Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2411.02089

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

Manqi Xu·Ye Guo·Hongbin Sun
View original source ↗Read source document ↗Export metadata ↓

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.

Explore related subjects

eess.SYcs.SY

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Manqi Xu, Ye Guo, Hongbin Sun. 2024-11-04. Bidding and Dispatch Strategies with Flexibility Quantification and Pricing for Electric Vehicle Aggregator in Joint Energy-Regulation Market. https://arxiv.org/abs/2411.02089

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

On finite-horizon approximation of an infinite-horizon feedback Nash equilibrium in discrete-time LQ games

Computing feedback Nash equilibria (FNEs) in infinite-horizon discrete-time linear-quadratic (LQ) dynamic games remains computationally challenging. Inspired by model predictive control (MPC) in single-agent optimal control, we address this challenge with a finite-horizon strategy for approximating one such FNE. The finite-horizon strategy is as follows. Each player $i$ has an individual prediction horizon $T^i$. At each stage, player $i$ envisions an auxiliary $T^i$-stage game, computes its unique FNE, and implements only the first-stage control. Our main results are as follows. First, we give parameter conditions that guarantee geometric convergence of the coupled Riccati iteration to a stabilizing solution. Second, under these conditions, the finite-horizon strategies stabilize the system, and each player's total cost converges to the limiting FNE cost as all prediction horizons tend to infinity. Third, we derive an explicit upper bound on this cost gap that decreases geometrically with the shortest prediction horizon. This bound tells us how long the prediction horizons need to be for a given accuracy. The strategy is tractable and implementable, as it avoids directly solving the coupled algebraic Riccati equations of the infinite-horizon game.

eess.SY↗
arXiv2026

Direct and Indirect Data-Driven Control with Prior Information about the Equilibrium Manifold

By hinging on the assumption that a system to be controlled is fully unknown, many data-driven control approaches do not leverage available or readily inferable priors. In contrast to this viewpoint, this paper analyzes the impact of using the system's equilibrium subspace to inform direct and indirect linear quadratic regulation. For the indirect case, we show how including a constraint on the equilibrium subspace in the identification problem changes the statistical properties of the learned model. In particular, we show that enforcing consistency with respect to the equilibrium subspace leads to a reduction in the estimator variance that, in turn, enhances model-based control performance. In the direct case, we show how this prior can be leveraged to gain insight into the controlled system without requiring an explicit identification step. These results are supported by both numerical and experimental evidence, showcasing the advantages of explicitly leveraging the equilibrium manifold as a prior in data-driven control.

eess.SY↗
arXiv2026

Bearing-Only Formation Tracking Control for Euler-Lagrange Multi-Agent Systems Without Inter-Agent Communication

This paper investigates communication-free bearing-only formation tracking control for multi-agent systems governed by Euler-Lagrange dynamics. Distinct from existing results that can only stabilize a stationary formation, this work considers a scenario where the leaders move with time-varying velocities while the inter-agent communication is absent. In this setup, the leaders' states (position and velocity) are unavailable to all followers and cannot be estimated via distributed observers. A novel adaptive distributed control scheme is developed to address this problem. The design exploits the fact that bearing rates contain the projected relative-velocity information, which, together with bearing rigidity, provides a rigidity-based damping mechanism for compensating the unavailable velocity error. Moreover, this damping mechanism is incorporated into a bearing-driven auxiliary variable to construct a surrogate velocity error, facilitating the adaptive control design for EL dynamics. Furthermore, since this damping mechanism necessitates sufficient bearing rigidity, we characterize a rigidity-preserving set and establish its forward invariance, thereby guaranteeing such rigidity via initial conditions. Via a Filippov-based Lyapunov analysis, the proposed scheme is shown to achieve local practical formation tracking in the sense that the velocity error converges to zero and the position error is uniformly ultimately bounded. As a corollary, for the constant-velocity case, asymptotic tracking is achieved without initial-condition restriction. The simulation results verify the effectiveness of the proposed control law.

eess.SY↗