Calculation of Day-Ahead Frequency Regulation Capacity for Virtual Power Plants Based on Analytical Target Cascading Method

doi:10.11930/j.issn.1004-9649.202303003

Abstract

Abstract:

In recent years, with the large-scale integration of clean energy such as photovoltaic and wind power, the power grid is under growing stress from the demand of the system's frequency regulation. Meanwhile, the access of distributed resources such as micro gas turbines and energy storage with stable frequency regulation performance has provided new ideas to improve the system's frequency regulation capability. Therefore, an optimized scheduling model based on target cascade analysis is proposed in this paper, which aims to aggregate distributed resources through virtual power plants to participate in the frequency regulation ancillary service market. Specifically, a bi-level optimization scheduling model is established for both the distribution network layer and the virtual power plant layer. The distribution network coordinates the complementary resources of the virtual power plant to improve resource utilization efficiency. Then, the virtual power plant conducts optimization scheduling among internal participants, and the chance-constrained programming is applied to constrain the uncertainty of the frequency regulation signal. Finally, the target cascade analysis method is used to solve the established model, and the effectiveness and feasibility of the optimization scheduling model are verified through simulation examples. The results has demonstrated the effectiveness of the proposed day-ahead dispatching model. Moreover, the virtual power plant can effectively reduce its operating cost by participating in the regulation auxiliary service.

Key words: virtual power plant, regulation auxiliary service, analysis target cascading, optimal scheduling, chance constrained

Ying ZHANG, Yan WEN, Yu JI, Juan ZUO, Wenbo WANG. Calculation of Day-Ahead Frequency Regulation Capacity for Virtual Power Plants Based on Analytical Target Cascading Method[J]. Electric Power, 2024, 57(1): 82-90.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202303003

https://www.electricpower.com.cn/EN/Y2024/V57/I1/82

Figures/Tables 11

Fig.1 VPP control architecture

Fig.2 VPP connectional architecture

Fig.3 ATC computational framework

Fig.4 ATC optimization process

Fig.5 Improved IEEE 33 bus distribution network

Fig.6 Distribution network dispatch results

Fig.7 VPP dispatch results

Fig.8 Total cost of distribution network

Table 1 Distribution network cost under different convergence tolerances

收敛容差	配电网成本/元	收敛容差	配电网成本/元
0.005	38553	0.5	38557
0.050	38554	5.0	38572

Fig.9 Energy storage charge state at different confidence levels

Table 2 Energy storage benefits at different confidence levels

置信水平	收益/元	置信水平	收益/元
0.95	680.0	0.70	791.1
0.90	761.1	0.60	798.1
0.80	780.9

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