UK energy security: challenges, threats and solutions

Over the last few decades, the debate about 'Peak Oil' became increasingly common and frustrating to governments, oil companies and individuals. Also in the last decade or so, some unusual events took places, which have raised the concern about the future of energy resources. i These events and the peak oil debate lead policy makers, particularly in the industrialised countries, to consider what is known today as 'Energy Security'. The UK is one of these countries that fears the unknown future should petroleum resources worldwide become scarce or vanish. After the dwindling of the North Sea production, the UK found itself on the brink of losing its energy self-sufficiency (Macalister, 2010). This paper sets the following questions: has the UK's oil and gas production peaked yet? If so, does the UK have a serious energy security problem? and if so, how this problem may be solved and what are the possible short, medium and long-term solutions for such a concern? In answering these questions, the paper discusses the concerns and challenges to the UK energy security and brings about the Government plans for tackling these concerns. In other words, the paper seeks to uncover the UK energy security position and shed light on any possible challenges and threats to this position. It also highlights the solutions to the energy security concern. Our analysis is based on quantitative data extracted from Governmental and industrial sources. It is found that the UK does not experience an energy security problem on the short to medium-term, but it may suffer energy insecurity on the longer-term

Climate policy costs of spatially unbalanced growth in electricity demand: the case of datacentres. ESRI Working Paper No. 657 March 2020

We investigate the power system implications of the anticipated expansion in electricity demand by datacentres. We perform a joint optimisation of Generation and Transmission Expansion Planning considering uncertainty in future datacentre growth under various climate policies. Datacentre expansion imposes significant extra costs on the power system, even under the cheapest policy option. A renewable energy target is more costly than a technology-neutral carbon reduction policy, and the divergence in costs increases non-linearly in electricity demand. Moreover, a carbon reduction policy is more robust to uncertainties in projected demand than a renewable policy. High renewable targets crowd out other low-carbon options such as Carbon Capture and Sequestration. The results suggest that energy policy should be reviewed to focus on technology-neutral carbon reduction policies

Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions

Economic Analysis of Feed-in Tariffs for Generating Electricity from Renewable Energy Sources

Feed-in tariffs, renewable energy