Enhanced representations of lithium-ion batteries in power systems models and their effect on the valuation of energy arbitrage applications

Sakti, Apurba; Gallagher, Kevin G.; Sepulveda, Nestor A.; Uçkun, Canan; Vergara, Claudio; Sisternes, Fernando J. de; Dees, Dennis W.; Botterud, Audun

doi:10.1016/j.jpowsour.2016.12.063

Cited by 56 publications

(36 citation statements)

References 26 publications

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“…3(a) and 3(b), the efficiency of a battery improves for higher SOC and C-rates closer to 1C. This result corresponds to previous experimental analyses performed in [15], [1], [16]. The discharging efficiency lowers for higher current values and low SOC, dropping as much as 33% from its maximum value.…”

Section: B Charging and Discharging Battery Efficienciessupporting

confidence: 84%

“…To characterize battery usage on an operation optimization model, it is necessary to derive an expression for the battery's performance, both for charging and discharging regimes. The battery discharging efficiency is given by (16). The symbol p dis denotes the power discharged to the electric grid, and so p dis = v · i.…”

Section: B Charging and Discharging Battery Efficienciesmentioning

confidence: 99%

“…This substitution can be done without the need for evaluating the bilinear products of (19) because η out is dependent on the power provided by the battery, p dis , and the SOC, e.g. a function of i and v eq , see (16). Therefore, the values of p out can be obtained in terms of p dis and SOC, which are the variables of interest in operation models implementing electric energy storage through batteries.…”

Section: Linear Reformulation Approachmentioning

confidence: 99%

“…The model simulates the battery performance based on the battery state during the operation, as in [14]. A Mixed Integer Linear Programming (MILP) model for representing the behavior of a Li-ion battery pack based on the battery's electrochemical behavior was developed by Sakti et al [16]. In this MILP model, the power limits and battery efficiency are expressed as a function of the SOC and the power output.…”

mentioning

confidence: 99%

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Non-Ideal Linear Operation Model for Li-Ion Batteries

Gonzalez-Castellanos

Pozo

Bischi

2020

IEEE Trans. Power Syst.

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Currently, the characterization of electric energy storage units used for power system operation and planning models relies on two major assumptions: charge and discharge efficiencies, and power limits are constant and independent of the electric energy storage state of charge. This approach can misestimate the available storage flexibility.This work proposes a detailed model for the characterization of steady-state operation of Li-ion batteries in optimization problems. The model characterizes the battery performance, including non-linear charge and discharge power limits and efficiencies, as a function of the state of charge and requested power. We then derive a linear reformulation of the model without introducing binary variables, which achieves high computational efficiency, while providing high approximation accuracy. The proposed model characterizes more accurately the performance and technical operational limits associated with Li-ion batteries than those present in classical ideal models.The developed battery model has been compared with three modelling approaches: the complete non-convex formulation; an ideal model typically used in the power system community; and a mixed integer linear reformulation approach. The models have been tested on a network-constrained economic dispatch for a 24bus system. Based on the simulations, we observed approximately 12% of energy mismatches between schedules that use an ideal model and those that use the model proposed in this study.

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Section: B Charging and Discharging Battery Efficienciessupporting

confidence: 84%

Section: B Charging and Discharging Battery Efficienciesmentioning

confidence: 99%

Section: Linear Reformulation Approachmentioning

confidence: 99%

mentioning

confidence: 99%

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Non-Ideal Linear Operation Model for Li-Ion Batteries

Gonzalez-Castellanos

Pozo

Bischi

2020

IEEE Trans. Power Syst.

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“…Thus, further study is required to determine the discharge durations at which the cost of a flow battery installation might first penetrate into the range of breakeven costs such as those shown in these figures. Future works on this topic should consider detailed ESS dispatch models, including bids for each five minute period in RTM [28], to determine the extent to which increased temporal granularity can achieve higher values of revenue. An optimal bidding strategy across both DAM and RTM could also be attractive when both choices are available to the same optimization process [29,30].…”

Section: Breakeven Overnight Installed Costmentioning

confidence: 99%

Potential Arbitrage Revenue of Energy Storage Systems in PJM

et al. 2017

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Abstract:The volatility of electricity prices is attracting interest in the opportunity of providing net revenue by energy arbitrage. We analyzed the potential revenue of a generic Energy Storage System (ESS) in 7395 different locations within the electricity markets of Pennsylvania-New Jersey-Maryland interconnection (PJM), the largest U.S. regional transmission organization, using hourly locational marginal prices over the seven-year period 2008-2014. Assuming a price-taking ESS with perfect foresight in the real-time market, we optimized the charge-discharge profile to determine the maximum potential revenue for a 1 MW system as a function of energy/power ratio, or rated discharge duration, from 1 to 14 h, including a limited analysis of sensitivity to round-trip efficiency. We determined minimum potential revenue with a similar analysis of the day-ahead market. We presented the distribution over the set of nodes and years of price, price volatility, and maximum potential arbitrage revenue. From these results, we determined the breakeven overnight installed cost of an ESS below which arbitrage would be profitable, its dependence on rated discharge duration, its distribution over grid nodes, and its variation over the years. We showed that dispatch into real-time markets based on day-ahead market settlement prices is a simple, feasible method that raises the lower bound on the achievable arbitrage revenue.

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