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

Smart grids are expected to play a central role in any transition to a low-carbon energy future, and much research is currently underway on practically every area of smart grids. However, it is evident that even basic aspects such as theoretical and operational definitions, are yet to be agreed upon and be clearly defined. Some aspects (efficient management of supply, including intermittent supply, two-way communication between the producer and user of electricity, use of IT technology to respond to and manage demand, and ensuring safe and secure electricity distribution) are more commonly accepted than others (such as smart meters) in defining what comprises a smart grid. It is clear that smart grid developments enjoy political and financial support both at UK and EU levels, and from the majority of related industries. The reasons for this vary and include the hope that smart grids will facilitate the achievement of carbon reduction targets, create new employment opportunities, and reduce costs relevant to energy generation (fewer power stations) and distribution (fewer losses and better stability). However, smart grid development depends on additional factors, beyond the energy industry. These relate to issues of public acceptability of relevant technologies and associated risks (e.g. data safety, privacy, cyber security), pricing, competition, and regulation; implying the involvement of a wide range of players such as the industry, regulators and consumers. The above constitute a complex set of variables and actors, and interactions between them. In order to best explore ways of possible deployment of smart grids, the use of scenarios is most adequate, as they can incorporate several parameters and variables into a coherent storyline. Scenarios have been previously used in the context of smart grids, but have traditionally focused on factors such as economic growth or policy evolution. Important additional socio-technical aspects of smart grids emerge from the literature review in this report and therefore need to be incorporated in our scenarios. These can be grouped into four (interlinked) main categories: supply side aspects, demand side aspects, policy and regulation, and technical aspects.

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

A novel mechanical analogy based battery model for SoC estimation using a multi-cell EKF

The future evolution of technological systems dedicated to improve energy efficiency will strongly depend on effective and reliable Energy Storage Systems, as key components for Smart Grids, microgrids and electric mobility. Besides possible improvements in chemical materials and cells design, the Battery Management System is the most important electronic device that improves the reliability of a battery pack. In fact, a precise State of Charge (SoC) estimation allows the energy flows controller to exploit better the full capacity of each cell. In this paper, we propose an alternative definition for the SoC, explaining the rationales by a mechanical analogy. We introduce a novel cell model, conceived as a series of three electric dipoles, together with a procedure for parameters estimation relying only on voltage measures and a given current profile. The three dipoles represent the quasi-stationary, the dynamics and the istantaneous components of voltage measures. An Extended Kalman Filer (EKF) is adopted as a nonlinear state estimator. Moreover, we propose a multi-cell EKF system based on a round-robin approach to allow the same processing block to keep track of many cells at the same time. Performance tests with a prototype battery pack composed by 18 A123 cells connected in series show encouraging results.Comment: 8 page, 12 figures, 1 tabl