Degradation Cost Analysis of Li-Ion Batteries in the Capacity Market with Different Degradation Models

Abstract

Increased deployment of intermittent renewable energy plants raises concerns about energy security and energy affordability. Capacity markets (CMs) have been implemented to provide investment stability to generators and secure energy generation by reducing the number of shortage hours. The research presented in this paper contributes to answering the question of whether batteries can provide cost effective back up services for one year in this market. The analysis uses an equivalent circuit lithium ion battery model coupled with two degradation models (empirical and semi-empirical) to account for capacity fade during battery lifetime. Depending on the battery’s output power, four de-rating factors of 0.5 h, 1 h, 2 h and 4 h are considered to study which de-rating strategy can result in best economic profit. Two scenarios for the number of shortage hours per year in the CM are predicted based on the energy demand data of Great Britain and recent research. Results show that the estimated battery profit is maximum with 2 h and 1 h de-rating factors and minimum with 4 h and 0.5 h. Depending on the battery degradation model used, battery degradation cost can considerably impact the potential profit if the battery’s temperature is not controlled with adequate thermal management system. The empirical and semi-empirical models predict that the degradation cost is minimum at 5 °C and 25 °C respectively. Moreover, both models predict degradation is minimum at lower battery charge levels. While the battery’s capacity fade can be minimized to make some profits from the CM service, the increased shortage hours can make providing this service not economically viable.

Challenging Practices of Algebraic Battery Life Models through Statistical Validation and Model Identification via Machine-Learning

“…LIB degradation varies substantially based on environmental conditions and use case, making it challenging to estimate the degradation cost and system profitability. [3][4][5][6] At the root of estimating degradation cost is estimating the state-of-health (SOH) of a cell, which is some measure of the cell performance relative to its initial state. It is possible to simply measure battery SOH, but this approach is not predictive; a predictive model is required to estimate the future state of the battery from its present state.…”

“…The flexibility to handle both small and large training data sets, fast computation, and ease-of-implementation inherent to algebraic reduced-order models naturally lends them to use many complex optimizations, where implementing numerical electrochemical models may be computationally prohibitive. 4,[7][8][9] The main challenge of utilizing a reduced-order battery lifetime model is in identifying an algebraic expression that predicts cell behavior accurately and extrapolates safely. This process is challenging for even the simplest battery degradation mechanisms.…”

Multi-Objective Optimization for Optimal Allocation and Coordination of Wind and Solar DGs, BESSs and Capacitors in Presence of Demand Response

“…The BESS capability to store energy decreases over time resulting in capacity fade, power fade or both. This is as a result of complex electrochemical and mechanical processes inside the battery that can take place simultaneously [33]. In this study, the BESS degradation is considered as a cost per kWh of discharged energy [34].…”

“…The multi-objective function (*)3) consists of three objective functions which should be optimized simultaneously. *)3 = )31, )32, ) 33 23 In order to optimize several conflicting objective functions, various methods have been proposed in the literature such as weighted sum method, ε-constraint method, and goal programming method. In this paper, ε-constraint method is used to solve the proposed multi-objective optimization model.…”

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