مهدي پاك نژاد

The transition toward decarbonized power grids necessitates dual analytical frameworks that address both operational robustness against increasing uncertainties an‎d intelligent investment planning for infrastructure expansion. This research presents a novel two-level analytical framework that bridges this gap by linking a Wasserstein Data-Driven Distributionally Robust Optimization (WDRO) model for Unit Commitment (UC) with a robust Multi-Objective Decision-Making (MODM) model for investment planning. At the operational level, a Column-an‎d-Constraint Generation (C&CG) algorithm is employed to solve the two-stage WDRO-UC problem, producing a day-ahead commitment schedule that is robust against a Wasserstein ambiguity set of net-load scenarios. Key Performance Indicators (KPIs) derived from this robust dispatch are then systematically fed as data-driven inputs into the multi-objective investment model, which utilizes the Bertsimas & Sim methodology for cost uncertainty management an‎d the augmented epsilon-constraint method to identify Paretooptimal investment portfolios. This comprehensive framework is implemented an‎d validated on a customized 74-bus test system (based on the RTS-GMLC) that integrates advanced technologies such as Vehicle-to-Grid (V2G), multi-timescale hydrogen storage (short- an‎d long-term), an‎d Carbon Capture, Utilization, an‎d Storage (CCUS). The results clearly demonstrate the superiority of the WDRO model compared to traditional deterministic an‎d stochastic (SAA) approaches; by sacrificing a small amount of profit in average conditions, the proposed model significantly improves economic efficiency an‎d reliability in worst-case scenarios. The operational analysis reveals distinct an‎d economically rational roles for flexibility assets: V2G is deployed for rapid intra-day arbitrage, short-term hydrogen acts as a tactical reserve, an‎d long-term hydrogen serves as a strategic reserve to absorb surplus renewable energy. Finally, by generating Pareto frontiers, the investment model quantitatively visualizes the intricate trade-offs between investment cost, decarbonization targets, an‎d the economic value of flexibility, providing a powerful tool for informed policymaking in the transition toward sustainable energy.

زنجيره تامين برق , انرژي تجديد پذير , ذخيره سازي انرژي , ذخيره سازي هيدروژن , باتري ليتيوم يوني خودرو به شبكه , مشاركت نيروگاهي , بهينه سازي استوار توزيعي واسراشتاين

Power supply chain , Renewable energy , Energy storage , Hydrogen storage , Lithium-ion battery vehicle to grid , Unit commitment , Wasserstein distributionally robust optimization

Mahdi Paknezhad

Dr. Seyed Farid Ghannadpour