Chance-constrained energy-reserve co-optimization scheduling of wind-photovoltaic-hydrogen integrated energy systems

An optimal energy-reserve scheduling model of wind-photovoltaic-hydrogen integrated energy systems (WPH-IES) with multi-type energy storage devices including electric, thermal and hydrogen is presented in this paper. The chance-constrained programming (CCP) theory is utilized to model the impact of renewable and load uncertainties on reserve constraints. Considering the non-convex of proposed CCP model, an improved discretized step transformation (DST) method is proposed to transform the CCP problem into a solv-able mixed integer linear programming (MILP) formulation. In addition, a critical threshold value selection approach is developed to reduce the number of constraints and improve solution efficiency. Case studies demonstrate that the proposed energy-reserve model can reduce the total operating cost on the premise of ensuring the safety of system operation. The combined improved DST and critical threshold value selection method can reduce computational burden and improve the accuracy of scheduling results.(c) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

Automated summary

This paper presents an optimal energy-reserve scheduling model for wind-photovoltaic-hydrogen integrated energy systems with multi-type energy storage devices. The model considers the impact of renewable and load uncertainties on reserve constraints using chance-constrained programming theory. An improved discretized step transformation method is proposed to convert the non-convex CCP problem into a solvable mixed integer linear programming formulation. Additionally, a critical threshold value selection approach is developed to reduce constraints and improve solution efficiency. Case studies show that the proposed model reduces operating costs while ensuring system safety. The combined method of improved discretized step transformation and critical threshold value selection reduces computational burden and improves scheduling accuracy.