Robust multi-time-scale scheduling of microgrids with renewable energy interpretation and bidirectionally controlled electric vehicles using adaptive Harris hawks optimization

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Authors: Zhuoting Cheng, Rendong Ji, Hai Tao, Ahmed N. Abdalla, Xiu Tang, Shifeng Li
Cited by: 0
References: 30

Abstract

The increasing penetration of renewable energy sources and bidirectionally controlled electric vehicles into microgrids introduces significant uncertainty in generation output, electricity market prices, and user behavior. These uncertainties pose substantial challenges to achieving reliable, economical, and flexible microgrid scheduling. This paper proposes a robust multi-time-scale scheduling framework that integrates renewable energy interpretation with advanced optimization techniques. A distributionally robust optimization model is developed for day-ahead planning, capturing renewable energy variability and electric vehicle dynamics through an ambiguity set constructed from historical data. The non-convex scheduling problem is solved using an adaptive Harris Hawks Optimization algorithm, which enhances convergence stability and search diversity. During real-time operation, a model predictive control strategy refines dispatch decisions every 15 minutes based on updated forecasts, ensuring responsiveness to operational fluctuations. The microgrid system under study includes wind turbines, photovoltaic units, microturbines, battery energy storage, and a fleet of bidirectionally controlled electric vehicles. Simulation results demonstrate that the proposed framework significantly reduces operating costs, mitigates power fluctuations, and enhances renewable energy utilization compared to conventional deterministic methods. These findings validate the effectiveness of the proposed strategy in delivering resilient, cost-effective, and renewable-integrated microgrid scheduling under uncertainty. • Adaptive HHO enables cost-effective, low-fluctuation microgrid control. • DRO + AHHO improves EV-integrated microgrid resilience & efficiency. • DRO + MPC model boosts RE use, stabilizes grid under uncertainty. • Robust microgrid scheduling cuts cost 8.7%, boosts RE use by 5.6%. • Multi-scale microgrid strategy enhances stability, RE use, and savings.

Fields of Study & Subjects

Electric Vehicles and Infrastructure Electric and Hybrid Vehicle Technologies Microgrid Control and Optimization

Publication details

Journal / source
Unconventional Resources
Volume
9
Pages
100305–100305
Publication date
1 January 2026
Language
EN