摘要
针对目前风能、光能、柴油发电机组多能互补模型中考虑弃风光能源的浪费,在系统构建中加入电解水制氢系统和氢燃料电池系统,利用可再生能源弃电实现零碳排放制氢,增强新能源电力的消纳,建立电力系统经济调度模型。将电解水制氢系统、氢燃料电池与风光火多能互补电力系统结合,利用电力系统满足负荷后多余的可再生能源弃电进行电解水制氢,并利用氢燃料电池对不足的电力进行补足,辅助火电机组进行调峰,氢储能的加入能够提升系统对其孤岛环境的电力供应能力。提出基于一种改进非支配排序遗传算法2(NSGA2)来求解目标函数,利用测试函数对改进前后的算法进行性能测试,通过实例数据进行模拟仿真,仿真结果表明,在不增污染气体排放量的同时降低了燃料费用,验证了在解决电力系统经济调度问题方面的可行性和有效性。
Considering the waste of abandoned wind energy in the current multi-energy complementary model of wind energy,light energy and diesel generator set.The electrolytic water hydrogen production system and hydrogen fuel cell system are added into the system construction,and the renewable energy waste power is utilized to achieve zero carbon emission hydrogen production,enhance the absorption of new energy power,and establish the economic dispatching model of power system.The combination of electrolytic water hydrogen production system,hydrogen fuel cell and wind-solar fire multi-energy complementary power system,the use of power system to meet the load of excess renewable energy abandoned electricity for hydrogen electrolysis water hydrogen production,and the use of hydrogen fuel cell to supplement the insufficient power,auxiliary thermal power unit peak load,the addition of hydrogen energy storage can improve the system\s power supply capacity for its isolated island environment.An improved non-dominated sorting genetic algorithm 2(NSGA2)is proposed to solve the objective function.The test function is used to test the performance of the algorithm before and after the improvement.The simulation results show that the fuel cost is reduced without increasing the emission of polluting gas.It proves the feasibility and effectiveness in solving the economic dispatching problem of power system.
作者
孙斌
王迪
Sun Bin;Wang Di(Jilin Institute of Chemical Technology,Jilin,Jilin 132000,China;Northeast Electric Power University,Jilin,Jilin 132000,China)
出处
《机电工程技术》
2024年第3期258-263,共6页
Mechanical & Electrical Engineering Technology
关键词
多能互补
经济调度
多目标优化
NSGA2
新能源消纳
multi-energy complementary
economic dispatching
multi-objective optimization
NSGA2
new energy consumption
