Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2609.31017

Resource-Aware Rolling-Horizon Controller for Renewable-Based Virtual Power Plants Providing Automatic Frequency Restoration Reserves

Marco Vinicio Avendaño-Caiza·Juan Diego Rios-Peñaloza·Javier Roldán-Pérez·José Luis Rodríguez-Amenedo·Milan Prodanovic
View original source ↗Read source document ↗Export metadata ↓

Abstract

Virtual power plants (VPPs) are an effective solution for increasing the penetration of renewable energy sources (RES) in power systems. Moreover, VPPs can provide ancillary services if generators and loads are closely connected and properly coordinated. This aspect has been addressed in several studies, yet they mostly focus on the dispatch optimisation level, leaving real-time operation aspects unresolved. To close this gap, a central controller for VPPs based on a rolling-horizon optimisation is proposed in this work. Its main objective is to maximise revenues while delivering frequency restoration services. A day-ahead optimisation firstly defines the power setpoint and the upward and downward reserves of the VPP. Then, the rolling-horizon optimiser, executed every minute, distributes the power and reserve references between the VPP units to achieve their close tracking while taking into account resource availability (wind speed, irradiance, etc.), operational constraints and battery degradation. The effectiveness of the controller is tested using a two-area interconnected power system under different RES generation and demand profiles. The simulations are performed in MATLAB/Simulink and include detailed dynamic models of the VPP elements, demonstrating the applicability of the algorithm to real systems. The day-ahead and the rolling-horizon optimisation problems are modelled using YALMIP and solved using Gurobi. The obtained results demonstrate the VPP supporting grid frequency restoration by optimally allocating its resources, even under the presence of forecast uncertainty.

Explore related subjects

eess.SYcs.SY

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Marco Vinicio Avendaño-Caiza, Juan Diego Rios-Peñaloza, Javier Roldán-Pérez, José Luis Rodríguez-Amenedo, Milan Prodanovic. 2026-09-25. Resource-Aware Rolling-Horizon Controller for Renewable-Based Virtual Power Plants Providing Automatic Frequency Restoration Reserves. https://arxiv.org/abs/2609.31017

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

KEEP EXPLORING

Related papers

arXiv2026

From Data to Sliding Mode Control of Uncertain Large-Scale Networks with Unknown Dynamics

In this paper, we develop a compositional data-driven approach for the global stabilization of large-scale nonlinear networks with unknown dynamics and external perturbations. We first collect data along a single trajectory of each unknown nominal subsystem during a finite-time experiment. The data collected from each nominal subsystem are then used to design a feedback law that renders each nominal closed-loop subsystem input-to-state stable (ISS), certified by its corresponding ISS Lyapunov function. We derive conditions as data-dependent semidefinite programs that simultaneously yield local ISS controllers and the corresponding ISS Lyapunov functions. To cancel the effect of external perturbations on subsystem dynamics and, consequently, on the whole network dynamics, we then design a local integral sliding mode (ISM) controller for each subsystem using the collected data. Under a small-gain compositional condition, we employ data-driven ISS Lyapunov functions designed for the subsystems to construct a control Lyapunov function for the network, guaranteeing that the nominal closed-loop network is globally asymptotically stable (GAS) at the origin. We then extend this compositional result to perturbed networks, proving that the synthesized ISM controllers render the origin of the closed-loop network GAS even in the presence of perturbations. We demonstrate the efficacy of the proposed data-driven approach on large-scale interconnected networks with five distinct interconnection topologies.

eess.SY↗
arXiv2026

Simultaneous improvement of control and estimation for battery management systems

Standard battery management systems treat the control and state estimation problems as decoupled objectives, relying on certainty equivalence controllers that are blind to the varying observability induced by nonlinear open-circuit voltage models. In this paper, we show that for a broad class of objectives, including the peak shaving and valley filling scenarios common in grid-connected energy storage, the expected cost of a stochastic battery system can be exactly parametrized by the conditional mean and covariance of the state of charge. This reformulation reveals a direct coupling between the control input and estimation quality, a coupling that certainty equivalence controllers ignore, and motivates a dual-control approach in which the controller actively reduces estimation uncertainty by driving the state to high observability regions without compromising the control objective. We derive a deterministic surrogate to this stochastic cost and pose the dual-control problem as a computationally tractable model predictive control problem. We validate our approach on a nine-battery system tracking a time-varying reference trajectory. We report simultaneous improvements in tracking cost (a 28\% reduction) and state estimation error (up to 18\% reduction). The estimation improvement is reported across different state estimators: extended Kalman filter, unscented Kalman filter, and a moving horizon estimator, confirming that the estimation improvement of our approach is not restricted to a specific state observer.

eess.SY↗
arXiv2026

Time-To-Reach Separation and Safety Filtering for Safe, Fair, and Efficient Multi-Agent Coordination

Advanced Air Mobility operations are expected to significantly increase aerial traffic in urban airspace, requiring autonomous traffic management systems to ensure collision-free operations in highly congested environments. In this paper, we propose a multi-agent coordination framework that uses minimum time-to-reach (TTR) as a unifying metric for priority assignment, temporal separation, and safety filtering. We focus on the problem of coordinating multiple aerial vehicles merging into an air corridor while maintaining safe separation between vehicles. Vehicles are assigned arrival-consistent priority based on TTR, and target TTR values are used to enforce temporal spacing, which induces spatial separation. A priority-consistent safety filtering layer based on Hamilton-Jacobi reachability value functions promotes collision avoidance while minimally modifying the reference guidance. Simulation results in a highly congested corridor merging scenario show that the proposed method improves safety, fairness, and efficiency compared to time-optimal guidance and priority-agnostic safety filtering.

eess.SY↗