Ageing and Efficiency Aware Battery Dispatch for Arbitrage Markets Using Mixed Integer Linear Programming †

Holger C. Hesse, Volkan Kumtepeli, Michael Schimpe, Jorn Reniers, David A. Howey, Anshuman Tripathi, Youyi Wang and Andreas Jossen
Additional contact information
Holger C. Hesse: Department of Electrical and Computer Engineering, Technical University of Munich (TUM), 80333 Munich, Germany
Volkan Kumtepeli: Energy Research Institute @ NTU, Interdisciplinary Graduate Programme, Nanyang Technological University, Singapore 637371, Singapore
Michael Schimpe: Department of Electrical and Computer Engineering, Technical University of Munich (TUM), 80333 Munich, Germany
Jorn Reniers: Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK
David A. Howey: Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK
Anshuman Tripathi: Energy Research Institute @ NTU, Nanyang Technological University, Singapore 637141, Singapore
Youyi Wang: School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore
Andreas Jossen: Department of Electrical and Computer Engineering, Technical University of Munich (TUM), 80333 Munich, Germany

Energies, 2019, vol. 12, issue 6, 1-28

Abstract: To achieve maximum profit by dispatching a battery storage system in an arbitrage operation, multiple factors must be considered. While revenue from the application is determined by the time variability of the electricity cost, the profit will be lowered by costs resulting from energy efficiency losses, as well as by battery degradation. In this paper, an optimal dispatch strategy is proposed for storage systems trading on energy arbitrage markets. The dispatch is based on a computationally-efficient implementation of a mixed-integer linear programming method, with a cost function that includes variable-energy conversion losses and a cycle-induced battery capacity fade. The parametrisation of these non-linear functions is backed by in-house laboratory tests. A detailed analysis of the proposed methods is given through case studies of different cost-inclusion scenarios, as well as battery investment-cost scenarios. An evaluation with a sample intraday market data set, collected throughout 2017 in Germany, offers a potential monthly revenue of up to 8762 EUR/MWh cap installed capacity, without accounting for the costs attributed to energy losses and battery degradation. While this is slightly above the revenue attainable in a reference application—namely, primary frequency regulation for the same sample month (7716 EUR/MWh cap installed capacity)—the situation changes if costs are considered: The optimisation reveals that losses in battery ageing and efficiency reduce the attainable profit by up to 36% for the most profitable arbitrage use case considered herein. The findings underline the significance of considering both ageing and efficiency in battery system dispatch optimisation.

Keywords: efficiency; storage; battery ageing; arbitrage; market; optimisation; mixed-integer-linear-programming; piece-wise affine approximation; utility-scale; frequency regulation; primary control reserve; lithium-ion (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: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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