Over the last 40 years, demand for electricity has increased by over 18%. The rising use of electrified heating and transport in our daily lives is set to push this demand still higher. National consumption is catching up on available supply, increasing the risk of blackouts and leaving us poorly equipped to handle sudden spikes in demand. According to figures from National Grid, the available capacity margin could drop to around 1.2% this winter.
At the same time, ageing infrastructure and parallel technological development means one–fifth of UK power stations are due to close by 2020 with monumental investment (estimated at £110bn) required to replace them and upgrade the National Grid.
With figures suggesting that electricity generation currently accounts for around a third of the UK’s CO2 emissions, the government is keen to lower its impact. It has a target to cover 15 % of the UK’s energy demand from renewable sources by 2020, and meet legally binding emissions reduction targets (80 % by 2050). While renewable sources are generally CO2 neutral, their often intermittent supply coupled with the current lack of energy storage capabilities, results in even further challenges in balancing irregular supply with consumer demand.
In the future smart grids will help manage the electricity grid by allowing information to be exchanged across the entire system. Smart grids are able to redirect supply efficiently to areas of demand, and integrate renewable effectively into the wider energy mix, minimising carbon emissions as a result.
Whilst smart grids look set to tackle problems of supply and demand, however, they will still require significant investment. Assessing which investment options will provide the best return in terms of emissions reductions will allow regulators and investors to implement the most effective methods. In order to achieve this, we require consistency and confidence in the manner in which emissions savings are measured, made possible by implementing a standardised carbon savings model.
At the highest level, it is important to have a model that represents the electricity grid as a mathematical simulation consisting of continually changing inputs and outputs, accounting for generation methods and consumption patterns that vary depending on conditions and demand.
The National Physical Laboratory (NPL), the UK’s National Measurement Institute has developed an independent, adaptable and tested model to measure the amount of carbon that can be saved in electricity generation and consumption, when different technologies and methodologies are employed.
Based on Ensemble Kalman Filter (EnKF) forecast and optimisation (EnOpt) routines, it uses actual electricity generation and consumption data, to ensure the output represents real-world scenarios. The resulting estimation of carbon savings are provided through a statistically robust and rigorous methodology, and the uncertainty quantifications included have been tested on a number of case studies.
NPL has used its Carbon Savings Model to analyse carbon reduction delivered by BT’s Demand Site Management Programme, which includes activities such as TRIAD load management. BT’s purpose is to use the power of communications to make a better world. They are committed to using their technologies to respond to the climate change challenge. The NPL carbon savings model allowed them to prove how their participation in Demand Side Management programmes (like TRIAD) supports this commitment by actively reducing the UK’s carbon emissions.
NPL’s Carbon Savings Model provides a robust and proven tool to calculate the savings that could be achieved and can ensure steady measurements to allow clear comparisons between low carbon investment opportunities on electricity infrastructure.
The UK faces a three-dimensional challenge in the field of energy policy as it sets out to ensure the security of its energy supply, address climate change and deal with fluctuating energy costs. It is critical that we ensure investments in energy are made with confidence in the knowledge that they will address these problems. Having a consistent model that can really contextualise energy usage will help provide confidence to investors and maximise the economic, societal and environmental impact of investment in the smart grid, and ensure that the UK’s energy network is as efficient as it can possibly be.