基于鲁棒多目标优化模型的可再生能源消纳分析
|
摘要
电力系统中接入高比例可再生能源后,间歇式电源的出力波动性将给电力系统的安全稳定运行带来挑战,亟需研究应对高比例可再生能源出力波动的电力系统优化调度方法。首先分析了"源-网-荷-储"等灵活性资源的调用成本以及常规火电机组的使用对环境的影响,结合两者构建鲁棒多目标优化调度模型,从而实现对间歇式电源出力波动性的抑制。进而引入保守度调节因子确定不同的计算场景,然后采用NSGA-II算法进行求解。最后,采用上饶地区实际网络对所提的优化调度方法进行测算,验证了所提模型和方法的可行性和有效性。
With the high proportion penetration of renewable energy, the volatility of intermittent power output will bring challenges to the safe and stable operation of power system. It is urgently requested to research the optimal scheduling method to cope with the output volatility of renewable energy. Firstly, the call costs of resources, such as source-net-load-reserve and the impact of the use of conventional thermal power units on the environment are analyzed,a robust multi-objective optimal scheduling model is constructed to achieve the suppression of the output volatility of the power supply. Furthermore,the conservative adjustment factor is introduced to determine different calculation scenarios,and then solved by NSGA-II algorithm. Finally, the optimal scheduling method proposed in this paper is verified and measured by the actual network in Shangrao area, which verifies the feasibility and effectiveness of the proposed model and method.
With the high proportion penetration of renewable energy, the volatility of intermittent power output will bring challenges to the safe and stable operation of power system. It is urgently requested to research the optimal scheduling method to cope with the output volatility of renewable energy. Firstly, the call costs of resources, such as source-net-load-reserve and the impact of the use of conventional thermal power units on the environment are analyzed,a robust multi-objective optimal scheduling model is constructed to achieve the suppression of the output volatility of the power supply. Furthermore,the conservative adjustment factor is introduced to determine different calculation scenarios,and then solved by NSGA-II algorithm. Finally, the optimal scheduling method proposed in this paper is verified and measured by the actual network in Shangrao area, which verifies the feasibility and effectiveness of the proposed model and method.
引文
[1]白建华,辛颂旭,刘俊,等.中国实现高比例可再生能源发展路径研究[J].中国电机工程学报,2015,35(14):3699-3705.BAI Jianhua,XIN Songxu,LIU Jun,et al. Research on the development path of high proportion of renewable energy in China[J]. Proceedings of the CSEE, 2015, 35(14):3699-3705.
[2]童光毅.关于当代能源转型方向的探讨[J].智慧电力,2018,46(10):1-3.TONG Guangyi. Probe into modern energy structure transition[J]. Smart Power,2018,46(10):1-3.
[3]国家能源局.2017年能源工作指导意见[Z].北京:国家能源局,2017.
[4]国家能源局.国家能源局关于做好2016年度风电并网消纳有关工作的通知(国能新能[2016]74号)[Z].北京:国家能源局,2016.
[5]鲁宗相,李海波,乔颖.高比例可再生能源并网的电力系统灵活性评价与平衡机理[J].中国电机工程学报,2017,37(1):9-19.LU Zongxiang,LI Haibo,QIAO Ying. Flexibility evaluation and supply/demand balance principle of power system with high-penetration renewable electricity[J]. Proceedings of the CSEE,2017,37(1):9-19.
[6]鲁宗相,李海波,乔颖.含高比例可再生能源电力系统灵活性规划及挑战[J].电力系统自动化,2016,40(13):147-158.LU Zongxian,LI Haibo,QIAO Ying. Power system flexible planning and challenge with high proportion of renewable energy[J]. Automation of E lectric Power Systems, 2016,40(13):147-158.
[7]肖定垚,王承民,曾平良,等.考虑短时灵活性需求及资源调用成本的灵活性资源优化调度[J].华东电力,2014(5):809-815.XIAO Dingyo,WANG Chengmin,ZENG Pingliang,et al.Flexibility resource otimization dispatch considering shortterm flexibility demand and invoking cost[J]. East China Electric Power,2014,(5):809-815.
[8] PATERAKS N G, ERDINC 0, BAKIRTZIS A G, et al.Load-following reserves procurementconsidering flexible demand-side resources under high wind power penetration[J]. IEEE Transactions on Power Systems, 2015, 30(3):1337-1350.
[9]肖定垚,王承民,曾平良,等.考虑可再生能源电源功率不确定性的电源灵活性评价[J].电力自动化设备,2015,35(7):120-125.XIAO Dingyao,WANG Chengmin,ZENG Pingliang,et al.Flexible evaluation on energy considering of uncertainty of renewable energy power[J]. Electric Power Automation Equipment,2015,35(7):120-125.
[10]李志刚,吴文传,张伯明.消纳大规模风电的鲁棒区间经济调度(二)不确定集合构建与保守度调节[J].电力系统自动化,2014,38(21):32-38.LI Zhigang,WU Wenchuan,ZHANG Boming. Robust interval economic scheduling for large-scale wind power generation(ii)uncertain set construction and conservative adjustment[J].Automation of Electric Power Systems,2014,38(21):32-38.
[11]叶荣,陈皓勇,王钢,等.多风电场并网时安全约束机组组合的混合整数规划解法[J],电力系统自动化,2010,34(5):1-5.YE Rong,CHEN Haoyong,WANG Gang, et al. A mixed integer programming method for security-constrained unit commitment with multiple wind farms[J]. Automation of Electric Power Systems,2010,34(5):1-5.
[12] International Energy Agency. Harnessing variable renewables:a guide to the balancing challenge[R]. Paris:OECD Publishing,2011.
[13]李宏仲,吕振邦,朱佳明,等.考虑经济性的日前风电消纳动态评估方法[J].电网技术,2017,41(4):1261-1268.LI Hongzhong,LYU Zhenbang,ZHUJiaming,et al. Dynamic evaluation on day-ahead wind power accommodation with economic consideration[J]. Power System Technology,2017,41(4):1261-1268.
[14]高小涛,,盛昌栋.1000MW机组锅炉掺烧贫煤NO_x排放的燃烧优化[J].电力工程技术,2018,37(3):14-20.GAO Xiaotao, SHENG Changdong. Combustion optimization of NO_x emissions from a lean coal mixed with 1000MW units[J].Electric Engineering&Technology,2018,37(3):14-20.
[15]李德波,曾庭华,廖永进,等.广东省首台超洁净排放燃煤机组现场测试分析[J].广东电力,2016,29(4):16-21.LI Debo,ZENG Tinghua,LIAO Yongjin, et al. Field test analysis of the first ultra-clean emission coal-fired unit in Guangdong Province[J].Guangdong Electric Power,2016,29(4):16-21.
[16]唐程辉,张凡,梁才,等.考虑电池损耗和分布式可再生能源的储能调度[J].智慧电力,2018,46(12):1-6.TANG Chenghui,ZHANG Fan, LIANG Cai, et al.Considering battery loss and energy storage scheduling of distributed renewable energy[J]. Smart Power, 2018, 46(12):1-6.
[17]卓结彬,李亮枫.考虑不确定性风电功率预测的风电-抽水蓄能联合优化运行[J].陕西电力,2015,43(4):19-23.ZHUO Jiebin,LI Liangfeng.Considering the uncertainty of wind power prediction for wind power-pumped storage optimization joint operation[J] Shaanxi Electric Power,2015,43(4):19-23.
[18] DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multiobjective genetic algorithm:NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2):182-197.
[19]金虹,衣进.当前储能市场和储能经济性分析[J].储能科学与技术,2012,1(2):103-111.JIN Hong,YIJin.Analysis of current energy storage market and energy storage economy[J]. Energy Storage Science and Technology,2012,1(2):103-111.
[20]梁惠施,程林,苏剑,等.微网的成本效益分析[J].中国电机工程学报,2011,31(S1):38-44.LIANG Huishi,CHENG Lin,SU Jian,et al. Cost benefit analysis for microgrid[J]. Proceedings of the CSEE,2011,31(S1):38-44.
[2]童光毅.关于当代能源转型方向的探讨[J].智慧电力,2018,46(10):1-3.TONG Guangyi. Probe into modern energy structure transition[J]. Smart Power,2018,46(10):1-3.
[3]国家能源局.2017年能源工作指导意见[Z].北京:国家能源局,2017.
[4]国家能源局.国家能源局关于做好2016年度风电并网消纳有关工作的通知(国能新能[2016]74号)[Z].北京:国家能源局,2016.
[5]鲁宗相,李海波,乔颖.高比例可再生能源并网的电力系统灵活性评价与平衡机理[J].中国电机工程学报,2017,37(1):9-19.LU Zongxiang,LI Haibo,QIAO Ying. Flexibility evaluation and supply/demand balance principle of power system with high-penetration renewable electricity[J]. Proceedings of the CSEE,2017,37(1):9-19.
[6]鲁宗相,李海波,乔颖.含高比例可再生能源电力系统灵活性规划及挑战[J].电力系统自动化,2016,40(13):147-158.LU Zongxian,LI Haibo,QIAO Ying. Power system flexible planning and challenge with high proportion of renewable energy[J]. Automation of E lectric Power Systems, 2016,40(13):147-158.
[7]肖定垚,王承民,曾平良,等.考虑短时灵活性需求及资源调用成本的灵活性资源优化调度[J].华东电力,2014(5):809-815.XIAO Dingyo,WANG Chengmin,ZENG Pingliang,et al.Flexibility resource otimization dispatch considering shortterm flexibility demand and invoking cost[J]. East China Electric Power,2014,(5):809-815.
[8] PATERAKS N G, ERDINC 0, BAKIRTZIS A G, et al.Load-following reserves procurementconsidering flexible demand-side resources under high wind power penetration[J]. IEEE Transactions on Power Systems, 2015, 30(3):1337-1350.
[9]肖定垚,王承民,曾平良,等.考虑可再生能源电源功率不确定性的电源灵活性评价[J].电力自动化设备,2015,35(7):120-125.XIAO Dingyao,WANG Chengmin,ZENG Pingliang,et al.Flexible evaluation on energy considering of uncertainty of renewable energy power[J]. Electric Power Automation Equipment,2015,35(7):120-125.
[10]李志刚,吴文传,张伯明.消纳大规模风电的鲁棒区间经济调度(二)不确定集合构建与保守度调节[J].电力系统自动化,2014,38(21):32-38.LI Zhigang,WU Wenchuan,ZHANG Boming. Robust interval economic scheduling for large-scale wind power generation(ii)uncertain set construction and conservative adjustment[J].Automation of Electric Power Systems,2014,38(21):32-38.
[11]叶荣,陈皓勇,王钢,等.多风电场并网时安全约束机组组合的混合整数规划解法[J],电力系统自动化,2010,34(5):1-5.YE Rong,CHEN Haoyong,WANG Gang, et al. A mixed integer programming method for security-constrained unit commitment with multiple wind farms[J]. Automation of Electric Power Systems,2010,34(5):1-5.
[12] International Energy Agency. Harnessing variable renewables:a guide to the balancing challenge[R]. Paris:OECD Publishing,2011.
[13]李宏仲,吕振邦,朱佳明,等.考虑经济性的日前风电消纳动态评估方法[J].电网技术,2017,41(4):1261-1268.LI Hongzhong,LYU Zhenbang,ZHUJiaming,et al. Dynamic evaluation on day-ahead wind power accommodation with economic consideration[J]. Power System Technology,2017,41(4):1261-1268.
[14]高小涛,,盛昌栋.1000MW机组锅炉掺烧贫煤NO_x排放的燃烧优化[J].电力工程技术,2018,37(3):14-20.GAO Xiaotao, SHENG Changdong. Combustion optimization of NO_x emissions from a lean coal mixed with 1000MW units[J].Electric Engineering&Technology,2018,37(3):14-20.
[15]李德波,曾庭华,廖永进,等.广东省首台超洁净排放燃煤机组现场测试分析[J].广东电力,2016,29(4):16-21.LI Debo,ZENG Tinghua,LIAO Yongjin, et al. Field test analysis of the first ultra-clean emission coal-fired unit in Guangdong Province[J].Guangdong Electric Power,2016,29(4):16-21.
[16]唐程辉,张凡,梁才,等.考虑电池损耗和分布式可再生能源的储能调度[J].智慧电力,2018,46(12):1-6.TANG Chenghui,ZHANG Fan, LIANG Cai, et al.Considering battery loss and energy storage scheduling of distributed renewable energy[J]. Smart Power, 2018, 46(12):1-6.
[17]卓结彬,李亮枫.考虑不确定性风电功率预测的风电-抽水蓄能联合优化运行[J].陕西电力,2015,43(4):19-23.ZHUO Jiebin,LI Liangfeng.Considering the uncertainty of wind power prediction for wind power-pumped storage optimization joint operation[J] Shaanxi Electric Power,2015,43(4):19-23.
[18] DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multiobjective genetic algorithm:NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2):182-197.
[19]金虹,衣进.当前储能市场和储能经济性分析[J].储能科学与技术,2012,1(2):103-111.JIN Hong,YIJin.Analysis of current energy storage market and energy storage economy[J]. Energy Storage Science and Technology,2012,1(2):103-111.
[20]梁惠施,程林,苏剑,等.微网的成本效益分析[J].中国电机工程学报,2011,31(S1):38-44.LIANG Huishi,CHENG Lin,SU Jian,et al. Cost benefit analysis for microgrid[J]. Proceedings of the CSEE,2011,31(S1):38-44.