Demand Response in Smart Grids

The Special Issue “Demand Response in Smart Grids” includes 11 papers on a variety of topics. The success of this Special Issue demonstrates the relevance of demand response programs and events in the operation of power and energy systems at both the distribution level and at the wide power system level. This reprint addresses the design, implementation, and operation of demand response programs, with focus on methods and techniques to achieve an optimized operation as well as on the electricity consumer

Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems

The electrical power system is undergoing a revolution enabled by advances in telecommunications, computer hardware and software, measurement, metering systems, IoT, and power electronics. Furthermore, the increasing integration of intermittent renewable energy sources, energy storage devices, and electric vehicles and the drive for energy efficiency have pushed power systems to modernise and adopt new technologies. The resulting smart grid is characterised, in part, by a bi-directional flow of energy and information. The evolution of the power grid, as well as its interconnection with energy storage systems and renewable energy sources, has created new opportunities for optimising not only their techno-economic aspects at the planning stages but also their control and operation. However, new challenges emerge in the optimization of these systems due to their complexity and nonlinear dynamic behaviour as well as the uncertainties involved.This volume is a selection of 20 papers carefully made by the editors from the MDPI topic “Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems”, which was closed in April 2022. The selected papers address the above challenges and exemplify the significant benefits that optimisation and nonlinear control techniques can bring to modern power and energy systems

State-of-the-Art Renewable Energy in Korea

Nowadays, renewable energy plays an important role in our daily lives. This Special Issue addresses the current trend in the use of renewable energy in South Korea. The first aspect is a renewable-based power system, where both main and ancillary supplies are sourced from renewable energies; the second aspect is a distribution network for renewable energy; and the last aspect is a nanogrid network technology. Renewable energy requires many innovations over existing power infrastructure and regulation. These articles show the changing trend in various sectors in Korea

Advanced Modeling, Control, and Optimization Methods in Power Hybrid Systems - 2021

The climate changes that are becoming visible today are a challenge for the global research community. In this context, renewable energy sources, fuel cell systems and other energy generating sources must be optimally combined and connected to the grid system using advanced energy transaction methods. As this reprint presents the latest solutions in the implementation of fuel cell and renewable energy in mobile and stationary applications such as hybrid and microgrid power systems based on the Energy Internet, blockchain technology and smart contracts, we hope that they will be of interest to readers working in the related fields mentioned above

Mathematical programming-based models for the distribution networks' decarbonization

(English) Climate change is pushing to decarbonize worldwide economies and forcing fossil fuel-based power systems to evolve into power systems based mainly on renewable energies sources (RES). Thus, increasing the energy generated from renewables in the energy supply mix involves transversal challenges at operational, market, political and social levels due to the stochasticity associated with these technologies and their capacity to generate energy at a small scale close to the consumption point. In this regard, the power generation uncertainty can be handled through battery storage systems (BSS) that have become competitive over the last few years due to a significant price reduction and are a potential alternative to mitigate the technical network problems associated with the intermittency of the renewables, providing flexibility to store/supply energy when is required. On the other hand, the capacity of low-cost generation from small-scale power systems (distributed or decentralized generation (DG)) represents an opportunity for both customers and the power system operators. i.e., customers can generate their energy, reduce their network dependency, and participate actively in eventual local energy markets (LEM), while the power system operator can reduce the system losses and increase the power system quality against unexpected external failures. Nevertheless, incorporating these structures and operational frameworks into distribution networks (DN) requires developing sophisticated tools to support decision-making related to the optimal integration of the distributed energy resources (DER) and assessing the performance of new DNs with high DERs penetration under different operational scenarios. This thesis addresses the distribution networks' decarbonization challenge by developing novel algorithms and applying different optimization techniques through three subtopics. The first axis addresses the optimal sizing and allocation of DG and BSS into a DN from deterministic and stochastic approaches, considering the technical network limitation, the electric vehicle (EV) presence, the users capacity to modify their load consumption, and the DG capability to generate reactive power for voltage stability. Besides, a novel algorithm is developed to solve the deterministic and stochastic models for multiple scenarios providing an accurate DERs capacity that should be installed to decrease the external network dependency. The second subtopic assesses the DN capacity to face unlikely scenarios like primary grid failure or natural disasters preventing the energy supply through a deterministic model that modifies the unbalance DN topology into multiple virtual microgrids (VM) balanced, considering the power supplied by DG and the flexibility provided by the storage devices (SD) and demand response (DR). The third axis addresses the emerging transactive energy (TE) schemes in DNs with high DERs penetration at a residential level through two stochastic approaches to model a Peer-to-peer (P2P) energy trading. To this end, the capability of a P2P energy trading scheme to operate on different markets as day-ahead, intraday, flexibility, and ancillary services (AS) market is assessed, while an algorithm is developed to manage the users' information under a decentralized design.(Català) El cambio climático está obligando a descarbonizar las economías de todo el mundo forzando a los sistemas de energía basados en combustibles fósiles a evolucionar hacia sistemas de energía basados principalmente en fuentes de energía renovables (FER). Así, incrementar la energía generada a partir de renovables en el mix energético está implicando retos transversales a nivel operativo, de mercado, político y social debido a la estocasticidad asociada a estas tecnologías y su capacidad de generar electricidad a pequeña escala cerca al punto de consumo. En este sentido, la incertidumbre en la generación de energía eléctrica puede ser manejada a través de sistemas de almacenamiento en baterías (BSS) que se han vuelto competitivos en los últimos años debido a una importante reducción de precios y son una potencial alternativa para mitigar los problemas técnicos de red asociados a la intermitencia de las renovables, proporcionando flexibilidad para almacenar/suministrar energía cuando sea necesario. Por otro lado, la capacidad de generación a bajo costo a partir de sistemas eléctricos de pequeña escala (generación distribuida o descentralizada (GD)) representa una oportunidad tanto para los clientes como para los operadores del sistema eléctrico. Es decir, los clientes pueden generar su energía, reducir su dependencia de la red y participar activamente en eventuales mercados locales de energía (MLE), mientras que el operador del sistema eléctrico puede reducir las pérdidas del sistema y aumentar la calidad del sistema eléctrico frente a fallas externas inesperadas. Sin embargo, incorporar estas estructuras y marcos operativos en las redes de distribución (RD) requiere desarrollar herramientas sofisticadas para apoyar la toma de decisiones relacionadas con la integración óptima de los recursos energéticos distribuidos (RED) y evaluar el desempeño de las nuevas RD con alta penetración de RED bajo diferentes escenarios de operación. Esta tesis aborda el desafío de la descarbonización de las redes de distribución mediante el desarrollo de algoritmos novedosos y la aplicación de diferentes técnicas de optimización a través de tres dimensiones. El primer eje aborda el dimensionamiento y localización óptimos de GD y BSS en una RD desde enfoques determinísticos y estocásticos, considerando la limitación técnica de la red, la presencia de vehículos eléctricos (VE), la capacidad de los usuarios para modificar su consumo de carga y la capacidad de GD para generar potencia reactiva para la estabilidad del voltaje. Además, se desarrolla un algoritmo novedoso para resolver los modelos determinísticos y estocásticos para múltiples escenarios proporcionando una capacidad precisa de RED que debe instalarse para disminuir la dependencia de la red externa. El segundo subtema evalúa la capacidad de la RD para enfrentar escenarios improbables como fallas en la red primaria o desastres naturales que impidan el suministro de energía, a través de un modelo determinista que modifica la topología de la RD desequilibrada en múltiples microrredes virtuales (MV) balanceadas, considerando la potencia suministrada por GD y la flexibilidad proporcionada por los dispositivos de almacenamiento y respuesta a la demanda (DR). El tercer eje aborda los esquemas emergentes de energía transactiva en RDs con alta penetración de RED a nivel residencial a través de dos enfoques estocásticos para modelar un comercio de energía Peer-to-peer (P2P). Para ello, se evalúa la capacidad de un esquema de comercialización de energía P2P para operar en diferentes mercados como el mercado diario, intradiario, de flexibilidad y de servicios complementarios, a la vez que se desarrolla un algoritmo para gestionar la información de los usuarios bajo un esquema descentralizado.Postprint (published version

Low-carbon Energy Transition and Planning for Smart Grids

With the growing concerns of climate change and energy crisis, the energy transition from fossil-based systems to a low-carbon society is an inevitable trend. Power system planning plays an essential role in the energy transition of the power sector to accommodate the integration of renewable energy and meet the goal of decreasing carbon emissions while maintaining the economical, secure, and reliable operations of power systems. In this thesis, a low-carbon energy transition framework and strategies are proposed for the future smart grid, which comprehensively consider the planning and operation of the electricity networks, the emission control strategies with the carbon response of the end-users, and carbon-related trading mechanisms. The planning approach considers the collaborative planning of different types of networks under the smart grid context. Transportation electrification is considered as a key segment in the energy transition of power systems, so the planning of charging infrastructure for electric vehicles (EVs) and hydrogen refueling infrastructure for fuel cell electric vehicles is jointly solved with the electricity network expansion. The vulnerability assessment tools are proposed to evaluate the coupled networks towards extreme events. Based on the carbon footprint tracking technologies, emission control can be realized from both the generation side and the demand side. The operation of the low-carbon oriented power system is modeled in a combined energy and carbon market, which fully considers the carbon emission right trading and renewable energy certificates trading of the market participants. Several benchmark systems have been used to demonstrate the effectiveness of the proposed planning approach. Comparative studies to existing approaches in the literature, where applicable, have also been conducted. The simulation results verify the practical applicability of this method

Data-driven modelling for demand response from large consumer energy assets

Demand response (DR) is one of the integral mechanisms of today’s smart grids. It enables consumer energy assets such as flexible loads, standby generators and storage systems to add value to the grid by providing cost-effective flexibility. With increasing renewable generation and impending electric vehicle deployment, there is a critical need for large volumes of reliable and responsive flexibility through DR. This poses a new challenge for the electricity sector. Smart grid development has resulted in the availability of large amounts of data from different physical segments of the grid such as generation, transmission, distribution and consumption. For instance, smart meter data carrying valuable information is increasingly available from the consumers. Parallel to this, the domain of data analytics and machine learning (ML) is making immense progress. Data-driven modelling based on ML algorithms offers new opportunities to utilise the smart grid data and address the DR challenge. The thesis demonstrates the use of data-driven models for enhancing DR from large consumers such as commercial and industrial (C&I) buildings. A reliable, computationally efficient, cost-effective and deployable data-driven model is developed for large consumer building load estimation. The selection of data pre-processing and model development methods are guided by these design criteria. Based on this model, DR operational tasks such as capacity scheduling, performance evaluation and reliable operation are demonstrated for consumer energy assets such as flexible loads, standby generators and storage systems. Case studies are designed based on the frameworks of ongoing DR programs in different electricity markets. In these contexts, data-driven modelling shows substantial improvement over the conventional models and promises more automation in DR operations. The thesis also conceptualises an emissions-based DR program based on emissions intensity data and consumer load flexibility to demonstrate the use of smart grid data in encouraging renewable energy consumption. Going forward, the thesis advocates data-informed thinking for utilising smart grid data towards solving problems faced by the electricity sector

INTER-ENG 2020

These proceedings contain research papers that were accepted for presentation at the 14th International Conference Inter-Eng 2020 ,Interdisciplinarity in Engineering, which was held on 8–9 October 2020, in Târgu Mureș, Romania. It is a leading international professional and scientific forum for engineers and scientists to present research works, contributions, and recent developments, as well as current practices in engineering, which is falling into a tradition of important scientific events occurring at Faculty of Engineering and Information Technology in the George Emil Palade University of Medicine, Pharmacy Science, and Technology of Târgu Mures, Romania. The Inter-Eng conference started from the observation that in the 21st century, the era of high technology, without new approaches in research, we cannot speak of a harmonious society. The theme of the conference, proposing a new approach related to Industry 4.0, was the development of a new generation of smart factories based on the manufacturing and assembly process digitalization, related to advanced manufacturing technology, lean manufacturing, sustainable manufacturing, additive manufacturing, and manufacturing tools and equipment. The conference slogan was “Europe’s future is digital: a broad vision of the Industry 4.0 concept beyond direct manufacturing in the company”

Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems