热电联产型家庭能源枢纽调度优化模型研究
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摘要
为改进现有能源利用方式并减少能源支付成本,本文提出了一个由电动汽车、光伏系统和储能系统等组成的热电联产型家庭能源枢纽模型。以能源支付成本最小化为目标,对所提出的模型进行优化调度。通过控制响应电器的能耗,调度电动汽车和储能系统的充放电,协调光伏出力来确定能源枢纽的最佳运行模式,并采用粒子群优化算法对模型进行求解。为证明模型的合理性和有效性,以某地区智能电网环境下冬季家庭负荷数据作为仿真算例进行验证。仿真结果表明,在电价最高的11∶00~16∶00时段,天然气全部分配给CHP用以转化为电能和热能,减少了从电网的购电量,节约了成本。该研究减少了家庭能源支付费用,为智能家庭能源消费的优化调度提供了理论依据。
In order to improve the existing energy utilization mode and reduce the energy payment cost,this paper proposes a cogeneration-type household energy hub model consisting of electric vehicles,photovoltaic systems and energy storage systems.With the goal of minimizing the cost of energy payment,the proposed home energy hub model is optimized and dispatched.By controlling the energy consumption of the responding appliances,scheduling the charging and discharging of electric vehicles and energy storage systems,and coordinating the photovoltaic output,the optimal operating mode of the energy hub is determined.And the particle swarm optimization algorithm is used to solve the model.In order to prove the rationality and effectiveness of the model,the winter household load data in a smart grid environment in a certain area is used as a simulation example for verification.The simulation results show that in the 11~16 period with the highest electricity price,all natural gas is allocated to CHP for conversion into electric energy and heat energy,which reduces the purchase of electricity from the grid and saves costs.The study reduced household energy payments.
In order to improve the existing energy utilization mode and reduce the energy payment cost,this paper proposes a cogeneration-type household energy hub model consisting of electric vehicles,photovoltaic systems and energy storage systems.With the goal of minimizing the cost of energy payment,the proposed home energy hub model is optimized and dispatched.By controlling the energy consumption of the responding appliances,scheduling the charging and discharging of electric vehicles and energy storage systems,and coordinating the photovoltaic output,the optimal operating mode of the energy hub is determined.And the particle swarm optimization algorithm is used to solve the model.In order to prove the rationality and effectiveness of the model,the winter household load data in a smart grid environment in a certain area is used as a simulation example for verification.The simulation results show that in the 11~16 period with the highest electricity price,all natural gas is allocated to CHP for conversion into electric energy and heat energy,which reduces the purchase of electricity from the grid and saves costs.The study reduced household energy payments.
引文
[1]张延宇,曾鹏,臧传治,等.智能电网环境下家庭能源管理系统研究综述[J].电力系统保护与控制,2014,42(18):144-154.
[2]徐建军,王保娥,闫丽梅,等.混合能源协同控制的智能家庭能源优化控制策略[J].电工技术学报,2017,32(12):214-223.
[3]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.
[4]林峰,宋楠,舒少龙,等.接入可再生能源家庭能耗优化策略研究[J].系统仿真技术,2016,12(1):1-5,12.
[5]苏粟,蒋小超,王玮,等.计及电动汽车和光伏-储能的微网能量优化管理[J].电力系统自动化,2015,39(9):164-171.
[6]王毅,张宁,康重庆.能源互联网中能量枢纽的优化规划与运行研究综述及展望[J].中国电机工程学报,2015,35(22):5669-5681.
[7]张钦,王锡凡,王建学,等.电力市场下需求响应研究综述[J].电力系统自动化,2008,32(3):97-107.
[8]冯霄,钱立伦.热电联产中热,电分摊比的合理确定[J].工程热物理学报,1997,18(4):409-412.
[9] Rastegar M,Fotuhi-Firuzabad M,Lehtonen M.Home Load Management in a Residential Energy Hub[J].Electric Power Systems Research,2015,119(119):322-328.
[10]陈思.智能电网中考虑电动汽车储能特性的家庭用电策略研究[D].成都:电子科技大学,2014.
[11]黄焘,马溪原,雷金勇,等.考虑分时电价和需求响应的家庭型用户侧微电网优化运行[J].南方电网技术,2015,9(4):47-53.
[12] Geidl M,Koeppel G,Favre-Perrod P,et al.Energy Hubs for the Future[J].Power&Energy Magazine IEEE,2007,5(1):24-30.
[13] Ren H,Gao W,Ruan Y.Optimal Sizing for Residential CHP System[J].Applied Thermal Engineering,2008,28(5):514-523.
[14]段培永,刘聪聪,段晨旭,等.基于粒子群优化的室内动态热舒适度控制方法[J].信息与控制,2013,42(1):100-110.
[15]盛万兴,史常凯,孙军平,等.智能用电中自动需求响应的特征及研究框架[J].电力系统自动化,2013,37(23):1-7.
[16]蔡乾乾,刘西强,段善旭,等.基于改进粒子群算法的微电网能量管理模型[J].电器与能效管理技术,2016,39(4):51-57.
[17]周任军,李绍金,李红英,等.空间粒子群优化算法及其在电力系统环保经济负荷分配中的应用[J].电力自动化设备,2014,34(9):7-12.
[18]周超,田立军.基于粒子群优化算法的电压暂降监测点优化配置[J].电工技术学报,2014,29(4):181-187.
[19]程声烽,程小华,杨露.基于改进粒子群算法的小波神经网络在变压器故障诊断中的应用[J].电力系统保护与控制,2014,42(19):37-42.
[20]陈晓高,熊保鸿.基于微网技术的家庭光伏发电及能量管理系统[J].太阳能,2014,17(12):20-22.
[2]徐建军,王保娥,闫丽梅,等.混合能源协同控制的智能家庭能源优化控制策略[J].电工技术学报,2017,32(12):214-223.
[3]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.
[4]林峰,宋楠,舒少龙,等.接入可再生能源家庭能耗优化策略研究[J].系统仿真技术,2016,12(1):1-5,12.
[5]苏粟,蒋小超,王玮,等.计及电动汽车和光伏-储能的微网能量优化管理[J].电力系统自动化,2015,39(9):164-171.
[6]王毅,张宁,康重庆.能源互联网中能量枢纽的优化规划与运行研究综述及展望[J].中国电机工程学报,2015,35(22):5669-5681.
[7]张钦,王锡凡,王建学,等.电力市场下需求响应研究综述[J].电力系统自动化,2008,32(3):97-107.
[8]冯霄,钱立伦.热电联产中热,电分摊比的合理确定[J].工程热物理学报,1997,18(4):409-412.
[9] Rastegar M,Fotuhi-Firuzabad M,Lehtonen M.Home Load Management in a Residential Energy Hub[J].Electric Power Systems Research,2015,119(119):322-328.
[10]陈思.智能电网中考虑电动汽车储能特性的家庭用电策略研究[D].成都:电子科技大学,2014.
[11]黄焘,马溪原,雷金勇,等.考虑分时电价和需求响应的家庭型用户侧微电网优化运行[J].南方电网技术,2015,9(4):47-53.
[12] Geidl M,Koeppel G,Favre-Perrod P,et al.Energy Hubs for the Future[J].Power&Energy Magazine IEEE,2007,5(1):24-30.
[13] Ren H,Gao W,Ruan Y.Optimal Sizing for Residential CHP System[J].Applied Thermal Engineering,2008,28(5):514-523.
[14]段培永,刘聪聪,段晨旭,等.基于粒子群优化的室内动态热舒适度控制方法[J].信息与控制,2013,42(1):100-110.
[15]盛万兴,史常凯,孙军平,等.智能用电中自动需求响应的特征及研究框架[J].电力系统自动化,2013,37(23):1-7.
[16]蔡乾乾,刘西强,段善旭,等.基于改进粒子群算法的微电网能量管理模型[J].电器与能效管理技术,2016,39(4):51-57.
[17]周任军,李绍金,李红英,等.空间粒子群优化算法及其在电力系统环保经济负荷分配中的应用[J].电力自动化设备,2014,34(9):7-12.
[18]周超,田立军.基于粒子群优化算法的电压暂降监测点优化配置[J].电工技术学报,2014,29(4):181-187.
[19]程声烽,程小华,杨露.基于改进粒子群算法的小波神经网络在变压器故障诊断中的应用[J].电力系统保护与控制,2014,42(19):37-42.
[20]陈晓高,熊保鸿.基于微网技术的家庭光伏发电及能量管理系统[J].太阳能,2014,17(12):20-22.