A Computationally Efficient Rule-Based Scheduling Algorithm for Battery Energy Storage Systems

Lorenzo Becchi, Elisa Belloni, Marco Bindi, Matteo Intravaia (), Francesco Grasso, Gabriele Maria Lozito and Maria Cristina Piccirilli
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Lorenzo Becchi: Department of Information Engineering, Università degli Studi di Firenze, Via di Santa Marta 3, 50139 Firenze, Italy
Elisa Belloni: Department of Engineering, Università di Perugia, Via G. Duranti n.93, 06125 Perugia, Italy
Marco Bindi: Department of Information Engineering, Università degli Studi di Firenze, Via di Santa Marta 3, 50139 Firenze, Italy
Matteo Intravaia: Department of Information Engineering, Università degli Studi di Firenze, Via di Santa Marta 3, 50139 Firenze, Italy
Francesco Grasso: Department of Information Engineering, Università degli Studi di Firenze, Via di Santa Marta 3, 50139 Firenze, Italy
Gabriele Maria Lozito: Department of Information Engineering, Università degli Studi di Firenze, Via di Santa Marta 3, 50139 Firenze, Italy
Maria Cristina Piccirilli: Department of Information Engineering, Università degli Studi di Firenze, Via di Santa Marta 3, 50139 Firenze, Italy

Sustainability, 2024, vol. 16, issue 23, 1-21

Abstract: This paper presents a rule-based control strategy for the Battery Management System (BMS) of a prosumer connected to a low-voltage distribution network. The main objective of this work is to propose a computationally efficient algorithm capable of managing energy flows between the distribution network and a prosumer equipped with a photovoltaic (PV) energy production system. The goal of the BMS is to maximize the prosumer’s economic revenue by optimizing the use, storage, sale, and purchase of PV energy based on electricity market information and daily production/consumption curves. To achieve this goal, the method proposed in this paper consists of developing a rule-based algorithm that manages the prosumer’s Battery Energy Storage System (BESS). The rule-based approach in this type of problem allows for the reduction of computational costs, which is of fundamental importance in contexts where many users will be coordinated simultaneously. This means that the BMS presented in this work could play a vital role in emerging Renewable Energy Communities (RECs). From a general point of view, the method requires an algorithm to process the load and generation profiles of the prosumer for the following three days, together with the hourly price curve. The output is a battery scheduling plan for the timeframe, which is updated every hour. In this paper, the algorithm is validated in terms of economic performance achieved and computational times on two experimental datasets with different scenarios characterized by real productions and loads of prosumers for over a year. The annual economic results are presented in this work, and the proposed rule-based approach is compared with a linear programming optimization algorithm. The comparison highlights similar performance in terms of economic revenue, but the rule-based approach guarantees 30 times lower processing time.

Keywords: battery management system; rule-based optimization; linear programming optimization; smart grids (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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