计及需求响应柔性调节的分布鲁棒DG优化配置
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摘要
弃电现象制约着风电、光伏的发展,采用需求响应措施提高配网对清洁能源的消纳是一种有效的措施,但传统处理分布式电源(distributed generation,DG)出力或需求响应不确定性的方法具有很大的局限性,因此,该文建立计及需求响应柔性调节的配电网两阶段分布鲁棒DG优化配置线性模型。首先,该模型以配电公司运营商年利润最大化为优化目标,考虑各种投资约束和运行约束,并将清洁能源出力和负荷的差值作为基于实时电价的需求响应的定价依据,以提高配电网对清洁能源的消纳率;其次,通过基于多面体的线性化技巧和McCormick方法,将原始混合整数非线性模型线性化,建立混合整数线性规划模型;然后,充分利用需求响应和DG出力的历史数据,构建数据驱动的两阶段分布鲁棒DG优化配置模型,其中第一阶段确定DG的投资方案,第二阶段模拟投入DG后的系统运行,并同时考虑不确定性概率分布置信集合的1-范数和∞-范数约束;最后,采用列与约束生成(columnandconstraintgeneration,CCG)算法对分布鲁棒模型进行求解,并基于IEEE33节点系统验证所提模型的有效性。
The development of wind power and photovoltaic is mainly restricted by wind and solar curtailment.Demand response(DR) is an effective measure to improve the consumption of clean energy for distribution system.Traditionally, however, it has great limitations on the method of dealing with the uncertainty associated with distributed generation(DG) output or DR. A two-stage distributionally robust DG optimal allocation linear model considering DR flexible adjustment was formulated. The annual profit of distribution system operator(DSO) considering various investment and operational constraints was maximized. In order to improve the consumption of clean energy for distribution system, the author regarded the difference between clean energy output and load as the pricing basis of DR based on real-time price(RTP). Then, through the polyhedral-based linearization and McCormick's method, the original mixed integer nonlinear programming(MINLP) model was converted to a mixed integer linear programming(MILP) model.Moreover, taking advantage of the historical data of DR and DG output, a data-driven two-stage distributionally robust DG optimal allocation linear model was presented. The first stage determined the investment plan of DG, and in the second stage,the system operation stage after investing DG, the 1-norm and∞-norm constraints of the uncertainty probability distribution confidence set were incorporated. Finally, the proposed model was simulated on the IEEE33-node system and solved by column and constraint generation(CCG) algorithm. Numerical results demonstrate the effectiveness of the proposed model.
The development of wind power and photovoltaic is mainly restricted by wind and solar curtailment.Demand response(DR) is an effective measure to improve the consumption of clean energy for distribution system.Traditionally, however, it has great limitations on the method of dealing with the uncertainty associated with distributed generation(DG) output or DR. A two-stage distributionally robust DG optimal allocation linear model considering DR flexible adjustment was formulated. The annual profit of distribution system operator(DSO) considering various investment and operational constraints was maximized. In order to improve the consumption of clean energy for distribution system, the author regarded the difference between clean energy output and load as the pricing basis of DR based on real-time price(RTP). Then, through the polyhedral-based linearization and McCormick's method, the original mixed integer nonlinear programming(MINLP) model was converted to a mixed integer linear programming(MILP) model.Moreover, taking advantage of the historical data of DR and DG output, a data-driven two-stage distributionally robust DG optimal allocation linear model was presented. The first stage determined the investment plan of DG, and in the second stage,the system operation stage after investing DG, the 1-norm and∞-norm constraints of the uncertainty probability distribution confidence set were incorporated. Finally, the proposed model was simulated on the IEEE33-node system and solved by column and constraint generation(CCG) algorithm. Numerical results demonstrate the effectiveness of the proposed model.
引文
[1]颜伟,吕冰,赵霞,等.计及需求响应影响的配网可控DG和开关协调规划[J].电网技术,2014,38(8):2220-2224.Yan Wei,LüBing,Zhao Xia,et al.Coordinated allocation of controllable distributed generation and switchers in distribution system considering demand response[J].Power System Technology,2014,38(8):2220-2224(in Chinese).
[2]张沈习,袁加妍,程浩忠,等.主动配电网中考虑需求侧管理和网络重构的分布式电源规划方法[J].中国电机工程学报,2016,36(S1):1-9.Zhang Shenxi,Yuan Jiayan,Cheng Haozhong,et al.Optimal distributed generation planning in active distribution network considering demand side management and network reconfiguration[J].Proceedings of the CSEE,2016,36(S1):1-9(in Chinese).
[3]Asensio M,de Quevedo P M D,Mu?oz-Delgado G,et al.Joint distribution network and renewable energy expansion planning considering demand response and energy storage-Part II:stochastic programming model[J].IEEE Transactions on Smart Grid,2018,9(2):667-675.
[4]李秀磊,耿光飞,季玉琦,等.主动配电网中储能和需求侧响应的联合优化规划[J].电网技术,2016,40(12):3803-3810.Li Xiulei,Geng Guangfei,Ji Yuqi,et al.Integrated optimal planning of energy storage and demand side response in active power distribution network[J].Power System Technology,2016,40(12):3803-3810(in Chinese).
[5]高红均,刘俊勇.考虑不同类型DG和负荷建模的主动配电网协同规划[J].中国电机工程学报,2016,36(18):4911-4922.Gao Hongjun,Liu Junyong.Coordinated planning considering different types of DG and load in active distribution network[J].Proceedings of the CSEE,2016,36(18):4911-4922(in Chinese).
[6]谭心怡,吕林,高红均,等.考虑DG功率非线性特性和综合负荷建模的DG和DR协同规划[J].电网技术,2018,42(9):2958-2966.Tan Xinyi,LüLin,Gao Hongjun,et al.Coordinated planning of DG and DR considering nonlinear power characteristics of DG and synthetic load model[J].Power System Technology,2018,42(9):2958-2966(in Chinese).
[7]Zeng Bo,Zhang Jianhua,Yang Xu,et al.Integrated planning for transition to low-carbon distribution system with renewable energy generation and demand response[J].IEEE Transactions on Power Systems,2014,29(3):1153-1165.
[8]Ahmed H M A,Eltantawy A B,Salama M M A.Aplanning approach for the network configuration of AC-DC hybrid distribution systems[J].IEEE Transactions on Smart Grid,2018,9(3):2203-2213.
[9]Gao Yajing,Hu Xiaobo,Yang Wenhai,et al.Multi-objective bilevel coordinated planning of distributed generation and distribution network frame based on multiscenario technique considering timing characteristics[J].IEEE Transactions on Sustainable Energy,2017,8(4):1415-1429.
[10]Arias N B,Tabares A,Franco J F,et al.Robust joint expansion planning of electrical distribution systems and EV charging stations[J].IEEE Transactions on Sustainable Energy,2018,9(2):884-894.
[11]Gao Hongjun,Liu Junyong,Liu Youbo,et al.A robust model for multistage distribution network planning[C]//2015 International Symposium on Smart Electric Distribution Systems and Technologies.Vienna,Austria:IEEE,2015:37-41.
[12]Mu?oz-Delgado G,Contreras J,Arroyo J M.Multistage generation and network expansion planning in distribution systems considering uncertainty and reliability[J].IEEETransactions on Power Systems,2016,31(5):3715-3728.
[13]温俊强,曾博,张建华.配电网中分布式风电可调鲁棒优化规划[J].电网技术,2016,40(1):227-233.Wen Junqiang,Zeng Bo,Zhang Jianhua.Adjustable robust optimization for distributed wind power planning in distribution network[J].Power System Technology,2016,40(1):227-233(in Chinese).
[14]Amjady N,Attarha A,Dehghan S,et al.Adaptive robust expansion planning for a distribution network with DERs[J].IEEE Transactions on Power Systems,2018,33(2):1698-1715.
[15]周任军,闵雄帮,童小娇,等.电力环保经济调度矩不确定分布鲁棒优化方法[J].中国电机工程学报,2015,35(13):3248-3256.Zhou Renjun,Min Xiongbang,Tong Xiaojiao,et al.Distributional robust optimization under moment uncertainty of environmental and economic dispatch for power system[J].Proceedings of the CSEE,2015,35(13):3248-3256(in Chinese).
[16]Zhao Chaoyue,Guan Yongpei.Data-driven stochastic unit commitment for integrating wind generation[J].IEEETransactions on Power Systems,2016,31(4):2587-2596.
[17]税月,刘俊勇,高红均,等.考虑风电不确定性的电气能源系统两阶段分布鲁棒协同调度[J].电力系统自动化,2018,42(13):43-50,75.Shui Yue,Liu Junyong,Gao Hongjun,et al.Two-stage distributed robust cooperative dispatch for integrated electricity and natural gas energy systems considering uncertainty of wind power[J].Automation of Electric Power Systems,2018,42(13):43-50,75(in Chinese).
[18]Bagheri A,Wang Jianhui,Zhao Chaoyue.Data-driven stochastic transmission expansion planning[J].IEEETransactions on Power Systems,2017,32(5):3461-3470.
[19]Ding Tao,Yang Qingrun,Yang Yongheng,et al.Adata-driven stochastic reactive power optimization considering uncertainties in active distribution networks and decomposition method[J].IEEE Transactions on Smart Grid,2018,9(5):4994-5004.
[20]焦薇羽.计及负荷需求响应的分布式电源并网规划[D].吉林:东北电力大学,2017.Jiao Weiyu.Demand response and load of the distributed power grid planning[D].Jilin:Northeast Electric Power University,2017(in Chinese).
[21]Owaifeer M A,Al-Muhaini M.MILP-based technique for smart self-healing grids[J].IET Generation,Transmission&Distribution,2018,12(10):2307-2316.
[22]郭清元,吴杰康,莫超,等.基于混合整数二阶锥规划的新能源配电网电压无功协同优化模型[J].中国电机工程学报,2018,38(5):1385-1396.Guo Qingyuan,Wu Jiekang,Mo Chao,et al.A model for multi-objective coordination optimization of voltage and reactive power in distribution networks based on mixed integer second-order cone programming[J].Proceedings of the CSEE,2018,38(5):1385-1396(in Chinese).
[23]Zare A,Chung C Y,Zhan Junpeng,et al.Adistributionally robust chance-constrained MILP model for multistage distribution system planning with uncertain renewables and loads[J].IEEE Transactions on Power Systems,2018,33(5):5248-5262.
[24]Doostizadeh M,Ghasemi H.A day-ahead electricity pricing model based on smart metering and demand-side management[J].Energy,2012,46(1):221-230.
[2]张沈习,袁加妍,程浩忠,等.主动配电网中考虑需求侧管理和网络重构的分布式电源规划方法[J].中国电机工程学报,2016,36(S1):1-9.Zhang Shenxi,Yuan Jiayan,Cheng Haozhong,et al.Optimal distributed generation planning in active distribution network considering demand side management and network reconfiguration[J].Proceedings of the CSEE,2016,36(S1):1-9(in Chinese).
[3]Asensio M,de Quevedo P M D,Mu?oz-Delgado G,et al.Joint distribution network and renewable energy expansion planning considering demand response and energy storage-Part II:stochastic programming model[J].IEEE Transactions on Smart Grid,2018,9(2):667-675.
[4]李秀磊,耿光飞,季玉琦,等.主动配电网中储能和需求侧响应的联合优化规划[J].电网技术,2016,40(12):3803-3810.Li Xiulei,Geng Guangfei,Ji Yuqi,et al.Integrated optimal planning of energy storage and demand side response in active power distribution network[J].Power System Technology,2016,40(12):3803-3810(in Chinese).
[5]高红均,刘俊勇.考虑不同类型DG和负荷建模的主动配电网协同规划[J].中国电机工程学报,2016,36(18):4911-4922.Gao Hongjun,Liu Junyong.Coordinated planning considering different types of DG and load in active distribution network[J].Proceedings of the CSEE,2016,36(18):4911-4922(in Chinese).
[6]谭心怡,吕林,高红均,等.考虑DG功率非线性特性和综合负荷建模的DG和DR协同规划[J].电网技术,2018,42(9):2958-2966.Tan Xinyi,LüLin,Gao Hongjun,et al.Coordinated planning of DG and DR considering nonlinear power characteristics of DG and synthetic load model[J].Power System Technology,2018,42(9):2958-2966(in Chinese).
[7]Zeng Bo,Zhang Jianhua,Yang Xu,et al.Integrated planning for transition to low-carbon distribution system with renewable energy generation and demand response[J].IEEE Transactions on Power Systems,2014,29(3):1153-1165.
[8]Ahmed H M A,Eltantawy A B,Salama M M A.Aplanning approach for the network configuration of AC-DC hybrid distribution systems[J].IEEE Transactions on Smart Grid,2018,9(3):2203-2213.
[9]Gao Yajing,Hu Xiaobo,Yang Wenhai,et al.Multi-objective bilevel coordinated planning of distributed generation and distribution network frame based on multiscenario technique considering timing characteristics[J].IEEE Transactions on Sustainable Energy,2017,8(4):1415-1429.
[10]Arias N B,Tabares A,Franco J F,et al.Robust joint expansion planning of electrical distribution systems and EV charging stations[J].IEEE Transactions on Sustainable Energy,2018,9(2):884-894.
[11]Gao Hongjun,Liu Junyong,Liu Youbo,et al.A robust model for multistage distribution network planning[C]//2015 International Symposium on Smart Electric Distribution Systems and Technologies.Vienna,Austria:IEEE,2015:37-41.
[12]Mu?oz-Delgado G,Contreras J,Arroyo J M.Multistage generation and network expansion planning in distribution systems considering uncertainty and reliability[J].IEEETransactions on Power Systems,2016,31(5):3715-3728.
[13]温俊强,曾博,张建华.配电网中分布式风电可调鲁棒优化规划[J].电网技术,2016,40(1):227-233.Wen Junqiang,Zeng Bo,Zhang Jianhua.Adjustable robust optimization for distributed wind power planning in distribution network[J].Power System Technology,2016,40(1):227-233(in Chinese).
[14]Amjady N,Attarha A,Dehghan S,et al.Adaptive robust expansion planning for a distribution network with DERs[J].IEEE Transactions on Power Systems,2018,33(2):1698-1715.
[15]周任军,闵雄帮,童小娇,等.电力环保经济调度矩不确定分布鲁棒优化方法[J].中国电机工程学报,2015,35(13):3248-3256.Zhou Renjun,Min Xiongbang,Tong Xiaojiao,et al.Distributional robust optimization under moment uncertainty of environmental and economic dispatch for power system[J].Proceedings of the CSEE,2015,35(13):3248-3256(in Chinese).
[16]Zhao Chaoyue,Guan Yongpei.Data-driven stochastic unit commitment for integrating wind generation[J].IEEETransactions on Power Systems,2016,31(4):2587-2596.
[17]税月,刘俊勇,高红均,等.考虑风电不确定性的电气能源系统两阶段分布鲁棒协同调度[J].电力系统自动化,2018,42(13):43-50,75.Shui Yue,Liu Junyong,Gao Hongjun,et al.Two-stage distributed robust cooperative dispatch for integrated electricity and natural gas energy systems considering uncertainty of wind power[J].Automation of Electric Power Systems,2018,42(13):43-50,75(in Chinese).
[18]Bagheri A,Wang Jianhui,Zhao Chaoyue.Data-driven stochastic transmission expansion planning[J].IEEETransactions on Power Systems,2017,32(5):3461-3470.
[19]Ding Tao,Yang Qingrun,Yang Yongheng,et al.Adata-driven stochastic reactive power optimization considering uncertainties in active distribution networks and decomposition method[J].IEEE Transactions on Smart Grid,2018,9(5):4994-5004.
[20]焦薇羽.计及负荷需求响应的分布式电源并网规划[D].吉林:东北电力大学,2017.Jiao Weiyu.Demand response and load of the distributed power grid planning[D].Jilin:Northeast Electric Power University,2017(in Chinese).
[21]Owaifeer M A,Al-Muhaini M.MILP-based technique for smart self-healing grids[J].IET Generation,Transmission&Distribution,2018,12(10):2307-2316.
[22]郭清元,吴杰康,莫超,等.基于混合整数二阶锥规划的新能源配电网电压无功协同优化模型[J].中国电机工程学报,2018,38(5):1385-1396.Guo Qingyuan,Wu Jiekang,Mo Chao,et al.A model for multi-objective coordination optimization of voltage and reactive power in distribution networks based on mixed integer second-order cone programming[J].Proceedings of the CSEE,2018,38(5):1385-1396(in Chinese).
[23]Zare A,Chung C Y,Zhan Junpeng,et al.Adistributionally robust chance-constrained MILP model for multistage distribution system planning with uncertain renewables and loads[J].IEEE Transactions on Power Systems,2018,33(5):5248-5262.
[24]Doostizadeh M,Ghasemi H.A day-ahead electricity pricing model based on smart metering and demand-side management[J].Energy,2012,46(1):221-230.