AI for Visibility and Forecasting of Renewable Generation aims to improve metered and behind the meter solar and wind generation forecasts to procure flexibility and reduce curtailment more efficiently, and better inform network investment. This will consist of the development of a machine learning algorithm that takes timeseries data from commercial customers and satellite imagery, weather forecasts and other open data to provide a live forecast service. This forecast will improve on the spatial and temporal granularity of existing forecasting, supporting the more efficient procurement of flexibility services and curtailment of generation, ultimately leading to financial savings for the DSO and customers, as well as CO2 savings. 

Benefits

The total forecasted benefits including social return on investment for UK Power Networks are £2,378k over RIIO-ED2 and £5,459k over RIIO-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 
Reduction in flexibility procurement 
The base costs were calculated using published flexibility costs (ukpowernetworks.opendatasoft.com/explore/dataset/UK Power Networks-flexibility-dispatches/), 40% of costs for UK Power Networks in the four months through to October 2023 are paid out to reduce the oversupply of generation, usually weather dependent renewables, when demand is low. The base cost is £26,029k over RIIO-ED2 and £98,675k over RIIO-ED3. 
The UK Power Networks DSO’s forecasting team uses forecasts of renewable generation across the UK Power Networks regions to predict these events and to procure flexibility services. Through having access to more accurate forecasts, the DSO will be able to forecast 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. We estimate this would save in the order of 1.5% of costs due to such future events. 
From the published figures, 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 1.5% on flexibility procurement, this delivers a cumulative saving of £559k over four years to end of RIIO-ED2.  Benefits will be £1.48m in RIIO-ED3.  
 
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. OCF’s 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.   
ESO – 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 ESO’s grid awareness, allowing better estimates of renewable generation, better forecasts and ultimately better decisions on grid connections.  
 
Quantified Societal Benefits 
Social benefits for this project are forecasted to total £1,819k across RIIO-ED2, and £3,979k through RIIO-ED3.  This equates to a total SROI of 11.02 
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 using the same approach that UK Power Networks used in our RIIO-ED2 Business Plan. This takes the forecast uncurtailed annual energy generated by solar and onshore wind and assumes a 10% reduction each year. We have assumed that this innovation would contribute to 10% of that figure. Using an expected financial benefit for customers of £50/MWh, as well as the carbon intensity of the grid and carbon price each year, we arrive at the following social return on investment (SROI) benefits. 
Customers are expected to save £1.7m in RIIO-ED2 and £4m across RIIO-ED3, and there are carbon savings worth £185K and £641k in RIIO-ED2 and RIIO-ED3 respectively.    
Total NPV: (Base - Method + (Benefits (DNO/DSO) + (Societal Benefits)) = £7,598k