Optimal Management of a Virtual Power Plant with Active Demand Using a MILP–MPC Scheme

The increasing penetration of non-conventional renewable energy sources poses operational flexibility challenges that motivate the coordination of distributed energy resources via active demand management. This study proposes an optimal operational strategy for a Virtual Power Plant (VPP) integrating conventional thermal generation, renewable sources, battery energy storage, main grid transactions, and active demand mechanisms. The optimization model is formulated as a Mixed-Integer Linear Programming (MILP) problem embedded within a receding horizon Model Predictive Control (MPC) framework. Active demand is represented through an aggregated shiftable load and distinct curtailable loads. Shiftable-load operation is regulated through power, ramping, and energy-balance constraints, whereas curtailable loads are governed by user discomfort penalties, economic incentives, and daily curtailment budgets. Renewable forecasting uncertainties are mitigated using a hybrid physical-LSBoost model that reduces prediction errors by up to 93.0%. Operational results demonstrate total operating cost reductions ranging from 2.08% to 15.34%, alongside an 8.65% peak demand reduction. A comprehensive sensitivity analysis reveals that active demand participation is governed by distinct qualitative mechanisms: strict budget bounds, a steep penalty threshold for curtailment activation, and a threshold-type saturation response to temporal price spreads for shiftable loads. Furthermore, high-resolution 5-min online operation confirms the real-time viability of the scheme, maintaining solver execution times under 1.69s per iteration. It is concluded that coordinating active demand, storage, and MPC enhances both economic and technical VPP performance, though carbon emission abatement relies heavily on thermal dispatch dynamics, highlighting the need to explicitly balance economic and environmental trade-offs.