This research develops a comprehensive method to solve a combinatorial problem consisting of distribution system reconfiguration, capacitor allocation, and renewable energy resources sizing and siting simultaneously a...This research develops a comprehensive method to solve a combinatorial problem consisting of distribution system reconfiguration, capacitor allocation, and renewable energy resources sizing and siting simultaneously and to improve power system's accountability and system performance parameters. Due to finding solution which is closer to realistic characteristics, load forecasting, market price errors and the uncertainties related to the variable output power of wind based DG units are put in consideration. This work employs NSGA-II accompanied by the fuzzy set theory to solve the aforementioned multi-objective problem. The proposed scheme finally leads to a solution with a minimum voltage deviation, a maximum voltage stability, lower amount of pollutant and lower cost. The cost includes the installation costs of new equipment, reconfiguration costs, power loss cost, reliability cost, cost of energy purchased from power market, upgrade costs of lines and operation and maintenance costs of DGs. Therefore, the proposed methodology improves power quality, reliability and security in lower costs besides its preserve, with the operational indices of power distribution networks in acceptable level. To validate the proposed methodology's usefulness, it was applied on the IEEE 33-bus distribution system then the outcomes were compared with initial configuration.展开更多
综合能源系统(integrated energy system,IES)参与电力现货市场交易时,由于市场供需关系的变化导致交易价格具有不确定性。因此,对综合能源系统运行边际成本进行精细化分析,研究充分利用综合能源系统灵活性资源参与市场的最优调度策略...综合能源系统(integrated energy system,IES)参与电力现货市场交易时,由于市场供需关系的变化导致交易价格具有不确定性。因此,对综合能源系统运行边际成本进行精细化分析,研究充分利用综合能源系统灵活性资源参与市场的最优调度策略。首先,分析了外部现货市场环境下市场价格不确定性典型场景处理方法,并研究了综合能源系统内部多种源荷可调资源及运行成本结构;其次,建立了在电力市场价格不确定性条件下考虑系统边际成本交易优化模型,并提出沙猫群优化算法进行求解。最后,通过对实际案例的仿真验证。结果表明:该策略不仅可以降低IES的运行成本,还能增强其对市场价格不确定性的适应能力,为综合能源系统在电力现货市场环境下的运行提供了新的思路和方法,有助于实现能源系统参与市场调度的经济性和可靠性双重优化。展开更多
随着能源互联网战略的深入实施,可再生能源与微电网的参与度不断攀升,系统中不确定性因素显著增加,各参与主体间的合作与竞争关系变得愈发错综复杂。从垂直和水平两个层面建立了电网、服务商及多微电网混合博弈双层电能交易体系。在垂...随着能源互联网战略的深入实施,可再生能源与微电网的参与度不断攀升,系统中不确定性因素显著增加,各参与主体间的合作与竞争关系变得愈发错综复杂。从垂直和水平两个层面建立了电网、服务商及多微电网混合博弈双层电能交易体系。在垂直层面提出主从博弈的思想,以服务商为主导者、微电网为从属者。构建不确定性问题分阶段优化的分段鲁棒优化模型,实现不确定性的差异化调度,提高鲁棒优化的灵活性。利用布尔-列和约束生成(Bool-Column and constraint generation,B-C&CG)算法求解模型,并把整个模型分为主问题和子问题:主问题优化电价不确定性问题,子问题优化源荷不确定问题。在水平层面搭建纳什谈判模型,通过交替方向乘子(alternating direction method of multipliers,ADMM)算法求解水平层面微电网之间的电能交互模型。利用分布式求解方法得出交易价格策略,再结合拉格朗日乘子法,交替优化各分部并更新乘子,得出各微电网之间的最佳交易电价。仿真结果表明,所提方案兼顾了系统的鲁棒性、经济性及灵活性,缩减了各微电网的成本并充分保护了各微网的隐私。展开更多
电鳗觅食优化算法EEFO(Electric Eel Foraging Optimization)在迭代过程中会出现全局探索能力不足、容易陷入局部最优和收敛速度慢的问题。同时,算法的性能受到参数设置的影响,需要仔细调整和优化。对此,提出了一种多策略改进的电鳗觅...电鳗觅食优化算法EEFO(Electric Eel Foraging Optimization)在迭代过程中会出现全局探索能力不足、容易陷入局部最优和收敛速度慢的问题。同时,算法的性能受到参数设置的影响,需要仔细调整和优化。对此,提出了一种多策略改进的电鳗觅食优化算法(IEEFO)。首先,调整能量因子策略,引入了双曲正切能量因子,使算法在迭代过程中提前加入开发行为,从而快速发现最优种群,加快收敛速度;之后,改进扰动因子,扩大电鳗游走的位置范围,有利于种群的全局寻优;然后,在迁徙阶段加入正弦余弦策略,促进算法的局部开发;最后,在每次迭代之后,加入透镜成像反向学习的策略来扩大搜索空间,使得算法跳出局部最优并加速收敛到全局最优解。将IEEFO分别与6种基本算法、4种单策略改进的电鳗觅食优化算法进行对比,对13个基准函数进行仿真实验,对IEEFO算法进行性能评估。实验结果表明,IEEFO相比于对比算法收敛速度更快,全局寻优能力更强,算法总体性能有显著提升。此外,通过一个机械优化设计实验进行测试分析,进一步验证了IEEFO的有效性和适用性。展开更多
文摘This research develops a comprehensive method to solve a combinatorial problem consisting of distribution system reconfiguration, capacitor allocation, and renewable energy resources sizing and siting simultaneously and to improve power system's accountability and system performance parameters. Due to finding solution which is closer to realistic characteristics, load forecasting, market price errors and the uncertainties related to the variable output power of wind based DG units are put in consideration. This work employs NSGA-II accompanied by the fuzzy set theory to solve the aforementioned multi-objective problem. The proposed scheme finally leads to a solution with a minimum voltage deviation, a maximum voltage stability, lower amount of pollutant and lower cost. The cost includes the installation costs of new equipment, reconfiguration costs, power loss cost, reliability cost, cost of energy purchased from power market, upgrade costs of lines and operation and maintenance costs of DGs. Therefore, the proposed methodology improves power quality, reliability and security in lower costs besides its preserve, with the operational indices of power distribution networks in acceptable level. To validate the proposed methodology's usefulness, it was applied on the IEEE 33-bus distribution system then the outcomes were compared with initial configuration.
文摘综合能源系统(integrated energy system,IES)参与电力现货市场交易时,由于市场供需关系的变化导致交易价格具有不确定性。因此,对综合能源系统运行边际成本进行精细化分析,研究充分利用综合能源系统灵活性资源参与市场的最优调度策略。首先,分析了外部现货市场环境下市场价格不确定性典型场景处理方法,并研究了综合能源系统内部多种源荷可调资源及运行成本结构;其次,建立了在电力市场价格不确定性条件下考虑系统边际成本交易优化模型,并提出沙猫群优化算法进行求解。最后,通过对实际案例的仿真验证。结果表明:该策略不仅可以降低IES的运行成本,还能增强其对市场价格不确定性的适应能力,为综合能源系统在电力现货市场环境下的运行提供了新的思路和方法,有助于实现能源系统参与市场调度的经济性和可靠性双重优化。
文摘随着能源互联网战略的深入实施,可再生能源与微电网的参与度不断攀升,系统中不确定性因素显著增加,各参与主体间的合作与竞争关系变得愈发错综复杂。从垂直和水平两个层面建立了电网、服务商及多微电网混合博弈双层电能交易体系。在垂直层面提出主从博弈的思想,以服务商为主导者、微电网为从属者。构建不确定性问题分阶段优化的分段鲁棒优化模型,实现不确定性的差异化调度,提高鲁棒优化的灵活性。利用布尔-列和约束生成(Bool-Column and constraint generation,B-C&CG)算法求解模型,并把整个模型分为主问题和子问题:主问题优化电价不确定性问题,子问题优化源荷不确定问题。在水平层面搭建纳什谈判模型,通过交替方向乘子(alternating direction method of multipliers,ADMM)算法求解水平层面微电网之间的电能交互模型。利用分布式求解方法得出交易价格策略,再结合拉格朗日乘子法,交替优化各分部并更新乘子,得出各微电网之间的最佳交易电价。仿真结果表明,所提方案兼顾了系统的鲁棒性、经济性及灵活性,缩减了各微电网的成本并充分保护了各微网的隐私。
文摘电鳗觅食优化算法EEFO(Electric Eel Foraging Optimization)在迭代过程中会出现全局探索能力不足、容易陷入局部最优和收敛速度慢的问题。同时,算法的性能受到参数设置的影响,需要仔细调整和优化。对此,提出了一种多策略改进的电鳗觅食优化算法(IEEFO)。首先,调整能量因子策略,引入了双曲正切能量因子,使算法在迭代过程中提前加入开发行为,从而快速发现最优种群,加快收敛速度;之后,改进扰动因子,扩大电鳗游走的位置范围,有利于种群的全局寻优;然后,在迁徙阶段加入正弦余弦策略,促进算法的局部开发;最后,在每次迭代之后,加入透镜成像反向学习的策略来扩大搜索空间,使得算法跳出局部最优并加速收敛到全局最优解。将IEEFO分别与6种基本算法、4种单策略改进的电鳗觅食优化算法进行对比,对13个基准函数进行仿真实验,对IEEFO算法进行性能评估。实验结果表明,IEEFO相比于对比算法收敛速度更快,全局寻优能力更强,算法总体性能有显著提升。此外,通过一个机械优化设计实验进行测试分析,进一步验证了IEEFO的有效性和适用性。
