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

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.

A systematic review of machine learning techniques related to local energy communities

In recent years, digitalisation has rendered machine learning a key tool for improving processes in several sectors, as in the case of electrical power systems. Machine learning algorithms are data-driven models based on statistical learning theory and employed as a tool to exploit the data generated by the power system and its users. Energy communities are emerging as novel organisations for consumers and prosumers in the distribution grid. These communities may operate differently depending on their objectives and the potential service the community wants to offer to the distribution system operator. This paper presents the conceptualisation of a local energy community on the basis of a review of 25 energy community projects. Furthermore, an extensive literature review of machine learning algorithms for local energy community applications was conducted, and these algorithms were categorised according to forecasting, storage optimisation, energy management systems, power stability and quality, security, and energy transactions. The main algorithms reported in the literature were analysed and classified as supervised, unsupervised, and reinforcement learning algorithms. The findings demonstrate the manner in which supervised learning can provide accurate models for forecasting tasks. Similarly, reinforcement learning presents interesting capabilities in terms of control-related applications.publishedVersio

Demand Side Management of Electric Vehicles in Smart Grids: A survey on strategies, challenges, modeling, and optimization

The shift of transportation technology from internal combustion engine (ICE) based vehicles to electricvehicles (EVs) in recent times due to their lower emissions, fuel costs, and greater efficiency hasbrought EV technology to the forefront of the electric power distribution systems due to theirability to interact with the grid through vehicle-to-grid (V2G) infrastructure. The greater adoptionof EVs presents an ideal use-case scenario of EVs acting as power dispatch, storage, and ancillaryservice-providing units. This EV aspect can be utilized more in the current smart grid (SG) scenarioby incorporating demand-side management (DSM) through EV integration. The integration of EVswith DSM techniques is hurdled with various issues and challenges addressed throughout thisliterature review. The various research conducted on EV-DSM programs has been surveyed. This reviewarticle focuses on the issues, solutions, and challenges, with suggestions on modeling the charginginfrastructure to suit DSM applications, and optimization aspects of EV-DSM are addressed separatelyto enhance the EV-DSM operation. Gaps in current research and possible research directions have beendiscussed extensively to present a comprehensive insight into the current status of DSM programsemployed with EV integration. This extensive review of EV-DSM will facilitate all the researchersto initiate research for superior and efficient energy management and EV scheduling strategies andmitigate the issues faced by system uncertainty modeling, variations, and constraints

Flexibility services for distribution network operation

On the way towards a low carbon electricity system, flexibility has become one of the main sources for achieving it. Flexibility can be understood as the ability of a power system to cope with the variability and uncertainty of demand and supply. Both the generation-side and the demand-side can provide it. This research is focused on the role of the demand-side flexibility for providing a service to the distribution system operator, who manages the medium and low-voltage network. By activating this flexibility from the demand-side to the distribution network operator, the latter can avoid or mitigate congestions in the network and prevent grid reinforcement. This thesis starts with analyzing the current state of the art in the field of local electricity markets, setting the baseline for flexibility products in power systems. As a result of the previous analysis, the definition of flexibility is developed more specifically, considering the flexible assets to be controlled, the final client using this flexibility and the time horizon for this flexibility provision. Following the previous step, an aggregated flexibility forecast model is developed, considering a flexibility portfolio based on different flexible assets such as electric vehicles, water boilers, and electric space heaters. The signal is then modeled under a system-oriented approach for providing a service to the distribution network operator under the operation timeline on a day-ahead basis. The flexibility required by the distribution network operator is then calculated through an optimization problem, considering the flexibility activation costs and the network power flow constraints. Finally, since this scenario aims to lower the environmental impacts of the power system, its sustainability is assessed with the life-cycle assessment, considering the entire life cycle and evaluating it in terms of greenhouse gas emissions. This approach enhances the analysis of the potential role of flexibility in the power system, quantifying whether, in all cases, there is a reduction of emissions when shifting the consumption from peak hours to non-peak hours.En el camí cap a un sistema elèctric amb baixes emissions de carboni, la flexibilitat s'ha convertit en una de les principals fonts per aconseguir-ho. La flexibilitat es pot entendre com la capacitat d'un sistema de reaccionar davant la variabilitat i la incertesa provocades per la demanda i la generació. Tant la part de la generació com el costat de la demanda tenen actius per a poder proporcionar-ho. La recerca presentada en aquest manuscrit està enfocada en el paper de la flexibilitat oferta per la demanda, per a proporcionar un servei a l'operador del sistema de distribució, que gestiona les xarxes de mitja i baixa tensió. Gràcies a l'activació de la flexibilitat de la demanda, l'operador de les xarxes de distribució pot evitar o mitigar la congestió de la xarxa i evitar-ne les inversions per a reforçar-la, així com el seu impacte ambiental. Aquesta tesi comença amb l'anàlisi de l'estat de l'art en el camp dels mercats d'electricitat locals, establint-ne la línia base per a la definició dels productes de flexibilitat en els sistemes elèctrics. Com a resultat de l'estudi anterior, la definició de flexibilitat es desenvolupa més específicament, considerant els actius flexibles que han de controlar-se, el client final que utilitza aquesta flexibilitat i l'horitzó temporal per a aquesta disposició de flexibilitat. A continuació es desenvolupa un model de predicció de flexibilitat agregada, considerant una cartera de flexibilitat basada en diferents actius flexibles, com ara vehicles elèctrics, calderes d'aigua i escalfadors elèctrics, gestionats per la figura de l’agregador. El senyal es modela sota un enfocament orientat al sistema per proporcionar un servei a l'operador de la xarxa de distribució, per un horitzó temporal corresponent a l'operació de la xarxa de mitja i baixa tensió. El resultat és un model de la flexibilitat que pot oferir l’agregador. Una vegada desenvolupat el model de flexibilitat pel costat de l’agregador, la tesi s’enfoca al càlcul de la flexibilitat requerida per l’operador de la xarxa de distribució. Això es desenvolupa mitjançant un problema d'optimització, tenint en compte els costos d'activació de la flexibilitat, la localització dels punts on s’injectarà la flexibilitat i les restriccions de flux de potència de la xarxa de distribució. Finalment, atès que aquest escenari pretén reduir l'impacte mediambiental del sistema elèctric, la seva sostenibilitat s'avalua considerant tot el cicle de vida de les tecnologies que hi participen, i avaluant-la en termes d'emissions de gasos d'efecte d'hivernacle. L'ús d'aquest enfocament millora l'anàlisi del potencial paper de la flexibilitat en el sistema elèctric, quantificant si, en tots els casos, hi ha una reducció de les emissions traslladant el consum de les hores punta a hores vall.Postprint (published version

Scenarios for the Development of Smart Grids in the UK: Literature Review

This Working Paper reviews the existing literature on the socio-technical aspects of smart grid development. This work was undertaken as part of the Scenarios for the Development of Smart Grids in the UK project

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

No abstractUK Energy Research Centr

Applications of Agent-Based Methods in Multi-Energy Systems—A Systematic Literature Review

The need for a greener and more sustainable energy system evokes a need for more extensive energy system transition research. The penetration of distributed energy resources and Internet of Things technologies facilitate energy system transition towards the next generation of energy system concepts. The next generation of energy system concepts include “integrated energy system”, “multi-energy system”, or “smart energy system”. These concepts reveal that future energy systems can integrate multiple energy carriers with autonomous intelligent decision making. There are noticeable trends in using the agent-based method in research of energy systems, including multi-energy system transition simulation with agent-based modeling (ABM) and multi-energy system management with multi-agent system (MAS) modeling. The need for a comprehensive review of the applications of the agent-based method motivates this review article. Thus, this article aims to systematically review the ABM and MAS applications in multi-energy systems with publications from 2007 to the end of 2021. The articles were sorted into MAS and ABM applications based on the details of agent implementations. MAS application papers in building energy systems, district energy systems, and regional energy systems are reviewed with regard to energy carriers, agent control architecture, optimization algorithms, and agent development environments. ABM application papers in behavior simulation and policy-making are reviewed with regard to the agent decision-making details and model objectives. In addition, the potential future research directions in reinforcement learning implementation and agent control synchronization are highlighted. The review shows that the agent-based method has great potential to contribute to energy transition studies with its plug-and-play ability and distributed decision-making process

Prosumer communities and relationships in smart grids: A literature review, evolution and future directions

Smart grids are robust, self-healing networks that allow bidirectional propagation of energy and information within the utility grid. This introduces a new type of energy user who consumes, produces, stores and shares energy with other grid users. Such a user is called a "prosumer." Prosumers' participation in the smart grid is critical for the sustainability and long-term efficiency of the energy sharing process. Thus, prosumer management has attracted increasing attention among researchers in recent years. This paper systematically examines the literature on prosumer community based smart grid by reviewing relevant literature published from 2009 to 2018 in reputed energy and technology journals. We specifically focus on two dimensions namely prosumer community groups and prosumer relationships. Based on the evaluated literature, we present eight propositions and thoroughly describe several future research directions

Advancements in Enhancing Resilience of Electrical Distribution Systems: A Review on Frameworks, Metrics, and Technological Innovations

This comprehensive review paper explores power system resilience, emphasizing its evolution, comparison with reliability, and conducting a thorough analysis of the definition and characteristics of resilience. The paper presents the resilience frameworks and the application of quantitative power system resilience metrics to assess and quantify resilience. Additionally, it investigates the relevance of complex network theory in the context of power system resilience. An integral part of this review involves examining the incorporation of data-driven techniques in enhancing power system resilience. This includes the role of data-driven methods in enhancing power system resilience and predictive analytics. Further, the paper explores the recent techniques employed for resilience enhancement, which includes planning and operational techniques. Also, a detailed explanation of microgrid (MG) deployment, renewable energy integration, and peer-to-peer (P2P) energy trading in fortifying power systems against disruptions is provided. An analysis of existing research gaps and challenges is discussed for future directions toward improvements in power system resilience. Thus, a comprehensive understanding of power system resilience is provided, which helps in improving the ability of distribution systems to withstand and recover from extreme events and disruptions