提升风能消纳清洁供热项目的经济性优化配置
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摘要
在中国三北地区,热电联产机组"以热定电"的运行调节模式是供暖季出现严重弃风的重要原因之一.推进电供热、配置蓄热设备是促进风电消纳、降低系统能耗的有效方式.为实现清洁供热项目的经济性最优,提出了一种基于遗传算法和传统机组组合的双层优化模型.首先,在分析热、电能源供需的基础上,给出了区域能源系统的典型结构;然后,将日内净收益作为经济性优化的定量评价指标;通过分析能源系统的短期优化调度方式,得到最终的双层优化模型.在该模型中,外层优化利用遗传算法对不同的配置组合情况进行全局搜索,内层优化则通过求解线性规划给出特定组合条件下清洁供热项目的日内净收益.案例分析表明,清洁供热项目的经济性最优配置与区域能源系统的热、电负荷、风电出力特性和社会经济条件有关;同时,遗传算法可稳定收敛到全局最优点附近,优化计算结果完全满足工程应用的要求.另外,虽然清洁供热改造能够显著降低系统的弃风量,但出于经济性考虑,弃风并不能完全消除.
In three-north regions of China, power regulating abilities of cogeneration units are constrained significantly by their compulsory heat outputs. Such regulation mode is one of the key reasons for the large amounts of wind curtailment in the last few years in winter. Introducing electric heating boilers and thermal energy devices are effective ways to promote wind power integration and reduce the energy consumption of district heat and power system. In order to get the optimal economic configurations of the clean heating reconstruction projects, a two-layer optimization model based on the genetic algorithms and traditional unit commitments is proposed in this paper. First, by analysing the supply and demand relationship of heat and power, a typical structure of the district energy system(DES) in northern China was given. Then, the daily net benefits were defined as the quantitative economic indexes for any reconstruction projects. Finally, the two-layer optimization model was obtained by integrating short-term dispatch models of the DES. In this model, the genetic algorithms are used to search for the most economical configuration combinations globally, and the fitness values of the population are solved by the inner linear programming. Case studies indicate that the optimal configurations of the clean heating project were affected by several factors including the heat and power demands, characteristic of the wind power outputs, and socioeconomic conditions. Although differences exist between several independent genetic optimizations, any of these solutions are good enough to meet the requirements of engineering application. In addition, although more wind power can be integrated by introducing electric heating boilers and thermal energy devices, there is no need to integrate all wind power because of the law of diminishing return.
In three-north regions of China, power regulating abilities of cogeneration units are constrained significantly by their compulsory heat outputs. Such regulation mode is one of the key reasons for the large amounts of wind curtailment in the last few years in winter. Introducing electric heating boilers and thermal energy devices are effective ways to promote wind power integration and reduce the energy consumption of district heat and power system. In order to get the optimal economic configurations of the clean heating reconstruction projects, a two-layer optimization model based on the genetic algorithms and traditional unit commitments is proposed in this paper. First, by analysing the supply and demand relationship of heat and power, a typical structure of the district energy system(DES) in northern China was given. Then, the daily net benefits were defined as the quantitative economic indexes for any reconstruction projects. Finally, the two-layer optimization model was obtained by integrating short-term dispatch models of the DES. In this model, the genetic algorithms are used to search for the most economical configuration combinations globally, and the fitness values of the population are solved by the inner linear programming. Case studies indicate that the optimal configurations of the clean heating project were affected by several factors including the heat and power demands, characteristic of the wind power outputs, and socioeconomic conditions. Although differences exist between several independent genetic optimizations, any of these solutions are good enough to meet the requirements of engineering application. In addition, although more wind power can be integrated by introducing electric heating boilers and thermal energy devices, there is no need to integrate all wind power because of the law of diminishing return.
引文
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[19]王彩霞,李琼慧,谢国辉.风电供热提高低谷风电消纳能力评估[J].中国电力,2013,46(12):100WANG Caixia,LI Qionghui,XIE Guohui.Evaluation of wind power heating in facilitating wind power integration capability during vally load period[J].Electric Power,2013,46(12):100.DOI:10.3969/j.issn.1004-9649.2013.12.021
[20]王钤,潘险险,陈迎,等.基于实测数据的风电场风速-功率模型的研究[J].电力系统保护与控制,2014,42(2):23WANG Qian,PAN Xianxian,CHEN Ying,et al.Study of wind speed-active power model for wind farm based on measured data[J].Power System Protection and Control,2014,42(2):23.DOI:10.7667/j.issn.1674-3415.2014.02.004
[21]郎斌斌,穆钢,严干贵,等.联网风电机组风速-功率特性曲线的研究[J].电网技术,2008,32(12):70LANG Binbin,MU Gang,YAN Gangui,et al.Research on wind speed vs output power characteristic curve of wind power generator interconnected with power grid[J].Power System Technology,2008,32(12):70.DOI:10.13335/j.1000-3673.pst.2008.12.008
[2]舒印彪,张智刚,郭剑波,等.新能源消纳关键因素分析及解决措施研究[J].中国电机工程学报,2017,37(1):1SHU Yinbiao,ZHANG Zhigang,GUO Jianbo,et al.Study on key factors and solution of renewable energy accommodation[J].Proceedings of the CSEE,2017,37(1):1.DOI:10.13334/j.0258-8013.pcsee.162555
[3]LUO Guoliang,LI Yanling,TANG Wenjun,et al.Wind curtailment of China's wind power operation:evolution,causes and solutions[J].Renewable and Sustainable Energy Reviews,2016,53:1190.DOI:10.1016/j.rser.2015.09.075
[4]NUYTTEN T,CLAESSENS B,PAREDIS K,et al.Flexibility of a combined heat and power system with thermal energy storage for district heating[J].Applied Energy,2013,104:583.DOI:10.1016/j.apenergy.2012.11.029
[5]LUND H.Large-scale integration of wind power into different energy systems[J].Energy,2015,30(13):2402.DOI:10.1016/j.energy.2004.11.001
[6]MEIBOM P,KIVILUOMA J,BARTH R,et al.Value of electric heat boilers and heat pumps for wind power integration[J].Wind Energy,2007,10(4):321.DOI:10.1002/we.224
[7]FITZGERALD N,FOLEY A M,MCKEOGH E.Integrating wind power using intelligent electric water heating[J].Energy,2012,48(1):135.DOI:10.1016/j.energy.2012.03.014
[8]LUND H,MLLER B,MATHIESEN B V,et al.The role of district heating in future renewable energy systems[J].Energy,2010,35(3):1381.DOI:10.1016/j.energy.2009.11.023
[9]LUND H,WERNER S,WILTSHIRE R,et al.4th Generation District Heating(4GDH):integrating smart thermal grids into future sustainable energy systems[J].Energy,2014,68:1.DOI:10.1016/j.energy.2014.02.089
[10]于炎娟,陈红坤,姜欣,等.促进风电消纳的蓄热罐运行策略[J].电力系统自动化,2017,41(7):37.DOI:10.7500/AEPS20160510006YU Yanjuan,CHEN Hongkun,JIANG Xin,et al.Operation strategy for heat storage tank to improve wind power accommodation[J].Automation of Electric Power Systems,2017,41(7):37.DOI:10.7500/AEPS20160510006
[11]孙杰,张炳达,杨俊鹏.微网热电储能容量配置优化[J].天津理工大学学报,2017,33(2):1SUN Jie,ZHANG Bingda,YANG Junpeng.Energy storage capacity optimization for micro grid including electricity and heat[J].Journal of Tianjin University of Technology,2017,33(2):1.DOI:10.3969/j.issn.1673-095X.2017.002.001
[12]KATULIC'S,CˇEHIL M,BOGDAN.A novel method for finding the optimal heat storage tank capacity for a cogeneration power plant[J].Applied Thermal Engineering,2014,65(1/2):530.DOI:10.1016/j.applthermaleng.2014.01.051
[13]吕泉,李玲,朱全胜,等.三种弃风消纳方案的节煤效果与国民经济性比较[J].电力系统自动化,2015,39(7):75LYU Quan,LI Ling,ZHU Quansheng,et al.Comparison of coalsaving effect and national economic indices of three feasible curtailed wind power accommodation strategies[J].Automation of Electric Power Systems,2015,(7):75.DOI:10.7500/AEPS20140125001
[14]吕泉,陈天佑,王海霞,等.配置储热后热电机组调峰能力分析[J].电力系统自动化,2014,38(11):34LYU Quan,CHEN Tianyou,WANG Haixia,et al.Analysis on peak-load regulation ability of cogeneration unit with heat accumulator[J].Automation of Electric Power Systems,2014,38(11):34.DOI:10.7500/AEPS20130724002
[15]HEDEGAARD K,MATHIESEN B V,LUND H,et al.Wind power integration using individual heat pumps analysis of different heat storage options[J].Energy,2012,47(1):284.DOI:10.1016/j.energy.2012.09.030
[16]HEDEGAARD K,M NSTER M.Influence of individual heat pumps on wind power integration-Energy system investments and operation[J].Energy Conversion and Management,2013,75:673.DOI:10.1016/j.enconman.2013.08.015
[17]郭宇航,胡博,万凌云,等.含热泵的热电联产型微电网短期最优经济运行[J].电力系统自动化,2015,39(14):16GUO Yuhang,HU Bo,WAN Lingyun,et al.Optimal economic short-term scheduling of CHP microgrid incorporating heat pump[J].Automation of Electric Power Systems,2015,39(14):16.DOI:10.7500/AEPS20141127011
[18]崔杨,陈志,严干贵,等.基于含储热热电联产机组与电锅炉的弃风消纳协调调度模型[J].中国电机工程学报,2016,36(15):4072CUI Yang,CHEN Zhi,YAN Gangui,et al.Coordinated wind power accommodating dispatch model based on electric boiler and CHP with thermal energy storage[J].Proceedings of the CSEE,2016,36(15):4072.DOI:10.13334/j.0258-8013.pcsee.152406
[19]王彩霞,李琼慧,谢国辉.风电供热提高低谷风电消纳能力评估[J].中国电力,2013,46(12):100WANG Caixia,LI Qionghui,XIE Guohui.Evaluation of wind power heating in facilitating wind power integration capability during vally load period[J].Electric Power,2013,46(12):100.DOI:10.3969/j.issn.1004-9649.2013.12.021
[20]王钤,潘险险,陈迎,等.基于实测数据的风电场风速-功率模型的研究[J].电力系统保护与控制,2014,42(2):23WANG Qian,PAN Xianxian,CHEN Ying,et al.Study of wind speed-active power model for wind farm based on measured data[J].Power System Protection and Control,2014,42(2):23.DOI:10.7667/j.issn.1674-3415.2014.02.004
[21]郎斌斌,穆钢,严干贵,等.联网风电机组风速-功率特性曲线的研究[J].电网技术,2008,32(12):70LANG Binbin,MU Gang,YAN Gangui,et al.Research on wind speed vs output power characteristic curve of wind power generator interconnected with power grid[J].Power System Technology,2008,32(12):70.DOI:10.13335/j.1000-3673.pst.2008.12.008
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