Effect of Battery Degradation on Multi-Service Portfolios of Energy Storage

Abstract

Abstract-In an electricity market environment, energy storage plant owners are remunerated for the provision of services to multiple electricity sectors. Some of these services, however, may accelerate battery aging and degradation and hence this needs to be properly balanced against associated services remunerations. In this framework, we propose a combined economic-degradation model to quantify effects of operational policies (mainly focused on constraining State of Charge -SOC-to prescribed levels in order to reduce effects of aging) on gross revenue, multi-service portfolios, degradation and lifespan of distributed energy storage plants that can provide multiple services to energy and balancing market participants and Distribution Network Operators (DNO). Through various case studies based on the Great Britain (GB) system, we demonstrate that although operational policies focused on battery damage reduction will lead to a revenue loss in the shortterm, such loss can be more than compensated by long-term revenues due to a lengthier battery lifespan. We also demonstrate that operational policies to reduce battery degradation mainly affect services related to the energy (rather than balancing) market, which represents a smaller proportion of the overall revenue streams of a distributed storage plant. The model is also used to study effects of ambient temperature fluctuations.Index Terms-Distributed energy storage, multi-service portfolios, degradation, temperature control, power system economics. I. NOMENCLATURE A. Parameters

Control of Battery Storage Systems for the Simultaneous Provision of Multiple Services

“…The relevance of application sinergies for energy storage devices has been pointed out in general terms in [9]. Several works, in the existing technical literature, propose approaches to provide simultaneously multiple grid services and demonstrate their effectiveness by simulations [8], [10]- [20]. These references differ from each other for the kind of services they provide and how they account for BESS operational constraints.…”

“…local objectives (LO) ), like congestion management, voltage regulation at LV and MV level or selfconsumption. The applications described in [8], [10]- [20] are designed to provide combinations of the aforementioned services, as summarized in Table I. In such references, operation scheduling problems for energy storage systems considering multiple services are formulated. These aim at maximising the economic revenue generated for a standalone storage systems exploiting multiple revenue streams.…”

Improving optimal control of grid-connected lithium-ion batteries through more accurate battery and degradation modelling

“…x is the state of charge z and hence the battery state space model f consists of one equation (4), with two constraints g (5). The revenue per unit of time R is the product of the power and the price (6).…”

“…Many previous economic assessments of storage have included a battery degradation model, usually an empirical correlation based on fitting of measured degradation tests e.g. [2,4,5,6]. Although empirical models can provide valuable insight, they should be used with caution [7,8].…”

Hierarchical Energy Management System for Microgrid Operation Based on Robust Model Predictive Control

“…Likewise, SoC min = 0.2 and SoC max = 0.8 are the minimum and maximum values allowed for the SoC. These were set to increase the lifespan of the batteries, because capacity fade is typically accelerated by operating profiles with high average SoC and deep discharge levels [34]. To ensure operation within these limits, the SoC value is estimated based on an unscented Kalman filter [35], with outer feedback correction loops as presented in [36].…”