A model predictive control framework with customer-priority tiers for virtual power plant resilience during extreme weather: A UK heatwave case study

TL;DR

This paper presents a Model Predictive Control framework for Virtual Power Plants that prioritizes critical loads during extreme weather events, improving resilience and operational efficiency in a UK heatwave case study.

Contribution

It introduces a multi-objective MPC approach with customer-priority tiers for VPP coordination during extreme weather, incorporating real-world data and uncertainties.

Findings

MPC improves system resilience by 11-20% compared to traditional methods.

The framework effectively manages PV, batteries, and loads during heatwaves.

Simulation results demonstrate practical viability under forecast errors.

Abstract

Due to changes in frequency and intensity of extreme weather events, such as heatwaves and storms, power systems around the globe are having to deal with increased imbalance between demand and supply and additional risk of loss of supply, calling for advanced control strategies that strengthen system resilience. This paper develops a Model Predictive Control (MPC) framework for coordination of Virtual Power Plants (VPPs) that manages photovoltaic (PV) systems, batteries, and loads before, during, and after extreme weather events. A multi-objective mixed-integer quadratically constrained program is solved to enforce customer-priority tiers, serving critical loads first, while minimizing operating cost and PV curtailment under network and device constraints. Simulations on the IEEE 33-bus distribution network with real UK heatwave data show that, under realistic forecast errors and modeling uncertainties, MPC improves resilience by 11-20% relative to traditional full-horizon optimization. These results indicate the practical viability of receding-horizon coordination for resilient, low-carbon VPP operation during extreme weather.