Introduction: Decarbonisation will change the demand that needs to be supplied from the electricity transmission system. Some examples are heat pumps, electric vehicles, distributed generation and improved insulation. Some of these will increase electricity demand whereas others will reduce it. Approximately 15GW of distributed generation is anticipated to be connected. National Grid therefore needs to understand the impact of different take up rates of these developments on the real time electricity demand and the uncertainties associated with them to ensure that we continue to be able to accurately forecast demand so we can operate securely and economically in to the future.

National Grid currently forecasts maximum demand to an accuracy of approximately 1%. Decarbonisation to meet the EU and Government 2020 CO2 emission targets will change the nature of electricity demand and potentially reduce our ability to accurately forecast it. Increasing amounts of intermittent generation being connected to the transmission system, 30GW by 2020, also means that demand at all times of the day becomes important and not just the historic evening peak. Accurate demand forecasting ensures that the correct amount of response and reserves are held to ensure that electricity is supplied securely and reliably, and at an economic cost that is ultimately borne by electricity consumers.

Objectives: This project will produce a flexible computer model of current and future electricity system demand for use in near term demand forecasting. It will first understand the current makeup of demand and will then introduce the impacts of expected changes in demand as decarbonisation drives changes in domestic, commercial and industrial demand. This model will enable different scenarios to be examined to understand the different influences on a range of developments such as heat pumps, electric vehicles, distributed generation and improved insulation and the consequential impact on final electricity demand. This will help to define what developments are needed to improve near term demand forecasting.