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

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.

Energy Arbitrage Optimization With Battery Storage: 3D-MILP for Electro-Thermal Performance and Semi-Empirical Aging Models

“…For profit-optimized system control with consideration of variable costs, Schimpe et al introduced the metric marginal costs which calculated the costs for energy losses and battery degradation occurring under operation of battery systems [20]. In the subsequent work by Hesse et al, the cost metric was implemented in battery system control through mixed integer linear programming (MILP) formulation [7]. The implementation showed MILP as a stable real-time control technique for battery systems with variable price signals.…”

“…The implementation showed MILP as a stable real-time control technique for battery systems with variable price signals. Therefore, MILP appears to be a suitable approach for the problem formulated within the exact solution approaches, for a more detailed discussion on the individual advantages and shortcomings, the reader is referred to the original paper by Hesse et al [7].…”

“…In this study, we focus on including detailed battery costs in the optimization algorithm and build a so-called an aging-aware battery management system (BMS) that helps to assess and optimize economic profitability more accurately. Although different battery dispatch optimization formulations are used in the literature [7]- [9], the effects of battery temperature are typically not fully included. However, as discussed in [10], temperature plays an important role in battery characteristics such as cell resistance, entropy, hysteresis voltage and self-discharge.…”

Energy Storage System Analysis Review for Optimal Unit Commitment

“…(a). Energy arbitrage: Buy energy (charge ESS) during the lower price and sell energy (discharge ESS) during the higher price [95][96][97]. (b).…”

Operation of the Hybrid Photovoltaic-Battery System on the Electricity Market—Simulation, Real-Time Tests and Cost Analysis

“…He et al [23] studied the optimal bidding strategy of the battery energy storage operating on electricity markets, taking into account performance-based regulation and battery lifespan. Hesse et al [24] optimized a dispatch strategy for a storage participating in German arbitrage energy market using mixed-integer programming model. Levelized cost of electricity (LCOE) calculations of PV systems were presented by International Energy Agency and Nuclear Energy Agency [3], while more recent report on the subject was published by International Renewable Energy Agency (IRENA) [25].…”