Abstract
The output of wind turbine is volatile and difficult to predict. Energy storage can help wind turbine offset the deviation between forecast and actual output. Based on the concept of sharing economy, there will be more alliance for wind turbines and energy storage in the electricity market. However, an open question is how the wind-energy storage alliance’s participation affects market clearing and the profits of market participants. Therefore, a stochastic bi-level optimization model is proposed to describe the bidding behavior of wind-energy storage alliances in energy and frequency regulation markets. At the same time, a new quantitative index of bidding behavior is defined—regulation participation ratio. Considering the uncertainty of wind turbine output, the profits of wind-energy storage alliance are maximized in the upper level. The lower level minimizes the power purchase cost of distribution system operator (DSO) for the joint market clearing. The bi-level model is transformed into a mixed integer linear programming (MILP) model by Karush-Kuhn-Tucker (KKT) conditions, strong duality theory and large M method. Regulation participation ratio is set to different values in the case analysis, so as to analyze the influence of the alliance’s bidding behavior on market. Moreover, the economic impact of alliance on wind turbine and energy storage is compared.
Highlights
The bi-level optimization model is transformed into the single-level mathematical planning problem with equilibrium constraints (MPEC)
MPEC is transformed into mixed integer linear programming (MILP) model by strong duality theory and large M method
In this paper, a bi-level bidding optimization model of windenergy storage alliance is established by introducing regulation participation ratio
Summary
Peiyue Li et al.: Market Impact of Wind-Energy Storage Alliance Strategic Bidding under Uncertainty (October 2021). Reference [12] proposes virtual bidding to improve the market power of retailers, in which the demand uncertainty of strategic retailers is represented by scenarios. In order to reduce the impact of electric vehicles rapid charging stations on the power grid, reference [13] proposes the bidding strategy of rapid charging stations with energy storage systems in energy and reserve markets. Considering electric vehicle cluster as a storage system, reference [16] proposes an optimal bidding framework for regional energy internet in day-ahead markets. Based on multi-stage stochastic programming, reference [26] proposes the bidding strategies for virtual power plants in day-ahead and intraday markets. To maximize the profits of market participants, it is necessary to define a quantitative index for the bidding behavior to study the market impact brought by the decision
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