Peak demand shaving and load-levelling using a combination of bin packing and subset sum algorithms for electrical energy storage system scheduling

• Published in: IET science, measurement & technology Vol. 10; no. 5; pp. 477 - 484
• Main Authors: Agamah, Simon U, Ekonomou, Lambros
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 01.08.2016
• Subjects: Algorithms; bin packing; charging phase; discharges (electric); discharging phase; electrical energy storage system scheduling; electricity; electricity demand profile; Energy storage; heuristic algorithms; load‐levelling; Mathematical analysis; mathematical equations; one‐dimensional bin packing problem; peak demand shaving; Peak load; Phases; Regular Papers; Schedules; set theory; Shaving; subset sum algorithms; Transformations (mathematics); viable operation schedule; one-dimensional bin packing problem; heuristic algorithms; electrical energy storage system scheduling; discharging phase; electricity; discharges (electric); set theory; subset sum algorithms; electricity demand profile; bin packing; load-levelling; charging phase; energy storage; viable operation schedule; mathematical equations; peak demand shaving
• ISSN: 1751-8822; 1751-8830
• DOI: 10.1049/iet-smt.2015.0218

An algorithm that uses demand profile information and a minimal set of energy storage system (ESS) parameters is formulated in this study for obtaining ESS operation schedules to achieve peak demand shaving and load-levelling. The algorithm is developed by formulating an electricity demand profile and ESS as a combination of the one-dimensional bin packing problem and the subset sum problem using sets of mathematical equations. The electrical system undergoes specific transformations in the charging and discharging phases of the ESS and heuristic algorithms for solving these problems are applied to obtain a viable operation schedule to meet these objectives. A comparative analysis with another algorithm is presented for verification. Results for a case study are also presented alongside a discussion to show a practical application of the algorithm.