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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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

Received: 08 April 2024

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TM614

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	Li Junhui
	Pan Yahui
	Mu Gang
	Li Cuiping
	Jia Chen

Cite this article:

Li Junhui,Pan Yahui,Mu Gang等. Hierarchical Optimal Control Strategy for Storage Cluster-Assisted Thermal Unit Peaking in High-Ratio Wind Power System[J]. Transactions of China Electrotechnical Society, 0, (): 2492932-2492932.

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