Optimisation and Risk Management Techniques for a Virtual Power Plant

Virtual Power Plants (VPPs) face significant challenges in managing the uncertainty and variability of distributed energy resources (DERs), which can result in high trading risk and deter investment. This paper proposes and evaluates two advanced optimisation techniques—stochastic programming and robust optimisation—to derive risk-aware bidding strategies for VPP participation in the day-ahead and balancing electricity markets. These methods are benchmarked against a deterministic, expectation-based model. The novelty of this work lies in the comparative application of stochastic and robust frameworks to VPP bidding strategy design under real-world uncertainty, the introduction of scenario-based wind and conventional generation models, and the integration of energy storage into the optimisation framework to assess its impact on profitability and risk mitigation. Through a series of simulations using actual market data from the UK (Elexon), we evaluate three generation portfolio configurations—conventional, renewable, and aggregated. The results show that while stochastic optimisation consistently achieves the highest expected profit, the robust model ensures the highest minimum profit under worst-case conditions. Moreover, combining DER types and integrating battery storage further enhances profitability and reduces exposure to imbalance penalties. These findings provide valuable insights for the development of intelligent, risk-aware trading strategies for VPP operators.