基于智能电能表的居民需求响应协同策略
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摘要
随着新能源大量接入,传统由发电跟踪负荷变化的运行模式面临挑战。需求响应是重要的调度资源,信息技术的发展提高了居民负荷的响应能力。在此背景下,负荷服务实体(LSE)通过电价机制协调用户的响应实现供需互动。首先,对家用电器设备的分类和建模得到响应电价的优化模型,建立了基于智能电能表的家庭能源管理系统(HEMS)。其次,根据负荷服务实体的供电成本函数得到电价制定模型。以电价和响应功率作为互动信息,协调不同用户的响应,进行迭代计算直到收敛,实现整体优化。考虑到用户优化会导致总成本振荡无法收敛,在优化目标函数中添加连续两次迭代间负荷变化的惩罚项。最后,通过算例仿真,分析了上述协同优化策略对LSE和用户的影响,验证了所提策略在平滑功率曲线和降低用户成本的效果。
With the massive access of renewable sources,the traditional operation method of controlling generation to deal with load fluctuation is being challenged. Demand response (DR) is an important scheduling resource,and the advancement of information technology improves the responsiveness of residential load. In this context,the load service entity (LSE) realizes the supply-demand interaction of residential loads through price mechanism. Firstly,home energy management system (HEMS) based on smart meter is established by optimizing operation of household equipment to minimize energy cost. Secondly,the pricing mechanism of the LSE is derived according to the power supply cost function. Electricity prices and response power of HEMS are used as interactive information to coordinate the responses of users and optimize the overall load. Considering that the distributed optimization of users may fail to converge,penalty for load changes between two consecutive iterations are added to the objective function. Finally,the influence of the above coordination strategy on LSE and users is analyzed through simulations,and the effectiveness on smoothing the power curve and reducing household energy cost is verified.
With the massive access of renewable sources,the traditional operation method of controlling generation to deal with load fluctuation is being challenged. Demand response (DR) is an important scheduling resource,and the advancement of information technology improves the responsiveness of residential load. In this context,the load service entity (LSE) realizes the supply-demand interaction of residential loads through price mechanism. Firstly,home energy management system (HEMS) based on smart meter is established by optimizing operation of household equipment to minimize energy cost. Secondly,the pricing mechanism of the LSE is derived according to the power supply cost function. Electricity prices and response power of HEMS are used as interactive information to coordinate the responses of users and optimize the overall load. Considering that the distributed optimization of users may fail to converge,penalty for load changes between two consecutive iterations are added to the objective function. Finally,the influence of the above coordination strategy on LSE and users is analyzed through simulations,and the effectiveness on smoothing the power curve and reducing household energy cost is verified.
引文
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[12]Safdarian A,Fotuhi-Firuzabad M,Lehtonen M.Optimal Residential Load Management in Smart Grids:A Decentralized Framework[J].IEEE Transactions on Smart Grid,2016,7(4):1836-1845.
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[14]Li Y,Ng B L,Trayer M,et al.Automated Residential Demand Response:Algorithmic Implications of Pricing Models[J].IEEE Transactions on Smart Grid,2012,3(4):1712-1721.
[15]Yang P,Chavali P,Gilboa E,et al.Parallel Load Schedule Optimization With Renewable Distributed Generators in Smart Grids[J].IEEE Transactions on Smart Grid,2013,4(3):1431-1441.
[2]U.S.Department of Energy,‘‘Benefits of demand response in electricity markets and recommendations for achieving them’’U.S.Dept.Energy,Washington,DC,Tech.Rep.,Feb.2006.
[3]Nolan S,O’Malley M.Challenges and barriers to demand response deployment and evaluation[J].Applied Energy,2015,152:1-10.
[4]Zipperer A,Aloise-Young P A,Suryanarayanan S,et al.Electric Energy Management in the Smart Home:Perspectives on Enabling Technologies and Consumer Behavior[J].Proceedings of the IEEE,2013,101(11):2397-2408.
[5]Bozchalui M C,Hashmi S A,Hassen H,et al.Optimal Operation of Residential Energy Hubs in Smart Grids[J].IEEE Transactions on Smart Grid,2012,3(4):1755-1766.
[6]周磊,李扬.分时电价环境下基于家居能量管理系统的家居负荷建模与优化运行[J].电网技术,2015,39(2):367-374.Zhou Lei,Li Yang.Modeling and optimal dispatch for residential load based on home energy management system under time-of-use pricing[J].Power System Technology,2015,39(2):367-374.
[7]张华一,文福拴,张璨,等.计及舒适度的家庭能源中心运行优化模型[J].电力系统自动化,2016,40(20):32-39.ZhangHuayi,Wen Fushuan,Zhang Can,et al.Operation Optimization Model of Home Energy Hubs Considering Comfort Level of Customers[J].Automation of Electric Power Systems,2016 40(20):32-39.
[8]Muratori M,Rizzoni G.Residential Demand Response:Dynamic Energy Management and Time-Varying Electricity Pricing[J].IEEE Transactions on Power Systems,2016,31(2):1108-1117.
[9]高赐威,李倩玉,李慧星,等.基于负荷聚合商业务的需求响应资源整合方法与运营机制[J].电力系统自动化,2013,37(17):78-86.Gao Ciwei,Li Qianyu,Li Huixing,et al.Methodology and Operation Mechanism of Demand Response Resources In-tegration Based on Load Aggregator[J].Automation of Electric Power Systems,2013,37(17):78-86.
[10]Callaway D S,Hiskens I A.Achieving Controllability of Electric Loads[J].Proceedings of the IEEE,2011,99(1):184-199.
[11]曾鸣,武赓,王昊婧,等.智能用电背景下考虑用户满意度的居民需求侧响应调控策略[J].电网技术,2016,40(10):2917-2923.Zemg Ming,Wu Geng,Wang Haojing,et al.Regulation Strategies of Demand Response Considering User Satisfaction Under Smart Power Background[J].Power System Technology,2016,40(10):2917-2923.
[12]Safdarian A,Fotuhi-Firuzabad M,Lehtonen M.Optimal Residential Load Management in Smart Grids:A Decentralized Framework[J].IEEE Transactions on Smart Grid,2016,7(4):1836-1845.
[13]Chapman A C,VerbicˇG,Hill D J.Algorithmic and Strategic Aspects to Integrating Demand-Side Aggregation and Energy Management Methods[J].IEEE Transactions on Smart Grid,2016,PP(99):1-13.
[14]Li Y,Ng B L,Trayer M,et al.Automated Residential Demand Response:Algorithmic Implications of Pricing Models[J].IEEE Transactions on Smart Grid,2012,3(4):1712-1721.
[15]Yang P,Chavali P,Gilboa E,et al.Parallel Load Schedule Optimization With Renewable Distributed Generators in Smart Grids[J].IEEE Transactions on Smart Grid,2013,4(3):1431-1441.