A hierarchical two-level MILP optimization model for the management of grid-connected BESS considering accurate physical model. (15th March 2023)

• Record Type: Journal Article
• Authors: Nebuloni, Riccardo; Meraldi, Lorenzo; Bovo, Cristian; Ilea, Valentin; Berizzi, Alberto; Sinha, Snigdh; Tamirisakandala, Raviteja Bharadwaj; Raboni, Pietro
• Is Part Of: Applied energy. Volume 334(2023)
• Publication Date: 2023-03-15
• Subjects: Battery degradation -- BESS -- EMS -- MILP -- Stress factors -- Variable efficiency; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2023.120697
• Languages: English
• ISSNs: 0306-2619

Abstract

Abstract: This work proposes a new Energy Management System (EMS) for Battery Energy Storage Systems (BESS). The goal is to make a BESS profitable in the new environment considering massive use of batteries that can be foreseen in the next future, due to the predictive increase of clean energy resources. The developed EMS considers two levels of optimization. The first level models the participation of the BESS in an Ancillary Service Market and schedules the BESS. The second level, the most innovative, is responsible for optimally distributing the power set-points obtained previously among the various battery banks considering, in addition to the battery aging, also the different efficiencies of battery banks, converters, and transformers. Moreover, this second-level manages both active and reactive power flows, and losses. Both optimization algorithms have been modeled as Mixed Integer Linear Programming (MILP) and implemented in GAMS using CPLEX as a solver. The results are encouraging: compared with the common industrial practice in which the load profile is equally shared among the individual batteries within a BESS, the two new proposed EMS strategies guarantee for a long period of operation (10-years) a consistent reduction in the number of batteries replacement (around 47%), thus ensuring significant cost savings. Moreover, the proposed BESS model accurately approximates the real physical behavior of the system, leading to an average error in State-of Energy (SoE) evaluation below 0.6%, which is almost one order of magnitude lower than the ones obtained by simpler models from literature with degradation only SoE-dependent. Highlights: Linear degradation and efficiency models for BESS components in optimization problems. Complex but accurate and robust model for practical applications. New Energy Management System strategies for systems made of different batteries. Short-term studies show the accuracy and efficiency of the model. Long-term studies show the impact on the number of battery replacements.