高比例风电系统中储能集群辅助火电机组调峰分层优化控制策略

doi:10.19595/j.cnki.1000-6753.tces.240545

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摘要

为应对大规模风电能源并网给电力系统调峰带来的严峻挑战,提出了一种储能集群辅助火电机组调峰的分层优化控制策略。在火-储调控层,提出基于储能集群调峰盈余系数的火储调峰优化控制策略,通过储能调峰盈余系数的引入合理预留储能调峰出力空间,提高整体调峰效果;在储能集群内部调控层,为减缓储能电站老化提出多目标约束下各储能电站优化控制策略,考虑网络潮流约束、调控一致性约束、出力公平性约束等技术经济性约束条件,兼顾储能寿命衰减慢、节点负荷率平衡度低及运行成本低等多目标模型利用遗传算法计算得到各储能电站出力初值,与上层调峰任务分配中储能所需出力修正迭代,最终确定各储能电站最优出力。通过算例仿真得出在高峰时段本文所提策略能实现调峰需求下降87.40 MW,在满足新能源完全消纳前提下,总运行成本下降3.62%,且各储能电站循环寿命次数都有不同程度的增加,其中锂电池储能电站增加了4.38%,从经济性及调峰技术性两方面验证了本文所提策略的有效性。

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关键词 ：储能集群, 电力系统调峰, 电池寿命, 分层优化控制策略

Abstract：

To address the challenges associated with integrating large-scale wind power into the power grid, we propose a layered optimization control strategy. This strategy employs energy storage clusters to support thermal power unit peaking. In the fire-storage control layer, an optimization control strategy is introduced based on the peak-regulating surplus coefficient of the energy storage cluster. The goal of this strategy is to reserve space for energy storage peak-regulating output by introducing the storage peak-regulating surplus coefficient, thereby enhancing the overall peak-regulating effect. In the internal control layer of the storage cluster, a multi-objective constraint optimization control strategy is proposed to mitigate the aging of the storage power station. This strategy considers various technical and economic constraints such as network tidal current constraints, regulation consistency constraints, output constraints, and fairness constraints. To determine the optimal power output of each energy storage power station, a multi-objective model is employed that takes into account factors such as slow decay of storage life, low node load rate balance, and low operating costs. The initial value of power output for each storage power station is obtained using genetic algorithms and iterated with the corrected power output of storage energy in the upper layer of the peak regulation task assignment. Through simulation, the proposed strategy can achieve a peak demand reduction of 87.40 MW, while also reducing the total operating cost by 3.62%. Additionally, the number of cycle lives for each energy storage power station increases to varying degrees, including a 4.38% increase for lithium battery storage plants. The effectiveness of the proposed strategy is verified from both an economic and technical standpoint.
In order to verify the effectiveness of the above control strategies, this paper takes an actual wind power and load data from a certain location in China for simulation analysis. The specific conclusions are as follows:
The peak optimization control strategy predicated on the surplus coefficient diminishes the peak shifting demand by 87.40MW in comparison to traditional methods, achieving a 100% wind power consumption rate. Utilizing the residual adjustable capacity of thermal power units to charge the energy storage stations leads to a 0.22% increment in their operating costs (from 365.78 ¥ to 527.68 ¥). However, this results in an 80% extension in the operation time of the energy storage, with no wind abandonment or load loss, culminating in a total operational cost reduction of 1.70×10⁵ ¥ or 3.62% relative to traditional strategies.
By elevating the life loss cost of the energy storage stations within the lower model, while ensuring equitable load rate equalization, pumped storage stations with lower cycle aging costs are empowered, with the converse true for various electrochemical stations. Computational outcomes indicate that the depth of discharge for Lithium Battery Station 1 is reduced by 5.38% under the proposed strategy, increasing its cycle life by 188 times, and for Lithium Battery Station 2 by 4.62%.
Varying the surplus coefficient of energy storage peaking reveals its relationship with operational costs and peaking demands. Optimal economy and peaking effects are achieved when selecting a State of Charge (SOC_tj) of 0.88 and a surplus coefficient of 0.3.

Key words： Energy storage cluster power system peaking remaining useful life two-layer optimal control strategy

收稿日期: 2024-04-08

PACS:

TM614

通讯作者: 李翠萍女,1982年生,副教授,硕士生导师,研究方向为储能技术在电力系统中的应用。E-mail：licuipingabc@163.com

作者简介: 李军徽男,1995年生,博士,教授,研究方向为新能源发电联网运行关键技术和储能技术的应用等。E-mail: lijunhui@neepu.edu.cn

引用本文:

李军徽, 潘雅慧, 穆钢, 李翠萍, 贾晨. 高比例风电系统中储能集群辅助火电机组调峰分层优化控制策略[J]. 电工技术学报, 0, (): 2492932-2492932. Li Junhui, Pan Yahui, Mu Gang, Li Cuiping, Jia Chen. Hierarchical Optimal Control Strategy for Storage Cluster-Assisted Thermal Unit Peaking in High-Ratio Wind Power System. Transactions of China Electrotechnical Society, 0, (): 2492932-2492932.

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