Self-Organizing Aggregation Operation Scheduling Method for Virtual Power Plant

doi:10.12204/j.issn.1000-7229.2021.04.009

Abstract

Abstract:

This paper presented a bottom-up self-organizing aggregation operation scheduling method for virtual power plants, aiming at reducing the amount of regulation in the process of scheduling by dynamic self-organization aggregation among distributed energy resources (DER). Firstly, according to the output characteristics of DER, the consistency between which and the load is evaluated with the tracking coefficient, and the relevant optimal control model is given. As the basis of self-organizing aggregation, the basic intelligence of DER is considered. Secondly, the framework of coalition formation games is introduced, and the aggregating utility function and its corresponding benefit sharing mechanism are proposed, on the basis of which the self-organizing aggregation conditions of DER are established. Finally, according to Pareto rules, the "merge-split" mechanism is proposed, which can promote the orderly evolution of virtual power plants and form a self-organizing aggregation operation scheduling(SAOS) for virtual power plants. The results show that SAOS can be constituted dynamically according to the output characteristics of DER. Its overall scheduling quantity is equivalent to that of centralized optimization, and is significantly reduced compared with that of independent optimization. The calculation amount and calculation time of SAOS are equivalent to those of independent optimization, and significantly lower than centralized optimization.

Key words: virtual power plant (VPP), self-organization, coalition formation games, dynamic coalition, load tracking

CLC Number:

TM73

WANG Fen, LI Zhiyong, SHAO Jie, ZHOU Huan, FAN Shuai, HE Guangyu. Self-Organizing Aggregation Operation Scheduling Method for Virtual Power Plant[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(4): 79-88.

Figures/Tables 7

Fig.1 Self-organizing aggregation response process of VPP

Fig.2 Flow chart of VPP self-organizing aggregation

Table 1 Load tracking coefficients in three patterns

Fig.3 Aggregate load tracking of VPP1

Table 2 Adjustment in three patterns

Fig.4 Load adjustments of DER1 in three patterns

Table 3 Computational efficiency in three patterns

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