Implementing Dynamic Virtual Power Plants with Model Predictive Control

Abstract

Power systems are integrating more distributed energy resources (DERs) to meet decarbonization targets. Yet inverter-based generation reduces system inertia and increases the need for fast-acting dynamic ancillary services. Virtual power plants (VPPs) aggregate heterogeneous DERs to provide such services. However, existing approaches do not directly combine prescribed dynamic responses for fast frequency and voltage regulation with explicit enforcement of device-and distribution-network constraints in co-located VPPs. This paper proposes a model predictive control (MPC) framework for dynamic VPPs that tracks grid code-specified behaviour encoded by a desired transfer function while enforcing device-and feeder-level constraints. The framework implements a non-uniform prediction horizon that preserves fine near-term resolution for fast disturbance response while extending look-ahead without uniformly increasing computational burden. Case studies on a modified IEEE 33-bus feeder demonstrate close tracking of frequency and voltage regulation targets with practical real-time feasibility under suitable disturbances, and graceful degradation when requests exceed VPP capacity. The modular design accommodates diverse grid codes and DER portfolios, positioning the framework as a practical tool for evolving ancillary service markets.