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Forecasting optimal running profiles for behind the meter batteries

Case studies

How we helped an industrial client assess the value of installing a battery energy storage system at one of its sites taking account of onsite solar generation and demand.

The background

We worked with an industrial client who was considering building a battery storage asset on sites that had existing (or planned) onsite renewable generation and demand load. Locating a battery on an active commercial or industrial site means the asset can allow the site to avoid paying high import power prices (including non-commodity elements), by discharging to supply demand at those times. The asset can then charge at times of low import prices, or make use of any excess renewable generation, using this to charge the battery instead of exporting to the grid at an unfavourable price.

We provided the client with both forecasts of the expected profit over the battery’s lifetime under different strategies and configurations, and licenced a model for in-house client teams to perform their own analysis across multiple sites and scenarios

Our solution

After agreeing market assumptions with the client, LCP’s stochastic dispatch model was then run to obtain granular price forecasts for the next 20-30 years across multiple market scenarios. This pricing data, along with site-specific onsite generation and demand profiles, was then used in our Standalone Battery Optimisation Model. This model allows users to input prices and expected on-site load, and gives an optimal storage profile (using the same algorithm employed in LCP EnVision) and expected net profit for each day considered.

These actions accounted for any opportunities for the asset to charge either from excess onsite generation or from the grid, as well as discharge either to supply onsite demand or export to the grid. Alternative strategies were also considered, such as the asset competing in the frequency response market instead of supplying onsite demand.

The outcome

Our analysis was used to inform decisions around onsite battery assets, including the operating strategy and asset configuration. Also, LCP’s standalone battery optimisation model was licensed to allow the client to perform their own analysis on a range of other potential sites and configurations.

-£75-£50-£25£0£25£50£75£100£125£150-1.50-1.00-0.50 -0.501.001.502.002.503.0000:00:0000:30:0001:00:0001:30:0002:00:0002:30:0003:00:0003:30:0004:00:0004:30:0005:00:0005:30:0006:00:0006:30:0007:00:0007:30:0008:00:0008:30:0009:00:0009:30:0010:00:0010:30:0011:00:0011:30:0012:00:0012:30:0013:00:0013:30:0014:00:0014:30:0015:00:0015:30:0016:00:0016:30:0017:00:0017:30:0018:00:0018:30:0019:00:0019:30:0020:00:0020:30:0021:00:0021:30:0022:00:0022:30:0023:00:0023:30:00Price, £/MWh, Profit, £Output, MWExample day: Optimal running profile for a behind the meter batterySolar GenerationOnsite DemandBattery UtilisationOnsite demand net of solarOnsite demand net of solar & batteryChange in Profit due to BatteryImport PriceExport Price

How we can help

We can help our clients with a wide range of issues from whole system modelling to individual power plant and policy impact analysis.

Strategic advice aimed at maximising the benefits and minimising the risks associated with the Capacity Market and Contracts for Difference.

We advise on the optimal dispatch of assets within the wholesale market, forward planning of maintenance activities and valuation of commercial upgrades to plant.

We provide detailed forecasts of the GB and Irish power markets, using our EnVision modelling framework. This can provide both short-term and long-term forecasts of all key system metrics, from system wide to individual assets.

We work extensively in the Irish market, providing market modelling and analysis to investors, generation owners and governments.

LCP Enact is an advanced data integration and analytics platform for the UK’s electricity short term markets, allowing power traders to make more informed decisions.

Our understanding of market dynamics and modelling experience allow us to offer evidence-based recommendations on complex policy and regulatory issues that allow fully informed decision-making.

We combine bottom-up unit-level modelling with market and policy insights to quantify the value and understand the risk associated with any generation asset. 

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