Distributionally Robust Bidding Strategy of Energy-Regulation Market for Electric Vehicle Aggregator based on Scenario Tree Probabilities

doi:10.12204/j.issn.1000-7229.2023.12.015

Abstract

Abstract:

The bidding strategy of an electric vehicle aggregator (EVA) in the energy-regulation market determines the value of electric vehicle flexibility, whereas the bidding decision-making process of an EVA is influenced by many uncertain factors based on the market. Therefore, to address the uncertainty modeling problem of market price and regulation signals, a distributionally robust modeling method that considers the multi-timescale correlation of uncertainty factors is proposed. First, a scenario tree model based on the hierarchical clustering method is proposed to depict the temporal correlation of different market uncertainties. Second, a multi-time-scale bidding decision model of EVA participation in the energy-regulation market based on two-stage optimization is established. A fuzzy set of scenario tree probabilities is constructed based on mixed norm distance to realize the solution of EVA bidding decisions under the framework of distributionally robust optimization. Then, the four-layer robustness problem of max-min-max-min constructed in this study is solved using a column and constraint generation method. Finally, the advantages of the proposed model in solving the two-stage uncertainty optimization problem and improving the economy of the bidding strategy are verified by simulation.

Key words: scenario tree, electric vehicle, regulation market, distributionally robust optimization, bidding decision-making

CLC Number:

TM73

AI Xin, HU Huanyu, HU Junjie, WANG Kunyu, WANG Haoyang, WANG Zhe. Distributionally Robust Bidding Strategy of Energy-Regulation Market for Electric Vehicle Aggregator based on Scenario Tree Probabilities[J]. ELECTRIC POWER CONSTRUCTION, 2023, 44(12): 174-184.

Figures/Tables 12

Fig.1 Flowchart of scenario tree modeling based on hierarchical clustering

Fig.2 Flowchart of solving model based on C&CG algorithm

Fig.3 Cumulative regulation energy scenario clustering results under day-ahead scenario 1

Fig.4 Cumulative mileage signal scenario clustering results under day-ahead scenario 1

Table 1

Fig.5 Day-ahead energy market bidding strategies of different models

Fig.6 Real-time energy market bidding strategies of different models

Fig.7 Real-time regulation market bidding strategies of different models

Fig.A1 Temporal correlation diagram of hierarchical data

Fig.C1 Clustering results of typical day-ahead scenarios

Table C1

Table C2

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