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A method for applying combinatorial optimization algorithms to Energy Storage System (ESS) scheduling is presented in this paper. Scheduling is essential for the integration of ESS in electrical networks at grid level or at consumer level to achieve the objectives of integration such as constraint management or energy cost reduction and for efficient storage dispatch. It also shows that for a time-of-use (ToU) tariff scheme based on the shape of the demand profile with higher prices tied to peak periods, effective load-leveling, and peak demand reduction always leads to energy cost reduction. While other methods usually require more information such as generation cost curves or ToU tariffs to schedule ESS, the proposed method uses only demand profile information and ESS parameters to achieve load-leveling and peak demand reduction and also considers the entire optimization time horizon. This is done by combining heuristic bin packing and subset sum algorithms with specific modifications to the standard forms and through transformations. A case study is presented in which the algorithm is used to schedule household ESS with repurposed electric vehicle batteries and the results are compared to a demand response scheme on the same setup. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]