Forecasting unmetered solar energy with AI aims to improve visibility and forecasting of unmetered solar generation. This will allow UK Power Networks to better anticipate network power flows. This should deliver lower flexibility procurement costs, reduced renewable curtailment and better-informed long-term network planning, resulting in lower costs for end users. The project will develop a machine learning (ML) algorithm to infer the capacity of unmetered solar generation installed behind substations. Capacity estimates feed into a solar forecast algorithm to produce forecasts of unmetered solar generation at primary substations. This forms an important input to the forecasting team’s system modelling. This capacity is currently hidden from UK Power Networks’ forecasting capabilities, and having improved estimates will allow better understanding of rooftop solar capacity growth to help calibrate strategic reinforcement planning.

Benefits

The total forecasted benefits including social return on investment for UK Power Networks are £901k over RIIO-ED2 and £2,024k over ED3.

The quantifiable financial benefit arises from increased efficiency in flexibility markets and the resultant lowering of costs to support the distribution network where there is high renewable penetration. The quantifiable social return on investment arises from the reduction of curtailment of renewable generation.

Quantified Financial Benefit

The financial benefits for this project are forecasted to be £370k across RIIO-ED2, and £823k through ED3. The details and assumptions of these calculations is below.

Reduction in flexibility procurement

The UK Power Networks DSO’s forecasting team uses forecasts of renewable generation across the UK Power Networks regions to procure flexibility services. Through having access to more accurate forecasts, the DSO will be able to estimate likely constraints more accurately. This will allow the DSO to procure and dispatch flexibility services on fewer occasions, and at lower volumes while maintaining the same risk appetite.

Open Climate Fix’s solar forecast work with NESO reduced the demand error by 7%. Given that this project expands on this work by also improving the capacity estimation, this would likely be a conservative estimate. Based on this, UK Power Networks should be able to purchase flexibility more precisely by this amount.  However, solar is not always the driver of flexibility requirements, and there is already good solar forecasting for metered capacity.  We have therefore estimated that 15% of the improvement in error is likely to be attributed to the new solution, providing 1.05%

UK Power Networks’ Flexibility Dispatches Dataset shows that oversupply flexibility services cost over £500,000 over a four-month period (at time of writing), or around £5.4m per year. Flexibility is expected to grow as renewable generation increases on the existing infrastructure, so we have assumed an annual growth rate from 2025 to 2028. If we assume a modest efficiency gain of 1.05% on flexibility procurement, this delivers the cumulative savings outlined above.

Quantified Societal Benefits

Social benefits for this project are forecasted to total £531k across RIIO-ED2, and £1,201k through ED3 This corresponds to a social return that is 12.37 times the original investment. The details and assumptions for these calculations are below.

Reduced Curtailment

Improved forecasting will enable the DSO to more smartly manage distributed energy resources (DERs), including distributed energy resources management system (DERMS) settings and dynamic trim limits. These will enable the DSO to reduce the amount of curtailment that it enforces on generators providing a financial benefit to generators and a carbon reduction saving.

The benefits of reduced curtailment have been modelled by taking the total reduction in curtailment due to dynamic outage management, and then applying a 1.05% scaling factor to account for the proportion of this reduction that will likely be attributable to the new service.  This is then multiplied by the carbon benefit and energy price (£85/MWh).

Carbon savings are worth £70K and £261k in RIIO-ED2 and ED3 respectively.

Non quantified benefits:

Tailored forecasts

To enable whole system coordination, the service should be tailored to UK Power Networks. This requires regional aggregations of renewable generation at the primary and secondary level that are probabilistically coherent with each other and with demand forecasts. This means probability distributions must be aligned or correlated, not simply aggregated. Forecasts for individual weather ensemble members can be used to align with UK Power Networks’ internal forecasts.  The control room will have access to a user interface (UI) which can be deployed for granular real-time visibility.

Behind the meter renewable capacity

There is a significant amount of renewable generation behind the meter.  A significant proportion of this embedded renewable capacity is unmetered or invisible to UK Power Networks. At the national level the uncertainty in the estimated total capacity of installed solar photovoltaic (PV) is the most significant source of error when estimating national or regional solar PV generation. To quote a recent paper from Sheffield Solar, “We find that the capacity error, at ±5%, dominates the yield calculation error, at < ±1% and leads to an overall error in GB solar PV output estimates of ±5.1%” [https://www.sciencedirect.com/science/article/pii/S1364032121012636]. This uncertainty nationally of up to 10% would tend to increase as you move to smaller aggregation levels as the averaging of noise across multiple regions is reduced. Therefore, at grid supply point (GSP) or primary level, the error could be double that figure. The project includes research and development to improve the energy accounting of renewables in the UK Power Networks’ regions. The research uses half-hourly substation load figures and the modelling of expected renewable generation given known weather conditions to infer the behind the meter renewable capacity in each primary and secondary substation area. This should have a significant impact on the accuracy of the end forecast as well as giving UK Power Networks a much-improved capacity map. 

NESO – better grid connections & grid awareness

The outputs of this project (the capacity map) could be used as part of improving the overall energy accounting of renewable capacity in the UK Power Networks’ region.  These forecasts and capacity estimates could aid the NESO’s grid awareness, allowing better estimates of renewable generation, better forecasts and ultimately better decisions on grid connections.