Stochastic-Distributionally Robust Joint Optimisation for Multi-Stage Planning of Park Integrated Energy System Considering V2G Response and PV Uncertainty

Abstract

This paper proposes a multi-stage planning framework for park integrated energy systems (PIES), integrating stochastic optimisation and distributionally robust optimisation (DRO) to address the uncertainties of vehicle-to-grid (V2G) response and photovoltaic (PV) generation. The framework fully leverages the potential of electric vehicles (EVs) stationed in the park during working hours as schedulable energy storage while ensuring their energy needs for post-work commutes. The arrival and departure times of individual EVs, as well as their energy demands for post-work travel, are modelled using Monte Carlo simulation. A price incentive mechanism is introduced to encourage EV owners to participate in scheduling, with explicit consideration of EV battery degradation. Furthermore, a scenario probability-driven DRO method is employed to manage PV generation uncertainty. Simulation results demonstrate that, for long-term planning with steadily increasing demands, the proposed multi-stage approach effectively avoids redundant equipment configuration and enhances economics compared to a one-shot decision. V2G participation significantly reduces equipment investment costs, operational expenses, and carbon emissions. Meanwhile, the DRO planning model achieves an optimal balance between economic efficiency and planning robustness by combining the benefits of stochastic and robust optimisation.