Joint Scheduling Method for Peak and Frequency Regulation in Unit Commitment Using Battery Energy Storage Systems

doi:10.16183/j.cnki.jsjtu.2024.404

Abstract

Abstract:

Battery energy storage (BES) is one of the most effective resources for consuming renewable energy and ensuring frequency security. With large-scale integration into power grids, BESs coordinate with thermal power units to participate in peak regulation and frequency regulation, and must reserve sufficient capacity in advance. The required frequency regulation capacity is determined by the implicit relationship between frequency and power. Existing methods linearize the frequency regulation capacity model according to the mixed integer linear programming framework of the unit commitment problem, which ensure computational efficiency at the cost of increased approximation errors. To address the trade-off between model accuracy and computational efficiency, this paper develops a frequency-power relationship model based on the universal approximation theorem of deep neural network (DNN), which not only keeps the structure of unit commitment problem unchanged but also theoretically approximates the original model with arbitrary accuracy. Based on this method, a joint scheduling method for battery energy storage in unit commitment is proposed. First, the mechanism of multi-source joint frequency regulation is analyzed, and a control strategy is developed in which energy storage provides fast power support in response to load step disturbance. According to this control strategy, a DNN is built to predict the maximum disturbance that the system can withstand, and a frequency regulation capacity reservation method for energy storage is proposed. Then, a joint scheduling model for BES peak and frequency regulation is developed to optimize the unit operating state and the capacity allocation of energy storage for the above services. Finally, the simulation results of case studies based on the IEEE 39-bus system verify that the proposed method can ensure the frequency security of the system under the maximum step disturbance, and improve the economy of the system operation, demonstrating the superiority of data-driven methods in representing frequency fluctuations.

Key words: battery energy storage (BES), deep neural network (DNN), peak regulation, frequency regulation, unit commitment

CLC Number:

TM732

LIU Hang, LI Canbing, LIU Jianzhe, WU Yuhang, SHI Minjie. Joint Scheduling Method for Peak and Frequency Regulation in Unit Commitment Using Battery Energy Storage Systems[J]. Journal of Shanghai Jiao Tong University, 2026, 60(7): 1045-1054.

Figures/Tables 11

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负荷节点	f_na	负荷节点	f_na
L3	59.7255	L23	59.7323
L4	59.7259	L24	59.7282
L7	59.7259	L25	59.7276
L8	59.7257	L26	59.7272
L12	59.7272	L27	59.7260
L15	59.7266	L28	59.7281
L16	59.7283	L29	59.7303
L18	59.7260	L31	59.7278
L20	59.7304	L39	59.7280
L21	59.7296

预测方法分类	最大误差/MW	平均误差中值/MW	平均绝对误差/MW	平均绝对百分比误差/%	R²
DNN	6.6801	1.3689	1.5922	0.2284	0.9978
LR	10.5949	4.7557	4.8690	0.5284	0.9851
SVR	21.4414	14.6063	14.0228	1.6371	0.9636

场景	系统运行总成本	火电机组运行成本	电池储能老化成本
Case 1	9214.3	9214.3	0
Case 2	9042.2	9014.3	27.9
Case 3	8939.5	8911.3	28.2

方法分类	可承受最大扰动功率/MW	误差功率/MW	机组组合成本/万元
现有方法^[7]	1027.53	222.33	8955.9
本文方法	1248.65	1.21	8939.5