Risk-Aware Multi-Market Scheduling of Virtual Power Plants with Dynamic Network Tariffs
For power system operators and VPP aggregators, this work provides a more realistic scheduling tool that accounts for local flexibility procurement via dynamic tariffs, though the approach is incremental.
This paper proposes a two-stage stochastic optimization framework for virtual power plant multi-market scheduling that integrates device-level constraints, network limitations, and uncertainties, using conditional value-at-risk for risk preferences and Benders decomposition for tractability. Results show risk aversion reduces profit volatility, and dynamic network tariffs can reduce expected profitability by up to 65% with limited flexibility gains.
As the penetration of distributed energy resources (DERs) increases, harnessing their flexibility becomes critical for power system operations. Virtual power plants (VPPs) offer a promising solution. However, most existing scheduling tools rely on simplified DER or grid models and largely overlook local flexibility procurement mechanisms such as dynamic network tariffs. This paper proposes a two-stage stochastic optimization framework for VPP multi-market scheduling that integrates detailed device-level constraints, network limitations, and operational and market uncertainties. Conditional value-at-risk is incorporated to represent risk preferences, and Benders decomposition ensures tractability with extensive scenario sets. The model jointly optimizes bidding across energy and reserve markets while explicitly accounting for local flexibility procurement through dynamic network tariffs. The results from a realistic case study show that both risk-neutral and risk-averse strategies exploit arbitrage opportunities. However, risk aversion reduces profit volatility through closer alignment with physical dispatch. Dynamic tariffs unlock local flexibility by shifting demand across the day, though strong tariff signals reduce expected profitability by up to 65% with limited additional flexibility gains.