Bidding and Dispatch Strategies with Flexibility Quantification and Pricing for Electric Vehicle Aggregator in Joint Energy-Regulation Market

TL;DR

This paper develops an online bidding and dispatch strategy for electric vehicle aggregators in joint energy-regulation markets, leveraging EV flexibility as a tradable commodity to enhance profitability and system efficiency.

Contribution

It introduces a method to quantify EV flexibility, incorporates it into a bidding model, and employs stochastic model predictive control for real-time decision-making.

Findings

Proposed flexibility quantification method enables tradable EV flexibility.

The bidding model effectively incorporates flexibility procurement.

Numerical experiments confirm real-time applicability and effectiveness.

Abstract

Managing and unlocking the flexibility hidden in electric vehicles (EVs) has emerged as a critical yet challenging task towards low-carbon power and energy systems. This paper focuses on the online bidding and dispatch strategies for an EV aggregator (EVA) in a joint energy-regulation market, considering EVs' flexibility contributions and compensations. A method for quantifying EV flexibility as a tradable commodity is proposed, allowing the EVA to set flexibility prices based on bid-in supply curves. An EVA bidding model in the joint market incorporate flexibility procurement is formulated. The stochastic model predictive control technique is employed to solve the bidding problem online and address the uncertainties from the electricity markets and the EVs. A power dispatch protocol that ensures a profitable and feasible allocation based on EV flexibility contribution is proposed. An affine mapping control strategy can be derived based on parametric linear programming, enables online indexing of optimal solutions given the regulation signals to avoid repeatedly solving the problem. Numerical experiments show the effectiveness of the proposed scheme, and the solution methodology can be applied in real-time.