Optimizing virtual power plant coordination through locational marginal flexibility under network constraints.

Area of Science:

• Energy Systems Engineering
• Optimization Theory
• Computational Economics

Background:

• Deep decarbonization of energy systems necessitates advanced optimization frameworks.
• Virtual Power Plants (VPPs) require integrated management of long-term dynamics, operational reliability, and contractual stability under uncertainty.
• Existing models often struggle to simultaneously address these complex, interacting factors.

Purpose of the Study:

• To develop a comprehensive modeling and solution framework for the long-term welfare optimization of VPPs.
• To jointly consider seasonal, annual, and rolling time horizons under various operational and environmental constraints.
• To ensure computational tractability and resilience against stochastic variations in renewable energy output and demand.

Main Methods:

• Formulation of a unified welfare objective function internalizing costs, penalties, risks, and carbon charges.
• Integration of a distributionally robust optimization layer with scenario reduction, stability metrics, and fairness tracking.
• Application of constraints for network feasibility, renewable integration, reliability assurance, and carbon accountability.

Main Results:

• Demonstrated effectiveness on a 33-bus system with heterogeneous VPPs.
• Achieved 8-13% reduction in total seasonal welfare costs and up to 45% reduction in curtailment.
• Lowered overload probabilities on critical lines by 20-30% and quantified carbon abatement drivers.

Conclusions:

• The proposed framework offers a rigorous and adaptable blueprint for optimizing low-carbon VPP systems under uncertainty.
• Quantitative improvements in both economic welfare and system reliability were achieved.
• Carbon abatement is a multi-mechanistic outcome driven by curtailment relief, redispatch, loss reduction, and contract rebalancing.