基于多代理理论的微电网分布式优化控制方法研究
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摘要
微电网作为分布式发电的有效管理形式，对于推进清洁能源的发展，增加新能源在配电网中的渗透率，提高配电网的稳定性、可靠性具有重要的意义。但是由于微电网内部分布式电源的间歇性和波动性以及微电网分布式接入配电网的方式，导致微电网本身以及微电网与配电网之间的优化控制问题难以解决。研究微电网的优化控制方法，多微电网之间的协调控制策略对于保证微电网的可靠经济运行具有重要的意义，为推进智能配电网的建设提供理论指导。
由于微电网运行模式多样、可控程度不同，优化问题的局部约束条件各异，全局运行信息难以收集等问题，造成传统的集中式优化控制方法无论是从模型构建还是计算成本方面都难以胜任含多微电网复杂系统的优化控制问题求解，而基于多代理的分布式控制方法对于求解这类分布式问题具有明显优势。因此，本文采用多代理控制理论构建了微电网混合优化控制框架结构，依据微电网的不同运行环境，从系统到个体设计了相应的优化控制算法和策略。首先针对含多微电网的配电网系统，提出了分布式约束优化算法对微电网进行分布式优化控制，解决集中控制难以求解难题，其次针对配电网中个体微电网，建立了集中式优化算法对微电网进行集中式控制，保持集中控制对小规模系统快速求解优势，最后结合微电网内不同的DG给出了各自的执行控制方法。本文的主要研究内容和创新点包括：
（1）结合多Agent理论，建立了完整的微电网混合式优化控制结构，研究基于多Agent分布式理论的分布式优化控制算法，将分布式优化控制问题视为分布式约束优化问题（DCOP），以势博弈理论为基础，提出了符合多代理（MAS）分布式理论的DCOP求解算法Weighted Regret Monitoring Distributed SimulatedAnnealing（WRM-DSAN）。算法以局部优化为基础，通过Agent之间的通信交流以及优化控制规则进行全局优化。测试结果表明所提出的分布式优化算法结构上满足分布式设计要求，并且能够保证算法几乎处处收敛到全局最优纳什均衡，较集中式有更好的复杂网络分布式求解能力。
（2）以IEEE39节点系统为基础，通过微电网优化配置，建立了含多微电网的配电网系统。按照DCOP理论将微电网优化控制模型转化成为适合分布式求解的分布式约束优化问题，设定微电网中每一个Agent的优化目标函数，同时考虑局部优化约束和全局优化约束的关系，采用所提出的势博弈分布式优化算法WRM-DSAN进行求解，考虑分析Agent环状和网状控制结构对优化控制结果的影响，证实了微电网分布式优化控制的可行性，通过结果对比表明，系统在优化后获得了更好的整体效益，且网状控制结构具有更好的故障容忍能力。
（3）针对小规模微电网的动态集中优化控制方法，提出了基于粒子群算法理论的Information Exchange Particle Swarm Optimization（IEPSO）集中式优化控制算法，建立了微电网动态集中优化控制模型，包括DG优化配置，风电以及负载的波动模型，集中优化控制的数学模型。考虑DG以及负载变化的情况下，制定微电网储能的控制策略，分析了不同储能贡献率对优化运行的影响，结果表明动态储能贡献率具有更好的协调效果。
（4）建立了包括风电，小水电和储能底层执行DG的控制模型，并按照CERTS规范建立了微电网仿真平台，对组网后微电网进行仿真，同时研究并搭建了微电网在Matlab/simulink环境下微电网的数字仿真平台，为后续微电网的研究提供实验环境。
As an effective manager for distributed generation, microgrid plays an importantrole in clean energy development, stability and reliability of future distribution network,and can also improve the penetration of renewable energy. However, DGs in microgridare characterized by fluctuation and intermittent and microgrids are distributedembedded into distribution network, which lead to the microgrid and microgrids indistribution network is difficult to control. The research of optimization control methodfor microgrid and coordination control strategy between microgrids could providevaluable references for reliable and economic operation of microgrid and developmentof smart distribution network.
Generally, different controllable level of microgrid has different operation mode,where the local constraints for optimization control are varied and sometime the globalinformation is not available. For these complex distribution networks withmulti-microgrid, the centralized optimization control method is not suitable for dealingwith the control problem because of model building and computing cost, but it issuggested that multi-agent based distributed control method should be good at solvingsuch problems. Therefore, a hybrid microgrid optimization control framework is buildin this thesis based on multi-agent control theory, and the corresponding optimizationcontrol algorithms and strategies from top to bottom structure are designed for differentoperating environment. First, a distributed constrained optimization algorithm which isused to distributed optimization control for microgrid is proposed on the top controllevel of multi-microgrid; Second, in order to maintain a rapid control advantage ofcentralized method for small system, a centralized optimization algorithm is presentedin the middle control level for single microgrid; Finally, different execution controlapproaches for different DG at the bottom control level in microgrid are build. The mainresearch contents of this thesis are shown as follows:
(1) Based on multi-agent theory, the optimization control system of microgrid isestablished first in this thesis, where the distributed control problem is regarded as adistributed constraint optimization problem (DCOP). To meet the requirement ofpotential game theory, the distributed optimization control algorithm WRM-DSAN isproposed. In this algorithm, global optimization that is based on local searching can beexecuted due to the communication between Agents and optimization control rules. Test results show that the proposed distributed optimization algorithm can satisfy therequirement of distributed design structure and the algorithm can converge to globaloptimal Nash equilibrium almost everywhere. It is proved that distributed optimizationalgorithm is more suitable for solving complex system problems in contrast withcentralized algorithms.
(2) Distribution network with microgrids is build based on the IEEE39system.According to DCOP theory, the optimization control model of microgrid is consideredas an distributed constraint optimization problem. Utility function for each Agent inmicrogrid are formulated with the local optimization constraints. And potential gamedistributed optimization algorithm, WRM-DSAN, is applied to solve this distributedconstraint optimization problems. Under the ring and mesh control structure, theoptimal control results are analyzed, and it is indicated that distributed optimizationcontrol method is viable for multi-microgrid. After optimization, it is easy to find thatthe mesh control structure has better fault tolerance ability.
(3) Firstly, a centralized optimization control algorithm IEPSO is designed fordynamic centralized control of small microgrid system. Then a dynamic centralizedoptimization control model of microgrid is established, including DG placement model,wind power and load fluctuation model and centralized optimization controlmathematical model. Considering the change of load and DG, energy storage controlstrategy of microgrid is formulated, and the effects of different DG contribution rate onthe optimal operation are analyzed. Results indicate that the dynamic DG contributionrate can provided more effective coordination control.
(4) Microgrid simulation platform is built under the standard of CERTS with windpower, small hydro and energy storage model. Control performance of microgrid issimulated under island operation mode. Finally, the digital simulation platform ofmicrogrid is built in Matlab/simulink for future research of microgrid control.
由于微电网运行模式多样、可控程度不同，优化问题的局部约束条件各异，全局运行信息难以收集等问题，造成传统的集中式优化控制方法无论是从模型构建还是计算成本方面都难以胜任含多微电网复杂系统的优化控制问题求解，而基于多代理的分布式控制方法对于求解这类分布式问题具有明显优势。因此，本文采用多代理控制理论构建了微电网混合优化控制框架结构，依据微电网的不同运行环境，从系统到个体设计了相应的优化控制算法和策略。首先针对含多微电网的配电网系统，提出了分布式约束优化算法对微电网进行分布式优化控制，解决集中控制难以求解难题，其次针对配电网中个体微电网，建立了集中式优化算法对微电网进行集中式控制，保持集中控制对小规模系统快速求解优势，最后结合微电网内不同的DG给出了各自的执行控制方法。本文的主要研究内容和创新点包括：
（1）结合多Agent理论，建立了完整的微电网混合式优化控制结构，研究基于多Agent分布式理论的分布式优化控制算法，将分布式优化控制问题视为分布式约束优化问题（DCOP），以势博弈理论为基础，提出了符合多代理（MAS）分布式理论的DCOP求解算法Weighted Regret Monitoring Distributed SimulatedAnnealing（WRM-DSAN）。算法以局部优化为基础，通过Agent之间的通信交流以及优化控制规则进行全局优化。测试结果表明所提出的分布式优化算法结构上满足分布式设计要求，并且能够保证算法几乎处处收敛到全局最优纳什均衡，较集中式有更好的复杂网络分布式求解能力。
（2）以IEEE39节点系统为基础，通过微电网优化配置，建立了含多微电网的配电网系统。按照DCOP理论将微电网优化控制模型转化成为适合分布式求解的分布式约束优化问题，设定微电网中每一个Agent的优化目标函数，同时考虑局部优化约束和全局优化约束的关系，采用所提出的势博弈分布式优化算法WRM-DSAN进行求解，考虑分析Agent环状和网状控制结构对优化控制结果的影响，证实了微电网分布式优化控制的可行性，通过结果对比表明，系统在优化后获得了更好的整体效益，且网状控制结构具有更好的故障容忍能力。
（3）针对小规模微电网的动态集中优化控制方法，提出了基于粒子群算法理论的Information Exchange Particle Swarm Optimization（IEPSO）集中式优化控制算法，建立了微电网动态集中优化控制模型，包括DG优化配置，风电以及负载的波动模型，集中优化控制的数学模型。考虑DG以及负载变化的情况下，制定微电网储能的控制策略，分析了不同储能贡献率对优化运行的影响，结果表明动态储能贡献率具有更好的协调效果。
（4）建立了包括风电，小水电和储能底层执行DG的控制模型，并按照CERTS规范建立了微电网仿真平台，对组网后微电网进行仿真，同时研究并搭建了微电网在Matlab/simulink环境下微电网的数字仿真平台，为后续微电网的研究提供实验环境。
As an effective manager for distributed generation, microgrid plays an importantrole in clean energy development, stability and reliability of future distribution network,and can also improve the penetration of renewable energy. However, DGs in microgridare characterized by fluctuation and intermittent and microgrids are distributedembedded into distribution network, which lead to the microgrid and microgrids indistribution network is difficult to control. The research of optimization control methodfor microgrid and coordination control strategy between microgrids could providevaluable references for reliable and economic operation of microgrid and developmentof smart distribution network.
Generally, different controllable level of microgrid has different operation mode,where the local constraints for optimization control are varied and sometime the globalinformation is not available. For these complex distribution networks withmulti-microgrid, the centralized optimization control method is not suitable for dealingwith the control problem because of model building and computing cost, but it issuggested that multi-agent based distributed control method should be good at solvingsuch problems. Therefore, a hybrid microgrid optimization control framework is buildin this thesis based on multi-agent control theory, and the corresponding optimizationcontrol algorithms and strategies from top to bottom structure are designed for differentoperating environment. First, a distributed constrained optimization algorithm which isused to distributed optimization control for microgrid is proposed on the top controllevel of multi-microgrid; Second, in order to maintain a rapid control advantage ofcentralized method for small system, a centralized optimization algorithm is presentedin the middle control level for single microgrid; Finally, different execution controlapproaches for different DG at the bottom control level in microgrid are build. The mainresearch contents of this thesis are shown as follows:
(1) Based on multi-agent theory, the optimization control system of microgrid isestablished first in this thesis, where the distributed control problem is regarded as adistributed constraint optimization problem (DCOP). To meet the requirement ofpotential game theory, the distributed optimization control algorithm WRM-DSAN isproposed. In this algorithm, global optimization that is based on local searching can beexecuted due to the communication between Agents and optimization control rules. Test results show that the proposed distributed optimization algorithm can satisfy therequirement of distributed design structure and the algorithm can converge to globaloptimal Nash equilibrium almost everywhere. It is proved that distributed optimizationalgorithm is more suitable for solving complex system problems in contrast withcentralized algorithms.
(2) Distribution network with microgrids is build based on the IEEE39system.According to DCOP theory, the optimization control model of microgrid is consideredas an distributed constraint optimization problem. Utility function for each Agent inmicrogrid are formulated with the local optimization constraints. And potential gamedistributed optimization algorithm, WRM-DSAN, is applied to solve this distributedconstraint optimization problems. Under the ring and mesh control structure, theoptimal control results are analyzed, and it is indicated that distributed optimizationcontrol method is viable for multi-microgrid. After optimization, it is easy to find thatthe mesh control structure has better fault tolerance ability.
(3) Firstly, a centralized optimization control algorithm IEPSO is designed fordynamic centralized control of small microgrid system. Then a dynamic centralizedoptimization control model of microgrid is established, including DG placement model,wind power and load fluctuation model and centralized optimization controlmathematical model. Considering the change of load and DG, energy storage controlstrategy of microgrid is formulated, and the effects of different DG contribution rate onthe optimal operation are analyzed. Results indicate that the dynamic DG contributionrate can provided more effective coordination control.
(4) Microgrid simulation platform is built under the standard of CERTS with windpower, small hydro and energy storage model. Control performance of microgrid issimulated under island operation mode. Finally, the digital simulation platform ofmicrogrid is built in Matlab/simulink for future research of microgrid control.
引文
[1] F. Blaabjerg, R. Teodorescu, M. Liserre, and A. V. Timbus. Overview of control and gridsynchronization for distributed power generation systems [J]. IEEE Transactions on IndustrialElectronics,2006,53(05):1398-1409.
[2] M. A. Kashem, and G. Ledwich. Improvement of power supply to rural customers throughgrid-interactive distributed generation [J]. International Journal of Global Energy Issues,2006,26(03):341-360.
[3] B. Awad, J. Wu, and N. Jenkins. Control of distributed generation [J]. Elektrotechnik undInformationstechnik,2008,125(12):409-414.
[4] N. C. Nair, and L. Zhang. SmartGrid: Future networks for New Zealand power systemsincorporating distributed generation [J]. Energy Policy,2009,37(09):3418-3427.
[5] P. Cheng, C. Chen, T. Lee, and S. Kuo. A cooperative imbalance compensation method fordistributed-generation interface converters [J]. IEEE Transactions on Industry Applications,2009,45(02):805-815.
[6] A. Zangeneh, S. Jadid, and A. Rahimi-Kian. Promotion strategy of clean technologies indistributed generation expansion planning [J]. Renewable Energy,2009,34(12):2765-2773.
[7] A. Piccolo, and P. Siano. Evaluating the impact of network investment deferral on distributedgeneration expansion [J]. IEEE Transactions on Power Systems,2009,24(03):1559-1567.
[8] D. Trebolle, T. Gomez, R. Cossent, and P. Frias. Distribution planning with reliabilityoptions for distributed generation [J]. Electric Power Systems Research,2010,80(02):222-229.
[9] Y. Wu, C. Lee, L. Liu, and S. Tsai. Study of reconfiguration for the distribution system withdistributed generators [J]. IEEE Transactions on Power Delivery,2010,25(03):1678-1685.
[10] F. Wang, J. L. Duarte, and M. A. M. Hendrix. Design and analysis of active power controlstrategies for distributed generation inverters under unbalanced grid faults [J]. IETGeneration, Transmission and Distribution,2010,04(08):905-916.
[11] R. Cossent, T. Gomez, and L. Olmos. Large-scale integration of renewable and distributedgeneration of electricity in Spain: Current situation and future needs [J]. Energy Policy,2011,39(12):8078-8087.
[12] R. H. Salim, M. Oleskovicz, and R. A. Ramos. Power quality of distributed generationsystems as affected by electromechanical oscillations-definitions and possible solutions [J].IET Generation, Transmission and Distribution,2011,5(11):1114-1123.
[13] J. N. Brass, S. Carley, L. M. Maclean, and E. Baldwin. Power for development: A review ofdistributed generation projects in the developing world [J]. Annual Review of Environmentand Resources,2012,37:107-136.
[14] F. Meng, D. Haughton, B. Chowdhury, and M. L. Crow, et al. Distributed generation andstorage optimal control with state estimation [J]. IEEE Transactions on Smart Grid,2013,4(4):2266-2273.
[15] H. Nikkhajoei, and R. H. Lasseter. Distributed generation interface to the CERTS microgrid[J]. IEEE Transactions on Power Delivery,2009,24(03):1598-1608.
[16] R. H. Lasseter, J. H. Eto, B. Schenkman, and J. Stevens, et al. CERTS microgrid laboratorytest bed [J]. IEEE Transactions on Power Delivery,2011,26(01):325-332.
[17]王成山,肖朝霞,王守相.微网综合控制与分析[J].电力系统自动化,2008,32(07):98-103.
[18] A. Chaouachi, R. M. Kamel, R. Andoulsi, and K. Nagasaka. Multiobjective intelligent energymanagement for a microgrid [J]. IEEE Transactions on Industrial Electronics,2013,60(04):1688-1699.
[19]王成山,高菲,李鹏,等.低压微网控制策略研究[J].中国电机工程学报,2012,32(25):2-9.
[20]黄晓东,郝木凯,陈柔伊,等.微网中储能系统的控制与分析[J].可再生能源,2012,30(02):23-27.
[21] R. Arghandeh, M. Pipattanasomporn, and S. Rahman. Flywheel energy storage systems forride-through applications in a facility microgrid [J]. IEEE Transactions on Smart Grid,2012,03(04):1955-1962.
[22]唐西胜,邓卫,齐智平.基于储能的微网并网/离网无缝切换技术[J].电工技术学报,2011,26(S1):279-284.
[23]陈伟,石晶,任丽,等.微网中的多元复合储能技术[J].电力系统自动化,2010,34(01):112-115.
[24] H. Dagdougui, and R. Sacile. Decentralized control of the power flows in a network of smartmicrogrids modeled as a team of cooperative agents [J]. IEEE Transactions on ControlSystems Technology,2014,22(02):510-519.
[25] C. Ahn, and H. Peng. Decentralized voltage control to minimize distribution power loss ofmicrogrids [J]. IEEE Transactions on Smart Grid,2013,04(03):1297-1304.
[26] J. M. Guerrero, M. Chandorkar, T. Lee, and P. C. Loh. Advanced control architectures forintelligent microgridspart i: Decentralized and hierarchical control [J]. IEEE Transactions onIndustrial Electronics,2013,60(04):1254-1262.
[27] S. Anand, B. G. Fernandes, and J. M. Guerrero. Distributed control to ensure proportionalload sharing and improve voltage regulation in low-voltage DC microgrids [J]. IEEETransactions on Power Electronics,2013,28(04):1900-1913.
[28] T. Logenthiran, D. Srinivasan, and A. M. Khambadkone. Multi-agent system for energyresource scheduling of integrated microgrids in a distributed system [J]. Electric PowerSystems Research,2011,81(01):138-148.
[29] F. Li, M. Wu, Y. He, and X. Chen. Optimal control in microgrid using multi-agentreinforcement learning [J]. ISA Transactions,2012,51(06):743-751.
[30] C. Yoo, I. Chung, H. Lee, and S. Hong. Intelligent control of battery energy storage formulti-agent based microgrid energy management [J]. Energies,2013,06(10):4956-4979.
[31] H. Kim, T. Kinoshita, and M. Shin. A multiagent system for autonomous operation ofislanded microgrids based on a power market environment [J]. Energies,2010,03(12):1972-1990.
[32] C. M. Colson, M. H. Nehrir, and R. W. Gunderson. Multi-agent microgrid powermanagement [C]. Proceedings of18th IFAC World Congress.2011:3678-3683.
[33] P. Basak, A. K. Saha, S. Chowdhury, and S. P. Chowdhury. Microgrid: Control techniquesand modeling [C].44th International Universities Power Engineering Conference.2009:1-5.
[34] K. De Brabandere, K. Vanthournout, J. Driesen, and G. Deconinck, et al. Control ofmicrogrids [C].2007IEEE Power Engineering Society General Meeting.2007:1-7.
[35] A. A. Zaidi, and F. Kupzog. Microgrid automation-A self-configuring approach [C].12thIEEE International Multitopic Conference.2008:565-570.
[36] X. Wang, F. Blaabjerg, Z. Chen, and J. M. Guerrero. A centralized control architecture forharmonic voltage suppression in islanded microgrids [C].37th Annual Conference of theIEEE Industrial Electronics Society.2011:3070-3075.
[37] K. T. Tan, X. Y. Peng, P. L. So, and Y. C. Chu, et al. Centralized control for paralleloperation of distributed generation inverters in microgrids [J]. IEEE Transactions on SmartGrid,2012,03(04):1977-1987.
[38] M. K. Zadeh, B. Zahedi, M. Molinas, and L. E. Norum. Centralized stabilizer for marine DCmicrogrid [C].39th Annual Conference of the IEEE Industrial Electronics Society.2013:3359-3363.
[39] M. Barnes, G. Ventakaramanan, J. Kondoh, and R. Lasseter, et al. Real-world MicroGrids-An overview [C].2007IEEE International Conference on System of Systems Engineering.2007:1-8.
[40] C. Wang, X. Li, L. Guo, and Y. Li. A seamless operation mode transition control strategy fora microgrid based on master-slave control [J]. Science China Technological Sciences,2012,55(06):1644-1654.
[41] M. Chamana, and S. B. Bayne. Modeling, control and power management of inverterinterfaced sources in a microgrid [C].201133rd International Telecommunications EnergyConference.2011:1-7.
[42] Y. Zhao, R. Cheng, X. Gong, and G. Zhao. Control strategy of microgrid based on distributedgeneration [C].2012International Conference on Frontiers of Advanced Materials andEngineering Technology.2012:1277-1280.
[43] B. Zhao, X. Zhang, and J. Chen. Integrated microgrid laboratory system [J]. IEEETransactions on Power Systems,2012,27(04):2175-2185.
[44] Z. Y. Yu, M. Lu, Z. N. Wang, and Y. G. Zhang. A droop control strategy with impedancecompensation for low voltage microgrid [C].20133rd International Conference onMachinery Electronics and Control Engineering.2014:245-248.
[45] S. W. Yang, P. Li, C. Wang, and J. M. Li. Decoupling droop control method for powerdistributionof microgrid [C].20132nd International Conference on Energy andEnvironmental Protection.2013:1354-1357.
[46] R. Majumder, B. Chaudhuri, A. Ghosh, and R. Majumder, et al. Improvement of stability andload sharing in an autonomous microgrid using supplementary droop control loop [J]. IEEETransactions on Power Systems,2010,25(02):796-808.
[47] C. Jin, M. Gao, X. Lv, and M. Chen. A seamless transfer strategy of islanded andgrid-connected mode switching for microgrid based on droop control [C].4th Annual IEEEEnergy Conversion Congress and Exposition.2012:969-973.
[48] C. M. Colson, M. H. Nehrir, and C. Wang. Ant colony optimization for microgridmulti-objective power management [C].2009IEEE/PES Power Systems Conference andExposition.2009:1-7.
[49] A. G. Tsikalakis, and N. D. Hatziargyriou. Centralized control for optimizing microgridsoperation [J]. IEEE Transactions on Energy Conversion,2008,23(01):241-248.
[50] M. Ding, Y. Y. Zhang, M. Q. Mao, and W. Yang, et al. Operation optimization for microgridsunder centralized control [C].2nd International Symposium on Power Electronics forDistributed Generation Systems.2010:984-987.
[51] F. Pilo, G. Pisano, and G. G. Soma. Neural implementation of MicroGrid central controllers
[C]. INDIN2007-5th IEEE International Conference on Industrial Informatics.2007:925-930.
[52] K. T. Tan, P. L. So, Y. C. Chu, and M. Z. Q. Chen. Coordinated Control and EnergyManagement of Distributed Generation Inverters in a Microgrid [J]. IEEE Transactions onPower Delivery,2013,28(02):704-713.
[53] A. L. Dimeas, and N. D. Hatziargyriou. A MAS architecture for microgrids control [C].13thInternational Conference on Intelligent Systems Application to Power Systems.2005:402-406.
[54] Z. Xiao, T. Li, M. Huang, and J. Shi, et al. Hierarchical MAS based control strategy formicrogrid [J]. Energies,2010,03(09):1622-1638.
[55] F. Li, M. Wu, Y. He, and X. Chen. Optimal control in microgrid using multi-agentreinforcement learning [J]. ISA Transactions,2012,51(06):743-751.
[56] C. Dou, and B. Liu. Multi-agent based hierarchical hybrid control for smart microgrid [J].IEEE Transactions on Smart Grid,2013,04(02):771-778.
[57] A. L. Dimeas, and N. D. Hatziargyriou. Operation of a multiagent system for microgridcontrol [J]. IEEE Transactions on Power Systems,2005,20(03):1447-1455.
[58] F. Y. S. Eddy, and H. B. Gooi. Multi-agent system for optimization of microgrids [C].8thInternational Conference on Power Electronics-ECCE Asia:"Green World with PowerElectronics".2011:2374-2381.
[59] K. Hirayama, and M. Yokoo. The distributed breakout algorithms [J]. Artificial Intelligence,2005,161(1-2):89-115.
[60] W. Zhang, G. Wang, Z. Xing, and L. Wittenburg. Distributed stochastic search anddistributed breakout: Properties, comparison and applications to constraint optimizationproblems in sensor networks [J]. Artificial Intelligence,2005,161(1-2):55-87.
[61] K. Krishna, K. Ganeshan, and D. J. Ram. Distributed simulated annealing algorithms for jobshop scheduling [J]. IEEE Transactions on Systems, Man and Cybernetics,1995,25(07):1102-1109.
[62] A. C. Chapman, A. Rogers, N. R. Jennings, and D. S. Leslie. A unifying framework foriterative approximate best-response algorithms for distributed constraint optimizationproblems [J]. Knowledge Engineering Review,2011,26(04):411-444.
[63] M. Wooldridge, J. P. Muller, and M. Tambe. Agent theories, architectures, and languages: Abibliography [C]. Proceedings of the1995Conference on Intelligent Agents II.1996:408.
[64] B. Wilsker. Study of Multi-Agent Collaboration Theories.[J]. citeseerx,1996:96-449.
[65] Y. Shoham. Agent Oriented Programming: An Overview of the Framework and Summary ofRecent Research.[J]. Springer Semin Immun,1994,808:123-129.
[66] M. Wooldridge. An introduction to multiagent systems [M]. John Wiley&Sons,2009.
[67] M. Pipattanasomporn, H. Feroze, and S. Rahman. Multi-agent systems in a distributed smartgrid: Design and implementation [C].2009IEEE/PES Power Systems Conference andExposition.2009:1-8.
[68] H. Zeilinger, T. Deutsch, B. Muller, and R. Lang. Bionic inspired decision making unit modelfor autonomous agents [C]. ICCC2008-IEEE6th International Conference onComputational Cybernetics.2008:259-264.
[69]黄晶.基于多Agent分布式约束优化问题求解方法研究[D].吉林大学,2008.
[70] T. Logenthiran, D. Srinivasan, and A. M. Khambadkone. Multi-agent system for energyresource scheduling of integrated microgrids in a distributed system [J]. Electric PowerSystems Research,2011,81(01):138-148.
[71] C. M. Colson, and M. H. Nehrir. Algorithms for distributed decision-making for multi-agentmicrogrid power management [C].2011IEEE Power and Energy Society General Meeting.2011:1-8.
[72] Z. Wang, R. Yang, and L. Wang. Intelligent multi-agent control for integrated building andmicro-grid systems [C].2011IEEE PES Innovative Smart Grid Technologies (ISGT).2011:1-7.
[73]颜跃进,李舟军,陈跃新.多Agent系统体系结构[J].计算机科学,2001,28(05):77-80.
[74]郭胜辉.基于多智能体的多移动机器人协调控制技术[D].河北工业大学,2006.
[75] K. Tuyls, and G. Weiss. Multiagent learning: Basics, challenges, and prospects [C].2012:41-52.
[76] J. Lygeros, D. N. Godbole, and S. Sastry. Multiagent hybrid system design using game theoryand optimal control [C]. Proceedings of the199635th IEEE Conference on Decision andControl.1996:1190-1195.
[77]王世通.基于多属性综合效用协商的协作运营冲突协调方法研究[D].河北工业大学,2009.
[78] F. M. T. Brazier, C. M. Jonker, F. J. Jungen, and J. Treur. Distributed scheduling to support acall center: A cooperative multiagent approach [J]. Applied Artificial Intelligence,1999,13(1-2):65-90.
[79] K. Hwang, S. Tan, M. Hsiao, and C. Wu. Cooperative multiagent congestion control forhigh-speed networks [J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics,2005,35(02):255-268.
[80] L. Panait, K. Sullivan, and S. Luke. Lenient learners in cooperative multiagent systems [C].Fifth International Joint Conference on Autonomous Agents and Multiagent Systems,AAMAS, May8,2006-May12,2006.2006:801-803.
[81] S. Ikenoue, M. Asada, and K. Hosoda. Cooperative behavior acquisition by asynchronouspolicy renewal that enables simultaneous learning in multiagent environment [C].2002IEEE/RSJ International Conference on Intelligent Robots and Systems.2002:2728-2734.
[82] P. J. Modi, W. Shen, M. Tambe, and M. Yokoo. Adopt: Asynchronous distributed constraintoptimization with quality guarantees [J]. Artificial Intelligence,2005,161(1-2):149-180.
[83] S. Hoseini Semnani, and K. Zamanifar. The power of ants in solving Distributed ConstraintSatisfaction Problems [J]. Applied Soft Computing Journal,2012,12(02):640-651.
[84] F. Lin, H. Kuo, and S. Lin. The enhancement of solving the distributed constraint satisfactionproblem for cooperative supply chains using multi-agent systems [J]. Decision SupportSystems,2008,45(04):795-810.
[85] M. Yokoo, E. H. Durfee, T. Ishida, and K. Kuwabara. Distributed constraint satisfactionproblem: formalization and algorithms [J]. IEEE Transactions on Knowledge and DataEngineering,1998,10(05):673-685.
[86] E. A. Sultanik, P. J. Modi, and W. C. Regli. On modeling multiagent task scheduling as adistributed constraint optimization problem [C].20th International Joint Conference onArtificial Intelligence.2007:1531-1536.
[87] K. B. Salah, and K. Ghedira. Using MAS to solve distributed constraint satisfaction andoptimization problems [C].2009International Conference on Artificial Intelligence, ICAI2009, July13,2009-July16,2009.2009:561-567.
[88] A. C. Chapman, A. Rogers, N. R. Jennings, and D. S. Leslie. A unifying framework foriterative approximate best-response algorithms for distributed constraint optimizationproblems [J]. Knowledge Engineering Review,2011,26(04):411-444.
[89] B. Faltings, and M. Yokoo. Introduction: Special issue on distributed constraint satisfaction[J]. Artificial Intelligence,2005,161(1):1-5.
[90]丁博,王怀民,史殿习,等.低约束密度分布式约束优化问题的求解算法[J].软件学报,2011,22(04):625-639.
[91] M. Yokoo, E. H. Durfee, T. Ishida, and K. Kuwabara. Distributed constraint satisfactionproblem: formalization and algorithms [J]. IEEE Transactions on Knowledge and DataEngineering,1998,10(05):673-685.
[92] T. Matsui, M. C. Silaghi, K. Hirayama, and M. Yokoo, et al. Distributed search methods forQuantified Distributed Constraint Optimization Problem [J]. Transactions of the JapaneseSociety for Artificial Intelligence,2013,28(01):43-56.
[93] E. A. Sultanik, P. J. Modi, and W. C. Regli. On modeling multiagent task scheduling as adistributed constraint optimization problem [C].20th International Joint Conference onArtificial Intelligence.2007:1531-1536.
[94] E. Bowring, M. Tambe, and M. Yokoo. Multiply-constrained distributed constraintoptimization [C]. Fifth International Joint Conference on Autonomous Agents and MultiagentSystems, AAMAS, May8,2006-May12,2006.2006:1413-1420.
[95]郭鹏,杨晓琴.博弈论与纳什均衡[J].哈尔滨师范大学自然科学学报,2006,22(04):25-28.
[96]贾文生,向淑文,杨剑锋,等.基于免疫粒子群算法的非合作博弈Nash均衡问题求解[J].计算机应用研究,2012,29(01):28-31.
[97] A. C. Chapman. Control of large distributed systems using games with pure strategy nashequilibria [D].University of Southampton,2009.
[98]路兆铭,张庚,孙勇,等.势博弈理论及在移动通信中的应用[C].2012年电力通信管理暨智能电网通信技术论坛.2013:551-555.
[99] W. H. Sandholm. Potential games with continuous player sets [J]. Journal of EconomicTheory,2001,97(01):81-108.
[100] J. Wang, Y. Xu, A. Anpalagan, and Q. Wu, et al. Optimal distributed interference avoidance:Potential game and learning [J]. European Transactions on Telecommunications,2012,23(04):317-326.
[101] R. Gao. Potential game for dynamic spectrum management in cognitive radio networks [J].Journal of Information and Computational Science,2013,10(06):1805-1817.
[102] N. Hayashi, T. Ushio, and T. Kanazawa. Potential game theoretic approach to power-awaremobile sensor coverage problem [J]. IEICE Transactions on Fundamentals of Electronics,Communications and Computer Sciences,2011, E94-A (03):929-936.
[103] T. Heikkinen. A potential game approach to distributed power control and scheduling [J].Computer Networks,2006,50(13):2295-2311.
[104] R. W. Rosenthal. A class of games possessing pure-strategy Nash equilibria [J]. InternationalJournal of Game Theory,1973,02(01):65-67.
[105] D. Monderer, and L. S. Shapley. Fictitious play property for games with identical interests [J].journal of economic theory,1996,68(01):258-265.
[106]杨光,蔚承建,王开,等.图形着色问题的分布式势博弈算法[J].计算机工程,2012,38(23):181-184.
[107] P. J. Modi, W. Shen, M. Tambe, and M. Yokoo. ADOPT: Asynchronous distributedconstraint optimization with quality guarantees [J]. Artificial Intelligence,2005,161(01):149-180.
[108] C. Lei, J. Selzer, and Y. Yang. Region-tree based stereo using dynamic programmingoptimization [C].2006IEEE Computer Society Conference on Computer Vision and PatternRecognition.2006:2378-2385.
[109] R. Mailler, and V. R. Lesser. Asynchronous Partial Overlay: A New Algorithm for SolvingDistributed Constraint Satisfaction Problems.[J]. J. Artif. Intell. Res.(JAIR),2006,25:529-576.
[110] S. Hart, and A. Mas Colell. A simple adaptive procedure leading to correlated equilibrium [J].Econometrica,2000,68(05):1127-1150.
[111] G. Arslan, J. R. Marden, and J. S. Shamma. Autonomous vehicle-target assignment: Agame-theoretical formulation [J]. Journal of Dynamic Systems, Measurement, and Control,2007,129(05):584-596.
[112] W. Zhang, G. Wang, Z. Xing, and L. Wittenburg. Distributed stochastic search anddistributed breakout: properties, comparison and applications to constraint optimizationproblems in sensor networks [J]. Artificial Intelligence,2005,161(01):55-87.
[113] J. W. Friedman, and C. Mezzetti. Learning in games by random sampling [J]. Journal ofEconomic Theory,2001,98(01):55-84.
[114] S. Singh, T. Jaakkola, M. L. Littman, and C. Szepesvári. Convergence results for single-stepon-policy reinforcement-learning algorithms [J]. Machine Learning,2000,38(03):287-308.
[115] J. R. Marden, G. Arslan, and J. S. Shamma. Regret based dynamics: convergence in weaklyacyclic games [C]. Proceedings of the6th international joint conference on Autonomousagents and multiagent systems.2007:42.
[116] R. D. McKelvey, and T. R. Palfrey. Quantal response equilibria for normal form games [J].Games and economic behavior,1995,10(01):6-38.
[117] M. Arshad, and M. C. Silaghi. Distributed simulated annealing [J]. Distributed ConstraintProblem Solving and Reasoning in Multi-Agent Systems,2004,112:1-13.
[118] P. La Mura, and M. R. Pearson. Simulated annealing of game equilibria: A simple adaptiveprocedure leading to Nash equilibrium [C]. International Workshop on the Logic and Strategyof Distributed Agents.2002:
[119] C. J. Bélisle. Convergence theorems for a class of simulated annealing algorithms on R [J].Journal of Applied Probability,1992,29:885-895.
[120]廖飞雄,马良.图着色问题的启发式搜索蚂蚁算法[J].计算机工程,2007,33(16):191-192.
[121]冯珊珊.基于图着色理论的聚类研究[D].太原理工大学,2013.
[122] R. T. Maheswaran, M. Tambe, E. Bowring, and J. P. Pearce, et al. Taking DCOP to the realworld: Efficient complete solutions for distributed multi-event scheduling [C]. Proceedings ofthe Third International Joint Conference on Autonomous Agents and MultiagentSystems-Volume1.2004:310-317.
[123]王西平.分布式算法在大规模图形着色中的应用[J].电子技术与软件工程,2014,(28):126.
[124] Y. Chen, and C. Shen. A Jacobian-free Newton-GMRES (m) method with adaptivepreconditioner and its application for power flow calculations [J]. Power Systems, IEEETransactions on,2006,21(03):1096-1103.
[125]史伟,赵政,薛桂香.基于智能Agent的复合网络演化模型[J].计算机应用,2008,28(11):2771-2773.
[126] Q. Li, M. Chen, M. Perc, and A. Iqbal, et al. Effects of adaptive degrees of trust oncoevolution of quantum strategies on scale-free networks [J]. Scientific reports,2013,03:1-7.
[127] Q. Li, A. Iqbal, M. Perc, and M. Chen, et al. Coevolution of quantum and classical strategieson evolving random networks [J]. PloS one,2013,08(07):1-10.
[128] Z. Xu, X. Hou, and J. Sun. Ant algorithm-based task scheduling in grid computing [C].2003.IEEE Electrical and Computer Engineering.2003:1107-1110.
[129] I. Y. Chung, W. Liu, D. A. Cartes, and S. I. Moon. Control parameter optimization formultiple distributed generators in a microgrid using particle swarm optimization [J].European Transactions on Electrical Power,2011,21(02):1200-1216.
[130] B. N. Alajmi, K. H. Ahmed, S. J. Finney, and B. W. Williams. Fuzzy-logic-control approachof a modified hill-climbing method for maximum power point in microgrid standalonephotovoltaic system [J]. Power Electronics, IEEE Transactions on,2011,26(04):1022-1030.
[131] M. A. Hassan, and M. A. Abido. Optimal design of microgrids in autonomous andgrid-connected modes using particle swarm optimization [J]. Power Electronics, IEEETransactions on,2011,26(03):755-769.
[132] J. Kim, J. Jeon, S. Kim, and C. Cho, et al. Cooperative control strategy of energy storagesystem and microsources for stabilizing the microgrid during islanded operation [J]. PowerElectronics, IEEE Transactions on,2010,25(12):3037-3048.
[133] F. Katiraei, and M. R. Iravani. Power management strategies for a microgrid with multipledistributed generation units [J]. Power Systems, IEEE Transactions on,2006,21(04):1821-1831.
[134] E. Alvarez, A. M. Campos, P. Arboleya, and A. J. Gutiérrez. Microgrid management with aquick response optimization algorithm for active power dispatch [J]. International Journal ofElectrical Power&Energy Systems,2012,43(01):465-473.
[135] S. N. Omkar, D. Mudigere, G. N. Naik, and S. Gopalakrishnan. Vector evaluated particleswarm optimization (VEPSO) for multi-objective design optimization of composite structures[J]. Computers&structures,2008,86(01):1-14.
[136] M. Greeff, and A. P. Engelbrecht. Solving dynamic multi-objective problems with vectorevaluated particle swarm optimisation [C].2008IEEE Congress on EvolutionaryComputation.2008:2917-2924.
[137] J. G. Vlachogiannis, and K. Y. Lee. Multi-objective based on parallel vector evaluatedparticle swarm optimization for optimal steady-state performance of power systems [J].Expert Systems with applications,2009,36(08):10802-10808.
[138] K. E. Parsopoulos, D. K. Tasoulis, and M. N. Vrahatis. Multiobjective optimization usingparallel vector evaluated particle swarm optimization [C]. Proceedings of the IASTEDinternational conference on artificial intelligence and applications (AIA2004).2004:823-828.
[139] D. Gies, and Y. Rahmat-Samii. Vector evaluated particle swarm optimization (VEPSO):optimization of a radiometer array antenna [C].2004IEEE Antennas and Propagation SocietyInternational Symposium.2004:2297-2300.
[140] S. Rao, and G. Jagadeesh. Vector evaluated particle swarm optimization (VEPSO) ofsupersonic ejector for hydrogen fuel cells [J]. Journal of Fuel Cell Science and Technology,2010,07(04):1004-1014.
[141] A. Genc, M. Erisoglu, A. Pekgor, and G. Oturanc, et al. Estimation of wind power potentialusing Weibull distribution [J]. Energy Sources,2005,27(09):809-822.
[142] D. Weisser. A wind energy analysis of Grenada: an estimation using the ‘Weibull’densityfunction [J]. Renewable Energy,2003,28(11):1803-1812.
[143] P. Piagi. Microgrid control [D].University of Wisconsin--Madison,2005.
[144] R. D. Zhao, Y. J. Wang, and J. S. Zhang. Maximum power point tracking control of the windenergy generation system with direct-driven permanent magnet synchronous generators [J].Proceedings of the CSEE,2009,29(27):106-111.
[145] J. Yao, Y. Liao, X. QU, and R. Liu. Optimal wind-energy tracking control of direct-drivenpermanent magnet synchronous generators for wind turbines [J]. Power System Technology,2008,32(10):11-15.
[146]张纯.微网双模式运行的控制策略研究[D].重庆大学,2011.
[147] E. De Jaeger, N. Janssens, B. Malfliet, and F. Van De Meulebroeke. Hydro turbine model forsystem dynamic studies [J]. IEEE Transactions on Power Systems,1994,09(04):1709-1715.
[148] R. Majumder, A. Ghosh, G. Ledwich, and F. Zare. Operation and control of hybrid microgridwith angle droop controller [C]. TENCON2010-2010IEEE Region10Conference.2010:509-515.
[2] M. A. Kashem, and G. Ledwich. Improvement of power supply to rural customers throughgrid-interactive distributed generation [J]. International Journal of Global Energy Issues,2006,26(03):341-360.
[3] B. Awad, J. Wu, and N. Jenkins. Control of distributed generation [J]. Elektrotechnik undInformationstechnik,2008,125(12):409-414.
[4] N. C. Nair, and L. Zhang. SmartGrid: Future networks for New Zealand power systemsincorporating distributed generation [J]. Energy Policy,2009,37(09):3418-3427.
[5] P. Cheng, C. Chen, T. Lee, and S. Kuo. A cooperative imbalance compensation method fordistributed-generation interface converters [J]. IEEE Transactions on Industry Applications,2009,45(02):805-815.
[6] A. Zangeneh, S. Jadid, and A. Rahimi-Kian. Promotion strategy of clean technologies indistributed generation expansion planning [J]. Renewable Energy,2009,34(12):2765-2773.
[7] A. Piccolo, and P. Siano. Evaluating the impact of network investment deferral on distributedgeneration expansion [J]. IEEE Transactions on Power Systems,2009,24(03):1559-1567.
[8] D. Trebolle, T. Gomez, R. Cossent, and P. Frias. Distribution planning with reliabilityoptions for distributed generation [J]. Electric Power Systems Research,2010,80(02):222-229.
[9] Y. Wu, C. Lee, L. Liu, and S. Tsai. Study of reconfiguration for the distribution system withdistributed generators [J]. IEEE Transactions on Power Delivery,2010,25(03):1678-1685.
[10] F. Wang, J. L. Duarte, and M. A. M. Hendrix. Design and analysis of active power controlstrategies for distributed generation inverters under unbalanced grid faults [J]. IETGeneration, Transmission and Distribution,2010,04(08):905-916.
[11] R. Cossent, T. Gomez, and L. Olmos. Large-scale integration of renewable and distributedgeneration of electricity in Spain: Current situation and future needs [J]. Energy Policy,2011,39(12):8078-8087.
[12] R. H. Salim, M. Oleskovicz, and R. A. Ramos. Power quality of distributed generationsystems as affected by electromechanical oscillations-definitions and possible solutions [J].IET Generation, Transmission and Distribution,2011,5(11):1114-1123.
[13] J. N. Brass, S. Carley, L. M. Maclean, and E. Baldwin. Power for development: A review ofdistributed generation projects in the developing world [J]. Annual Review of Environmentand Resources,2012,37:107-136.
[14] F. Meng, D. Haughton, B. Chowdhury, and M. L. Crow, et al. Distributed generation andstorage optimal control with state estimation [J]. IEEE Transactions on Smart Grid,2013,4(4):2266-2273.
[15] H. Nikkhajoei, and R. H. Lasseter. Distributed generation interface to the CERTS microgrid[J]. IEEE Transactions on Power Delivery,2009,24(03):1598-1608.
[16] R. H. Lasseter, J. H. Eto, B. Schenkman, and J. Stevens, et al. CERTS microgrid laboratorytest bed [J]. IEEE Transactions on Power Delivery,2011,26(01):325-332.
[17]王成山,肖朝霞,王守相.微网综合控制与分析[J].电力系统自动化,2008,32(07):98-103.
[18] A. Chaouachi, R. M. Kamel, R. Andoulsi, and K. Nagasaka. Multiobjective intelligent energymanagement for a microgrid [J]. IEEE Transactions on Industrial Electronics,2013,60(04):1688-1699.
[19]王成山,高菲,李鹏,等.低压微网控制策略研究[J].中国电机工程学报,2012,32(25):2-9.
[20]黄晓东,郝木凯,陈柔伊,等.微网中储能系统的控制与分析[J].可再生能源,2012,30(02):23-27.
[21] R. Arghandeh, M. Pipattanasomporn, and S. Rahman. Flywheel energy storage systems forride-through applications in a facility microgrid [J]. IEEE Transactions on Smart Grid,2012,03(04):1955-1962.
[22]唐西胜,邓卫,齐智平.基于储能的微网并网/离网无缝切换技术[J].电工技术学报,2011,26(S1):279-284.
[23]陈伟,石晶,任丽,等.微网中的多元复合储能技术[J].电力系统自动化,2010,34(01):112-115.
[24] H. Dagdougui, and R. Sacile. Decentralized control of the power flows in a network of smartmicrogrids modeled as a team of cooperative agents [J]. IEEE Transactions on ControlSystems Technology,2014,22(02):510-519.
[25] C. Ahn, and H. Peng. Decentralized voltage control to minimize distribution power loss ofmicrogrids [J]. IEEE Transactions on Smart Grid,2013,04(03):1297-1304.
[26] J. M. Guerrero, M. Chandorkar, T. Lee, and P. C. Loh. Advanced control architectures forintelligent microgridspart i: Decentralized and hierarchical control [J]. IEEE Transactions onIndustrial Electronics,2013,60(04):1254-1262.
[27] S. Anand, B. G. Fernandes, and J. M. Guerrero. Distributed control to ensure proportionalload sharing and improve voltage regulation in low-voltage DC microgrids [J]. IEEETransactions on Power Electronics,2013,28(04):1900-1913.
[28] T. Logenthiran, D. Srinivasan, and A. M. Khambadkone. Multi-agent system for energyresource scheduling of integrated microgrids in a distributed system [J]. Electric PowerSystems Research,2011,81(01):138-148.
[29] F. Li, M. Wu, Y. He, and X. Chen. Optimal control in microgrid using multi-agentreinforcement learning [J]. ISA Transactions,2012,51(06):743-751.
[30] C. Yoo, I. Chung, H. Lee, and S. Hong. Intelligent control of battery energy storage formulti-agent based microgrid energy management [J]. Energies,2013,06(10):4956-4979.
[31] H. Kim, T. Kinoshita, and M. Shin. A multiagent system for autonomous operation ofislanded microgrids based on a power market environment [J]. Energies,2010,03(12):1972-1990.
[32] C. M. Colson, M. H. Nehrir, and R. W. Gunderson. Multi-agent microgrid powermanagement [C]. Proceedings of18th IFAC World Congress.2011:3678-3683.
[33] P. Basak, A. K. Saha, S. Chowdhury, and S. P. Chowdhury. Microgrid: Control techniquesand modeling [C].44th International Universities Power Engineering Conference.2009:1-5.
[34] K. De Brabandere, K. Vanthournout, J. Driesen, and G. Deconinck, et al. Control ofmicrogrids [C].2007IEEE Power Engineering Society General Meeting.2007:1-7.
[35] A. A. Zaidi, and F. Kupzog. Microgrid automation-A self-configuring approach [C].12thIEEE International Multitopic Conference.2008:565-570.
[36] X. Wang, F. Blaabjerg, Z. Chen, and J. M. Guerrero. A centralized control architecture forharmonic voltage suppression in islanded microgrids [C].37th Annual Conference of theIEEE Industrial Electronics Society.2011:3070-3075.
[37] K. T. Tan, X. Y. Peng, P. L. So, and Y. C. Chu, et al. Centralized control for paralleloperation of distributed generation inverters in microgrids [J]. IEEE Transactions on SmartGrid,2012,03(04):1977-1987.
[38] M. K. Zadeh, B. Zahedi, M. Molinas, and L. E. Norum. Centralized stabilizer for marine DCmicrogrid [C].39th Annual Conference of the IEEE Industrial Electronics Society.2013:3359-3363.
[39] M. Barnes, G. Ventakaramanan, J. Kondoh, and R. Lasseter, et al. Real-world MicroGrids-An overview [C].2007IEEE International Conference on System of Systems Engineering.2007:1-8.
[40] C. Wang, X. Li, L. Guo, and Y. Li. A seamless operation mode transition control strategy fora microgrid based on master-slave control [J]. Science China Technological Sciences,2012,55(06):1644-1654.
[41] M. Chamana, and S. B. Bayne. Modeling, control and power management of inverterinterfaced sources in a microgrid [C].201133rd International Telecommunications EnergyConference.2011:1-7.
[42] Y. Zhao, R. Cheng, X. Gong, and G. Zhao. Control strategy of microgrid based on distributedgeneration [C].2012International Conference on Frontiers of Advanced Materials andEngineering Technology.2012:1277-1280.
[43] B. Zhao, X. Zhang, and J. Chen. Integrated microgrid laboratory system [J]. IEEETransactions on Power Systems,2012,27(04):2175-2185.
[44] Z. Y. Yu, M. Lu, Z. N. Wang, and Y. G. Zhang. A droop control strategy with impedancecompensation for low voltage microgrid [C].20133rd International Conference onMachinery Electronics and Control Engineering.2014:245-248.
[45] S. W. Yang, P. Li, C. Wang, and J. M. Li. Decoupling droop control method for powerdistributionof microgrid [C].20132nd International Conference on Energy andEnvironmental Protection.2013:1354-1357.
[46] R. Majumder, B. Chaudhuri, A. Ghosh, and R. Majumder, et al. Improvement of stability andload sharing in an autonomous microgrid using supplementary droop control loop [J]. IEEETransactions on Power Systems,2010,25(02):796-808.
[47] C. Jin, M. Gao, X. Lv, and M. Chen. A seamless transfer strategy of islanded andgrid-connected mode switching for microgrid based on droop control [C].4th Annual IEEEEnergy Conversion Congress and Exposition.2012:969-973.
[48] C. M. Colson, M. H. Nehrir, and C. Wang. Ant colony optimization for microgridmulti-objective power management [C].2009IEEE/PES Power Systems Conference andExposition.2009:1-7.
[49] A. G. Tsikalakis, and N. D. Hatziargyriou. Centralized control for optimizing microgridsoperation [J]. IEEE Transactions on Energy Conversion,2008,23(01):241-248.
[50] M. Ding, Y. Y. Zhang, M. Q. Mao, and W. Yang, et al. Operation optimization for microgridsunder centralized control [C].2nd International Symposium on Power Electronics forDistributed Generation Systems.2010:984-987.
[51] F. Pilo, G. Pisano, and G. G. Soma. Neural implementation of MicroGrid central controllers
[C]. INDIN2007-5th IEEE International Conference on Industrial Informatics.2007:925-930.
[52] K. T. Tan, P. L. So, Y. C. Chu, and M. Z. Q. Chen. Coordinated Control and EnergyManagement of Distributed Generation Inverters in a Microgrid [J]. IEEE Transactions onPower Delivery,2013,28(02):704-713.
[53] A. L. Dimeas, and N. D. Hatziargyriou. A MAS architecture for microgrids control [C].13thInternational Conference on Intelligent Systems Application to Power Systems.2005:402-406.
[54] Z. Xiao, T. Li, M. Huang, and J. Shi, et al. Hierarchical MAS based control strategy formicrogrid [J]. Energies,2010,03(09):1622-1638.
[55] F. Li, M. Wu, Y. He, and X. Chen. Optimal control in microgrid using multi-agentreinforcement learning [J]. ISA Transactions,2012,51(06):743-751.
[56] C. Dou, and B. Liu. Multi-agent based hierarchical hybrid control for smart microgrid [J].IEEE Transactions on Smart Grid,2013,04(02):771-778.
[57] A. L. Dimeas, and N. D. Hatziargyriou. Operation of a multiagent system for microgridcontrol [J]. IEEE Transactions on Power Systems,2005,20(03):1447-1455.
[58] F. Y. S. Eddy, and H. B. Gooi. Multi-agent system for optimization of microgrids [C].8thInternational Conference on Power Electronics-ECCE Asia:"Green World with PowerElectronics".2011:2374-2381.
[59] K. Hirayama, and M. Yokoo. The distributed breakout algorithms [J]. Artificial Intelligence,2005,161(1-2):89-115.
[60] W. Zhang, G. Wang, Z. Xing, and L. Wittenburg. Distributed stochastic search anddistributed breakout: Properties, comparison and applications to constraint optimizationproblems in sensor networks [J]. Artificial Intelligence,2005,161(1-2):55-87.
[61] K. Krishna, K. Ganeshan, and D. J. Ram. Distributed simulated annealing algorithms for jobshop scheduling [J]. IEEE Transactions on Systems, Man and Cybernetics,1995,25(07):1102-1109.
[62] A. C. Chapman, A. Rogers, N. R. Jennings, and D. S. Leslie. A unifying framework foriterative approximate best-response algorithms for distributed constraint optimizationproblems [J]. Knowledge Engineering Review,2011,26(04):411-444.
[63] M. Wooldridge, J. P. Muller, and M. Tambe. Agent theories, architectures, and languages: Abibliography [C]. Proceedings of the1995Conference on Intelligent Agents II.1996:408.
[64] B. Wilsker. Study of Multi-Agent Collaboration Theories.[J]. citeseerx,1996:96-449.
[65] Y. Shoham. Agent Oriented Programming: An Overview of the Framework and Summary ofRecent Research.[J]. Springer Semin Immun,1994,808:123-129.
[66] M. Wooldridge. An introduction to multiagent systems [M]. John Wiley&Sons,2009.
[67] M. Pipattanasomporn, H. Feroze, and S. Rahman. Multi-agent systems in a distributed smartgrid: Design and implementation [C].2009IEEE/PES Power Systems Conference andExposition.2009:1-8.
[68] H. Zeilinger, T. Deutsch, B. Muller, and R. Lang. Bionic inspired decision making unit modelfor autonomous agents [C]. ICCC2008-IEEE6th International Conference onComputational Cybernetics.2008:259-264.
[69]黄晶.基于多Agent分布式约束优化问题求解方法研究[D].吉林大学,2008.
[70] T. Logenthiran, D. Srinivasan, and A. M. Khambadkone. Multi-agent system for energyresource scheduling of integrated microgrids in a distributed system [J]. Electric PowerSystems Research,2011,81(01):138-148.
[71] C. M. Colson, and M. H. Nehrir. Algorithms for distributed decision-making for multi-agentmicrogrid power management [C].2011IEEE Power and Energy Society General Meeting.2011:1-8.
[72] Z. Wang, R. Yang, and L. Wang. Intelligent multi-agent control for integrated building andmicro-grid systems [C].2011IEEE PES Innovative Smart Grid Technologies (ISGT).2011:1-7.
[73]颜跃进,李舟军,陈跃新.多Agent系统体系结构[J].计算机科学,2001,28(05):77-80.
[74]郭胜辉.基于多智能体的多移动机器人协调控制技术[D].河北工业大学,2006.
[75] K. Tuyls, and G. Weiss. Multiagent learning: Basics, challenges, and prospects [C].2012:41-52.
[76] J. Lygeros, D. N. Godbole, and S. Sastry. Multiagent hybrid system design using game theoryand optimal control [C]. Proceedings of the199635th IEEE Conference on Decision andControl.1996:1190-1195.
[77]王世通.基于多属性综合效用协商的协作运营冲突协调方法研究[D].河北工业大学,2009.
[78] F. M. T. Brazier, C. M. Jonker, F. J. Jungen, and J. Treur. Distributed scheduling to support acall center: A cooperative multiagent approach [J]. Applied Artificial Intelligence,1999,13(1-2):65-90.
[79] K. Hwang, S. Tan, M. Hsiao, and C. Wu. Cooperative multiagent congestion control forhigh-speed networks [J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics,2005,35(02):255-268.
[80] L. Panait, K. Sullivan, and S. Luke. Lenient learners in cooperative multiagent systems [C].Fifth International Joint Conference on Autonomous Agents and Multiagent Systems,AAMAS, May8,2006-May12,2006.2006:801-803.
[81] S. Ikenoue, M. Asada, and K. Hosoda. Cooperative behavior acquisition by asynchronouspolicy renewal that enables simultaneous learning in multiagent environment [C].2002IEEE/RSJ International Conference on Intelligent Robots and Systems.2002:2728-2734.
[82] P. J. Modi, W. Shen, M. Tambe, and M. Yokoo. Adopt: Asynchronous distributed constraintoptimization with quality guarantees [J]. Artificial Intelligence,2005,161(1-2):149-180.
[83] S. Hoseini Semnani, and K. Zamanifar. The power of ants in solving Distributed ConstraintSatisfaction Problems [J]. Applied Soft Computing Journal,2012,12(02):640-651.
[84] F. Lin, H. Kuo, and S. Lin. The enhancement of solving the distributed constraint satisfactionproblem for cooperative supply chains using multi-agent systems [J]. Decision SupportSystems,2008,45(04):795-810.
[85] M. Yokoo, E. H. Durfee, T. Ishida, and K. Kuwabara. Distributed constraint satisfactionproblem: formalization and algorithms [J]. IEEE Transactions on Knowledge and DataEngineering,1998,10(05):673-685.
[86] E. A. Sultanik, P. J. Modi, and W. C. Regli. On modeling multiagent task scheduling as adistributed constraint optimization problem [C].20th International Joint Conference onArtificial Intelligence.2007:1531-1536.
[87] K. B. Salah, and K. Ghedira. Using MAS to solve distributed constraint satisfaction andoptimization problems [C].2009International Conference on Artificial Intelligence, ICAI2009, July13,2009-July16,2009.2009:561-567.
[88] A. C. Chapman, A. Rogers, N. R. Jennings, and D. S. Leslie. A unifying framework foriterative approximate best-response algorithms for distributed constraint optimizationproblems [J]. Knowledge Engineering Review,2011,26(04):411-444.
[89] B. Faltings, and M. Yokoo. Introduction: Special issue on distributed constraint satisfaction[J]. Artificial Intelligence,2005,161(1):1-5.
[90]丁博,王怀民,史殿习,等.低约束密度分布式约束优化问题的求解算法[J].软件学报,2011,22(04):625-639.
[91] M. Yokoo, E. H. Durfee, T. Ishida, and K. Kuwabara. Distributed constraint satisfactionproblem: formalization and algorithms [J]. IEEE Transactions on Knowledge and DataEngineering,1998,10(05):673-685.
[92] T. Matsui, M. C. Silaghi, K. Hirayama, and M. Yokoo, et al. Distributed search methods forQuantified Distributed Constraint Optimization Problem [J]. Transactions of the JapaneseSociety for Artificial Intelligence,2013,28(01):43-56.
[93] E. A. Sultanik, P. J. Modi, and W. C. Regli. On modeling multiagent task scheduling as adistributed constraint optimization problem [C].20th International Joint Conference onArtificial Intelligence.2007:1531-1536.
[94] E. Bowring, M. Tambe, and M. Yokoo. Multiply-constrained distributed constraintoptimization [C]. Fifth International Joint Conference on Autonomous Agents and MultiagentSystems, AAMAS, May8,2006-May12,2006.2006:1413-1420.
[95]郭鹏,杨晓琴.博弈论与纳什均衡[J].哈尔滨师范大学自然科学学报,2006,22(04):25-28.
[96]贾文生,向淑文,杨剑锋,等.基于免疫粒子群算法的非合作博弈Nash均衡问题求解[J].计算机应用研究,2012,29(01):28-31.
[97] A. C. Chapman. Control of large distributed systems using games with pure strategy nashequilibria [D].University of Southampton,2009.
[98]路兆铭,张庚,孙勇,等.势博弈理论及在移动通信中的应用[C].2012年电力通信管理暨智能电网通信技术论坛.2013:551-555.
[99] W. H. Sandholm. Potential games with continuous player sets [J]. Journal of EconomicTheory,2001,97(01):81-108.
[100] J. Wang, Y. Xu, A. Anpalagan, and Q. Wu, et al. Optimal distributed interference avoidance:Potential game and learning [J]. European Transactions on Telecommunications,2012,23(04):317-326.
[101] R. Gao. Potential game for dynamic spectrum management in cognitive radio networks [J].Journal of Information and Computational Science,2013,10(06):1805-1817.
[102] N. Hayashi, T. Ushio, and T. Kanazawa. Potential game theoretic approach to power-awaremobile sensor coverage problem [J]. IEICE Transactions on Fundamentals of Electronics,Communications and Computer Sciences,2011, E94-A (03):929-936.
[103] T. Heikkinen. A potential game approach to distributed power control and scheduling [J].Computer Networks,2006,50(13):2295-2311.
[104] R. W. Rosenthal. A class of games possessing pure-strategy Nash equilibria [J]. InternationalJournal of Game Theory,1973,02(01):65-67.
[105] D. Monderer, and L. S. Shapley. Fictitious play property for games with identical interests [J].journal of economic theory,1996,68(01):258-265.
[106]杨光,蔚承建,王开,等.图形着色问题的分布式势博弈算法[J].计算机工程,2012,38(23):181-184.
[107] P. J. Modi, W. Shen, M. Tambe, and M. Yokoo. ADOPT: Asynchronous distributedconstraint optimization with quality guarantees [J]. Artificial Intelligence,2005,161(01):149-180.
[108] C. Lei, J. Selzer, and Y. Yang. Region-tree based stereo using dynamic programmingoptimization [C].2006IEEE Computer Society Conference on Computer Vision and PatternRecognition.2006:2378-2385.
[109] R. Mailler, and V. R. Lesser. Asynchronous Partial Overlay: A New Algorithm for SolvingDistributed Constraint Satisfaction Problems.[J]. J. Artif. Intell. Res.(JAIR),2006,25:529-576.
[110] S. Hart, and A. Mas Colell. A simple adaptive procedure leading to correlated equilibrium [J].Econometrica,2000,68(05):1127-1150.
[111] G. Arslan, J. R. Marden, and J. S. Shamma. Autonomous vehicle-target assignment: Agame-theoretical formulation [J]. Journal of Dynamic Systems, Measurement, and Control,2007,129(05):584-596.
[112] W. Zhang, G. Wang, Z. Xing, and L. Wittenburg. Distributed stochastic search anddistributed breakout: properties, comparison and applications to constraint optimizationproblems in sensor networks [J]. Artificial Intelligence,2005,161(01):55-87.
[113] J. W. Friedman, and C. Mezzetti. Learning in games by random sampling [J]. Journal ofEconomic Theory,2001,98(01):55-84.
[114] S. Singh, T. Jaakkola, M. L. Littman, and C. Szepesvári. Convergence results for single-stepon-policy reinforcement-learning algorithms [J]. Machine Learning,2000,38(03):287-308.
[115] J. R. Marden, G. Arslan, and J. S. Shamma. Regret based dynamics: convergence in weaklyacyclic games [C]. Proceedings of the6th international joint conference on Autonomousagents and multiagent systems.2007:42.
[116] R. D. McKelvey, and T. R. Palfrey. Quantal response equilibria for normal form games [J].Games and economic behavior,1995,10(01):6-38.
[117] M. Arshad, and M. C. Silaghi. Distributed simulated annealing [J]. Distributed ConstraintProblem Solving and Reasoning in Multi-Agent Systems,2004,112:1-13.
[118] P. La Mura, and M. R. Pearson. Simulated annealing of game equilibria: A simple adaptiveprocedure leading to Nash equilibrium [C]. International Workshop on the Logic and Strategyof Distributed Agents.2002:
[119] C. J. Bélisle. Convergence theorems for a class of simulated annealing algorithms on R [J].Journal of Applied Probability,1992,29:885-895.
[120]廖飞雄,马良.图着色问题的启发式搜索蚂蚁算法[J].计算机工程,2007,33(16):191-192.
[121]冯珊珊.基于图着色理论的聚类研究[D].太原理工大学,2013.
[122] R. T. Maheswaran, M. Tambe, E. Bowring, and J. P. Pearce, et al. Taking DCOP to the realworld: Efficient complete solutions for distributed multi-event scheduling [C]. Proceedings ofthe Third International Joint Conference on Autonomous Agents and MultiagentSystems-Volume1.2004:310-317.
[123]王西平.分布式算法在大规模图形着色中的应用[J].电子技术与软件工程,2014,(28):126.
[124] Y. Chen, and C. Shen. A Jacobian-free Newton-GMRES (m) method with adaptivepreconditioner and its application for power flow calculations [J]. Power Systems, IEEETransactions on,2006,21(03):1096-1103.
[125]史伟,赵政,薛桂香.基于智能Agent的复合网络演化模型[J].计算机应用,2008,28(11):2771-2773.
[126] Q. Li, M. Chen, M. Perc, and A. Iqbal, et al. Effects of adaptive degrees of trust oncoevolution of quantum strategies on scale-free networks [J]. Scientific reports,2013,03:1-7.
[127] Q. Li, A. Iqbal, M. Perc, and M. Chen, et al. Coevolution of quantum and classical strategieson evolving random networks [J]. PloS one,2013,08(07):1-10.
[128] Z. Xu, X. Hou, and J. Sun. Ant algorithm-based task scheduling in grid computing [C].2003.IEEE Electrical and Computer Engineering.2003:1107-1110.
[129] I. Y. Chung, W. Liu, D. A. Cartes, and S. I. Moon. Control parameter optimization formultiple distributed generators in a microgrid using particle swarm optimization [J].European Transactions on Electrical Power,2011,21(02):1200-1216.
[130] B. N. Alajmi, K. H. Ahmed, S. J. Finney, and B. W. Williams. Fuzzy-logic-control approachof a modified hill-climbing method for maximum power point in microgrid standalonephotovoltaic system [J]. Power Electronics, IEEE Transactions on,2011,26(04):1022-1030.
[131] M. A. Hassan, and M. A. Abido. Optimal design of microgrids in autonomous andgrid-connected modes using particle swarm optimization [J]. Power Electronics, IEEETransactions on,2011,26(03):755-769.
[132] J. Kim, J. Jeon, S. Kim, and C. Cho, et al. Cooperative control strategy of energy storagesystem and microsources for stabilizing the microgrid during islanded operation [J]. PowerElectronics, IEEE Transactions on,2010,25(12):3037-3048.
[133] F. Katiraei, and M. R. Iravani. Power management strategies for a microgrid with multipledistributed generation units [J]. Power Systems, IEEE Transactions on,2006,21(04):1821-1831.
[134] E. Alvarez, A. M. Campos, P. Arboleya, and A. J. Gutiérrez. Microgrid management with aquick response optimization algorithm for active power dispatch [J]. International Journal ofElectrical Power&Energy Systems,2012,43(01):465-473.
[135] S. N. Omkar, D. Mudigere, G. N. Naik, and S. Gopalakrishnan. Vector evaluated particleswarm optimization (VEPSO) for multi-objective design optimization of composite structures[J]. Computers&structures,2008,86(01):1-14.
[136] M. Greeff, and A. P. Engelbrecht. Solving dynamic multi-objective problems with vectorevaluated particle swarm optimisation [C].2008IEEE Congress on EvolutionaryComputation.2008:2917-2924.
[137] J. G. Vlachogiannis, and K. Y. Lee. Multi-objective based on parallel vector evaluatedparticle swarm optimization for optimal steady-state performance of power systems [J].Expert Systems with applications,2009,36(08):10802-10808.
[138] K. E. Parsopoulos, D. K. Tasoulis, and M. N. Vrahatis. Multiobjective optimization usingparallel vector evaluated particle swarm optimization [C]. Proceedings of the IASTEDinternational conference on artificial intelligence and applications (AIA2004).2004:823-828.
[139] D. Gies, and Y. Rahmat-Samii. Vector evaluated particle swarm optimization (VEPSO):optimization of a radiometer array antenna [C].2004IEEE Antennas and Propagation SocietyInternational Symposium.2004:2297-2300.
[140] S. Rao, and G. Jagadeesh. Vector evaluated particle swarm optimization (VEPSO) ofsupersonic ejector for hydrogen fuel cells [J]. Journal of Fuel Cell Science and Technology,2010,07(04):1004-1014.
[141] A. Genc, M. Erisoglu, A. Pekgor, and G. Oturanc, et al. Estimation of wind power potentialusing Weibull distribution [J]. Energy Sources,2005,27(09):809-822.
[142] D. Weisser. A wind energy analysis of Grenada: an estimation using the ‘Weibull’densityfunction [J]. Renewable Energy,2003,28(11):1803-1812.
[143] P. Piagi. Microgrid control [D].University of Wisconsin--Madison,2005.
[144] R. D. Zhao, Y. J. Wang, and J. S. Zhang. Maximum power point tracking control of the windenergy generation system with direct-driven permanent magnet synchronous generators [J].Proceedings of the CSEE,2009,29(27):106-111.
[145] J. Yao, Y. Liao, X. QU, and R. Liu. Optimal wind-energy tracking control of direct-drivenpermanent magnet synchronous generators for wind turbines [J]. Power System Technology,2008,32(10):11-15.
[146]张纯.微网双模式运行的控制策略研究[D].重庆大学,2011.
[147] E. De Jaeger, N. Janssens, B. Malfliet, and F. Van De Meulebroeke. Hydro turbine model forsystem dynamic studies [J]. IEEE Transactions on Power Systems,1994,09(04):1709-1715.
[148] R. Majumder, A. Ghosh, G. Ledwich, and F. Zare. Operation and control of hybrid microgridwith angle droop controller [C]. TENCON2010-2010IEEE Region10Conference.2010:509-515.
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