一种日前发电调度与日前分时电价联合优化模型
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摘要
为充分发挥电价对市场需求的调节作用,有效引导需求侧资源参与电网调度,提出一种日前发电调度与日前分时电价(Time-of-Use, TOU)联合优化模型。考虑分时电价对用户收益的影响,以最大化社会福利和最小化峰谷差为目标函数,通过源荷两侧的协调调度来增强分时电价的实施效果。考虑负荷响应的差异性,对各类负荷实施不同的分时电价。计及电网公司让利约束,在保障用户和电网公司利益的前提下,电网公司以让利的形式将因分时电价带来的电网缓建获益适当转嫁给用户,以实现经济效益的共享。结合NSGA-II算法和优先顺序法求解该多目标优化问题,根据最大满意度法从Pareto解集中选取最优解。通过对修改的IEEE 39节点系统算例的仿真,验证了所提模型的有效性。
In order to give full play to the regulatory role of electricity price on demand, and effectively guide demand side resources to participate in scheduling, a joint optimization model of day-ahead generation scheduling and day-ahead time-of-use(TOU) price is proposed. Considering the impact of TOU on user benefits, the objective functions are the maximum social welfare and the minimum peak-valley difference, so as to enhance the effect of TOU by the coordinated scheduling of supply side and demand side. For the load response difference to the electricity price, different TOU schemes for different loads are designed. On the premise of protecting the interests of users and the utility companies, the utility companies transfer the benefits of delayed grid expansion to users to realize benefit sharing in the form of ceding profits, so the constraint of utility company ceding profits is considered in the model. The NSGA-II algorithm and priority list are used to solve this multi-objective optimization problem. The optimal solution is selected from the Pareto solutions by using the maximum satisfaction method. The simulation results of the modified IEEE 39-buses system show that the proposed method is effective.
In order to give full play to the regulatory role of electricity price on demand, and effectively guide demand side resources to participate in scheduling, a joint optimization model of day-ahead generation scheduling and day-ahead time-of-use(TOU) price is proposed. Considering the impact of TOU on user benefits, the objective functions are the maximum social welfare and the minimum peak-valley difference, so as to enhance the effect of TOU by the coordinated scheduling of supply side and demand side. For the load response difference to the electricity price, different TOU schemes for different loads are designed. On the premise of protecting the interests of users and the utility companies, the utility companies transfer the benefits of delayed grid expansion to users to realize benefit sharing in the form of ceding profits, so the constraint of utility company ceding profits is considered in the model. The NSGA-II algorithm and priority list are used to solve this multi-objective optimization problem. The optimal solution is selected from the Pareto solutions by using the maximum satisfaction method. The simulation results of the modified IEEE 39-buses system show that the proposed method is effective.
引文
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[14]顾伟,任佳依,高君,等.含分布式电源和可调负荷的售电公司优化调度模型[J].电力系统自动化,2017,41(14):37-44.GU Wei,REN Jiayi,GAO Jun,et al.Optimal dispatching model of electricity retailers considering distributed Generator and adjustable load[J].Automation of Electric Power Systems,2017,41(14):37-44.
[15]孙建军,张世泽,曾梦迪,等.考虑分时电价的主动配电网柔性负荷多目标优化控制[J].电工技术学报,2018,33(2):401-412.SUN Jianjun,ZHANG Shize,ZENG Mengdi,et al.Multi-objective optimal control for flexible load in active distribution network considering time-of-use tariff[J].Transactions of China Electrotechnical Society,2018,33(2):401-412.
[16]HE Yuqing,CHEN Yuehui,YANG Zhiqiang,et al.Areview on the influence of intelligent power consumption technologies on the utilization rate of distribution network equipment[J].Protection and Control of Modern Power Systems,2018,3(3):183-193.DOI:10.1186/s41601-018-0092-2.
[17]艾欣,周树鹏,赵阅群.考虑风电不确定性的用户侧分时电价研究[J].电网技术,2016,40(5):1529-1535.AI Xin,ZHOU Shupeng,ZHAO Yuequn.Study on time of use pricing of user side considering wind power uncertainty[J].Power System Technology,2016,40(5):1529-1535.
[18]张子泳,仉梦林,李莎.基于多目标粒子群算法的电力系统环境经济调度研究[J].电力系统保护与控制,2017,45(10):1-10.ZHANG Ziyong,ZHANG Menglin,LI Sha.Environmental/economic power dispatch based on multi-objective particle swarm constraint optimization algorithm[J].Power System Protection and Control,2017,45(10):1-10.
[19]颜远,林舜江,刘明波.考虑备用动作约束的含风电场电力系统多目标动态优化调度[J].电网技术,2018,42(2):479-486.YAN Yuan,LIN Shunjiang,LIU Mingbo.Multi-objective optimal dynamic dispatch of power system with wind farms considering reserve action constraints[J].Power System Technology,2018,42(2):479-486.
[20]赵波,汪湘晋,张雪松,等.考虑需求侧响应及不确定性的微电网双层优化配置方法[J].电工技术学报,2018,33(14):3284-3295.ZHAO Bo,WANG Xiangjin,ZHANG Xuesong,et al.Two-layer method of microgrid optimal sizing considering demand-side response and uncertainties[J].Transactions of China Electrotechnical Society,2018,33(14):3284-3295.
[21]易文飞,张艺伟,曾博,等.多形态激励型需求侧响应协同平衡可再生能源波动的鲁棒优化配置[J].电工技术学报,2018,33(23):5541-5554.YI Wenfei,ZHANG Yiwei,ZENG Bo,et al.Robust Optimization allocation for multi-type incentive-based demand response collaboration to balance renewable energy fluctuations[J].Transactions of China Electrotechnical Society,2018,33(23):5541-5554.
[22]杨锡运,张璜,修晓青,等.基于商业园区源/储/荷协同运行的储能系统多目标优化配置[J].电网技术,2017,41(12):3996-4003.YANG Xiyun,ZHANG Huang,XIU Xiaoqing,et al.Multi-objective optimal configuration of energy storage systems based on coordinated operation of source/storage/load in commercial park[J].Power System Technology,2017,41(12):3996-4003.
[23]董蓓,毛文博,李峰,等.考虑多类型柔性负荷的日前优化调度技术[J].电力工程技术,2018,37(6):97-102.DONG Bei,MAO Wenbo,LI Feng,et al.The technique of day-ahead optimized scheduling with multi-type of flexible loads[J].Electric Power Engineering Technology,2018,37(6):97-102.
[24]杨萌,艾欣,唐亮,等.计及风险规避的售电公司平衡市场优化交易策略研究[J].电网技术,2016,40(11):3300-3308.YANG Meng,AI Xin,TANG Liang,et al.Optimal trading strategy in balancing market for electricity retailer considering risk aversion[J].Power System Technology,2016,40(11):3300-3308.
[2]杨旭英,周明,李庚银.智能电网下需求响应机理分析与建模综述[J].电网技术,2016,40(1):220-226.YANG Xuying,ZHOU Ming,LI Gengyin.Survey on demand response mechanism and modeling in smart grid[J].Power System Technology,2016,40(1):220-226.
[3]陈丽娜,张智晟,于道林.基于广义需求侧资源聚合的电力系统短期负荷预测模型[J].电力系统保护与控制,2018,46(15):45-51.CHEN Lina,ZHANG Zhisheng,YU Daolin.Short-term load forecasting model of power system based on generalized demand side resources aggregation[J].Power System Protection and Control,2018,46(15):45-51.
[4]CHEN Qixin,ZHAO Xiangyu,GAN Dahua.Activereactive scheduling of active distribution system considering interactive load and battery storage[J].Protection and Control of Modern Power Systems,2017,2(2):320-330.DOI:10.1186/s41601-017-0060-2.
[5]陈培培,包宇庆,陈刚,等.计及需求响应电量约束的日前调度策略[J].电力工程技术,2018,37(4):9-13.CHEN Peipei,BAO Yuqing,CHEN Gang,et al.Aday-ahead scheduling strategy considering power constraint of demand response[J].Electric Power Engineering Technology,2018,37(4):9-13.
[6]姚建国,杨胜春,王珂,等.智能电网“源-网-荷”互动运行控制概念及研究框架[J].电力系统自动化,2012,36(21):1-6.YAO Jianguo,YANG Shengchun,WANG Ke,et al.Concept and research framework of smart grid“source-grid-load”interactive operation and control[J].Automation of Electric Power Systems,2012,36(21):1-6.
[7]周冬旭,张明,朱红,等.新电改形势下智能配电网调度互动研究应用[J].电力工程技术,2018,37(2):89-94.ZHOU Dongxu,ZHANG Ming,ZHU Hong,et al.Research and application of intelligent distribution network dispatching interactive under the background of electric power system reformation[J].Electric Power Engineering Technology,2018,37(2):89-94.
[8]刘小聪,王蓓蓓,李扬,等.智能电网下计及用户侧互动的发电日前调度计划模型[J].中国电机工程学报,2013,33(1):30-38.LIU Xiaocong,WANG Beibei,LI Yang,et al.Day-ahead generation scheduling model considering demand side interaction under smart grid paradigm[J].Proceedings of the CSEE,2013,33(1):30-38.
[9]别朝红,胡国伟,谢海鹏,等.考虑需求响应的含风电电力系统的优化调度[J].电力系统自动化,2014,38(13):115-120.BIE Zhaohong,HU Guowei,XIE Haipeng,et al.Optimal dispatch for wind power integrated systems considering demand response[J].Automation of Electric Power Systems,2014,38(13):115-120.
[10]鞠立伟,秦超,吴鸿亮,等.计及多类型需求响应的风电消纳随机优化调度模型[J].电网技术,2015,39(7):1839-1846.JU Liwei,QIN Chao,WU Hongliang,et al.Wind power accommodation stochastic optimization model with multi-type demand response[J].Power System Technology,2015,39(7):1839-1846.
[11]唐学军,韩佶,苗世洪,等.基于需求侧响应的多类型负荷协调控制模型[J].电力系统保护与控制,2017,45(16):116-123.TANG Xuejun,HAN Ji,MIAO Shihong,et al.Coordinated control model of multi-type load based on demand response[J].Power System Protection and Control,2017,45(16):116-123.
[12]李春燕,陈骁,张鹏,等.计及风电功率预测误差的需求响应多时间尺度优化调度[J].电网技术,2018,42(2):487-494.LI Chunyan,CHEN Xiao,ZHANG Peng,et al.Multitime-scale demand response dispatch considering wind power forecast error[J].Power System Technology,2018,42(2):487-494.
[13]黄伟,李宁坤,李玟萱,等.考虑多利益主体参与的主动配电网双层联合优化调度[J].中国电机工程学报,2017,37(12):3418-3428.HUANG Wei,LI Ningkun,LI Wenxuan,et al.Bi-level joint optimization dispatch of active distribution network considering the participation of multi-stakeholder[J].Proceedings of the CSEE,2017,37(12):3418-3428.
[14]顾伟,任佳依,高君,等.含分布式电源和可调负荷的售电公司优化调度模型[J].电力系统自动化,2017,41(14):37-44.GU Wei,REN Jiayi,GAO Jun,et al.Optimal dispatching model of electricity retailers considering distributed Generator and adjustable load[J].Automation of Electric Power Systems,2017,41(14):37-44.
[15]孙建军,张世泽,曾梦迪,等.考虑分时电价的主动配电网柔性负荷多目标优化控制[J].电工技术学报,2018,33(2):401-412.SUN Jianjun,ZHANG Shize,ZENG Mengdi,et al.Multi-objective optimal control for flexible load in active distribution network considering time-of-use tariff[J].Transactions of China Electrotechnical Society,2018,33(2):401-412.
[16]HE Yuqing,CHEN Yuehui,YANG Zhiqiang,et al.Areview on the influence of intelligent power consumption technologies on the utilization rate of distribution network equipment[J].Protection and Control of Modern Power Systems,2018,3(3):183-193.DOI:10.1186/s41601-018-0092-2.
[17]艾欣,周树鹏,赵阅群.考虑风电不确定性的用户侧分时电价研究[J].电网技术,2016,40(5):1529-1535.AI Xin,ZHOU Shupeng,ZHAO Yuequn.Study on time of use pricing of user side considering wind power uncertainty[J].Power System Technology,2016,40(5):1529-1535.
[18]张子泳,仉梦林,李莎.基于多目标粒子群算法的电力系统环境经济调度研究[J].电力系统保护与控制,2017,45(10):1-10.ZHANG Ziyong,ZHANG Menglin,LI Sha.Environmental/economic power dispatch based on multi-objective particle swarm constraint optimization algorithm[J].Power System Protection and Control,2017,45(10):1-10.
[19]颜远,林舜江,刘明波.考虑备用动作约束的含风电场电力系统多目标动态优化调度[J].电网技术,2018,42(2):479-486.YAN Yuan,LIN Shunjiang,LIU Mingbo.Multi-objective optimal dynamic dispatch of power system with wind farms considering reserve action constraints[J].Power System Technology,2018,42(2):479-486.
[20]赵波,汪湘晋,张雪松,等.考虑需求侧响应及不确定性的微电网双层优化配置方法[J].电工技术学报,2018,33(14):3284-3295.ZHAO Bo,WANG Xiangjin,ZHANG Xuesong,et al.Two-layer method of microgrid optimal sizing considering demand-side response and uncertainties[J].Transactions of China Electrotechnical Society,2018,33(14):3284-3295.
[21]易文飞,张艺伟,曾博,等.多形态激励型需求侧响应协同平衡可再生能源波动的鲁棒优化配置[J].电工技术学报,2018,33(23):5541-5554.YI Wenfei,ZHANG Yiwei,ZENG Bo,et al.Robust Optimization allocation for multi-type incentive-based demand response collaboration to balance renewable energy fluctuations[J].Transactions of China Electrotechnical Society,2018,33(23):5541-5554.
[22]杨锡运,张璜,修晓青,等.基于商业园区源/储/荷协同运行的储能系统多目标优化配置[J].电网技术,2017,41(12):3996-4003.YANG Xiyun,ZHANG Huang,XIU Xiaoqing,et al.Multi-objective optimal configuration of energy storage systems based on coordinated operation of source/storage/load in commercial park[J].Power System Technology,2017,41(12):3996-4003.
[23]董蓓,毛文博,李峰,等.考虑多类型柔性负荷的日前优化调度技术[J].电力工程技术,2018,37(6):97-102.DONG Bei,MAO Wenbo,LI Feng,et al.The technique of day-ahead optimized scheduling with multi-type of flexible loads[J].Electric Power Engineering Technology,2018,37(6):97-102.
[24]杨萌,艾欣,唐亮,等.计及风险规避的售电公司平衡市场优化交易策略研究[J].电网技术,2016,40(11):3300-3308.YANG Meng,AI Xin,TANG Liang,et al.Optimal trading strategy in balancing market for electricity retailer considering risk aversion[J].Power System Technology,2016,40(11):3300-3308.