含冰蓄冷空调的冷热电联供型微网多时间尺度优化调度
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摘要
冷热电联供型微网(CCHP-MG)对实现能源可持续发展和构建绿色低碳社会具有重要的应用价值,而内部复杂的能源结构与设备耦合关系、可再生能源的消纳和负荷波动的平抑给其优化运行带来了挑战。文中提出含冰蓄冷空调的CCHP-MG多时间尺度优化调度模型,研究冰蓄冷空调的不同运行方式对优化调度的影响。日前计划中通过多场景描述可再生能源的不确定性,侧重于一个运行优化周期内CCHP-MG的经济运行;日内调度基于日前计划方案,根据冷热电在不同时间尺度上的相关性和互补性,提出考虑冷热负荷变化的双层滚动优化平抑模型,求解各联供设备的调整出力。仿真结果表明:冰蓄冷空调的运行方式关系到CCHP-MG的综合效益的提高;多时间尺度优化调度模型不仅可以满足用户的冷、热、电能的需求,还能有效平抑日内阶段供需侧随机性波动,实现CCHP-MG经济及稳定运行。行带来了挑战。文中提出含冰蓄冷空调的CCHP-MG多时间尺度优化调度模型,研究冰蓄冷空调的不同运行方式对优化调度的影响。日前计划中通过多场景描述可再生能源的不确定性,侧重于一个运行优化周期内CCHP-MG的经济运行;日内调度基于日前计划方案,根据冷热电在不同时间尺度上的相关性和互补性,提出考虑冷热负荷变化的双层滚动优化平抑模型,求解各联供设备的调整出力。仿真结果表明:冰蓄冷空调的运行方式关系到CCHP-MG的综合效益的提高;多时间尺度优化调度模型不仅可以满足用户的冷、热、电能的需求,还能有效平抑日内阶段供需侧随机性波动,实现CCHP-MG经济及稳定运行。
Combined cooling, heating and power microgrid(CCHP-MG) has important application value for realizing sustainable energy development and building a low-carbon society. However, the complex energy structure and the coupling relationship between the equipment inside the systems, the renewable energy consumption and the smoothing of load fluctuations bring challenges to the optimal operation of CCHP-MG. This paper proposes a multi-time-scale optimal scheduling model of CCHP-MG with ice-storage air-conditioning, and studies the effects of different operation modes of the ice-storage air-conditioning system on the optimal scheduling. In the day-ahead scheduling, the uncertainty of renewable energy is represented by multi-scenarios, and the economic operation of the CCHP-MG during an optimization cycle is emphasized. Based on the day-ahead scheduling, a two-layer rolling optimization model is proposed in the intraday scheduling for smoothing the load fluctuation according to the coherency and complementarity of the cooling, heating and electricity load at different time scales. And the output power of each equipment can be identified. The simulation results show that the operation mode of ice-storage air-conditioning system is related to the improvement of comprehensive benefits of CCHP-MG, and the multi-time-scale optimal scheduling model can not only meet the requirements of users for cold, heat and electricity, but also can effectively smooth the random fluctuation on both supply and demand sides, and ensure economic and stable operation of CCHP-MG.
Combined cooling, heating and power microgrid(CCHP-MG) has important application value for realizing sustainable energy development and building a low-carbon society. However, the complex energy structure and the coupling relationship between the equipment inside the systems, the renewable energy consumption and the smoothing of load fluctuations bring challenges to the optimal operation of CCHP-MG. This paper proposes a multi-time-scale optimal scheduling model of CCHP-MG with ice-storage air-conditioning, and studies the effects of different operation modes of the ice-storage air-conditioning system on the optimal scheduling. In the day-ahead scheduling, the uncertainty of renewable energy is represented by multi-scenarios, and the economic operation of the CCHP-MG during an optimization cycle is emphasized. Based on the day-ahead scheduling, a two-layer rolling optimization model is proposed in the intraday scheduling for smoothing the load fluctuation according to the coherency and complementarity of the cooling, heating and electricity load at different time scales. And the output power of each equipment can be identified. The simulation results show that the operation mode of ice-storage air-conditioning system is related to the improvement of comprehensive benefits of CCHP-MG, and the multi-time-scale optimal scheduling model can not only meet the requirements of users for cold, heat and electricity, but also can effectively smooth the random fluctuation on both supply and demand sides, and ensure economic and stable operation of CCHP-MG.
引文
[1] 孙宏斌,潘昭光,郭庆来.多能流能量管理研究:挑战与展望[J].电力系统自动化,2016,40(15):1-8.SUN Hongbin, PAN Zhaoguang, GUO Qinglai. Energy management for multi-energy flow: challenges and prospects[J]. Automation of Electric Power Systems, 2016, 40(15): 1-8.
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[3] 蒋润花,曾蓉,李洪强,等.考虑气候条件及建筑类型等因素的分布式冷热电三联产系统的多目标优化及评估[J].中国电机工程学报,2016,36(12):3206-3214.JIANG Runhua, ZENG Rong, LI Hongqiang, et al. Multi-objective optimization and evaluation of distributed CCHP system considering influence of climate condition and building type[J]. Proceedings of the CSEE, 2016, 36(12): 3206-3214.
[4] 管霖,陈鹏,唐宗顺,等.考虑冷热电存储的区域综合能源站优化设计方法[J].电网技术,2016,40(10):2934-2943.GUAN Lin, CHEN Peng, TANG Zongshun, et al. Integrated energy station design considering cold and heat storage[J]. Power System Technology, 2016, 40(10): 2934-2943.
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[6] 刘星月,吴红斌.太阳能综合利用的冷热电联供系统控制策略和运行优化[J].电力系统自动化,2015,39(12):1-6.LIU Xingyue, WU Hongbin. A control strategy and operation optimization of combined cooling heating and power system considering solar comprehensive utilization[J]. Automation of Electric Power Systems, 2015, 39(12): 1-6.
[7] 王珺,顾伟,陆帅,等.结合热网模型的多区域综合能源系统协同规划[J].电力系统自动化,2016,40(15):17-24.WANG Jun, GU Wei, LU Shuai, et al. Coordinated planning of multi-district integrated energy system combining heating network model[J]. Automation of Electric Power Systems, 2016, 40(15): 17-24.
[8] 李正茂,张峰,梁军,等.计及附加机会收益的冷热电联供型微电网动态调度[J].电力系统自动化,2015,39(14):8-15.LI Zhengmao, ZHANG Feng, LIANG Jun, et al. Dynamic scheduling of CCHP type of microgrid considering additional opportunity income[J]. Automation of Electric Power Systems, 2015, 39(14): 8-15.
[9] 杨永标,于建成,李奕杰,等.含光伏和蓄能的冷热电联供系统调峰调蓄优化调度[J].电力系统自动化,2017,41(6):6-12.DOI:10.7500/AEPS20160610002.YANG Yongbiao, YU Jiancheng, LI Yijie, et al. Optimal load leveling dispatch of CCHP incorporating photovoltaic and storage[J]. Automation of Electric Power Systems, 2017, 41(6): 6-12. DOI: 10.7500/AEPS20160610002.
[10] 甘霖,陈瑜玮,刘育权,等.含可再生能源的微网冷-热-电多能流协同优化与案例分析[J].电力自动化设备,2017,37(6):275-281.GAN Lin, CHEN Yuwei, LIU Yuquan, et al. Coordinative optimization of multiple energy flows for microgrid with renewable energy resources and case study[J]. Electric Power Automation Equipment, 2017, 37(6): 275-281.
[11] 刘涤尘,马恒瑞,王波,等.含冷热电联供及储能的区域综合能源系统运行优化[J].电力系统自动化,2018,42(4):113-120.DOI:10.7500/AEPS20170512002.LIU Dichen, MA Hengrui, WANG Bo, et al. Operation optimization of regional integrated energy system with CCHP and energy storage system[J]. Automation of Electric Power Systems, 2018, 42(4): 113-120. DOI: 10.7500/AEPS20170512002.
[12] 张彦,张涛,孟繁霖,等.基于模型预测控制的能源互联网系统分布式优化调度研究[J].中国电机工程学报,2017,37(23):6829-6845.ZHANG Yan, ZHANG Tao, MENG Fanlin, et al. Model predictive control based distributed optimization and scheduling approach for the energy internet[J]. Proceedings of the CSEE, 2017, 37(23): 6829-6845.
[13] 窦晓波,徐忞慧,董建达,等.微电网改进多时间尺度能量管理模型[J].电力系统自动化,2016,40(9):48-55.DOU Xiaobo, XU Minhui, DONG Jianda, et al. Multi-time scale based improved energy management model for microgrid[J]. Automation of Electric Power Systems, 2016, 40(9): 48-55.
[14] 孙宇军,王岩,王蓓蓓,等.考虑需求响应不确定性的多时间尺度源荷互动决策方法[J].电力系统自动化,2018,42(2):106-113.DOI:10.7500/AEPS20170416004.SUN Yujun, WANG Yan, WANG Beibei, et al. Multi-time scale decision method for source-load interaction considering demand response uncertainty[J]. Automation of Electric Power Systems, 2018, 42(2): 106-113. DOI: 10.7500/AEPS20170416004.
[15] 窦晓波,晓宇,袁晓冬,等.基于改进模型预测控制的微电网能量管理策略[J].电力系统自动化,2017,41(22):56-65.DOI:10.7500/AEPS20170501001.DOU Xiaobo, XIAO Yu, YUAN Xiaodong, et al. Energy management strategy based on improved model predictive control for microgrid[J]. Automation of Electric Power Systems, 2017, 41(22): 56-65. DOI: 10.7500/AEPS20170501001.
[16] 徐立中,易永辉,朱承治,等.考虑风电随机性的微网多时间尺度能量优化调度[J].电力系统保护与控制,2014,42(23):1-8.XU Lizhong, YI Yonghui, ZHU Chengzhi, et al. Multi-time scale optimal energy dispatch of microgrid considering stochastic wind power[J]. Power System Protection and Control, 2014, 42(23): 1-8.
[17] BAO Zhejing, ZHOU Qin, YANG Zhihui, et al. A multi time-scale and multi energy-type coordinated microgrid scheduling solution: Part Ⅱ optimization algorithm and case studies[J]. IEEE Transactions on Power Systems, 2015, 30(5): 2267-2277.
[18] LUO Zhao, WU Zhi, LI Zhenyuan, et al. A two-stage optimization and control for CCHP microgrid energy management[J]. Applied Thermal Engineering, 2017, 125: 513-522.
[19] 吴鸣,骆钊,季宇,等.基于模型预测控制的冷热电联供型微网动态优化调度[J].中国电机工程学报,2017,37(24):7174-7184.WU Ming, LUO Zhao, JI Yu, et al. Optimal dynamic dispatch for combined cooling, heating and power microgrid based on model predictive control[J]. Proceedings of the CSEE, 2017, 37(24): 7174-7184.
[20] CHO H, MAGO P J, LUCK R, et al. Evaluation of CCHP systems performance based on operational cost, primary energy consumption, and carbon dioxide emission by utilizing an optimal operation scheme[J]. Applied Energy, 2009, 86(12): 2540-2549.
[21] 李正茂,张峰,梁军,等.含电热联合系统的微电网运行优化[J].中国电机工程学报,2015,35(14):3569-3576.LI Zhengmao, ZHANG Feng, LIANG Jun, et al. Optimization on microgrid with combined heat and power system[J]. Proceedings of the CSEE, 2015, 35(14): 3569-3576.
[22] 孙可,何德,李春筱,等.考虑冰蓄冷空调多模式的工厂综合能源系统多能协同优化模型[J].电力建设,2017,38(12):12-19.SUN Ke, HE De, LI Chunxiao, et al. Multi-energy cooperative optimization model of factory IES considering multi-model of ice storage[J]. Electric Power Construction, 2017, 38(12): 12-19.
[23] BAO Zhejing, ZHOU Qin, YANG Zhihui, et al. A multi time-scale and multi energy-type coordinated microgrid scheduling solution: Part Ⅰ model and methodology[J]. IEEE Transactions on Power Systems, 2015, 30(5): 2257-2266.
[24] WANG Jianhui, SHAHIDEHPOUR M, LI Zuyi. Security-constrained unit commitment with volatile wind power generation[J]. IEEE Transactions on Power Systems, 2008, 23(3): 1319-1327.
[25] WYSS G D, JORGENSEN K H. A user’s guide to LHS: Sandia’s Latin hypercube sampling software[R]. 1998.
[26] DUPACOVA J, GROWE-KUSKA N, ROMISCH W. Scenario reduction in stochastic programming—an approach using probability metrics[J]. Mathematical Programming, 2003, 95(3): 493-511.
[27] 张永宁.微电网多时间尺度能量优化管理方法研究[D].合肥:合肥工业大学,2016.ZHANG Yongning. Microgrid energy optimization management based on multi-time scale method[D]. Hefei: Hefei University of Technology, 2016.
[2] 靳小龙,穆云飞,贾宏杰,等.融合需求侧虚拟储能系统的冷热电联供楼宇微网优化调度方法[J].中国电机工程学报,2017,37(2):581-591.JIN Xiaolong, MU Yunfei, JIA Hongjie, et al. Optimal scheduling method for a combined cooling, heating and power building microgrid considering virtual storage system at demand side[J]. Proceedings of the CSEE, 2017, 37(2): 581-591.
[3] 蒋润花,曾蓉,李洪强,等.考虑气候条件及建筑类型等因素的分布式冷热电三联产系统的多目标优化及评估[J].中国电机工程学报,2016,36(12):3206-3214.JIANG Runhua, ZENG Rong, LI Hongqiang, et al. Multi-objective optimization and evaluation of distributed CCHP system considering influence of climate condition and building type[J]. Proceedings of the CSEE, 2016, 36(12): 3206-3214.
[4] 管霖,陈鹏,唐宗顺,等.考虑冷热电存储的区域综合能源站优化设计方法[J].电网技术,2016,40(10):2934-2943.GUAN Lin, CHEN Peng, TANG Zongshun, et al. Integrated energy station design considering cold and heat storage[J]. Power System Technology, 2016, 40(10): 2934-2943.
[5] 胡荣,马杰,李振坤,等.分布式冷热电联供系统优化配置与适用性分析[J].电网技术,2017,41(2):418-425.HU Rong, MA Jie, LI Zhenkun, et al. Optimal allocation and applicability analysis of distributed combined cooling-heating-power system[J]. Power System Technology, 2017, 41(2): 418-425.
[6] 刘星月,吴红斌.太阳能综合利用的冷热电联供系统控制策略和运行优化[J].电力系统自动化,2015,39(12):1-6.LIU Xingyue, WU Hongbin. A control strategy and operation optimization of combined cooling heating and power system considering solar comprehensive utilization[J]. Automation of Electric Power Systems, 2015, 39(12): 1-6.
[7] 王珺,顾伟,陆帅,等.结合热网模型的多区域综合能源系统协同规划[J].电力系统自动化,2016,40(15):17-24.WANG Jun, GU Wei, LU Shuai, et al. Coordinated planning of multi-district integrated energy system combining heating network model[J]. Automation of Electric Power Systems, 2016, 40(15): 17-24.
[8] 李正茂,张峰,梁军,等.计及附加机会收益的冷热电联供型微电网动态调度[J].电力系统自动化,2015,39(14):8-15.LI Zhengmao, ZHANG Feng, LIANG Jun, et al. Dynamic scheduling of CCHP type of microgrid considering additional opportunity income[J]. Automation of Electric Power Systems, 2015, 39(14): 8-15.
[9] 杨永标,于建成,李奕杰,等.含光伏和蓄能的冷热电联供系统调峰调蓄优化调度[J].电力系统自动化,2017,41(6):6-12.DOI:10.7500/AEPS20160610002.YANG Yongbiao, YU Jiancheng, LI Yijie, et al. Optimal load leveling dispatch of CCHP incorporating photovoltaic and storage[J]. Automation of Electric Power Systems, 2017, 41(6): 6-12. DOI: 10.7500/AEPS20160610002.
[10] 甘霖,陈瑜玮,刘育权,等.含可再生能源的微网冷-热-电多能流协同优化与案例分析[J].电力自动化设备,2017,37(6):275-281.GAN Lin, CHEN Yuwei, LIU Yuquan, et al. Coordinative optimization of multiple energy flows for microgrid with renewable energy resources and case study[J]. Electric Power Automation Equipment, 2017, 37(6): 275-281.
[11] 刘涤尘,马恒瑞,王波,等.含冷热电联供及储能的区域综合能源系统运行优化[J].电力系统自动化,2018,42(4):113-120.DOI:10.7500/AEPS20170512002.LIU Dichen, MA Hengrui, WANG Bo, et al. Operation optimization of regional integrated energy system with CCHP and energy storage system[J]. Automation of Electric Power Systems, 2018, 42(4): 113-120. DOI: 10.7500/AEPS20170512002.
[12] 张彦,张涛,孟繁霖,等.基于模型预测控制的能源互联网系统分布式优化调度研究[J].中国电机工程学报,2017,37(23):6829-6845.ZHANG Yan, ZHANG Tao, MENG Fanlin, et al. Model predictive control based distributed optimization and scheduling approach for the energy internet[J]. Proceedings of the CSEE, 2017, 37(23): 6829-6845.
[13] 窦晓波,徐忞慧,董建达,等.微电网改进多时间尺度能量管理模型[J].电力系统自动化,2016,40(9):48-55.DOU Xiaobo, XU Minhui, DONG Jianda, et al. Multi-time scale based improved energy management model for microgrid[J]. Automation of Electric Power Systems, 2016, 40(9): 48-55.
[14] 孙宇军,王岩,王蓓蓓,等.考虑需求响应不确定性的多时间尺度源荷互动决策方法[J].电力系统自动化,2018,42(2):106-113.DOI:10.7500/AEPS20170416004.SUN Yujun, WANG Yan, WANG Beibei, et al. Multi-time scale decision method for source-load interaction considering demand response uncertainty[J]. Automation of Electric Power Systems, 2018, 42(2): 106-113. DOI: 10.7500/AEPS20170416004.
[15] 窦晓波,晓宇,袁晓冬,等.基于改进模型预测控制的微电网能量管理策略[J].电力系统自动化,2017,41(22):56-65.DOI:10.7500/AEPS20170501001.DOU Xiaobo, XIAO Yu, YUAN Xiaodong, et al. Energy management strategy based on improved model predictive control for microgrid[J]. Automation of Electric Power Systems, 2017, 41(22): 56-65. DOI: 10.7500/AEPS20170501001.
[16] 徐立中,易永辉,朱承治,等.考虑风电随机性的微网多时间尺度能量优化调度[J].电力系统保护与控制,2014,42(23):1-8.XU Lizhong, YI Yonghui, ZHU Chengzhi, et al. Multi-time scale optimal energy dispatch of microgrid considering stochastic wind power[J]. Power System Protection and Control, 2014, 42(23): 1-8.
[17] BAO Zhejing, ZHOU Qin, YANG Zhihui, et al. A multi time-scale and multi energy-type coordinated microgrid scheduling solution: Part Ⅱ optimization algorithm and case studies[J]. IEEE Transactions on Power Systems, 2015, 30(5): 2267-2277.
[18] LUO Zhao, WU Zhi, LI Zhenyuan, et al. A two-stage optimization and control for CCHP microgrid energy management[J]. Applied Thermal Engineering, 2017, 125: 513-522.
[19] 吴鸣,骆钊,季宇,等.基于模型预测控制的冷热电联供型微网动态优化调度[J].中国电机工程学报,2017,37(24):7174-7184.WU Ming, LUO Zhao, JI Yu, et al. Optimal dynamic dispatch for combined cooling, heating and power microgrid based on model predictive control[J]. Proceedings of the CSEE, 2017, 37(24): 7174-7184.
[20] CHO H, MAGO P J, LUCK R, et al. Evaluation of CCHP systems performance based on operational cost, primary energy consumption, and carbon dioxide emission by utilizing an optimal operation scheme[J]. Applied Energy, 2009, 86(12): 2540-2549.
[21] 李正茂,张峰,梁军,等.含电热联合系统的微电网运行优化[J].中国电机工程学报,2015,35(14):3569-3576.LI Zhengmao, ZHANG Feng, LIANG Jun, et al. Optimization on microgrid with combined heat and power system[J]. Proceedings of the CSEE, 2015, 35(14): 3569-3576.
[22] 孙可,何德,李春筱,等.考虑冰蓄冷空调多模式的工厂综合能源系统多能协同优化模型[J].电力建设,2017,38(12):12-19.SUN Ke, HE De, LI Chunxiao, et al. Multi-energy cooperative optimization model of factory IES considering multi-model of ice storage[J]. Electric Power Construction, 2017, 38(12): 12-19.
[23] BAO Zhejing, ZHOU Qin, YANG Zhihui, et al. A multi time-scale and multi energy-type coordinated microgrid scheduling solution: Part Ⅰ model and methodology[J]. IEEE Transactions on Power Systems, 2015, 30(5): 2257-2266.
[24] WANG Jianhui, SHAHIDEHPOUR M, LI Zuyi. Security-constrained unit commitment with volatile wind power generation[J]. IEEE Transactions on Power Systems, 2008, 23(3): 1319-1327.
[25] WYSS G D, JORGENSEN K H. A user’s guide to LHS: Sandia’s Latin hypercube sampling software[R]. 1998.
[26] DUPACOVA J, GROWE-KUSKA N, ROMISCH W. Scenario reduction in stochastic programming—an approach using probability metrics[J]. Mathematical Programming, 2003, 95(3): 493-511.
[27] 张永宁.微电网多时间尺度能量优化管理方法研究[D].合肥:合肥工业大学,2016.ZHANG Yongning. Microgrid energy optimization management based on multi-time scale method[D]. Hefei: Hefei University of Technology, 2016.