Optimal Residential Battery Scheduling with Asset Lifespan Consideration

Couraud, B., Norbu, S., Andoni, M. , Robu, V., Gharavi, H. and Flynn, D. (2020) Optimal Residential Battery Scheduling with Asset Lifespan Consideration. In: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), The Hague, Netherlands, 26-28 October 2020, pp. 630-634. ISBN 9781728171005 (doi: 10.1109/ISGT-Europe47291.2020.9248889)

Abstract

Recent development of renewable generation and increasing penetration of electric vehicles have led to large volumes of residential battery storage systems connected at distribution networks. In this paper, we propose a control algorithm for residential batteries that determines optimal day-ahead battery scheduling and operation with the aim to minimize household energy bills and in the context of dynamic Time of Use (ToU) electricity tariffs. The proposed formulation of the optimization problem takes into consideration the battery's depreciation cost, which is determined by the accurate enumeration of battery cycles, including partial cycling i.e. battery cycles that do not start or end at 100% of State of Charge (SoC). A key advantage of the proposed formulation is that the problem can be solvable by use of linear programming. In addition, we study and compare the benefits of the optimisation-based algorithm with lifespan consideration to a simple heuristic-based battery control scheme and an optimisation-based algorithm without battery lifecycle consideration. Results show that battery lifespan consideration in the optimization algorithm does not necessarily yield to lower prosumer energy bills, when compared to other approaches, but it can lead to a lower depreciation cost of the battery.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Norbu, Mr Sonam and Andoni, Dr Merlinda and Flynn, Professor David
• Authors: Couraud, B., Norbu, S., Andoni, M., Robu, V., Gharavi, H., and Flynn, D.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity; College of Science and Engineering > School of Engineering > Systems Power and Energy
• ISBN: 9781728171005