The intermittency and volatility of wind and photovoltaic power generation exacerbate issues such as wind and solar curtailment,hindering the efficient utilization of renewable energy and the low-carbon development of...The intermittency and volatility of wind and photovoltaic power generation exacerbate issues such as wind and solar curtailment,hindering the efficient utilization of renewable energy and the low-carbon development of energy systems.To enhance the consumption capacity of green power,the green power system consumption optimization scheduling model(GPS-COSM)is proposed,which comprehensively integrates green power system,electric boiler,combined heat and power unit,thermal energy storage,and electrical energy storage.The optimization objectives are to minimize operating cost,minimize carbon emission,and maximize the consumption of wind and solar curtailment.The multi-objective particle swarm optimization algorithm is employed to solve the model,and a fuzzy membership function is introduced to evaluate the satisfaction level of the Pareto optimal solution set,thereby selecting the optimal compromise solution to achieve a dynamic balance among economic efficiency,environmental friendliness,and energy utilization efficiency.Three typical operating modes are designed for comparative analysis.The results demonstrate that the mode involving the coordinated operation of electric boiler,thermal energy storage,and electrical energy storage performs the best in terms of economic efficiency,environmental friendliness,and renewable energy utilization efficiency,achieving the wind and solar curtailment consumption rate of 99.58%.The application of electric boiler significantly enhances the direct accommodation capacity of the green power system.Thermal energy storage optimizes intertemporal regulation,while electrical energy storage strengthens the system’s dynamic regulation capability.The coordinated optimization of multiple devices significantly reduces reliance on fossil fuels.展开更多
Electromagnetic detection satellite（EDS） is a type of Earth observation satellite（EOS）. Satellites observation and data down-link scheduling plays a significant role in improving the efficiency of satellite observ...Electromagnetic detection satellite（EDS） is a type of Earth observation satellite（EOS）. Satellites observation and data down-link scheduling plays a significant role in improving the efficiency of satellite observation systems. However, the current works mainly focus on the scheduling of imaging satellites, little work focuses on the scheduling of EDSes for its specific requirements.And current works mainly schedule satellite resources and data down-link resources separately, not considering them in a globally optimal perspective. The EDSes and data down-link resources are scheduled in an integrated process and the scheduling result is searched globally. Considering the specific constraints of EDS, a coordinate scheduling model for EDS observation tasks and data transmission jobs is established and an algorithm based on the genetic algorithm is proposed. Furthermore, the convergence of our algorithm is proved. To deal with some specific constraints, a solution repairing algorithm of polynomial computing time is designed. Finally, some experiments are conducted to validate the correctness and practicability of our scheduling algorithms.展开更多
在用能形态转变关键期,有关园区级综合能源系统的试点项目集中涌现。多个园区以不同参与主体接入电力系统与热力系统构成的区域综合能源系统,带来利益冲突与交互功率不匹配问题。为此,基于交替方向乘子分布式算法,建立了多园区服务商与...在用能形态转变关键期,有关园区级综合能源系统的试点项目集中涌现。多个园区以不同参与主体接入电力系统与热力系统构成的区域综合能源系统,带来利益冲突与交互功率不匹配问题。为此,基于交替方向乘子分布式算法,建立了多园区服务商与综合能源供应商的两级协调优化运行模型。上级综合能源供应商运营管理电网与热网构成的区域综合能源系统,下级各园区服务商控制管理各园区级综合能源系统。上级模型中针对热电能源本身特性,建立供需实时平衡的配电网以及考虑热能传输延时性的热网模型;在下级模型中利用多能转换设备实现能源的梯级利用。该文提出的两级协调运行模型基于ADMM(alternatingdirectionmethod of multipliers)分布式算法,利用复制变量法解耦上下层交互功率保护参与主体隐私信息,利用惩罚因子调整参与主体调度策略,在多次迭代互动中实现上下层协调运行。以苏州同里某新能源智慧园区为算例,验证所建模型可保护各参与主体隐私,同时满足整体运行成本最低,并分析不同调度模式以及不同运行场景对系统运行结果的影响。展开更多
Most of the energy produced in the world is consumed by commercial and residential buildings.With the growth in the global economy and world demographics,this energy demand has become increasingly important.This has l...Most of the energy produced in the world is consumed by commercial and residential buildings.With the growth in the global economy and world demographics,this energy demand has become increasingly important.This has led to higher unit electricity prices,frequent stresses on the main electricity grid and carbon emissions due to inefficient energy management.This paper presents an energy-consumption management system based on time-shifting of loads according to the dynamic day-ahead electricity pricing.This simultaneously reduces the electricity bill and the peaks,while maintaining user comfort in terms of the operating waiting time of appliances.The proposed optimization problem is formulated mathematically in terms of multi-objective integer non-linear programming,which involves constraints and consumer preferences.For optimal scheduling,the management problem is solved using the hybridization of the particle swarm optimization algorithm and the branch-and-bound algorithm.Two techniques are proposed to manage the trade-off between the conflicting objectives.The first technique is the Pareto-optimal solutions classification using supervised learning methods.The second technique is called the lexicographic method.The simulations were performed based on residential building energy consumption,time-of-use pricing(TOU)and critical peak pricing(CPP).The algorithms were implemented in Python.The results of the current work show that the proposed approach is effective and can reduce the electricity bill and the peak-to-average ratio(PAR)by 28% and 49.32%,respectively,for the TOU tariff rate,and 48.91% and 47.87% for the CPP tariff rate by taking into account the consumer’s comfort level.展开更多
基金funded by the National Key Research and Development Program of China(2024YFE0106800)Natural Science Foundation of Shandong Province(ZR2021ME199).
文摘The intermittency and volatility of wind and photovoltaic power generation exacerbate issues such as wind and solar curtailment,hindering the efficient utilization of renewable energy and the low-carbon development of energy systems.To enhance the consumption capacity of green power,the green power system consumption optimization scheduling model(GPS-COSM)is proposed,which comprehensively integrates green power system,electric boiler,combined heat and power unit,thermal energy storage,and electrical energy storage.The optimization objectives are to minimize operating cost,minimize carbon emission,and maximize the consumption of wind and solar curtailment.The multi-objective particle swarm optimization algorithm is employed to solve the model,and a fuzzy membership function is introduced to evaluate the satisfaction level of the Pareto optimal solution set,thereby selecting the optimal compromise solution to achieve a dynamic balance among economic efficiency,environmental friendliness,and energy utilization efficiency.Three typical operating modes are designed for comparative analysis.The results demonstrate that the mode involving the coordinated operation of electric boiler,thermal energy storage,and electrical energy storage performs the best in terms of economic efficiency,environmental friendliness,and renewable energy utilization efficiency,achieving the wind and solar curtailment consumption rate of 99.58%.The application of electric boiler significantly enhances the direct accommodation capacity of the green power system.Thermal energy storage optimizes intertemporal regulation,while electrical energy storage strengthens the system’s dynamic regulation capability.The coordinated optimization of multiple devices significantly reduces reliance on fossil fuels.
基金supported by the National Natural Science Foundation of China(6110118461174159)
文摘Electromagnetic detection satellite（EDS） is a type of Earth observation satellite（EOS）. Satellites observation and data down-link scheduling plays a significant role in improving the efficiency of satellite observation systems. However, the current works mainly focus on the scheduling of imaging satellites, little work focuses on the scheduling of EDSes for its specific requirements.And current works mainly schedule satellite resources and data down-link resources separately, not considering them in a globally optimal perspective. The EDSes and data down-link resources are scheduled in an integrated process and the scheduling result is searched globally. Considering the specific constraints of EDS, a coordinate scheduling model for EDS observation tasks and data transmission jobs is established and an algorithm based on the genetic algorithm is proposed. Furthermore, the convergence of our algorithm is proved. To deal with some specific constraints, a solution repairing algorithm of polynomial computing time is designed. Finally, some experiments are conducted to validate the correctness and practicability of our scheduling algorithms.
文摘在用能形态转变关键期,有关园区级综合能源系统的试点项目集中涌现。多个园区以不同参与主体接入电力系统与热力系统构成的区域综合能源系统,带来利益冲突与交互功率不匹配问题。为此,基于交替方向乘子分布式算法,建立了多园区服务商与综合能源供应商的两级协调优化运行模型。上级综合能源供应商运营管理电网与热网构成的区域综合能源系统,下级各园区服务商控制管理各园区级综合能源系统。上级模型中针对热电能源本身特性,建立供需实时平衡的配电网以及考虑热能传输延时性的热网模型;在下级模型中利用多能转换设备实现能源的梯级利用。该文提出的两级协调运行模型基于ADMM(alternatingdirectionmethod of multipliers)分布式算法,利用复制变量法解耦上下层交互功率保护参与主体隐私信息,利用惩罚因子调整参与主体调度策略,在多次迭代互动中实现上下层协调运行。以苏州同里某新能源智慧园区为算例,验证所建模型可保护各参与主体隐私,同时满足整体运行成本最低,并分析不同调度模式以及不同运行场景对系统运行结果的影响。
基金supported by the Ministry of Higher Education,Scientific Research and Innovation,the Digital Development Agency(DDA)and the Centre National pour la Recherche Scientifique et Technique(CNRST)of Morocco(Alkhawarizmi/2020/39).
文摘Most of the energy produced in the world is consumed by commercial and residential buildings.With the growth in the global economy and world demographics,this energy demand has become increasingly important.This has led to higher unit electricity prices,frequent stresses on the main electricity grid and carbon emissions due to inefficient energy management.This paper presents an energy-consumption management system based on time-shifting of loads according to the dynamic day-ahead electricity pricing.This simultaneously reduces the electricity bill and the peaks,while maintaining user comfort in terms of the operating waiting time of appliances.The proposed optimization problem is formulated mathematically in terms of multi-objective integer non-linear programming,which involves constraints and consumer preferences.For optimal scheduling,the management problem is solved using the hybridization of the particle swarm optimization algorithm and the branch-and-bound algorithm.Two techniques are proposed to manage the trade-off between the conflicting objectives.The first technique is the Pareto-optimal solutions classification using supervised learning methods.The second technique is called the lexicographic method.The simulations were performed based on residential building energy consumption,time-of-use pricing(TOU)and critical peak pricing(CPP).The algorithms were implemented in Python.The results of the current work show that the proposed approach is effective and can reduce the electricity bill and the peak-to-average ratio(PAR)by 28% and 49.32%,respectively,for the TOU tariff rate,and 48.91% and 47.87% for the CPP tariff rate by taking into account the consumer’s comfort level.
