Inclusion of Battery SoH Estimation in Smart Distribution Planning With Energy Storage Systems

• Published in: IEEE transactions on power systems Vol. 36; no. 3; pp. 2323 - 2333
• Main Authors: Alrumayh, Omar, Wong, Steven, Bhattacharya, Kankar
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Batteries; Degradation; Distribution planning; Energy distribution; Energy management; Energy management systems; Energy storage; energy storage system; Estimation; home energy management system; Household appliances; Lithium-ion batteries; Load modeling; Local distribution companies; local distribution company; Mathematical model; Mathematical models; Neural networks; Planning; Rechargeable batteries; Residential energy; Schedules; smart grid; state of health; Storage systems; Stress concentration
• DOI: 10.1109/TPWRS.2020.3036448
• ISSN: 0885-8950

Energy storage systems (ESSs) can improve energy management in distribution grids, especially with the increasing penetration of home energy management systems (HEMSs) that schedule household appliances and render them as smart loads. A large number of uncoordinated HEMSs can result in significant changes to the aggregated load profile of the distribution system. This paper proposes a framework and mathematical model for integrating ESS in the distribution grid to minimize the operation cost of the local distribution company (LDC) and alleviate the impact of uncoordinated HEMS operation on the distribution grid. A novel neural network (NN) based state of health (SoH) estimator for a lithium-ion (Li-ion) battery based ESS is proposed, which is incorporated within the LDC's planning problem. The results show that the proposed estimation model is an accurate estimation of the SoH of the ESS. The LDC's planning decisions are also compared, considering SoH of the ESS vis-á-vis linear degradation and no-degradation models.