(471h) Techno-Economic Analysis of Electricity Storage Technologies for behind-the-Meter Applications

The electric power grid is one of the most sophisticated, complex and vital inventions of modern times (ErenoÄŸlu et al., 2019). Consisting of infrastructure for generating, transmitting and distributing electricity to hundreds of millions of end-users around the globe, it has become an indispensable amenity in present day. However, owing to unprecedented energy demands attributed to population growth, combined with growing concerns of climate change, a number of challenges have arisen (Bartolucci et al., 2019; ErenoÄŸlu et al., 2019). Some of these include the need for increased energy generation to meet future demand, reduction in greenhouse gas emissions, increased power reliability and flexibility.

Proposed solutions to address these challenges can be categorised in three: to decarbonise, decentralise and digitise energy. This entails, although it is not limited to, a reduction in fossil-based fuel use towards an increased share of cleaner and renewable energy sources (RES), and development of decentralised electrical power systems. RES such as solar, wind, hydro, biomass, have been introduced to a greater extent in the past decade in most regions of the world (REN21, 2019) both in large generating facilities and behind-the-meter (BTM) applications. These changes further introduce challenges of their own, particularly issues relating to power quality and reliability owing to the intermittent nature of renewable energy sources.

Electrical energy storage (EES) technologies have been proven to mitigate some of these problems. With EES, excess electrical energy generated can be stored for use at a different period – with higher loads or during blackouts. Whether they are used in BTM applications – small businesses, households, microgrids, etc. – or in the utility grids, EES devices have shown to improve power reliability, quality and flexibility (Ma et al., 2018). As the use of EES has grown from more traditional roles of just storing excess energy for use during periods of high demand, to more diverse roles such as load levelling, energy arbitrage, ancillary services, etc., it has become more important, and complicated, to determine the optimal choice and size of EES technologies, especially in BTM applications.

This work presents a techno-economic optimisation model for BTM EES technologies. Building on existing works (Fisher et al., 2019; Roberts & Brown, 2019), the proposed model aims to obtain the optimal size of a range of EES assets in light of their ability to access certain revenue streams. Storage technologies explored in this work are restricted to battery energy storage systems – Li-ion, flow, lead-acid batteries – over a range of sizes, capacities, degradation levels and/or power ratings. Revenue streams comprise a reduction in demand charges, energy costs, long term asset costs and/or participation in wholesale and ancillary services markets. Using available load profiles from a portfolio of commercial building types, the proposed model assesses the optimal choice and size of storage technologies that maximises the total financial benefits within different markets.

References

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Erenoğlu, A. K., Erdinç, O., & Taşcıkaraoğlu, A. (2019). History of electricity. In Pathways to a Smarter Power System (pp. 1–27). Elsevier. https://doi.org/10.1016/B978-0-08-102592-5.00001-6

Fisher, M., Apt, J., & Whitacre, J. F. (2019). Can flow batteries scale in the behind-the-meter commercial and industrial market? A techno-economic comparison of storage technologies in California. Journal of Power Sources, 420, 1–8. https://doi.org/10.1016/j.jpowsour.2019.02.051

Ma, T., Shen, L., & Li, M. (2018). Electrical energy storage for buildings. In Handbook of Energy Systems in Green Buildings (pp. 1079–1107). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-49120-1_44

REN21. (2019). Renewables 2019 Global Status Report. https://www.ren21.net/wp-content/uploads/2019/05/gsr_2019_full_report_en...

Roberts, D., & Brown, S. (2019). Flow batteries for energy management: Novel algebraic modelling approaches to properly assess their value. Journal of Energy Storage, 26(September), 100977. https://doi.org/10.1016/j.est.2019.100977