广告视角下智能小区实时定价博弈分析
|
摘要
考虑连续时间内供电商对用户的争抢行为,将广告微分博弈引入智能电网系统中以刻画供电商之间的相互竞争,并建立多供电商多用户Stackelberg博弈模型研究智能电网实时定价问题。通过求解用户侧优化问题及供电商之间非合作微分博弈纳什均衡,最终获得供电商与用户之间Stackelberg博弈均衡,以此得到供电商广告竞争下的智能电网实时电价。数值仿真结果表明:采取广告行为供电商的最大支付随其广告成本和定价先增后减,而随用户规模扩大而递增;另外,随着单位广告成本的增加,供电商统一电价逐渐降低,带来用户效用提高,且拥有较低广告成本的供电商售电较多。
Considering the competition behavior of electricity suppliers in continuous time slots, advertising differential game theory is introduced to depict the interaction among electricity suppliers. A Stackelberg game model with multiple leaders and multiple followers is formulated to study the real-time electricity pricing problem. By solving the optimal problem on user side and the non-cooperative differential Nash equilibrium among electricity suppliers, the Stackelberg game equilibrium between the suppliers and users is achieved. Finally, the real-time electricity price under the advertising competition of electricity suppliers is obtained. The results of numerical simulation show that the maximum payment of electricity suppliers taking advertising behavior increases first and then decreases with the increase of its advertising cost and price, but increases with the increase of user scale. In addition, with the increase of unit advertising cost, the uniform electricity price of electricity suppliers gradually decreases and the user's utility is improved. Moreover, the electricity suppliers with lower advertising cost sell more electricity.
Considering the competition behavior of electricity suppliers in continuous time slots, advertising differential game theory is introduced to depict the interaction among electricity suppliers. A Stackelberg game model with multiple leaders and multiple followers is formulated to study the real-time electricity pricing problem. By solving the optimal problem on user side and the non-cooperative differential Nash equilibrium among electricity suppliers, the Stackelberg game equilibrium between the suppliers and users is achieved. Finally, the real-time electricity price under the advertising competition of electricity suppliers is obtained. The results of numerical simulation show that the maximum payment of electricity suppliers taking advertising behavior increases first and then decreases with the increase of its advertising cost and price, but increases with the increase of user scale. In addition, with the increase of unit advertising cost, the uniform electricity price of electricity suppliers gradually decreases and the user's utility is improved. Moreover, the electricity suppliers with lower advertising cost sell more electricity.
引文
[1]ZHANG B,HAO Z,BO Z.New development in relay protection for smart grid[J].Protection&Control of Modern Power Systems,2016,1(1):121-127.
[2]梁甜甜,高赐威,王蓓蓓.智能电网下电力需求侧管理应用[J].电力自动化设备,2012,32(5):81-85.LIANG Tiantian,GAO Ciwei,WANG Beibei.Applications of demand side management in smart grid[J].Electric Power Automation Equipment,2012,32(5):81-85.
[3]SIANO P.Demand response and smart grids-A survey[J].Renewable&Sustainable Energy Reviews,2014,30(2):461-478.
[4]FARHANGI H.The path of the smart grid[J].IEEE Power&Energy Magazine,2009,8(1):18-28.
[5]SAMADI P,MOHSENIAN-RAD A H,SCHOBER R,et al.Optimal real-time pricing algorithm based on utility maximization for smart grid[C].IEEE International Conference on Smart Grid Communications.Oct 4-6,2010,Gaithersburg,MD,USA:IEEE,2010:415-420.
[6]SAMIMI A,NIKZAD M,MOHAMMADI M.Real-time electricity pricing of a comprehensive demand response model in smart grids[J].International Transactions on Electrical Energy Systems,2017,27(3):1-15.
[7]ZHU H,GAO Y,HOU Y,et al.Multi-time slots real-time pricing strategy with power fluctuation caused by operating continuity of smart home appliances[J].Engineering Applications of Artificial Intelligence,2018,71:166-174.
[8]SRINIVASAN D,RAJGARHIA S,RADHAKRISHNAN B M,et al.Game-Theory based dynamic pricing strategies for demand side management in smart grids[J].Energy,2017,126:132-143.
[9]刘晓峰,高丙团,罗京,等.基于非合作博弈的居民负荷分层调度模型[J].电力系统自动化,2017,41(14):54-60.LIU Xiaofeng,GAO Bingtuan,LUO Jing,et al.Non-cooperative game based hierarchical dispatch model of residential loads[J].Automation of Electric Power Systems,2017,41(14):54-60.
[10]YE M,HU G.Game design and analysis for price-based demand response:an aggregate game approach[J].IEEE Transactions on Cybernetics,2016,47(3):1-11.
[11]ZHANG Z,DENG R,YUAN T,et al.Bi-level demand response game with information sharing among consumer[J].Ifac Papersonline,2016,49(7):663-668.
[12]代业明,高岩,高红伟,等.具有电力需求预测更新的智能电网定价机制[J].电力系统自动化,2018,42(12):58-63.DAI Yeming,GAO Yan,GAO Hongwei,et al.Real-time pricing mechanism in smart grid with forecasting update of power demand[J].Automation of Electric Power Systems,2018,42(12):58-63.
[13]DAI Y,GAO Y,GAO H,et al.Real-time pricing scheme based on Stackelberg game in smart grid with multiple power suppliers[J].Neurocomputing,2017,260:149-156.
[14]高红伟.动态合作博弈[M].北京:科学出版社,2009:302-303.
[15]SRIKANTHA P,KUNDUR D.A DER attack-mitigation differential game for smart grid security analysis[J].IEEE Transactions on Smart Grid,2017,7(3):1476-1485.
[16]ARAI R,YAMAMOTO K,NISHIO T,et al.Differential gametheoretic analysis on information availability in decentralized demand-side energy management systems[J].IEICE Transactions on Communications,2014,97(9):1817-1825.
[17]FOROUZANDEHMEHR N,HAN Z,ZHENG R.Stochastic dynamic game between hydropower plant and thermal power plant in smart grid networks[J].IEEE Systems Journal,2016,10(1):88-96.
[18]ZHU Q,HAN Z,BASAR T.A differential game approach to distributed demand side management in smart grid[C]//IEEEInternational Conference on Communications.Ottawa,Canada:IEEE,2012:3345-3350.
[19]ZHU Q,BASAR T.A multi-resolution large population game framework for smart grid demand response management[C].Network Games,Control and Optimization(NetGCooP),20115th International Conference on IEEE,Paris,France:IEEE,2011:1-8.
[20]蔡希杰.基于微分博弈模型的寡头企业广告策略研究[D].上海:同济大学,2007.CAI Xijie.Study on advertising strategy of oligopoly based on differential game model[D].Shanghai:Tongji University,2007.
[21]张维迎.博弈论与信息经济学[M].上海:格致出版社,2012:107-110.
[22]代业明,高岩.基于智能电网需求侧管理的多零售商实时定价策略[J].中国电机工程学报,2014,34(25):4244-4249.DAI Yeming,GAO Yan.Real-time pricing strategy with multiretailers based on demand-side management for the smart grid[J].Proceedings of the CSEE,2014,34(25):4244-4249.
[23]DAI Y,GAO Y,GAO H,et al.A demand response approach considering retailer incentive mechanism based on Stackelberg game in smart grid with multi-retailers[J/OL].(2018-03-06).https://doi.org/10.1002/etep.2590.
[24]MAHARJAN S,ZHU Q,ZHANG Y,et al.Dependable demand response management in the smart grid:A Stackelberg game approach[J].IEEE Transactions on Smart Grid,2013,4(1):120-132.
[25]BOYD S VANDENBERGHE L.Convex optimization[M].New York,NY,USA:Cambridge University Press,2004:136-138.
[26]DOCKNER E J,J?RGENSEN S,LONG N V,et al.Differential games in economics and management science[M].Cambridge;Cambridge University Press,2000:46-58.
[2]梁甜甜,高赐威,王蓓蓓.智能电网下电力需求侧管理应用[J].电力自动化设备,2012,32(5):81-85.LIANG Tiantian,GAO Ciwei,WANG Beibei.Applications of demand side management in smart grid[J].Electric Power Automation Equipment,2012,32(5):81-85.
[3]SIANO P.Demand response and smart grids-A survey[J].Renewable&Sustainable Energy Reviews,2014,30(2):461-478.
[4]FARHANGI H.The path of the smart grid[J].IEEE Power&Energy Magazine,2009,8(1):18-28.
[5]SAMADI P,MOHSENIAN-RAD A H,SCHOBER R,et al.Optimal real-time pricing algorithm based on utility maximization for smart grid[C].IEEE International Conference on Smart Grid Communications.Oct 4-6,2010,Gaithersburg,MD,USA:IEEE,2010:415-420.
[6]SAMIMI A,NIKZAD M,MOHAMMADI M.Real-time electricity pricing of a comprehensive demand response model in smart grids[J].International Transactions on Electrical Energy Systems,2017,27(3):1-15.
[7]ZHU H,GAO Y,HOU Y,et al.Multi-time slots real-time pricing strategy with power fluctuation caused by operating continuity of smart home appliances[J].Engineering Applications of Artificial Intelligence,2018,71:166-174.
[8]SRINIVASAN D,RAJGARHIA S,RADHAKRISHNAN B M,et al.Game-Theory based dynamic pricing strategies for demand side management in smart grids[J].Energy,2017,126:132-143.
[9]刘晓峰,高丙团,罗京,等.基于非合作博弈的居民负荷分层调度模型[J].电力系统自动化,2017,41(14):54-60.LIU Xiaofeng,GAO Bingtuan,LUO Jing,et al.Non-cooperative game based hierarchical dispatch model of residential loads[J].Automation of Electric Power Systems,2017,41(14):54-60.
[10]YE M,HU G.Game design and analysis for price-based demand response:an aggregate game approach[J].IEEE Transactions on Cybernetics,2016,47(3):1-11.
[11]ZHANG Z,DENG R,YUAN T,et al.Bi-level demand response game with information sharing among consumer[J].Ifac Papersonline,2016,49(7):663-668.
[12]代业明,高岩,高红伟,等.具有电力需求预测更新的智能电网定价机制[J].电力系统自动化,2018,42(12):58-63.DAI Yeming,GAO Yan,GAO Hongwei,et al.Real-time pricing mechanism in smart grid with forecasting update of power demand[J].Automation of Electric Power Systems,2018,42(12):58-63.
[13]DAI Y,GAO Y,GAO H,et al.Real-time pricing scheme based on Stackelberg game in smart grid with multiple power suppliers[J].Neurocomputing,2017,260:149-156.
[14]高红伟.动态合作博弈[M].北京:科学出版社,2009:302-303.
[15]SRIKANTHA P,KUNDUR D.A DER attack-mitigation differential game for smart grid security analysis[J].IEEE Transactions on Smart Grid,2017,7(3):1476-1485.
[16]ARAI R,YAMAMOTO K,NISHIO T,et al.Differential gametheoretic analysis on information availability in decentralized demand-side energy management systems[J].IEICE Transactions on Communications,2014,97(9):1817-1825.
[17]FOROUZANDEHMEHR N,HAN Z,ZHENG R.Stochastic dynamic game between hydropower plant and thermal power plant in smart grid networks[J].IEEE Systems Journal,2016,10(1):88-96.
[18]ZHU Q,HAN Z,BASAR T.A differential game approach to distributed demand side management in smart grid[C]//IEEEInternational Conference on Communications.Ottawa,Canada:IEEE,2012:3345-3350.
[19]ZHU Q,BASAR T.A multi-resolution large population game framework for smart grid demand response management[C].Network Games,Control and Optimization(NetGCooP),20115th International Conference on IEEE,Paris,France:IEEE,2011:1-8.
[20]蔡希杰.基于微分博弈模型的寡头企业广告策略研究[D].上海:同济大学,2007.CAI Xijie.Study on advertising strategy of oligopoly based on differential game model[D].Shanghai:Tongji University,2007.
[21]张维迎.博弈论与信息经济学[M].上海:格致出版社,2012:107-110.
[22]代业明,高岩.基于智能电网需求侧管理的多零售商实时定价策略[J].中国电机工程学报,2014,34(25):4244-4249.DAI Yeming,GAO Yan.Real-time pricing strategy with multiretailers based on demand-side management for the smart grid[J].Proceedings of the CSEE,2014,34(25):4244-4249.
[23]DAI Y,GAO Y,GAO H,et al.A demand response approach considering retailer incentive mechanism based on Stackelberg game in smart grid with multi-retailers[J/OL].(2018-03-06).https://doi.org/10.1002/etep.2590.
[24]MAHARJAN S,ZHU Q,ZHANG Y,et al.Dependable demand response management in the smart grid:A Stackelberg game approach[J].IEEE Transactions on Smart Grid,2013,4(1):120-132.
[25]BOYD S VANDENBERGHE L.Convex optimization[M].New York,NY,USA:Cambridge University Press,2004:136-138.
[26]DOCKNER E J,J?RGENSEN S,LONG N V,et al.Differential games in economics and management science[M].Cambridge;Cambridge University Press,2000:46-58.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |