Stochastic Optimization of Microgrids With Hybrid Energy Storage Systems for Grid Flexibility Services Considering Energy Forecast Uncertainties

• Published in: IEEE transactions on power systems Vol. 36; no. 6; pp. 5537 - 5547
• Main Authors: Garcia-Torres, Felix, Bordons, Carlos, Tobajas, Javier, Real-Calvo, Rafael, Santiago, Isabel, Grieu, Stephane
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Distributed generation; Energy sources; Energy storage; Engineering Sciences; Flexibility; Hybrid systems; Hydrogen; Microgrids; Mixed integer; Optimal control; Optimization; optimization methods; Predictive control; Quadratic programming; Stochastic models; stochastic optimal control; Stochastic processes; Stochastic systems; Storage systems; Uncertainty
• ISSN: 0885-8950; 1558-0679
• DOI: 10.1109/TPWRS.2021.3071867

This paper presents a stochastic framework for the optimization of microgrids that has the functionality of providing flexibility services to System Operators (SOs) considering uncertainties in the energy forecast. The methodology is developed with the aim of being applied to complex microgrids composed of different distributed energy resources and hybrid energy storage systems (ESS). The associated optimization problem is operated in two stages: the first one performs a stochastic optimization of the microgrid in order to reserve an up/down regulation capacity with which to deal with the energy forecast uncertainties of the microgrid. The different microgrid devices are optimized by considering their operational costs in order to achieve their optimal operation in the Day-Ahead Market (DM). The second stage is used to re-schedule the initial planning according to the signal request and an economic offer from the SO. The control problem is developed using Stochastic Model Predictive Control (SMPC) techniques and Mixed-Integer Quadratic Programming (MIQP), owing to the presence of logic, integer, mixed and probabilistic variables. The simulation results show that the proposed methodology reduces the risk of undergoing up/down-penalty deviations in the Regulation Service Market (RM), also being able to provide flexibility services to the SOs, despite being subject to uncertainties in the energy forecast carried out for the microgrid.