基于粒子群文化算法的变电站规划研究
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摘要
随着我国经济的快速发展和人民生活水平的提高,全社会对电能的需求量越来越大,对电能质量要求越来越高。而我国当前电网普遍存在配电能力偏小、变电站容载比偏小、高压网架脆弱、供电可靠性低等缺点,这在一定程度上制约着经济等各方面的发展。近年来人们普遍意识到城市电网规划已经成为一项迫切任务。其中确定变电站容量与位置是城市电网规划中介于电力负荷预测和网络规划之间的关键环节,其结果直接影响未来电力系统的线路走径、网络结构、电网投资、运行经济性及供电可靠性等关键问题。
针对变电站优化规划的特点,本文根据文化算法的结构,分析若干智能算法作为文化算法框架中底层算法的可能性,最终选定粒子群算法,并设计文化算法上层空间的知识源约束条件。基于上面的分析,本文提出了一种用于变电站规划的新算法——粒子群文化算法,可以完成对新建变电站数量、容量、站址及供电范围等方面的优化。该方法首先根据容载比等方面的约束条件得到新建变电站的个数范围,然后采用0-1型整数规划方法得到容量组合的最优解和几组次优解。进而,再根据负荷密度函数,得到一组满足新建变电站个数范围的初始位置坐标。在此基础上,再将初始粒子群的相关信息导入到粒子群文化算法中进化迭代,最后完成待规划区的变电站位置、容量、供电范围等规划要求。
本文将粒子群文化算法运用到实际工程项目的算例中,并在年费用、收敛速度、运算时间三方面与其他算法进行了对比分析。规划结果表明本文算法在上述的三个指标中具有优势,特别是在年费用方面。本文算法继承粒子群算法对初始解要求不高的优点,这样加强了其在变电站优化规划工作的可操作性和实用性,使得其能运用于实际的工程项目中,为待规划区提供合适的变电站规划方案。
With the rapid development of our country’s economic and the improvement of people’s living standards, we demand much more electricity and better quality. But our country’s current power grid has some shortcomings, such as lack of distribution capacity, low capacity-load ratio of substation, vulnerable of high-voltage grid, low reliability. To some certain extents, it restricts economic and other aspects of development. In recent years people are generally aware that urban distribution network planning is becoming an urgent task. Substation Locating and Sizing is a core step of the urban distribution network planning between Load Forecasting and Network Planning, whose result will affect many aspects directly such as power line routing, network structure, power network investment, operation economy level and power supply reliability.
According to the structure of cultural algorithm based on characteristic of substation optimal planning, this paper analyses the possibility of a number of intelligent algorithms as an underlying algorithm of cultural algorithm’s framework. Eventually it selects PSO, and designs the cultural source knowledge of the upper space constraints. Based on the above analysis, this paper presents a novel method for substation planning--Particle Swarm of Culture Algorithm, which can optimize the quantities, locations, sizes and power supply areas of substation. Firstly, this method obtains the number of new substations in accordance with capacity-load constraints. Secondly, it uses 0-1 type integer programming approach to obtain the optimal combination of capacity and several groups of sub-optimal solution. Thirdly, under the load density function, this algorithm gets a group of substations’location coordinates, which satisfy the number of new-to-built substations. Fourthly, it puts the relevant information of initial particle swarm into the Particle Swarm of Culture Algorithm to compute and evolve. Finally, it completes the location, capacity, supply area of substation, and other requirements of the planning area.
This paper applies Particle Swarm of Culture Algorithm to the actual project examples, it compares and analyses in the aspects of the annual costs, convergency, and the computing time with other algorithm. The results show that the planning algorithm has advantages in the above-mentioned three indicators, especially in the aspect of annual cost. This method inherits the advantages from PSO, which is not sensitive to the initial solution. This strengthens its operability and practicality in Substation optimal planning, which makes it adapt to the actual projects, provides a suitable method to planning the substation.
针对变电站优化规划的特点,本文根据文化算法的结构,分析若干智能算法作为文化算法框架中底层算法的可能性,最终选定粒子群算法,并设计文化算法上层空间的知识源约束条件。基于上面的分析,本文提出了一种用于变电站规划的新算法——粒子群文化算法,可以完成对新建变电站数量、容量、站址及供电范围等方面的优化。该方法首先根据容载比等方面的约束条件得到新建变电站的个数范围,然后采用0-1型整数规划方法得到容量组合的最优解和几组次优解。进而,再根据负荷密度函数,得到一组满足新建变电站个数范围的初始位置坐标。在此基础上,再将初始粒子群的相关信息导入到粒子群文化算法中进化迭代,最后完成待规划区的变电站位置、容量、供电范围等规划要求。
本文将粒子群文化算法运用到实际工程项目的算例中,并在年费用、收敛速度、运算时间三方面与其他算法进行了对比分析。规划结果表明本文算法在上述的三个指标中具有优势,特别是在年费用方面。本文算法继承粒子群算法对初始解要求不高的优点,这样加强了其在变电站优化规划工作的可操作性和实用性,使得其能运用于实际的工程项目中,为待规划区提供合适的变电站规划方案。
With the rapid development of our country’s economic and the improvement of people’s living standards, we demand much more electricity and better quality. But our country’s current power grid has some shortcomings, such as lack of distribution capacity, low capacity-load ratio of substation, vulnerable of high-voltage grid, low reliability. To some certain extents, it restricts economic and other aspects of development. In recent years people are generally aware that urban distribution network planning is becoming an urgent task. Substation Locating and Sizing is a core step of the urban distribution network planning between Load Forecasting and Network Planning, whose result will affect many aspects directly such as power line routing, network structure, power network investment, operation economy level and power supply reliability.
According to the structure of cultural algorithm based on characteristic of substation optimal planning, this paper analyses the possibility of a number of intelligent algorithms as an underlying algorithm of cultural algorithm’s framework. Eventually it selects PSO, and designs the cultural source knowledge of the upper space constraints. Based on the above analysis, this paper presents a novel method for substation planning--Particle Swarm of Culture Algorithm, which can optimize the quantities, locations, sizes and power supply areas of substation. Firstly, this method obtains the number of new substations in accordance with capacity-load constraints. Secondly, it uses 0-1 type integer programming approach to obtain the optimal combination of capacity and several groups of sub-optimal solution. Thirdly, under the load density function, this algorithm gets a group of substations’location coordinates, which satisfy the number of new-to-built substations. Fourthly, it puts the relevant information of initial particle swarm into the Particle Swarm of Culture Algorithm to compute and evolve. Finally, it completes the location, capacity, supply area of substation, and other requirements of the planning area.
This paper applies Particle Swarm of Culture Algorithm to the actual project examples, it compares and analyses in the aspects of the annual costs, convergency, and the computing time with other algorithm. The results show that the planning algorithm has advantages in the above-mentioned three indicators, especially in the aspect of annual cost. This method inherits the advantages from PSO, which is not sensitive to the initial solution. This strengthens its operability and practicality in Substation optimal planning, which makes it adapt to the actual projects, provides a suitable method to planning the substation.
引文
[1]天津大学电力与能量系统研究所,城市电网规划计算机辅助决策系统,天津:天津大学,1995
[2] Lakervi E,Nurmi M,Present status in applying mathematical planning methods in AM/FM/GIS system,International Conference on Electricity Distribution-CIRED Birtningham(UK),1997:2~5
[3]张秋芳,余以文,城市变电站规划,山西建筑,2005,31(13):36~37
[4] Enver Masud,An interactive procedure for sizing and timing distribution substation using optimization techniques,IEEE Trans Power Apparatus and Systems,1974,93(5):1281~1286
[5] Dale M. Crawford,Jr. Stewart B. Hort,A Mathematical Optimization Technique for Locating and Sizing Distribution Substations, and Deriving their Optimal Service Areas,IEEE Transactions on Power Apparatus and Systems,1975,230~234
[6] G. L. Thompson,D .L. Wall,A Branch and Bound Model for Choosing Optimal Substation Locations,IEEE Transactions on Power Apparatus and Systems,1981,2683~2687
[7] D. I. Sun,D. R. Farris,P. J. Cote,R. R. Shoults,M. S. Chen,Optimal Distribution Substation and Primary Feeder Planning via the Fixed Charge Network Formulation,IEEE Transactions on Power Apparatus and Systems,1982,602~608
[8] T .H. Fawzi,S. M. EI-Sobki,A New Planning Model for Distribution IEEE Transactions on Power Apparatus and Systems,1983,3010~3017
[9] M .A. El-Kady,Computer-Aided Planning of Distribution Substation and Primary Feeders,IEEE Transactions on Power Apparatus and Systems,1984,1183~1189
[10] H. Lee Willis,H .N .Tram,R.W.Powell,Substation Sitting and Capacity Selection Based on Diversity Maximization,IEEE Transactions on Power Systems,1987,692~699
[11] Sun D I,Farris D R,Cote P J etal.,Optional distribution substation and primary feeder planning via the fixed charge network formulation,IEEE Trans on Power apparatus and systems,1982,101(3):602~609
[12] El-Kady M A,Computer-aided planning of distribution substations and systems,1984,103 (6):1183~1189
[13] Aoki K,Nara K,Sutoh T etal,New approximate optimization method for distribution systems planning,IEEE trans on power systems,1990,5(1):126~132
[14]奇文斌,南志远,综合评判模型在变电站选址中的应用,现代电力,1999,16(1):40~44
[15]陈大宇,肖峻,王成山,基于模糊层次分析法的城市电网规划决策综合评判,东北电力技术,2003(9):5~9
[16]刘增良,刘淑芬,用迭代法进行35kV变电站的站址选择,电力科学与工程,2003(2):36~37
[17]杨丽徒,贾德峰,任群等,数据融合技术在变电站选址中,电力自动化设备,2003,23(8):57~59
[18]周敏,程浩忠,周中明,优化理论在城市配电网变电站选址中的应用,供用电,2003,20(2):7~ 10
[19]杨丽徒,王家耀,贾德峰等,GIS与模糊模式识别理论在变电站选址中的应用,电力系统自动化,2003,27(18)
[20]鞠平,王勇,李靖霞,配电网综合规划的遗传算法,河海大学学报,2003,31(1)
[21]高炜欣,罗先觉,朱颖,贪心算法结合Hopfield神经网络优化配电变电站规划,电网技术,2004,28(7):73~76
[22]余贻鑫,王成山,葛少云等,一个新颖实用的城市电网规划计算机辅助决策系统,供用电,1994:11~14
[23]李惠,基于加权Voronoi图的变电站优化规划:[硕士学位论文],天津;天津大学,2006
[24] Robert R G,An Introduction to Cultural Algorithms In Pro. of the 3th annual Conf on Evolution Programming,Sebalk,A.V. Foel L J,River Edge,NJ,World Scientific Publishing,1994,131~136
[25] Durham W,Co-evolution:genes,culture,and human diversity,Stanford University Press,Stanford,CA,1994
[26] Renfrew A C,Dynamic Modeling in Archaeology:What,When,and Where Dynamic Modeling and the Study of Chang in Archaeology. In:S.E. van der Leeuw,ed. Edinburgh University Press,1994
[27] Reynolds R.G,On modeling the Evolution of Hunter-Gatherer Decision-Making Systems,Geographical Analysis,1978,31~46
[28] Reynolds R.G,An adaptive computer model of the evolution of agriculture for hunter-gatherers in the valley of Oaxaca,Mexico,Doctoral dissertation,Univ. of Michigan,Ann Arbor,1979
[29] Reynolds,R.G.,An Introduction to Cultural Algorithms,Proceedings of the Third Annual Conference on Evolutionary Programming,February 24-26,1994,San Diego,California,131~139
[30] Reynolds R,Chan J,Knowledge-Based Self-Adaptation in Evolutionary Programming Using Cultural Algorithms,Proc of IEEE Int’l Conf on Evolutionary Computation,1997,71~76
[31] Reynolds R,Chan J,Fuzzy Approaches to Acquiring Experimental Knowledge in Cultural Algorithms,Proc of the 9th IEEE Int’l Conf on Tools with Artificial Intelligence,1997,260~267
[32] Reynolds R,Zhu Shinin,Knowledge-Based Function Optimization Using FuzzyCultural Algorithms with Evolutionary Programming,IEEE Trans on Systems,Man,and Cybernetics,Part B:Cybernetics,2001,31(1):1~18
[33] Reynolds R,Al-Shehri H,The Use of Cultural Algorithms with Evolutionary Programming to Guide Decision Tree Induction in Large Databases,Proc of IEEE Int’l Conf on Computational Intelligence,1998,541~546
[34] Robert G,Reynolds R,Peng B etal,Cultural Evolution of Ensemble Learning for Problem Solving,Proc of IEEE Congress on Evolutionary Computation,2006,1119~1126
[35] Jin X,Reynolds R,Data Mining Using Cultural Algorithms and Regional Schemata,Proc of t he 14th Int’l Conf on Tools wit h Artificial Intelligence,2002,33~40
[36] Jin X,Reynolds R.G,Using Knowledge-Based System with Hierarchical Architecture to Guide the Search of Evolutionary Computation,International Journal on Artificial Intelligence Tools 2000,9(1):27~44
[37]张涤,杨燕,文化算法研究发展,计算机工程与科学,2007,29(10):29~31
[38]杜琼,周一届,新的进化算法—文化算法,计算机科学,2005,32(9):141~143
[39] Fogel L J,Artificial intelligence through simulate evolution,New York:John Wiley&Sons,1999,136~146.
[40]黄海燕,顾幸生,刘漫丹,求解约束优化问题的文化算法研究,自动化学报,2007,33(10):1115~1120
[41] Saleem Saleh,Knowledge-based solution to dynamic problems using cultural algorithms,Detroit Michigan:Wayne State University,2001
[42] Dorigo M,V Maniezzo,A Colorni,The Ant System:Optimization by a Colony of Cooperating Agents,IEEE Transactions on Systems,Man and Cybernetics,1996
[43] Eberhart R,Kennedy J,A new optimizer using swarm theory,Proceedings of the Sixth International Symposium on Micro Machine and Human Science,Nagoya:IEEE,1995,39~43
[44]周驰,高海兵,高亮等,粒子群优化算法,计算机应用研究,2003,12(8):7~11
[45] Becerra R L,Coello C A,Culturizing differential evolution for constrained optimization,IEEE Service Center,Mexico,2004:304~311
[46] Fukuyama Y,Fundamentals of particle swarm techniques,Lee K Y,Ei-Sharkawi M A. Modern Heuristic Optimization Techniques With Applications to Power Systems,IEEE Power Engineering Society,2002,45～51
[47] Eberhart R C,Kennedy J,A New Optimizer Using Particle Swarm Theory,Proceedings of Sixth International Symposium Micro Machine and Human Science,Nagoya,Japan,1995
[48] Shi Y H,Eberhart R C,Parameter Selection in Particle Swarm Optimization,Annual,1998
[49]于歆杰,王赞基,应用自适应指数比例变换的适应值共享遗传算法,系统工程理论与实践,2002,22(2):24-28
[50] Shi Y,Eberhart R C,Empirical study of particle swarm optimization, Proceeding of Congress on Evolutionary Computation,Piscataway,NJ:IEEE Service Center,1999,1945～1949
[51]陶友传,唐泳洪,段正澄,0-1整数规划的枚举算法,计算机应用研究,1992,18(3):33~34
[52]程冬时,张声年,关于求解0-1型整数规划的若干问题,江西电力职业技术学院学报,2006,19(3):31~32
[53]李忠卫,冯丽娟,王希云,0-1型整数规划解法刍议,太原重型机械学院学报,2004,25(4):310~312
[54] DAI Hong-wei,Yu Yi-xin,HUANG Chun-hua etal,Optimal Planning of Distribution Substation Locations and Sizes-model and Algorithm,Electrical Power & Energy Systems,1996,18(6):353~357
[2] Lakervi E,Nurmi M,Present status in applying mathematical planning methods in AM/FM/GIS system,International Conference on Electricity Distribution-CIRED Birtningham(UK),1997:2~5
[3]张秋芳,余以文,城市变电站规划,山西建筑,2005,31(13):36~37
[4] Enver Masud,An interactive procedure for sizing and timing distribution substation using optimization techniques,IEEE Trans Power Apparatus and Systems,1974,93(5):1281~1286
[5] Dale M. Crawford,Jr. Stewart B. Hort,A Mathematical Optimization Technique for Locating and Sizing Distribution Substations, and Deriving their Optimal Service Areas,IEEE Transactions on Power Apparatus and Systems,1975,230~234
[6] G. L. Thompson,D .L. Wall,A Branch and Bound Model for Choosing Optimal Substation Locations,IEEE Transactions on Power Apparatus and Systems,1981,2683~2687
[7] D. I. Sun,D. R. Farris,P. J. Cote,R. R. Shoults,M. S. Chen,Optimal Distribution Substation and Primary Feeder Planning via the Fixed Charge Network Formulation,IEEE Transactions on Power Apparatus and Systems,1982,602~608
[8] T .H. Fawzi,S. M. EI-Sobki,A New Planning Model for Distribution IEEE Transactions on Power Apparatus and Systems,1983,3010~3017
[9] M .A. El-Kady,Computer-Aided Planning of Distribution Substation and Primary Feeders,IEEE Transactions on Power Apparatus and Systems,1984,1183~1189
[10] H. Lee Willis,H .N .Tram,R.W.Powell,Substation Sitting and Capacity Selection Based on Diversity Maximization,IEEE Transactions on Power Systems,1987,692~699
[11] Sun D I,Farris D R,Cote P J etal.,Optional distribution substation and primary feeder planning via the fixed charge network formulation,IEEE Trans on Power apparatus and systems,1982,101(3):602~609
[12] El-Kady M A,Computer-aided planning of distribution substations and systems,1984,103 (6):1183~1189
[13] Aoki K,Nara K,Sutoh T etal,New approximate optimization method for distribution systems planning,IEEE trans on power systems,1990,5(1):126~132
[14]奇文斌,南志远,综合评判模型在变电站选址中的应用,现代电力,1999,16(1):40~44
[15]陈大宇,肖峻,王成山,基于模糊层次分析法的城市电网规划决策综合评判,东北电力技术,2003(9):5~9
[16]刘增良,刘淑芬,用迭代法进行35kV变电站的站址选择,电力科学与工程,2003(2):36~37
[17]杨丽徒,贾德峰,任群等,数据融合技术在变电站选址中,电力自动化设备,2003,23(8):57~59
[18]周敏,程浩忠,周中明,优化理论在城市配电网变电站选址中的应用,供用电,2003,20(2):7~ 10
[19]杨丽徒,王家耀,贾德峰等,GIS与模糊模式识别理论在变电站选址中的应用,电力系统自动化,2003,27(18)
[20]鞠平,王勇,李靖霞,配电网综合规划的遗传算法,河海大学学报,2003,31(1)
[21]高炜欣,罗先觉,朱颖,贪心算法结合Hopfield神经网络优化配电变电站规划,电网技术,2004,28(7):73~76
[22]余贻鑫,王成山,葛少云等,一个新颖实用的城市电网规划计算机辅助决策系统,供用电,1994:11~14
[23]李惠,基于加权Voronoi图的变电站优化规划:[硕士学位论文],天津;天津大学,2006
[24] Robert R G,An Introduction to Cultural Algorithms In Pro. of the 3th annual Conf on Evolution Programming,Sebalk,A.V. Foel L J,River Edge,NJ,World Scientific Publishing,1994,131~136
[25] Durham W,Co-evolution:genes,culture,and human diversity,Stanford University Press,Stanford,CA,1994
[26] Renfrew A C,Dynamic Modeling in Archaeology:What,When,and Where Dynamic Modeling and the Study of Chang in Archaeology. In:S.E. van der Leeuw,ed. Edinburgh University Press,1994
[27] Reynolds R.G,On modeling the Evolution of Hunter-Gatherer Decision-Making Systems,Geographical Analysis,1978,31~46
[28] Reynolds R.G,An adaptive computer model of the evolution of agriculture for hunter-gatherers in the valley of Oaxaca,Mexico,Doctoral dissertation,Univ. of Michigan,Ann Arbor,1979
[29] Reynolds,R.G.,An Introduction to Cultural Algorithms,Proceedings of the Third Annual Conference on Evolutionary Programming,February 24-26,1994,San Diego,California,131~139
[30] Reynolds R,Chan J,Knowledge-Based Self-Adaptation in Evolutionary Programming Using Cultural Algorithms,Proc of IEEE Int’l Conf on Evolutionary Computation,1997,71~76
[31] Reynolds R,Chan J,Fuzzy Approaches to Acquiring Experimental Knowledge in Cultural Algorithms,Proc of the 9th IEEE Int’l Conf on Tools with Artificial Intelligence,1997,260~267
[32] Reynolds R,Zhu Shinin,Knowledge-Based Function Optimization Using FuzzyCultural Algorithms with Evolutionary Programming,IEEE Trans on Systems,Man,and Cybernetics,Part B:Cybernetics,2001,31(1):1~18
[33] Reynolds R,Al-Shehri H,The Use of Cultural Algorithms with Evolutionary Programming to Guide Decision Tree Induction in Large Databases,Proc of IEEE Int’l Conf on Computational Intelligence,1998,541~546
[34] Robert G,Reynolds R,Peng B etal,Cultural Evolution of Ensemble Learning for Problem Solving,Proc of IEEE Congress on Evolutionary Computation,2006,1119~1126
[35] Jin X,Reynolds R,Data Mining Using Cultural Algorithms and Regional Schemata,Proc of t he 14th Int’l Conf on Tools wit h Artificial Intelligence,2002,33~40
[36] Jin X,Reynolds R.G,Using Knowledge-Based System with Hierarchical Architecture to Guide the Search of Evolutionary Computation,International Journal on Artificial Intelligence Tools 2000,9(1):27~44
[37]张涤,杨燕,文化算法研究发展,计算机工程与科学,2007,29(10):29~31
[38]杜琼,周一届,新的进化算法—文化算法,计算机科学,2005,32(9):141~143
[39] Fogel L J,Artificial intelligence through simulate evolution,New York:John Wiley&Sons,1999,136~146.
[40]黄海燕,顾幸生,刘漫丹,求解约束优化问题的文化算法研究,自动化学报,2007,33(10):1115~1120
[41] Saleem Saleh,Knowledge-based solution to dynamic problems using cultural algorithms,Detroit Michigan:Wayne State University,2001
[42] Dorigo M,V Maniezzo,A Colorni,The Ant System:Optimization by a Colony of Cooperating Agents,IEEE Transactions on Systems,Man and Cybernetics,1996
[43] Eberhart R,Kennedy J,A new optimizer using swarm theory,Proceedings of the Sixth International Symposium on Micro Machine and Human Science,Nagoya:IEEE,1995,39~43
[44]周驰,高海兵,高亮等,粒子群优化算法,计算机应用研究,2003,12(8):7~11
[45] Becerra R L,Coello C A,Culturizing differential evolution for constrained optimization,IEEE Service Center,Mexico,2004:304~311
[46] Fukuyama Y,Fundamentals of particle swarm techniques,Lee K Y,Ei-Sharkawi M A. Modern Heuristic Optimization Techniques With Applications to Power Systems,IEEE Power Engineering Society,2002,45～51
[47] Eberhart R C,Kennedy J,A New Optimizer Using Particle Swarm Theory,Proceedings of Sixth International Symposium Micro Machine and Human Science,Nagoya,Japan,1995
[48] Shi Y H,Eberhart R C,Parameter Selection in Particle Swarm Optimization,Annual,1998
[49]于歆杰,王赞基,应用自适应指数比例变换的适应值共享遗传算法,系统工程理论与实践,2002,22(2):24-28
[50] Shi Y,Eberhart R C,Empirical study of particle swarm optimization, Proceeding of Congress on Evolutionary Computation,Piscataway,NJ:IEEE Service Center,1999,1945～1949
[51]陶友传,唐泳洪,段正澄,0-1整数规划的枚举算法,计算机应用研究,1992,18(3):33~34
[52]程冬时,张声年,关于求解0-1型整数规划的若干问题,江西电力职业技术学院学报,2006,19(3):31~32
[53]李忠卫,冯丽娟,王希云,0-1型整数规划解法刍议,太原重型机械学院学报,2004,25(4):310~312
[54] DAI Hong-wei,Yu Yi-xin,HUANG Chun-hua etal,Optimal Planning of Distribution Substation Locations and Sizes-model and Algorithm,Electrical Power & Energy Systems,1996,18(6):353~357