The surge in interest in battery storage projects has highlighted a fundamental change in the energy market, as commercially viable systems become progressively more available

The deployment of physical energy storage assets can broadly be separated into two project categories. The first kind of project consists of grid-scale assets in “front of the meter”, which are usually implemented by industry partners on large grid connections. The second type is “behind the meter” batteries which provide an added layer of flexibility to energy consumption patterns of sites already connected to the electricity network – and offer tremendous potential to unlock previously inaccessible revenue streams for industrial and commercial customers.
Both project types require different approaches to select the best battery type and optimise operational strategy and performance over time.

Selecting the optimal battery operating strategy

Battery flexibility can unlock several non-mutually exclusive revenue streams. For example, a battery can be used to reduce site demand (for “behind the meter” projects), or export to Grid (for “front of the meter” opportunities) during peak price periods, reducing costs associated with wholesale, Duos, Triads and Capacity Market levy charges. Outside periods of peak tariffs, batteries can participate in the frequency response market and earn a revenue from National Grid for helping to dynamically balance electricity supply and demand.
The characteristics of Battery Energy Storage Systems (BESS) differ widely between manufacturers, with important factors to consider including capital and operating costs, power rating, energy storage capacity, energy density, cell chemistry, operating temperature, round-trip efficiency, self-discharge, degradation profile and tolerance to various depth of discharge. All these parameters have an influence on the economic viability of the project, so it is important to select the appropriate technical solution for a given project.
Once the different parameters are known, the determination of the most economical operating strategy becomes an optimisation problem in response to an aggregated electricity price signal and a potential frequency response revenue, under several constraints such as the battery technical characteristics and the site operational constraints (existing demand/generation on site if any, and import and export capacity).
The operating strategy might change over time, for example because one component of the price signal has changed, or if there is a new opportunity for flexibility that is more financially viable than current revenue streams. In that case, the optimisation process will be performed again and the operating strategy modified accordingly.

State of charge profile of a BESS doing peak price avoidance from 4PM to 7PM and participating in the frequency response market the rest of the time. The energy stored in the system is maximised before 4PM in order to optimise arbitrage revenues.

Choosing the right battery
The next crucial decision is choosing a battery that is optimal for a given project and operating strategy. The goal here is to select the battery that will be commercially viable under the constraints of a given project. For a “front of the meter” BESS the main factors driving the battery characteristics are the Authorised Supply Capacity (ASC) for importing and exporting, the capital and operational costs and the electricity tariffs for import and export.

There are additional parameters for a “behind the meter” battery. As most of these projects are implemented in sites with no or a small export capacity, the battery would respond to a low frequency event by discharging power into the site, reducing its overall energy consumption. It is therefore crucial to forecast the demand on site to choose the optimal battery size and tender an accurate power availability in the frequency response market.

The same approach can be used for generating sites (like wind or solar farms) where there must be sufficient potential for export in addition to the generating activity on site. The potential energy savings are also dependent on the demand and the site constraints, which might in return drive the optimal power/energy ratio of the BESS.

Managing battery state of charge and maintaining performance
Once installed, the challenge is to manage batteries while ensuring high performance following the operating strategy selected. A requirement of entering the frequency response market is to be able to provide the power tendered for 30 minutes at a time, which highlights the need for a performant state of charge management.

There is an inherent efficiency in BESS, with average efficiency ranging from 75% to 90 % for conventional systems. When used in the frequency response market, successive cycles of charge and discharge will progressively cause a net discharge of the battery, and ultimately cause the battery to be fully discharged if no corrective actions are taken. Similarly, if several large high frequency events happen in close succession, a frequency-responsive BESS might reach a high state of charge at which it will not be able to respond to high frequency events anymore.

State of charge of a 1MW/2MW.h frequency responsive battery. An appropriate state of charge management helps keep the energy stored in the battery at an optimal level over time.
A control strategy should ensure that the battery state of charge always stays within appropriate boundaries to meet its contracted obligations at any given point in time. It should also ensure that the total throughput of the battery (which is the cumulative sum of discharge processes over time) is minimised while in operation. A reduced throughput decreases the wear and tear of the battery, enhancing the BESS lifetime.
At A Circuit Ltd we are working with several customers to successfully operate batteries in the frequency response market, optimising their operating profile to maximise revenues, applying designed state of charge management techniques, while limiting the degradation of the battery lifetime to the lowest value possible.
(ref Rémi Boulineau Open Energi )