An Energy Management System for the Control of Battery Storage in a Grid-Connected Microgrid Using Mixed Integer Linear Programming

Marvin Barivure Sigalo, Ajit C. Pillai, Saptarshi Das and Mohammad Abusara
Additional contact information
Marvin Barivure Sigalo: Penryn Campus, College of Engineering, Mathematics and Physical Science, University of Exeter, Exeter TR10 9FE, UK
Ajit C. Pillai: Penryn Campus, College of Engineering, Mathematics and Physical Science, University of Exeter, Exeter TR10 9FE, UK
Saptarshi Das: Penryn Campus, College of Engineering, Mathematics and Physical Science, University of Exeter, Exeter TR10 9FE, UK
Mohammad Abusara: Penryn Campus, College of Engineering, Mathematics and Physical Science, University of Exeter, Exeter TR10 9FE, UK

Energies, 2021, vol. 14, issue 19, 1-14

Abstract: This paper proposes an energy management system (EMS) for battery storage systems in grid-connected microgrids. The battery charging/discharging power is determined such that the overall energy consumption cost is minimized, considering the variation in grid tariff, renewable power generation and load demand. The system is modeled as an economic load dispatch optimization problem over a 24 h horizon and solved using mixed integer linear programming (MILP). This formulation, therefore, requires knowledge of the expected renewable energy power production and load demand over the next 24 h. To achieve this, a long short-term memory (LSTM) network is proposed. The receding horizon (RH) strategy is suggested to reduce the impact of prediction error and enable real-time implementation of the EMS that benefits from using actual generation and demand data on the day. At each hour, the LSTM predicts generation and load data for the next 24 h, the dispatch problem is then solved and the battery charging or discharging command for only the first hour is applied in real-time. Real data are then used to update the LSTM input, and the process is repeated. Simulation results show that the proposed real-time strategy outperforms the offline optimization strategy, reducing the operating cost by 3.3%.

Keywords: energy management system; renewable energy; battery energy storage system; MILP; LSTM; RH (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/19/6212/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/19/6212/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:19:p:6212-:d:645934

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().