零碳园区电-氢混合储能系统多目标优化配置

doi:10.12204/j.issn.1000-7229.2022.08.001

电力建设 ›› 2022, Vol. 43 ›› Issue (8): 1-12.doi: 10.12204/j.issn.1000-7229.2022.08.001

• 新型电力系统背景下储能系统规划配置与运行控制·栏目主持李相俊教授级高工、帅智康教授、颜宁副教授· • 上一篇下一篇

零碳园区电-氢混合储能系统多目标优化配置

张驰¹(), 周骏¹(), 赵镔¹(), 李嘉乐²(), 杨博²()

1.云南电网有限责任公司昆明供电局,昆明市 654100
2.昆明理工大学电力工程学院, 昆明市 650500

收稿日期:2022-04-10 出版日期:2022-08-01 发布日期:2022-07-27
通讯作者: 杨博 E-mail:124834008@qq.com;7571053@qq.com;603889663@qq.com;614545513@qq.com;yangbo_ac@outlook.com
作者简介:张驰(1987),男,本科,工程师,主要从事配电网生产运维与新能源发电/储能系统规划等方面的工作,E-mail: 124834008@qq.com
周骏(1977),男,本科,工程师,主要从事新能源发电/储能系统规划与配网电压可靠性管理等方面的工作,E-mail: 7571053@qq.com
赵镔(1977),男,本科,工程师,主要从事新能源发电/储能系统规划与配网审查运维、配网作业、可靠性管理等方面的工作,E-mail: 603889663@qq.com
李嘉乐(1999),男,硕士研究生,主要研究方向为储能在配网侧的规划,E-mail: 614545513@qq.com
基金资助:
国家自然科学基金项目(61963020)

Multi-objective Optimal Configuration of Electricity-Hydrogen Hybrid Energy Storage System in Zero-Carbon Park

ZHANG Chi¹(), ZHOU Jun¹(), ZHAO Bin¹(), LI Jiale²(), YANG Bo²()

1. Kunming Power Supply Bureau of Yunnan Power Grid Co., Ltd., Kunming 654100, China
2. Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, China

Received:2022-04-10 Online:2022-08-01 Published:2022-07-27
Contact: YANG Bo E-mail:124834008@qq.com;7571053@qq.com;603889663@qq.com;614545513@qq.com;yangbo_ac@outlook.com
Supported by:
National Natural Science Foundation of China(61963020)

摘要/Abstract

摘要：

随着氢气生产和储存技术的快速发展,开发氢气储能系统(hydrogen energy storage systems, HESSs)将给能源和电力系统结构带来根本性变化。HESSs和电池储能系统(battery energy storage systems, BESSs)相结合进行协调优化可以解决多种能源供需之间的不平衡,并提高能源效率。为确保BESSs和HESSs规划的有效性,以最小全生命周期成本(life cycle cost, LCC)、系统网损、联络线交换功率偏差、负荷波动以及电压波动为目标,采用带精英策略的非支配排序遗传算法(non-dominated sorting genetic algorithm-II, NSGA2)求解储能系统(energy storage systems, ESSs)选址定容规划方案的Pareto非支配解集。并利用基于熵权法(entropy weight method, EWM)的灰靶决策在Pareto非支配解集中选取最佳折中解。另外,通过模糊核C-均值(fuzzy kernel C-means, FKCM)聚类算法获取源荷典型运行场景集,并基于扩展的IEEE-33节点系统进行仿真分析。仿真结果表明:NSGA2算法不仅实现了电-氢混合储能系统LCC最小,且其电压质量、功率稳定性、网损与负荷波动也显著优于对比算法。

关键词: “零碳”园区, 电池储能系统(BESSs), 氢气储能系统(HESSs), 模糊核C-均值, 带精英策略的非支配排序遗传算法(NSGA2)

Abstract:

With the rapid development of hydrogen production and storage technology, the development of hydrogen energy storage systems (HESSs) will bring fundamental changes to energy and power system structure. The coordinated optimization of HESS and battery energy storage system (BESS) can solve the imbalance between supply and demand of various energy sources and improve energy efficiency. In order to ensure the effectiveness of BESS and HESS planning, minimum life cycle cost (LCC), system network loss, switching power deviation, load fluctuation, and voltage fluctuation are chosen as the fitness function in this paper. Meanwhile, a non-dominated sorting genetic algorithm-II (NSGA2) with elite strategy is used to solve energy storage system (ESS) Pareto non-dominated solution set of site-constant volume planning scheme. The grey target decision based on entropy weight method (EWM) is used to select the best compromise solution in Pareto non-dominated solution set. Additionally, typical operation scenarios of source load are obtained by fuzzy kernel C-means (FKCM) clustering algorithm, and the simulation analysis is carried out on the basis of the extended IEEE 33-node system. Simulation results show that NSGA2 algorithm not only achieves the minimum LCC of the electricity-hydrogen hybrid energy storage system, but also improves voltage quality, power stability, network loss and load fluctuation compared to that of other algorithms.

Key words: zero-carbon park, battery energy storage systems (BESSs), hydrogen energy storage systems (HESSs), fuzzy kernel C-means (FKCM), non-dominated sorting genetic algorithm-II (NSGA2)

中图分类号:

TM715

张驰, 周骏, 赵镔, 李嘉乐, 杨博. 零碳园区电-氢混合储能系统多目标优化配置[J]. 电力建设, 2022, 43(8): 1-12.

ZHANG Chi, ZHOU Jun, ZHAO Bin, LI Jiale, YANG Bo. Multi-objective Optimal Configuration of Electricity-Hydrogen Hybrid Energy Storage System in Zero-Carbon Park[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(8): 1-12.

图/表 15

图1 与电网直连的电池储能和氢气储能系统模型

Fig.1 Typical configuration of BESSs and HESSs directly connected to power grid

图2 NSGA2算法用于电-氢储能系统流程图

Fig.2 The flowchart diagram of the proposed NSGA2 used for electricity-hydrogen hybrid energy storage system

图3 负荷、风电和光伏的典型日曲线

Fig.3 Typical diurnal curves of load, wind and PV power

表1 组合场景聚类结果

Table 1 Clustering results of composition scene

图4 扩展的IEEE-33节点系统拓扑

Fig.4 Topology of the extended IEEE 33-node system

表2 电网主要参数

Table 2 Main parameters of power grid

表3 BESSs与HESSs主要数据

Table 3 Main parameters of BESS and HESS

表4 分时电价

Table 4 Time-of-use electricity price

表5 两种算法的Pareto优化结果

Table 5 Pareto optimization results of the model obtained by two algorithms

表6 两种算法获得的BESSs和HESSs配置方案和优化结果

Table 6 BESS and HESS configuration schemes and optimization results obtained by two algorithms

图5 电储能系统与氢储能系统一天内充放电功率

Fig.5 Charging and discharging power of BESS and HESS in one day

图6 扩展的IEEE-33节点系统下两种算法的电网优化结果

Fig.6 Optimization results of network obtained by two algorithms in the extended IEEE 33-node system

图7 扩展的IEEE-33节点系统下平行坐标系Pareto前沿

Fig.7 Pareto front of parallel coordinate system under extended IEEE 33-node system

表7 配置不同储能系统的优化结果对比

Table 7 Comparison of optimization results with different energy storage configurations

图8 基于NSGA2算法对比接入不同储能系统的电网优化结果

Fig.8 Grid optimization results of different energy storage systems based on NSGA2 algorithm

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零碳园区电-氢混合储能系统多目标优化配置

Multi-objective Optimal Configuration of Electricity-Hydrogen Hybrid Energy Storage System in Zero-Carbon Park

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