基于IPSO算法的建筑级综合能源系统优化调度
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摘要
为节约建筑级综合能源系统的运营成本,提高电网供电的可靠性,本文提出了一种基于随机权重粒子群算法的建筑级综合能源系统优化调度策略,构建了建筑级综合能源系统优化调度模型,以运行能源成本最小化为目标函数,采用随机权重粒子群算法对模型进行求解,并引入实际算例进行仿真,对基于规则的控制策略和优化调度控制策略进行比较。仿真结果表明,与基于规则的控制策略相比,本文提出的优化调度策略可以显著降低运行能源成本,对电网的削峰填谷起到一定的促进作用。该研究为建筑级综合能源系统优化调度问题提供了理论依据。
In order to save the operation cost of building-level integrated energy system and improve the reliability of power supply,this paper proposes an optimal scheduling strategy for building-level integrated energy systems based on random weighted Particle Swarm Optimization algorithm and builds an optimized scheduling model for building-level integrated energy systems.The model uses the minimization of operating energy costs as the objective function,and adopts a random weighted particle swarm optimization algorithm.The model is solved.Actual examples are used to simulate and compare the rules-based control strategy and the optimal scheduling control strategy.The simulation results show that compared with the rule-based control strategy,the optimized scheduling strategy proposed in this paper can significantly reduce the operating energy cost(15.4%),and it plays a certain role in promoting the peak shaving and valley filling of the grid.
In order to save the operation cost of building-level integrated energy system and improve the reliability of power supply,this paper proposes an optimal scheduling strategy for building-level integrated energy systems based on random weighted Particle Swarm Optimization algorithm and builds an optimized scheduling model for building-level integrated energy systems.The model uses the minimization of operating energy costs as the objective function,and adopts a random weighted particle swarm optimization algorithm.The model is solved.Actual examples are used to simulate and compare the rules-based control strategy and the optimal scheduling control strategy.The simulation results show that compared with the rule-based control strategy,the optimized scheduling strategy proposed in this paper can significantly reduce the operating energy cost(15.4%),and it plays a certain role in promoting the peak shaving and valley filling of the grid.
引文
[1]戴锦,肖文波,胡芳雨,等.光伏发电性能物理预测模型的研究[J].电源技术,2018,42(2):262-266.
[2]彭亮.影响风力发电机组并网系统稳定性的因素分析[J].电子设计工程,2017,25(15):85-89.
[3]薛新白.含光伏蓄电池的冷热电联供系统运行模式评估[J].电源技术,2017,41(7):1035-1038.
[4]兰立雄,刘力卿,米增强.冷电联供系统的多目标运行优化[J].电力系统及其自动化学报,2016,28(5):62-68.
[5]Pelay U,Luo L,Fan Y,et al.Thermal energy storage systems for concentrated solar power plants[J].Renewable and Sustainable Energy Reviews,2017,79:82-100.
[6]史庆祥,霍继宝,左兴堂.小型分布式能源系统的优化设计[J].黑龙江科技信息,2016(3):68-68.
[7]司文伟.小型分布式能源系统中夹点技术的应用研究[J].中外企业家,2016(11):225-226.
[8]朱海强.小型天然气分布式能源系统的方案选择及性能[J].中国科技投资,2017(25):266.
[9]刘健,牛东晓,邢棉,等.基于动态电价的新能源实时调度定价与策略研究[J].电网技术,2014,38(5):1346-1351.
[10]郭晔,石峻玮,毛安家.考虑需求响应的CCHP系统中分布式电源的选址定容[J].电力建设,2017,38(12):43-50.
[11]冯明灿,谢宁,王承民,等.考虑瞬时性峰值负荷特性的配电网可靠性规划[J].电网技术,2015,39(3):757-762.
[12]Khushairi M M,Abdullah H,Hazran H.A Study on the optimization of control strategy of a thermal energy storage system for building air-conditioning[J].Procedia Engineering,2011,20:118-24.
[13]朱红,刘东,凌万水,等.有源配电网的馈线自动化效果评价方法[J].电力建设,2015,36(1):148-152.
[14]卜凡鹏,田世明,方芳,等.基于能源集线器模型的园区混合能源系统日前优化调度方法[J].电力系统及其自动化学报,2017,29(10):123-129.
[15]张斌,丁玉娟,韩硕,等.国内外溴化锂吸收式冷水机组产品标准简要对比[J].制冷与空调,2014,14(7):9-11.
[16]王安山.蒸汽型溴化锂冷水机组替代电动模块化水冷机组的应用分析[C]∥2017热电联产节能减排技术交流研讨会.长春:中国节能协会,2017:66-70.
[17]李刚,李峰,孔亮,等.不同电价下含储能系统的微电网经济调度[J].中国电力,2018,51(2):125-132.
[18]黄轩,张军,詹志辉.基于随机惯量权重的快速粒子群优化算法[J].计算机工程与设计,2009,30(3):647-650.
[19]赵志刚,黄树运,王伟倩.基于随机惯性权重的简化粒子群优化算法[J].计算机应用研究,2014,31(2):361-363.
[20]高扬,艾芊.含多微网稀疏通信优化的主动配电网分层分布式协调控制[J].电力系统自动化,2018,42(4):135-141.
[2]彭亮.影响风力发电机组并网系统稳定性的因素分析[J].电子设计工程,2017,25(15):85-89.
[3]薛新白.含光伏蓄电池的冷热电联供系统运行模式评估[J].电源技术,2017,41(7):1035-1038.
[4]兰立雄,刘力卿,米增强.冷电联供系统的多目标运行优化[J].电力系统及其自动化学报,2016,28(5):62-68.
[5]Pelay U,Luo L,Fan Y,et al.Thermal energy storage systems for concentrated solar power plants[J].Renewable and Sustainable Energy Reviews,2017,79:82-100.
[6]史庆祥,霍继宝,左兴堂.小型分布式能源系统的优化设计[J].黑龙江科技信息,2016(3):68-68.
[7]司文伟.小型分布式能源系统中夹点技术的应用研究[J].中外企业家,2016(11):225-226.
[8]朱海强.小型天然气分布式能源系统的方案选择及性能[J].中国科技投资,2017(25):266.
[9]刘健,牛东晓,邢棉,等.基于动态电价的新能源实时调度定价与策略研究[J].电网技术,2014,38(5):1346-1351.
[10]郭晔,石峻玮,毛安家.考虑需求响应的CCHP系统中分布式电源的选址定容[J].电力建设,2017,38(12):43-50.
[11]冯明灿,谢宁,王承民,等.考虑瞬时性峰值负荷特性的配电网可靠性规划[J].电网技术,2015,39(3):757-762.
[12]Khushairi M M,Abdullah H,Hazran H.A Study on the optimization of control strategy of a thermal energy storage system for building air-conditioning[J].Procedia Engineering,2011,20:118-24.
[13]朱红,刘东,凌万水,等.有源配电网的馈线自动化效果评价方法[J].电力建设,2015,36(1):148-152.
[14]卜凡鹏,田世明,方芳,等.基于能源集线器模型的园区混合能源系统日前优化调度方法[J].电力系统及其自动化学报,2017,29(10):123-129.
[15]张斌,丁玉娟,韩硕,等.国内外溴化锂吸收式冷水机组产品标准简要对比[J].制冷与空调,2014,14(7):9-11.
[16]王安山.蒸汽型溴化锂冷水机组替代电动模块化水冷机组的应用分析[C]∥2017热电联产节能减排技术交流研讨会.长春:中国节能协会,2017:66-70.
[17]李刚,李峰,孔亮,等.不同电价下含储能系统的微电网经济调度[J].中国电力,2018,51(2):125-132.
[18]黄轩,张军,詹志辉.基于随机惯量权重的快速粒子群优化算法[J].计算机工程与设计,2009,30(3):647-650.
[19]赵志刚,黄树运,王伟倩.基于随机惯性权重的简化粒子群优化算法[J].计算机应用研究,2014,31(2):361-363.
[20]高扬,艾芊.含多微网稀疏通信优化的主动配电网分层分布式协调控制[J].电力系统自动化,2018,42(4):135-141.