Long-Term Revenue Estimation for Battery Performing Arbitrage and Ancillary Services

Abstract

Energy storage revenue estimation is essential for analyzing financial feasibility of investment in batteries. We quantify the cycles of operation considering depth-of-discharge (DoD) of operational cycles and provide an algorithm to calculate equivalent 100% DoD cycles. This facilitates in comparing cycles of different DoDs. The battery life is frequently defined as a combination of cycle and calendar life. We propose a battery capacity degradation model based on the cycle and the calendar life and operational cycles. Using equivalent 100% DoD cycles and revenue generated, we calculate the dollars per cycle revenue of storage performing electricity price based arbitrage and ancillary services for load balancing in real time. Using PJM's (a regional transmission organization in the United States) real data we calculate short term and long term financial potential for the year of 2017. We observe that participating in ancillary services is significantly more beneficial for storage owners compared to participating in energy arbitrage.

Multimetric Event-Driven System for Long-Term Wireless Sensor Operation for SHM Applications

“…where 0.8 is a compensation factor for an external and environmental factor that affect battery life [36]. The average current consumption during operation can be expressed using (9).…”

Combined economic emission based resource allocation for electric vehicle enabled microgrids

“…The capacity loss and high cost of battery sum up the major financial liabilities of the EV owner and this will require financial compensation [12, 23]. Depth of discharge (DoD) plays an important role in estimating the number of charge cycles [24]. We consider a linear battery model which assumes the number of charge cycles multiplied by the DoD corresponds to 1 cycle operation of 100% DoD, i.e.…”

Optimal Storage Arbitrage under Net Metering using Linear Programming

“…Friction coefficient, denoted as η fric ∈ [0, 1], and introduced in [22] assists in reducing the operational life of the battery such that low returning transactions of charging and discharging are eliminated, thus increasing the operational life of the battery. In subsequent work, authors in [23] propose a framework to tune the value of friction coefficient for increasing operational life of battery. In a prior work, [24], we show that redefining η ch equal to η ch η fric and η dis equal to η dis η fric , we can control the cycles of operation by eliminating the low returning transactions by reducing the value of η fric .…”

“…13: end while V. NUMERICAL RESULTS For the numerical evaluation, we use battery parameters listed in Table II. The performance indices used for evaluating simulations are: • Arbitrage Gains: denotes the gains (made in absence of load and renewable) or reduction in the cost of consumption (made in presence of load and renewable) due to storage performing energy arbitrage under time-varying electricity prices, • Cycles of operation: In our prior work [23] we develop a mechanism to measure the number of cycles of operation based on depth-of-discharge (DoD) of energy storage operational cycles. Equivalent cycles of 100% DoD are identified.…”