A risk register for energy security: a UK case study

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonEnergy policy in many countries, driven by concerns about resource scarcity and environmental damage, is promoting a shift from fossil fuels to a variety of renewable sources. This has consequences both for sustainability and energy security, concepts which share common features, some of which are poorly defined or lacking good data. Using the Process Analysis Method for systematically selecting (sustainability) indicators, we recognised the need to account for risks arising from resource discovery and processing, conversion, and the use of the final energy vector. We analyse the whole of the fuel supply chain in a six stage process for 25 renewable and non-renewable fuels, both current and potential sources. We find that causes of risks can be categorised into seven groups, namely: economic, environmental, innovation, manufacturing, political, skills, and technical. Furthermore, we identify 34 specific causes of risk which we assess to compare their relative importance for the different fuels. In both structuring the problem, and quantifying individual risks we use published information and consultation with experts to ensure that the analysis has a broad range of inputs. All of these impinge on a national or supra-national assessment of energy security, which are important for the formulation of energy policy. Using the UK as a case study, we have applied our method to both reference and low carbon future energy system scenarios to calculate the levels of risk as the system composition changes. Our method underlines the need for assessments and data relating to many issues which are commonly not considered as part of energy security

Resource Rents, Democracy & the Eight Policy Lessons

We examine if resource revenues are likely to be managed more effectively with strong (or lack of) institutions and if so to contribute to economic development in resource abundant countries. We estimate a general model using evidence for the resource booms of the 1970-2012 period, resource rents, natural capital, socio-economic indicators and for institutions. Our results show 1) Countries with ample natural capital and subsoil wealth levels are associated to a healthier democracy which potentially mitigates the resource curse (RC); 2) High resource rents are negatively associated to weak institutional quality deepening the curse; 3) Long run economic growth is positively associated to natural capital but negatively associated for those countries that receive high resource rents. We recommend stronger transparency for revenue allocation, for sales of oil production, for the allocation of licences, and for revenue collection. One limitation is the lack of information: (energy) laws inducing economic growth. This paper contributes to explaining the long run impact of democratic change on managing resource revenue. Our three key conclusions are:1) Resource abundance across the world produces a strong income effect; 2) Institution quality emerges as the key mechanism from which the RC effect emanates; and 3)The RC effect does not appear in all countries at all times as some researchers argue.  Examinamos si las rentas de recursos naturales son probablemente mejor administradas bajo instituciones fuertes (o falta de estas), y de serlo así si eso contribuye al desarrollo económico de países con abundantes recursos naturales. Estimamos un modelo usando evidencia de booms (1970-2012) de recursos, rentas de recursos, capital natural, indicadores socio-económicos y de instituciones. Nuestros resultados son tres que 1) países con capital natural y riqueza del subsuelo están asociados a una sana democracia lo que mitiga la maldición de los recursos naturales (MRN); 2) altos niveles de renta están negativamente asociados a la baja calidad de instituciones lo que profundiza la maldición; 3) el crecimiento económico a largo plazo está asociado a el capital natural pero tal crecimiento esta negativamente asociado en países que registran altos percepciones de rentas. Recomendamos transparencia en: la distribución de rentas, las ventas de petróleo, distribución de licencias y la recaudación de rentas. Una limitación es la falta de información: leyes (sector energético) que produzcan el crecimiento. Explicamos el impacto de largo plazo de el cambio democrático sobre la gestión de la rentas. Nuestras tres conclusiones claves son: 1) La abundancia de recursos naturales en todo el mundo produce un fuerte efecto sobre los ingresos; 2) La calidad institucional emerge como un mecanismo clave del que emana el efecto MRN; y 3) El efecto MRN no aparece en todos los países en todo momento como argumentan algunos investigadores

FAR out? An examination of converging, diverging and intersecting smart grid futures in the United Kingdom

We describe a novel application of the field anomaly relaxation (FAR) method of scenario construction to the complex problem of smart grid development. We augment the FAR methodology with extensive expert input through all four steps to incorporate detailed knowledge of the technical, economic and policy issues relevant to informing scenarios for smart grid development in the United Kingdom. These steps inform scenarios useful to policymakers, regulators and the energy industry. We found this extended method to be flexible and reliable. Analysis of smart grid development yielded seven dimensions, allowing for portrayal of a complex and informed set of scenarios. The expert input and feedback identified branching points allowing switching between scenarios – a powerful dynamic feature to assist policy development for a fast-changing technological and regulatory landscape

Scenarios for the development of smart grids in the UK : literature review

No abstractUK Energy Research Centr

Scenarios for the development of smart grids in the UK: synthesis report

No abstractUK Energy Research Centr

UK Smart Grid development: an expert assessment of the benefits, pitfalls and functions

Open Access articleMaking electricity grids smarter is a challenging, long-term, and ambitious process. It consists of many possible transitions and involves many actors relevant to existing and potential functions of the grid. We applied a two round Policy Delphi process with a range of sectoral experts who discussed important drivers, barriers, benefits, risks and expected functions of smarter grids, to inform the development of smarter grids. Our analysis of these expert views indicates broad consensus of the necessity for smarter grids, particularly for economic and environmental reasons; yet stakeholders also associated a range of risks and barriers such as lack of investment, disengaged consumers, complexity and data privacy with measures to make the grid smarter. Different methods for implementing smarter grid functions were considered, all thought to be more likely in urban settings. Implications for policy and future research are considered.Natural Environment Research Counci

Policy and Regulation for smart grids in the United Kingdom

The UK has adopted legal obligations concerning climate change which will place increased stresses on the current 'traditional' model of centralised generation. This will include the stimulation of large volumes of intermittent generation, more distributed generation and larger and more variable loads at grid extremities, potentially including large volumes of electric vehicles and heat pumps. Smarter grids have been mooted as a major potential contributor to the decarbonisation of electricity, through facilitation of reduced losses, greater system efficiency, enhanced flexibility to allow the system to deal with intermittent sources and a number of other benefits. This article considers the different policy elements of what will be required for energy delivery in the UK to become smarter, the challenges this presents, the extent to which these are currently under consideration and some of the changes that might be needed in the future. © 2014 Elsevier Ltd

Predicting winning and losing businesses when changing electricity tariffs

By using smart meters, more data about how businesses use energy is becoming available to energy retailers (providers). This is enabling innovation in the structure and type of tariffs on offer in the energy market. We have applied Artificial Neural Networks, Support Vector Machines, and Naive Bayesian Classifiers to a data set of the electrical power use by 12,000 businesses (in 44 sectors) to investigate predicting which businesses will gain or lose by switching between tariffs (a two-classes problem). We have used only three features of each company: their business sector, load profile category, and mean power use. We are particularly interested in the switch between a static tariff (fixed price or time-of-use) and a dynamic tariff (half-hourly pricing). We have extended the two-classes problem to include a price elasticity factor (a three-classes problem). We show how the classification error for the two- and three-classes problems varies with the amount of available data. Furthermore, we used Ordinary Least Squares and Support Vector Regression models to compute the exact values of the amount gained or lost by a business if it switched tariff types. Our analysis suggests that the machine learning classifiers required less data to reach useful performance levels than the regression models

Clustering disaggregated load profiles using a Dirichlet process mixture model

This article has been made available through the Brunel Open Access Publishing Fund.The increasing availability of substantial quantities of power-use data in both the residential and commercial sectors raises the possibility of mining the data to the advantage of both consumers and network operations. We present a Bayesian non-parametric model to cluster load profiles from households and business premises. Evaluators show that our model performs as well as other popular clustering methods, but unlike most other methods it does not require the number of clusters to be predetermined by the user. We used the so-called 'Chinese restaurant process' method to solve the model, making use of the Dirichlet-multinomial distribution. The number of clusters grew logarithmically with the quantity of data, making the technique suitable for scaling to large data sets. We were able to show that the model could distinguish features such as the nationality, household size, and type of dwelling between the cluster memberships