Implementing dynamic virtual power plants with model predictive control

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 thesis proposes a model predictive control (MPC) framework for dynamic virtual power plants (DVPPs). It tracks grid code-specified behaviour encoded by a desired transfer function while enforcing device- and feeder-level constraints. The framework uses a non-uniform prediction horizon that preserves fine near-term resolution for fast disturbance response while extending look-ahead to account for storage and renewable availability effects without uniformly increasing computational burden. Case studies on a modified IEEE 33-bus feeder demonstrate close tracking of frequency and voltage regulation targets when the requested response remains within DVPP capability. The quadratic programming solve times support simulation-level real-time feasibility at the chosen 0.1~s sampling period in the tested MATLAB implementation. When requests exceed DVPP capacity, the controller provides a best-effort response while preserving battery state-of-charge limits, inverter limits, wind-turbine dynamics, feeder voltage bounds, and line-flow constraints. The modular design can accommodate diverse grid codes and DER portfolios, positioning the framework as a promising tool for evolving ancillary service markets.