Robust Scheduling of Virtual Power Plant under Exogenous and Endogenous Uncertainties

TL;DR

This paper introduces a robust optimization model for virtual power plants that accounts for both external market uncertainties and internal decision-dependent uncertainties, improving scheduling strategies.

Contribution

It presents a novel stochastic adaptive robust optimization model incorporating endogenous uncertainties, solved efficiently using duality, relaxation, and decomposition techniques.

Findings

Effective handling of market and reserve deployment uncertainties.

Improved scheduling strategies for virtual power plants.

Validation through simulation demonstrates practical applicability.

Abstract

Virtual power plant (VPP) provides a flexible solution to distributed energy resources integration by aggregating renewable generation units, conventional power plants, energy storages, and flexible demands. This paper proposes a novel model for determining the optimal offering strategy in the day-ahead energy-reserve market and the optimal self-scheduling plan. It considers exogenous uncertainties (or called decision-independent uncertainties, DIUs) associated with market clearing prices and available wind power generation, as well as the endogenous uncertainties (or called decision-dependent uncertainties, DDUs) pertaining to real-time reserve deployment requests. A tractable solution method based on strong duality theory, McCormick relaxation, and the Benders' decomposition to solve the proposed stochastic adaptive robust optimization with DDUs formulation is developed. Simulation results demonstrate the applicability of the proposed approach.