeriodicalId":320068,"journal":{"name":"2007 International Conference on Intelligent Systems Applications to Power Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114409290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-11-01DOI: 10.1109/ISAP.2007.4441691
Tsair-Fwu Lee, H. Wu, Ying-Chang Hsiao, P. Chao, F. Fang, M. Cho
We study the problem of dynamically scheduling a set of period stage control tasks controlling a set of large air conditioner loads (ACLs). To be able to solve the scheduling problem for realistic on-line cases, we utilize the technique of relaxed dynamic programming (RDP) algorithm to generate an optimal or near optimal daily control scheduling for ACLs with relaxing bounds. Field tests of controlling the ACLs located in the campus are tested on-site to demonstrate the effectiveness of the proposed load control strategy.
{"title":"Relaxed Dynamic Programming for Constrained Economic Direct Loads Control Scheduling","authors":"Tsair-Fwu Lee, H. Wu, Ying-Chang Hsiao, P. Chao, F. Fang, M. Cho","doi":"10.1109/ISAP.2007.4441691","DOIUrl":"https://doi.org/10.1109/ISAP.2007.4441691","url":null,"abstract":"We study the problem of dynamically scheduling a set of period stage control tasks controlling a set of large air conditioner loads (ACLs). To be able to solve the scheduling problem for realistic on-line cases, we utilize the technique of relaxed dynamic programming (RDP) algorithm to generate an optimal or near optimal daily control scheduling for ACLs with relaxing bounds. Field tests of controlling the ACLs located in the campus are tested on-site to demonstrate the effectiveness of the proposed load control strategy.","PeriodicalId":320068,"journal":{"name":"2007 International Conference on Intelligent Systems Applications to Power Systems","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125864009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-11-01DOI: 10.1109/ISAP.2007.4441678
Konrad Wojdan, K. Swirski, Tomasz Chomiak
The article presents an optimization method of combustion process in a power boiler. Immune inspired optimizer SILO is used to minimize CO and NOx emission. This solution is implemented in each of three units of Ostroleka Power Plant (Poland) and in the Newton Power Plant (USA). The result from the second SILO implementation in Newton Power Plant is presented. The results confirm that this solution is effective and usable in practice and it can be a good alternative to MPC controllers.
{"title":"Immune Inspired System for Chemical Process Optimization using the example of a Combustion Process in a Power Boiler","authors":"Konrad Wojdan, K. Swirski, Tomasz Chomiak","doi":"10.1109/ISAP.2007.4441678","DOIUrl":"https://doi.org/10.1109/ISAP.2007.4441678","url":null,"abstract":"The article presents an optimization method of combustion process in a power boiler. Immune inspired optimizer SILO is used to minimize CO and NOx emission. This solution is implemented in each of three units of Ostroleka Power Plant (Poland) and in the Newton Power Plant (USA). The result from the second SILO implementation in Newton Power Plant is presented. The results confirm that this solution is effective and usable in practice and it can be a good alternative to MPC controllers.","PeriodicalId":320068,"journal":{"name":"2007 International Conference on Intelligent Systems Applications to Power Systems","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121920088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-11-01DOI: 10.1109/ISAP.2007.4441637
H. Haroonabadi, M. Haghifam
Deregulation policy has caused some changes in the concepts of power systems reliability assessment and enhancement. In this paper, generation reliability is considered, and a method for its assessment using fuzzy logic is proposed. Monte Carlo simulation is used for reliability evaluation. Since generation reliability, merely focuses on interaction between generation complex and load, therefore in this paper, transmission and distribution systems are considered reliable. In this research, based on market type and its concentration, a fuzzy logic is proposed for modeling the market which is valid for all kinds of power pool markets. The proposed method is assessed on IEEE-reliability test system with satisfactory results. In all case studies, generation reliability indices are evaluated with different reserve margins and various load levels.
{"title":"Generation Reliability Assessment in Power Market Using Fuzzy Logic and Monte Carlo Simulation","authors":"H. Haroonabadi, M. Haghifam","doi":"10.1109/ISAP.2007.4441637","DOIUrl":"https://doi.org/10.1109/ISAP.2007.4441637","url":null,"abstract":"Deregulation policy has caused some changes in the concepts of power systems reliability assessment and enhancement. In this paper, generation reliability is considered, and a method for its assessment using fuzzy logic is proposed. Monte Carlo simulation is used for reliability evaluation. Since generation reliability, merely focuses on interaction between generation complex and load, therefore in this paper, transmission and distribution systems are considered reliable. In this research, based on market type and its concentration, a fuzzy logic is proposed for modeling the market which is valid for all kinds of power pool markets. The proposed method is assessed on IEEE-reliability test system with satisfactory results. In all case studies, generation reliability indices are evaluated with different reserve margins and various load levels.","PeriodicalId":320068,"journal":{"name":"2007 International Conference on Intelligent Systems Applications to Power Systems","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132125866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-11-01DOI: 10.1109/ISAP.2007.4441630
S. Small, B. Jeyasurya
Increased load forecasts can severely deteriorate the performance of a power system. Reactive compensation devices are a common method to allow a power system to return to an acceptable performance level for an expected load. Reactive power planning (RPP) is used to determine the optimal placement of reactive devices for a set of objectives. RPP is a large scale multiple objectives highly constrained and partially discrete optimization problem that is very difficult to solve. Evolutionary algorithms have been used to solve RPP problems. However, new multi-objective evolutionary computational techniques have shown the ability to consider an optimization problem's objectives independently for the determination of Pareto Optimal solutions. This paper aims at applying the Non-Dominated Sorting Genetic Algorithm II (NSGAII) to a multi-objective RPP. The results from the case study presented show that there is great potential in the use of evolutionary computation for solving the multi-objective RPP.
{"title":"Multi-Objective Reactive Power Planning: A Pareto Optimization Approach","authors":"S. Small, B. Jeyasurya","doi":"10.1109/ISAP.2007.4441630","DOIUrl":"https://doi.org/10.1109/ISAP.2007.4441630","url":null,"abstract":"Increased load forecasts can severely deteriorate the performance of a power system. Reactive compensation devices are a common method to allow a power system to return to an acceptable performance level for an expected load. Reactive power planning (RPP) is used to determine the optimal placement of reactive devices for a set of objectives. RPP is a large scale multiple objectives highly constrained and partially discrete optimization problem that is very difficult to solve. Evolutionary algorithms have been used to solve RPP problems. However, new multi-objective evolutionary computational techniques have shown the ability to consider an optimization problem's objectives independently for the determination of Pareto Optimal solutions. This paper aims at applying the Non-Dominated Sorting Genetic Algorithm II (NSGAII) to a multi-objective RPP. The results from the case study presented show that there is great potential in the use of evolutionary computation for solving the multi-objective RPP.","PeriodicalId":320068,"journal":{"name":"2007 International Conference on Intelligent Systems Applications to Power Systems","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133646513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-11-01DOI: 10.1109/ISAP.2007.4441618
Huy Huynh Nguyen, G. Baxter, L. Reznik
This paper presents an investigation and a comparative study of four different approaches namely ANSI/IEEE standard methods, Adaptive Neuro-Fuzzy Inference System (ANFIS), Multilayer Feedforward Neural Network (MFNN) and Elman Recurrent Neural Network (ERNN) to modeling and prediction of the top-oil temperature for the 8 MVA Oil Air (OA)-cooled and 27 MVA Forced Air (FA)-cooled class of power transformers. A comparison of the proposed techniques is presented for predicting top-oil temperature based on the historical data measured over a 35 day period for the first transformer and 4.5 days for the second transformer with either a half or a quarter hour sampling time. Comparison results indicate that hybrid neuro-fuzzy network is the best candidate for the analysis and predicting of power transformer top-oil temperature. The ANFIS demonstrated the paramount performance in temperature prediction in terms of Root Mean Square Error (RMSE) and peaks of error.
{"title":"Soft Computing Techniques to Model the Top-oil Temperature of Power Transformers","authors":"Huy Huynh Nguyen, G. Baxter, L. Reznik","doi":"10.1109/ISAP.2007.4441618","DOIUrl":"https://doi.org/10.1109/ISAP.2007.4441618","url":null,"abstract":"This paper presents an investigation and a comparative study of four different approaches namely ANSI/IEEE standard methods, Adaptive Neuro-Fuzzy Inference System (ANFIS), Multilayer Feedforward Neural Network (MFNN) and Elman Recurrent Neural Network (ERNN) to modeling and prediction of the top-oil temperature for the 8 MVA Oil Air (OA)-cooled and 27 MVA Forced Air (FA)-cooled class of power transformers. A comparison of the proposed techniques is presented for predicting top-oil temperature based on the historical data measured over a 35 day period for the first transformer and 4.5 days for the second transformer with either a half or a quarter hour sampling time. Comparison results indicate that hybrid neuro-fuzzy network is the best candidate for the analysis and predicting of power transformer top-oil temperature. The ANFIS demonstrated the paramount performance in temperature prediction in terms of Root Mean Square Error (RMSE) and peaks of error.","PeriodicalId":320068,"journal":{"name":"2007 International Conference on Intelligent Systems Applications to Power Systems","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133611298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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