| 引用本文: | 郭 威,杨 鹏,孙胜博,等.计及生物质能的热电联供系统经济运行优化策略[J].电力系统保护与控制,2021,49(11):88-96.[点击复制] |
| GUO Wei,YANG Peng,SUN Shengbo,et al.biomass energy; CHP; multi energy complementary; grey wolf optimization; particle swarm optimization; multi-objective optimization[J].Power System Protection and Control,2021,49(11):88-96[点击复制] |
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| 摘要: |
| 为提升我国农村地区生物质能利用效率，借助热电联供(CHP)系统高用能效率优势，给出一种计及生物质能的CHP系统经济运行优化策略。在考虑了运行功率及容量约束下，对生物质能燃气发电、光伏发电以及储能电池等的运行成本进行建模分析，将CHP系统运行经济性优化转化为求解最小系统运行成本问题。在优化问题求解过程中，为提升传统粒子群算法求解效率，采用灰狼-粒子群算法降低优化过程陷入局部最优概率，提升优化算法运行速度。仿真结果表明，灰狼-粒子群算法具有更快的算法收敛速度，能够改善计及生物质能的CHP系统用能经济性，有效降低农村地区用能费用，促进生物质能推广应用。 |
| 关键词: 生物质能 热电联供 多能互补 灰狼算法 粒子群算法 多目标优化 |
| DOI：DOI: 10.19783/j.cnki.pspc.200922 |
| 投稿时间：2020-08-02修订日期：2020-10-29 |
| 基金项目:国家自然科学基金项目资助(51806242)；国家电网河北省电力有限公司科技项目资助(B104DY200299) |
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| biomass energy; CHP; multi energy complementary; grey wolf optimization; particle swarm optimization; multi-objective optimization |
| GUO Wei1,YANG Peng2,SUN Shengbo2,LI Huan3,WANG Xiaotian1,ZHANG Xiuli1 |
| (1. State Grid Hebei Marketing Service Center, Shijiazhuang 050021, China; 2. State Grid Hebei Electric Power Co., Ltd.,
Shijiazhuang 050021, China; 3. College of Engineering, China Agricultural University, Beijing 100083, China) |
| Abstract: |
| To improve the utilization efficiency of biomass energy in rural areas of China, by virtue of the advantages of the high energy efficiency of a Combined Heat and Power (CHP) system, an optimization strategy for economic operation of such a system considering biomass energy is proposed. Given the constraints of operating power and capacity, the operational costs of biomass gas power and photovoltaic power generation and an energy storage battery are modeled and analyzed. The operational economy optimization of a CHP system is transformed into solving the minimum system operation cost problem. To improve the efficiency of a traditional Particle Swarm Optimization (PSO), the Gray Wolf Particle Swarm Optimization (GW-PSO) algorithm is used to reduce the probability of falling into a local optimum and improve the running speed of the algorithm. The simulation results show that the proposed GW-PSO algorithm has faster convergence, and can improve the energy efficiency of a CHP system with biomass energy. It can effectively reduce energy consumption in rural areas. This could promote the popularization and application of biomass energy.
This work is supported by the National Natural Science Foundation of China (No. 51806242) and the Science and Technology Project of State Grid Hebei Electric Power Co., Ltd. (No. B104DY200299). |
| Key words: biomass energy CHP multi energy complementary grey wolf optimization particle swarm optimization multi-objective optimization |
