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A review of the development of Smart Grid technologies
Abstract
Energy sustainability and environmental preservation have become worldwide concerns with the many manifestations of climate change and the continually increasing demand for energy. As cities and nations become more technologically advanced, electricity consumption rises to levels that may no longer be manageable if left unattended. The Smart Grid offers an answer to the shift to more sustainable technologies such as distributed generation and microgrids. A general public awareness and adequate attention from potential researchers and policy makers is crucial. This paper presents an overview of the Smart Grid with its general features, functionalities and characteristics. It presents the Smart Grid fundamental and related technologies and have identified the research activities, challenges and issues. It demonstrates how these technologies have shaped the modern electricity grid and continued to evolve and strengthen its role in the better alignment of energy demand and supply. Smart Grid implementation and practices in various locations are also unveiled. Concrete energy policies facilitate Smart Grid initiatives across the nations. Interestingly, Smart Grid practices in different regions barely indicate competition but rather an unbordered community of similar aspirations and shared lessons.
... Utilizing smart technologies represents the technological movement in energy transition together with supportive policies to incentivize consumers to take part in moving toward sustainability goals (European Commission, 2019). Such a movement accelerates the transition toward the smart grid paradigm (Tuballa & Abundo, 2016). One recent emerging actor in the transition toward smart grids is the smart service provider. ...
... The advent of distributed generation logic triggered a transformation in whole power grids especially in the distribution part, facilitating local and micro power generation that must be integrated into the grid. This has given rise to a new model of power grids that are reputed as "smart grids" (Tuballa & Abundo, 2016). In this new model of the energy grid, new actors also emerge to provide complementary products and services to instantiate new technologies that fulfill the needs of smart grids (Singh et al., 2022). ...
... Generally, there is no specific/single definition for smart grids (Tuballa & Abundo, 2016). In my viewpoint, it is easier to explain how traditional power grids transform into smart grids than to explain what essentially smart grids are. ...
... We address this question in the electricity sector which is currently undergoing a transformation towards renewable energy sources, facilitated by the implementation of smart grids, in order to meet future electricity demands [29,30]. The electricity sector encompasses the industries, networks, and institutions involved in the generation, transmission, distribution, sale, and utilization of electricity. ...
... Specifically, understanding the dynamics of both implicit and explicit exchanges between parties are essential for improving conflict outcomes. Additionally, as the implementation of smart grids is still in its early stages in the countries studied, future investigations could build on these findings by examining more advanced smart grid implementations to better understand and enhance conflict navigation and facilitate the energy transition [29,30]. Writingreview & editing, Writingoriginal draft, Funding acquisition. ...
There is an urgent need to decarbonize socio-technical systems for electricity. This urgency has led to the emergence of various types of intermediaries that bridge actors and their related resources, skills, and visions to catalyse change. While the roles intermediaries in energy transitions are well researched, their strategic positioning particularly the conflicts they engage in, their navigation, and outcomes-remains underexplored. Addressing this research gap is crucial for transition governance, as it enables effective coordination among actors. Through an analysis of the electricity sector's transformation, we identify competing interests among intermediaries for resources, power, and legitimacy, as well as differences in visions and intermediation practices , as key sources of conflict. Navigating these conflicts requires adaptive strategic approaches, mutuality, and dynamic collaboration among intermediaries. This work contributes by introducing a conflict-sensitive perspective, providing insights into how intermediaries navigate conflicts to drive transitions forward.
... Siemens not only produces wind turbines and solar modules, but also continuously improves the development of these technologies to meet the growing demand for renewable energy in Germany [41]. At the same time, Siemens actively proposes and promotes smart grid solutions, including smart meters, grid monitoring systems and energy management software [43]. These help to integrate renewable energy more effectively and improve the stability and efficiency of the grid. ...
... To solve it, Germany is witnessing a shift from coal to gas to reduce carbon emissions, as well as the use of gas turbines with hydrogen for lower CO2 intensity [40]. In addition, compared with conventional power grids, smart grid technologies help to reduce GHG emissions and thus contributing to energy efficiency and sustainability [43]. Hence, it could be suggested that the carbon footprint of traditional power grids is an important factor in global climate change. ...
In 2010, the German government officially initiated the Energiewende as the concept underpinning its energy transition, a plan to shift from nuclear and fossil fuels to renewables towards Green Germany. At the intersection of corporate activities and public policy, this dissertation aims to analyse the roles of corporations in facilitating the German Energiewende from the lens of stakeholder theory. It begins with unpacking the German Energiewende and corporate engagement with public policy. Then in the case of Siemens, it delves deeper into its non-market strategies in promoting renewables use, lobbying activities, decarbonisation cooperation and green finance. Siemenss non-market strategies are categorized into three roles: strategic adaptor, innovator and investor. From the lens of stakeholder theory, enterprises effectively interact with various stakeholders to jointly facilitate the formation and implementation of the German Energiewende. Overall, it examines the engagement of the private sector in public energy policies and offers insights into the broader discourse on long-term energy transition towards sustainability.
... For utilities, the adoption of the strategic model primarily involves enhancing grid efficiency, reducing energy loss during transmission, and optimizing energy use for consumers. Advanced grid management technologies, such as smart grids and energy storage systems, can help utilities monitor and adjust energy distribution in real-time to minimize waste (Tuballa & Abundo, 2016) [39] . Smart meters, which enable real-time data collection, can facilitate energy usage tracking at the consumer level, promoting energysaving behaviors and reducing demand peaks. ...
... For utilities, the adoption of the strategic model primarily involves enhancing grid efficiency, reducing energy loss during transmission, and optimizing energy use for consumers. Advanced grid management technologies, such as smart grids and energy storage systems, can help utilities monitor and adjust energy distribution in real-time to minimize waste (Tuballa & Abundo, 2016) [39] . Smart meters, which enable real-time data collection, can facilitate energy usage tracking at the consumer level, promoting energysaving behaviors and reducing demand peaks. ...
This paper presents a strategic model for carbon emission reduction through process optimization in the energy sector, aimed at addressing the urgent need to mitigate the environmental impact of energy production and consumption. Given the global push towards sustainability and the challenges associated with reducing carbon emissions, this model integrates a variety of process optimization techniques, including energy efficiency improvements, waste heat recovery, and the application of predictive analytics. It highlights the potential of emerging technologies such as renewable energy integration, smart grid solutions, and advanced data analytics to support the transition to a low-carbon energy system. The paper reviews current challenges, including regulatory frameworks and market dynamics, and evaluates existing strategies for emissions reduction in energy operations. Additionally, it identifies key gaps in existing models and introduces a comprehensive approach to overcome these challenges, proposing actionable recommendations for governments, energy companies, and researchers. The model's effectiveness is assessed through key performance indicators, such as CO2 emission reductions, cost savings, and energy efficiency improvements. The paper also explores the economic and environmental impacts, emphasizing the importance of sustainability, cost-effectiveness, and regulatory compliance. Ultimately, it suggests future research areas, particularly in machine learning, carbon capture technologies, and renewable energy integration, which will further enhance the model's applicability and scalability in the global energy landscape.
... Demand response [202] and load control [184] Fuzzy logic inference [203], LSTM [103], Naive Bayes classifier [204] Deep reinforcement learning [205] data of the appliance states, real-time electricity price, and outdoor temperature Energy activity analysis and monitoring ...
... Demand response [202] Q-learning [230] Consumer load profiles, wholesale energy price, retailer energy price ...
Decarbonization, decentralization and digitalization are the three key elements driving the twin energy transition. The energy system is evolving to a more data driven ecosystem, leading to the need of communication and storage of large amount of data of different resolution from the prosumers and other stakeholders in the energy ecosystem. While the energy system is certainly advancing, this paradigm shift is bringing in new privacy and security issues related to collection, processing and storage of data - not only from the technical dimension, but also from the regulatory perspective. Understanding data privacy and security in the evolving energy system, regarding regulatory compliance, is an immature field of research. Contextualized knowledge of how related issues are regulated is still in its infancy, and the practical and technical basis for the regulatory framework for data privacy and security is not clear. To fill this gap, this paper conducts a comprehensive review of the data-related issues for the energy system by integrating both technical and regulatory dimensions. We start by reviewing open-access data, data communication and data-processing techniques for the energy system, and use it as the basis to connect the analysis of data-related issues from the integrated perspective. We classify the issues into three categories: (i) data-sharing among energy end users and stakeholders (ii) privacy of end users, and (iii) cyber security, and then explore these issues from a regulatory perspective. We analyze the evolution of related regulations, and introduce the relevant regulatory initiatives for the categorized issues in terms of regulatory definitions, concepts, principles, rights and obligations in the context of energy systems. Finally, we provide reflections on the gaps that still exist, and guidelines for regulatory frameworks for a truly participatory energy system.
... Various nations, including the USA, China, Australia, England, Japan, and South Korea, are evaluating the smart grid as a means to mitigate CO2 emissions [84,109,110]. 540 Canada was founded for the smart grid awareness and promotion campaign, tasked with research and the formulation of various smart grid policies [84]. ...
... Various nations, including the USA, China, Australia, England, Japan, and South Korea, are evaluating the smart grid as a means to mitigate CO2 emissions [84,109,110]. 540 Canada was founded for the smart grid awareness and promotion campaign, tasked with research and the formulation of various smart grid policies [84]. ...
... Reference [15] proposed that the Smart Grid has emerged as a solution for enhancing energy sustainability and balancing supply and demand. This technology integrates distributed generation, microgrids, and power flow optimization, supported by well-structured energy policies and technological advancements [16][17][18]. ...
The Power Imbalance Management Agent (PIMA) is an innovative flexibility aggregator aimed at tackling the challenges of meeting electricity demand, particularly during peak periods. PIMA enhances the management of flexibility assets by bringing together resources from residential and commercial consumers, energy storage systems, and distributed generation. This article explores how PIMA applies the simplest form of market, known as the Cournot game, while considering market constraints and player behaviors. It aligns market participants to foster flexibility and satisfy network demands. The optimal and advantageous conditions for users and the network are examined, which determines the ideal price and quantity offered in the Cournot game for the players. If players operate under these conditions, PIMA will act as a valuable aggregator for both the network and its users. By modeling the strategic interactions among market players and the behavioral policies of PIMA, efficient management of flexibility resources while ensuring system stability is studied.
... The electric power system evolved over many decades, in this period, traditional networks evolved into sophisticated, reliable, efficient, and sustainable smart grids thanks to financial, technological, environmental, and political incentives [1][2][3]. Smart grids allow customers to actively participate in the electrical markets. The rapid advancements in vehicle-to-grid technology mean that plug-in electric vehicles, one type of distributed energy storage, are set to play a significant part in emergency reliability services [4][5][6]. ...
When there is a lag between generation and load in a power system, frequency deviation happens. The frequency deviation in the deregulated power system is caused by consumers switching to various DISCOs, which causes the load on DISCOs to fluctuate the frequency which causes the undesirable operation in the the power system. The EV aggregators are introduced in each control area to supply power to the DISCOs in the event of a contract violation. This paper presents a reinforcement learning controller for load frequency control of a Smart Deregulated Power System (SDPS) that consists of two control areas, each of which contains thermal, solar PV plants, and hydro, wind plants respectively. The superiority of reinforcement learning controller over model predictive and robust controllers is that the neural network is trained from the control and system parameters of the open access power system under various operating scenarios. The Reinforcement learning controller is called Actor-Critic agent based Deep Deterministic Policy Gradient (DDPG) controller tested on two area model of deregulated power system under different possible contract scenarios and under various operating conditions. The Actor-Cretic reinforcement learning approach for LFC compared to FOPI and PI controllers under different possible contract scenarios in smart deregulated environment.
... They provide improved demand-side management through smart meters and dynamic pricing strategies [31]. Smart grids increase efficiency and reduce peak loads by making real-time monitoring of power flow consumption and cost available to consumers [32]. Integrating EV and vehicle-to-grid (V2G) technologies allows bidirectional power flow between vehicles and the power grid [33]. ...
The world is increasingly turning to renewable energy sources (RES) to address climate change issues and achieve net-zero carbon emissions. Integrating RES into existing power grids is necessary for sustainability because the unpredictability and irregularity of the RES can affect grid stability and generate power quality issues, leading to equipment damage and increasing operational costs. As a result, the importance of RES is severely compromised. To tackle these challenges, traditional power systems (TPS) will have to become more innovative. Smart grids use advanced technology such as two-way communication between consumers and service providers, automated control, and real-time monitoring to manage power flow effectively. Inverters are effective tools for solving power quality problems in renewable-powered smart grids. However, their effectiveness depends on topology, control method and design. This review paper focuses on the role of multilevel inverters (MLIs) in mitigating power quality issues such as voltage sag, swell and total harmonics distortion (THD). The results shown here are through simulation studies using DC sources but can be extended to RES-integrated smart grids. The comprehensive review also examines the drawbacks of TPS to understand the importance and necessity of developing a smart power system. Finally, the paper discusses future trends in MLI control technology, addressing power quality problems in smart grid environments.
... During the First Industrial Revolution, electrical engineering contributed to the development of power generation technologies such as steam turbines and generators, which were instrumental in the expansion of electrification. The integration of these technologies into nationwide grid systems enabled large-scale transmission and distribution of electricity, substantially impacting industrial growth and urbanization [70]. Subsequently, advancements in power systems led to the modernization of electrical grids, enhancing their resilience and adaptability to emerging energy technologies. ...
The transforming power of electrical engineering (EE) on societal evolution, educational paradigms, university systems, and industrial revolutions is comprehensively reviewed in this study. This study emphasizes the critical contribution of EE in promoting technological development, improving learning approaches, and allowing sustainable industrial practices by methodically analyzing the co-evolution of these domains from Society 1.0 to 5.0, Education 1.0 to 4.0, University 1.0 to 4.0, and Industry 1.0 to 4.0. Unlike conventional stories that credit EE alone for development, this assessment critically examines the multidisciplinary character of progress and acknowledges the contributions of computer science, computer engineering, and artificial intelligence (AI) in forming the digital world. Focusing on fundamental technologies, including power systems, semiconductor devices, renewable energy integration, and automation, which have been the backbone of recent AI-driven advancements, this study offers a crucial contribution. This study clarifies EE’s special contribution of EE in the global technological revolution by separating its basic contributions from those resulting from its junction with computing disciplines. Furthermore underlined in this paper are EE’s contributions to smart infrastructure development, sustainable energy solutions, and society resilience. Presenting an evidence-based evaluation, this paper provides an insightful analysis for academics, teachers, and legislators, thus supporting EE’s basic enabler of multidisciplinary technical and societal advancement.
... The digital economy helps reduce energy consumption intensity by promoting technological innovation, enhancing energy efficiency (Charfeddine et al., 2024), and optimizing the energy mix (Huang et al., 2023). Based on blockchain and big data analytics, it enables the convergence of internet technologies with energy, driving the smarter and more efficient utilization of energy (Cheng et al., 2018;Tuballa & Abundo, 2016). Given that the burning of fossil fuels represents a significant source of carbon emissions, the digital economy achieves emission reductions at the source by improving the efficiency of traditional energy sources (Gao & He, 2024). ...
Achieving the “dual-carbon” goal is essential for deepening ecological civilization and a critical pathway to economic development. Utilizing provincial-level data (2012–2021) and constructs a mediation effect model to explore how digital economy influences carbon emissions. The findings indicate that the rapid expansion of digital economy reduces carbon emissions, and this finding has been verified by multiple robustness checks. Further analysis of the underlying mechanisms reveals that this effect operates through reduced energy intensity and increased FDI. In addition, the impact varies by region and scale, with more pronounced impacts in the eastern regions and provinces with larger populations. These findings provide valuable implications for fostering the integrated advancement between digital economy growth and carbon reduction efforts.
... Our focus is on UMC, where users/residents can adopt more flexible energy consumption patterns supported by systems that provide signals to encourage them to make more intelligent decisions about their energy use. Coupled with the emergence of smart grids in urban areas [23,24] and the integration of building smart energy management systems [25], this has elevated the significance of enhanced decision-making in charging operations [26,27]. ...
The transition to a decarbonized energy sector, driven by the integration of Renewable Energy Sources (RESs), smart building technology, and the rise of Electric Vehicles (EVs), has highlighted the need for optimized energy system planning. Increasing EV adoption creates additional challenges for charging infrastructure and grid demand, while proactive and informed decisions by residential EV users can help mitigate such challenges. Our work develops a smart residential charging framework that assists residents in making informed decisions about optimal EV charging. The framework integrates a machine-learning-based forecasting engine that consists of two components: a stacking and voting meta-ensemble regressor for predicting EV charging load and a bidirectional LSTM for forecasting national net energy exchange using real-world data from local road traffic, residential charging sessions, and grid net energy exchange flow. The combined forecasting outputs are passed through a data-driven weighting mechanism to generate probabilistic recommendations that identify optimal charging periods, aiming to alleviate grid stress and ensure efficient operation of local charging infrastructure. The framework’s modular design ensures adaptability to local charging infrastructure within or nearby building complexes, making it a versatile tool for enhancing energy efficiency in residential settings.
... In the EU, ongoing research and development (R&D) efforts in smart grids, battery storage, and hydrogen energy have significantly improved the efficiency and scalability of renewable energy systems [18]. Additionally, Tuballa and Abundo [19] highlighted that the EU has been actively promoting smart technologies, setting a goal for member states to install smart meters in 80% of households by 2020. This initiative aimed to improve energy efficiency and make grid management more effective. ...
The transition to renewable energy is a critical priority for the European Union. However, the roles of foreign direct investment and technological innovation in shaping renewable energy consumption remain unclear. This study examines their joint influence across 20 European Union countries from 2013 to 2023, employing Method of Moments Quantile Regression to capture varying effects under different market conditions. The findings reveal that technological innovation consistently enhances renewable energy consumption, strengthening its impact from 0.298 in the 10th to 0.488 in the 90th quantile, particularly in economies with a robust renewable energy infrastructure. However, FDI negatively affects renewable energy consumption across all quantiles, with coefficients ranging from −0.00000228 to −0.00000324, suggesting that foreign investments may not always align with clean energy goals. Additionally, inflation positively influences renewable energy consumption, implying that rising energy prices drive a shift toward renewables, while economic growth initially increases fossil fuel reliance before transitioning to cleaner sources. The study’s results emphasise the need for strong policy interventions to ensure that FDI aligns with renewable energy goals and that technological innovation continues to drive clean energy adoption.
... The shift towards a smarter power grid, commonly known as a smart grid, requires advanced monitoring and control by distribution system operators (DSOs), along with more complex coordination between producers and consumers [16,17]. Enhanced monitoring involves the better utilization of existing smart meters [18,19] and the installation of new devices that can provide more accurate information about voltage and power flows in the grid [20]. ...
In this paper, we perform a threat modeling of architectures for controlling the medium voltage (MV) part of the power grid, arguing the importance of this topic with a brief summary of serious cyber security attacks from the last decade. As more Distributed Energy Resources (DERs) are introduced into this part of the grid, the need to control these resources arises. A threat modeling of two alternative control architectures is performed to study two different aspects. Firstly, we study and compare the cyber security of the two architectures to determine whether one of them is inherently more secure than the other. While both architectures rely on 5G, one of the architectures uses a centralized design, while the other uses a distributed design. Our results indicate that at the current level of detail, contrary to common belief, it is difficult to draw definitive conclusions as to which architecture is more secure. The second aspect we study is the applied threat modeling method itself. We evaluate and test the method and suggest improvements.
... Smart grid cybersecurity has been extensively investigated by many researchers in recent years. The first paper explored here is the one that tried to integrate blockchain technology with machine learning for the purpose of protecting smart grids (Tuballa & Abundo, 2016). It targets the security of peer-to-peer energy transfer in some of the applications. ...
Smart grids fall at the intersection of conventional energy systems and modern informatics in the present digitalized energy environment. The growing number of linked devices and sensors in these networks leads to the generation of complex structures and vast quantities of data, presenting benefits and challenges. Safeguarding these complex structures against malicious intrusions and illegal activities is an important problem. The paper's main objective is to enhance smart grid security by utilizing the data mining and Artificial Intelligence (AI) approaches. As huge amounts of data are collected from the smart grids based on tiny and smart internet of things (IoT) devices, this data poses challenges as well as provides opportunities. The challenges come from analyzing this huge data, especially in real-time. At the same time, it provides opportunities to enhance the smart grid services and protection. Therefore, to overcome these challenges, this paper proposes a feedforward deep learning approach for data mining to secure the smart grid from different anomalies and allow the system to adapt to any risk it might face. Deep learning will allow the system to adjust dynamically to emerging risks. The proposed system has been examined using Power System Attack Datasets sourced from the Mississippi State University and Oak Ridge National Laboratory. The results show a detection accuracy of 91% just using 50% of the dataset features. Different percentages of the features are examined as well. However, we concluded that 50% of the features are enough for identifying the smart grid risks based on the given dataset.
... The concept of a smart grid provides a modern approach to efficiently managing and distributing electrical energy [1], [2], [3], [4]. Unlike traditional grids, smart grids integrate advanced technologies and communication systems to provide reliable, efficient, and sustainable power to consumers. ...
The concept of a decentralized smart grid has emerged as a viable approach to efficiently managing and distributing electrical energy. Ensuring the stability and reliability of the grid, particularly with the integration of renewable energy sources and the rise of prosumers, is a primary challenge in this domain. This study addresses this challenge by leveraging machine learning (ML) models and explainable artificial intelligence (XAI) techniques to predict the stability of a decentralized smart grid. A four-node star network implementing the decentralized smart grid control (DSGC) concept was investigated, and a dataset based on simulations of this network was utilized. Ten ML models, including Adaptive Boosting (AdaBoost), Artificial Neural Network (ANN), Gradient Boosting (GBoost), k-Nearest Neighbors (k-NN), Logistic Regression (LR), Naïve Bayes (NB), Random Forest (RF), Stochastic Gradient Descent (SGD), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost), were compared for their performance in predicting grid stability. Models were evaluated using various metrics, and XAI methods, specifically SHapley Additive exPlanations (SHAP) and Individual Conditional Expectation (ICE) plots, were employed to enhance the interpretability of the models. The results demonstrate that the ANN model outperformed the other ML algorithms, achieving an area under the curve (AUC) of 99.4% and showing commendable performance in terms of cumulative accuracy (CA), precision, recall, and F1-score, all of which reached 96.2%. The SHAP and ICE analyses provided a comprehensive understanding of the model’s behavior, highlighting the critical influence of parameters such as reaction time, nominal power, and price elasticity on the stability of the decentralized smart grid. The findings contribute to the development of more reliable and interpretable predictive models for decentralized smart grid stability, which can aid in the design and optimization of effective control mechanisms.
... Advancements in power electronics and control systems enhance the resilience and adaptability of grid operations. Ongoing research and development are crucial to tackle current difficulties and unveil new potentials [40]. ...
An integrated micro- and nano-grid with net-zero renewable energy is a sophisticated energy system framework aimed at attaining optimal efficiency and sustainability. This survey paper examines several contemporary research works in this domain. This document summarizes the latest papers selected for analysis to comprehend the current state-of-the-art, integration process, methodology, and research gaps. The objective of this review is to identify existing trends and ongoing transformations in this domain. At the conclusion of the study, emerging technologies for smart grid integration are offered, emphasizing Transactive Control, Blockchain Technology, and Quantum Cryptography, based on existing research gaps. Microgrids and nano-grids are localized energy systems capable of functioning alone or in tandem with larger power grids, offering resilience and adaptability. By incorporating renewable energy sources like solar, wind, and storage devices, these networks can produce and regulate energy locally, guaranteeing that the generated energy meets or surpasses the energy used. The incorporation of intelligent technology and control systems facilitates optimized energy distribution, real-time monitoring, and load balancing, advancing the objective of net-zero energy use. This strategy not only bolsters energy security but also markedly decreases carbon emissions, rendering it an essential element in the shift towards a sustainable and resilient energy future. The worldwide implementation of interconnected micro- and nano-grids utilizing net-zero renewable energy signifies a pivotal transition towards a sustainable and resilient energy future. These localized energy systems can function independently or in conjunction with conventional power grids, utilizing renewable energy sources like solar, wind, and advanced storage technology. Integrating these resources with intelligent control systems enables micro- and nano-grids to optimize energy production, distribution, and consumption at a detailed level, ensuring that communities and companies globally can attain net-zero energy usage. This method not only diminishes greenhouse gas emissions and reliance on fossil fuels but also improves energy security and grid stability in various places. These technologies, when implemented globally, provide a scalable answer to the issues of energy access, environmental sustainability, and climate change mitigation, facilitating a cleaner and more equal energy landscape worldwide.
... Real-time monitoring through smart meters and sensors distributed across the grid allows operators to continuously track performance, voltage, and frequency fluctuations, enabling them to take proactive measures to prevent issues before they impact operations. Predictive analytics, powered by AI-driven models, can anticipate variations in renewable energy output and demand, empowering grid operators to make preemptive adjustments that ensure a more balanced and reliable energy supply [36]. Together, these tools enable a more agile, responsive, and efficient grid, critical for maximizing the potential of renewable energy sources. ...
The global shift towards renewable energy systems is critical for combating climate change, enhancing energy security, and achieving sustainability. Hybrid renewable energy systems, which integrate multiple renewable sources such as solar, wind, and hydropower, promise increased reliability and efficiency in energy generation. However, their integration into national grids presents significant technical, economic, and regulatory challenges. This narrative review systematically analyzes the hurdles and opportunities associated with hybrid renewable energy systems, drawing from diverse case studies and technological advancements. It discusses technical challenges, including grid stability and interconnection compatibility, and economic issues related to high initial investments and financing. Furthermore, the review highlights regulatory barriers and the necessity for supportive frameworks. It concludes with insights into the potential of hybrid systems to foster technological innovation and contribute to sustainable development and climate change mitigation.
... The main goal of this project is to develop and implement a smart Internet of things energy monitoring system specifically designed for a solar-powered microgrid in Sierra Leone. Several ancillary goals have been established in the pursuit of this objective [8]. In order to collect data, these include building strong sensor networks, providing user-friendly interfaces for real-time monitoring, and making sure the system is sustainable and scalable in the local environment. ...
A smart IoT energy monitoring system for a solar-powered microgrid in Sierra Leone is implemented and presented in this study. Microgrids that are powered by solar energy have emerged as a promising approach to address the issues of consistent and sustainable energy availability in developing countries, especially in rural places[1]. For these microgrids to operate as best they can, however, effective resource management and monitoring are necessary. Here, we use Internet of Things (IoT) technology and Homer Pro software to create and install an advanced energy monitoring system. Homer Pro software is used for system design, optimization, and simulation, and it is integrated with Internet of Things (IoT) devices for data collecting, transfer, and analysis[2]. Real-time data on energy production, use, and storage is provided by the energy monitoring system. It also has an easy-to-use interface for controlling and monitoring in real-time. Users can monitor energy usage, spot inefficiencies, and optimize energy consumption thanks to the system's remote monitoring and control capabilities[3]. In this study, we examine viable substitutes for traditional power generating systems with the goal of delivering electricity in a cost-effective, dependable, and sustainable way. These substitutes include the utilization of solar renewable energy sources and less carbon-intensive technology. In addition, the study addresses the scalability, future prospects, and socioeconomic effects of smart IoT energy monitoring systems in relation to sustainable development[4]. In summary, this study advances the use of IoT applications in energy systems and provides workable solutions to improve resilience, efficiency, and access to energy in Sierra Leone.
... Smart Grids and Real-Time Data Management: The growth of smart grids is being greatly influenced by the effect of digitalization. A smart grid is a system of electrical distribution networks that incorporates monitoring and control devices that are integrated with communications systems (Tuballa & Abundo, 2016). The ability for such control is critical due to the need to integrate energy sources that are discontinuous in nature e.g. ...
Urban areas face numerous challenges related to population growth, resource management, environmental sustainability, and infrastructure efficiency. The integration of Internet of Things (IoT) and Artificial Intelligence (AI) technologies offers innovative solutions to tackle these urbanism problems. This article explores the application of IoT and AI in addressing various urban challenges, including traffic congestion, energy consumption, waste management, public safety, and urban planning. By leveraging real-time data collection, predictive analytics, and autonomous systems, IoT and AI solutions empower cities to optimize resource utilization, enhance service delivery, and improve overall quality of life for residents.
The pressing challenges of climate change, resource depletion, and environmental degradation necessitate innovative solutions that harness advanced technologies to promote sustainability and mitigate adverse environmental impacts. This article explores the diverse array of advanced technologies that hold promise for addressing key sustainability and environmental challenges. From renewable energy systems and smart grid technologies to sustainable agriculture practices and green manufacturing processes, these cutting-edge technologies offer opportunities to transition towards a more sustainable and resilient future. By leveraging the power of artificial intelligence, Internet of Things (IoT), and advanced materials, these technologies enable more efficient resource utilization, reduce greenhouse gas emissions, and enhance environmental conservation efforts. This article provides an overview of key advanced technologies for sustainability and environment, highlighting their potential benefits, challenges, and implications for addressing pressing global environmental issues.
Promoting a sustainable and low-carbon energy future through the integration of renewable energy is essential, yet it presents significant challenges due to the intermittent nature of resources such as solar and wind. This paper examines the technological and economic dimensions of AC, DC, and smart grids, concentrating on the optimization of costs, efficiency, stability, and scalability. Smart grids, enhanced by AI, IoT, and blockchain technologies, play a vital role in energy management optimization, predictive maintenance, and secure energy transactions. Furthermore, the incorporation of renewable energy sources, especially photovoltaics, presents challenges including intermittency, voltage fluctuations, and grid congestion. This paper emphasizes the necessity for updated grid codes and policies that guarantee system stability and the effective functioning of renewable energy systems. The implementation of these regulatory frameworks is crucial for facilitating the efficient integration of renewable energy into the grid, ensuring a reliable and secure power supply while advancing sustainability efforts.
Energy-saving practices are one of the best ways to address environmental issues. Unfortunately, energy-saving practices in Indonesian government agencies have not been optimally implemented. This article highlights the urgency of implementing energy-saving practices in Indonesian government institutions. More specifically, this article can explain why government institutions should practice energy conservation. A qualitative approach was performed in this study. The data of this study were obtained from the statute and conceptual approaches. The analysis technique utilized content analysis. This study examined the urgency of energy-saving practices in the government agency, setting from conceptual, legal, and regulatory perspectives, as well as energy-saving practices that several governments have carried out.
The development of renewable energy sources (RESs) is a key element of the energy policy in Poland and the European Union. The transition to green energy aims to reduce greenhouse gas emissions, enhance energy security, and decrease dependence on fossil fuels. In Poland, the RES sector, particularly photovoltaics and wind energy, is growing, which is changing the operation of energy generation. However, the increasing share of RESs in the energy mix presents challenges for the stability of the national power grid. This study focuses on renewable energy sources in Poland because the development of RESs is crucial for the country’s energy transition. Poland is striving to achieve its climate goals and reduce dependence on fossil fuels, and increasing the share of RESs in the national energy mix is a key element of energy policy. The transition to green energy aims to reduce greenhouse gas emissions, enhance energy security, and support sustainable development in Poland. (1) The aim of this article was to analyze the impact of RES development on power grids in Poland, identify key issues, and review adaptation strategies. (2) Methods such as a literature review and statistical data analysis were used to build scenarios. (3) In Poland, the RES sector is being developed through the application of both national and European policies. The main sources of renewable energy are wind energy and photovoltaics. (4) The introduction of technologies such as energy storage systems, smart grids, and advanced management strategies is a key response to the challenges posed by the development of renewable energy in Poland, particularly in relation to the stability of the power grid.
The smart grid presents an unparalleled opportunity to revolutionize the present scenario energy industry, ushering in a contemporary era of an upgraded network. In this advanced system, electric energy generation, electric energy transmission, and electric energy distribution are intelligently and collaboratively controlled via a two-way automation system, promoting responsiveness and efficiency. The applications and technologies of smart grids may vary in their functions and forms, but they all share common potential benefits. These include intelligent energy curtailment, seamless integration of demand response, distributed renewable generation, and energy storage solutions. This paper provides a comprehensive review covering the past two decades, encompassing recent advancements and prior research developments in the smart grid paradigm. The primary aim of this study is to conduct an application-focused survey, comprehensively examining each category and subcategory independently. The paper's introduction provides insights into the concept and structure of smart grids. It delves deeply into reviewing recent advances in energy data management within smart grids, pricing models in modernized power grids, and the key components of smart grid systems. Furthermore, the paper thoroughly explores recent advancements in network reliability. Conversely, the growing dependence on urban areas utilizing sophisticated communication technologies and their infrastructure raises concerns about data integrity. Hence, a dedicated subsection is devoted to highlighting the existing challenges and the latest state-of-the-art advancements in cybersecurity. Lastly, the review concludes by emphasizing the unfolding advancements in pricing mechanisms.
The smart grid enables the bidirectional flow of electricity and data, but during multidimensional electricity consumption data reporting, Smart Meters (SMs) may compromise users’ privacy by disclosing detailed electricity consumption data from various devices. Additionally, in addressing the needs of diverse electricity Data Users (DUs), it is essential to protect their legal rights. Consequently, it is necessary to implement dimension-level access control for multidimensional aggregated data. However, existing Multidimensional Data Aggregation (MDA) schemes often fail to provide efficient dimension-level access control while safeguarding users’ privacy. To address these problems, this paper establishes a smart grid model based on fog computing and introduces a privacy-preserving MDA scheme with dimension-level access control. Specifically, our scheme utilizes the threshold Paillier cryptosystem and the Chinese Remainder Theorem (CRT) to securely aggregate users’ multidimensional data. Meanwhile, our scheme employs digital signatures to ensure data integrity and implements Key-Policy Attribute-Based Encryption (KP-ABE) to enforce dimension-level access control. Comprehensive theoretical analysis indicates our scheme satisfies privacy, integrity, and authentication. Extensive experimental results demonstrate our scheme achieves a trade-off between dimension level access control and system overhead.
The concept of Explainable Cyber-Physical Systems (ExpCPSs) aims to provide clear explanations for system decisions and actions, which is crucial for understanding, managing, and controlling high-risk Cyber-Physical Systems (CPSs), such as intelligent energy distribution networks or Smart Grids (SGs). Existing approaches for ExpCPSs, however, are typically designed only for specific application areas, limiting their impact. This paper proposes a domain-independent framework for automatically generating system state explanations in a CPS through (i) a unified data model and (ii) novel explanation algorithms to improve systems transparency, interpretability, and trustworthiness. The proposed framework facilitates root cause analysis on CPSs independently of their domains, enabling users to query system states and receive comprehensive explanations. We evaluated our solution in a real-world Electric Vehicle (EV) charging garage use case, highlighting its feasibility and effectiveness.
This paper discussed the design and analysis of Photovoltaic (PV) system to supply lighting load for Renewable Energy Lab. The procedure of this paper was the measuring and collection of the basic meteorological data of solar radiation for Sohar-Oman, and then standalone system optimization simulation model was developed using the renewable energy software HOMER. Simulation model has been used to find out the best result optimization based on energy efficient system for the specific load. The optimization found that the PV array rated capacity is 0.7 kW, which produces 1,316 kWh/yr. The results indicate that the solar energy utilization is an attractive option with initial cost, net present cost of the system, and energy cost are 3,425 US, and 0.561 US/kWh. We conclude that using the PV system for different applications in Oman is justified on economic and technical grounds.
The move to "fuel-free" power mainly under the form of wind and solar photovoltaic makes a bigger challenge than ever before, because of the need to use energy storage for easy electricity transmission and distribution. That is why the integration of new primary sources requires more attention in the design, control, and management of the electric grid. This chapter provides an overview of energy storage systems applied to smart-grid applications. Several technologies are possible and a comparison in terms of effectiveness and prices is given for each of them in order to help the decision makers to make a choice. Special attention is given to lithium-ion batteries used in large facilities to support energy storage, the load leveling, and frequency regulation. With its ability to inject electricity contained in the batteries of cars on the grid, the vehicle-to-grid technology is also considered.
The Wireless Sensor Network (WSN) is a promoted technology applied in the communication system of smart grid, which enjoys low cost, low power dissipation, self-organization, and strong flexibility. This paper presents the applications of WSN in condition-based maintenance, smart metering, smart-home, fault location, distributed bus protection of power networks, as well as some other application in disaster prevention with the main technical characteristics of WSN. It not only proposes the basic design philosophy of WSN's applications in areas, but also summarizes the superiority of the communication systems using WSN in smart grid.
Cloud Computing has become more and more of a choice for storing and processing data due to the performance, scalability, availability and interoperability that it offers. The adoption of generic off-the-shelf Information and Communication Technologies (ICT) for the Smart Grid has come with many advantages, but also raised issues regarding the security, availability and reliability of the Smart Grid. The data generated by the Smart Grid's systems has the properties of a time series and its analysis needs to be secure and in real-time. As shown in this paper, cloud computing has become a real candidate for Smart Grid data analysis. We present the particularities of the data generated in the Smart Grid and the operations involved in its analysis. As well we compare existing solutions for stream data processing against this basic set of operations.
Plug-in Electric Vehicles (PEVs) are envisioned to be more popular during the next decade as part of Smart Grid implementations. Charging multiple PEVs at the same time within a power distribution area constitutes a major challenge for energy service providers. This paper discusses a priority-based approach for charging PEVs in a Smart Grid environment. In this work, ideas from the communication network paradigm are being utilized and tailored toward achieving the desired objective of monitoring and controlling PEVs electric load in Smart Grid. A detailed example is given to show how uncontrolled penetration of PEVs can impact distribution transformer reliability. The paper introduces the concept of Charging Quality of Service (CQoS) as a smart electric vehicle charging scheme and models it using a priority-controlled leaky bucket approach. The performance of such a model is investigated under the umbrella of a Smart Grid environment.
Microgrids can deploy traditional and/or renewable power sources to support remote sites. Utilizing renewable power sources requires more complicated control strategies to achieve acceptable power quality and maintain grid stability. In this research, we assume that the grid stability problem is already solved. As a next step, we focus on how the power can be dispatched from multiple power sources for improved grid efficiency. Isolated microgrids frequently require reconfigurations because of the grid expansion or component failures. Therefore, the control strategies ideally should be implemented in a plug-and-play fashion. Moreover, these strategies ideally require no pre-knowledge of the grid structure, and as little communication with neighboring power sources as possible. The control objective is to minimize a cost function that can be adjusted to reflect the desire to minimize energy cost and/or losses. An algorithm is designed to satisfy a derived necessary condition of function optimality. Such conditions are obtained by formulating Lagrange functions. An equivalent grid model approximates the grid structure which was later confirmed to represent the grid behavior adequately. For decentralized operations, we execute the distributed control sequentially using a simple token communication protocol. The performance of the combined system identification-Lagrange function minimization algorithm is demonstrated through simulations.
Numerous smart home systems have been created in the recent years, but they still lack of high interoperability and research has been focused on single smart technologies instead of the system interoperability as a whole. Furthermore, available systems are usually strongly technology-oriented and they neglect the user's satisfaction and the benefits’ analysis. In addition to this, modern systems impose the intelligent management of a huger amount of data, which needs to be properly coordinated to achieve higher performances and offer new energy-control services.
While Electric Vehicles (EVs) have received substantial investment from governments to support technological development and testing in real world conditions, uptakes levels have thus far been below expectations. This is widely acknowledged to be due to a range of concerns held by potential users including cost, range, reliability and availability for use. In this paper the role of through-life engineering in addressing these concerns will be discussed. The major components and subsystems used in EVs will be identified and their impact on the life span, operation and cost of EV ownership will be identified. Based upon this analysis recommendations will be made for future research and development aimed at improving the operational viability and life cycle cost.
Energy efficient operation of microgrids, a localized grouping of controllable loads with distributed energy resources like solar photovoltaic panels, requires the development of energy management systems (EMSs) with the capability of controlling the loads so as to optimize the aggregate performance of the microgrid. In microgrids comprising of buildings of different nature (residential, commercial, industrial, etc.), where the occupants exhibit heterogeneous occupancy schedules, the objective of an effective management strategy is to optimize the aggregate performance by intelligently exploiting the occupancy schedules and the intermittent production of solar energy. This paper presents a simulation-based optimization approach for the design of an EMS in grid-connected photovoltaic-equipped microgrids with heterogeneous occupancy schedule. The microgrid exchanges energy, buying or selling it, with the main grid and the EMS optimizes an aggregate multi-objective criterion that takes into account both the energy cost and the thermal comfort of the occupants of the microgrid. Simulative results obtained using a microgrid test case developed in EnergyPlus demonstrate the effectiveness of the proposed approach: the proposed EMS strategy is shown to take advantage of the occupancy information, intelligently and automatically changing the energy demand of each building according to the occupants’ behavior, and achieving relevant improvements with respect to alternative EMS strategies.
Energy policy in the United States has focused on three major goals: assuring a secure supply of energy, keeping energy costs low, and protecting the environment. In pursuit of those goals, government programs have been developed to improve the efficiency with which energy is utilized, to promote the domestic production of conventional energy sources, and to develop new energy sources, particularly renewable sources. Implementing these programs has been controversial because of varying importance given to different aspects of energy policy. For some, dependence on imports of foreign oil, particularly from the Persian Gulf, is the primary concern; for others, the indiscriminate use of fossil fuels, whatever their origin, is most important. The contribution of burning fossil fuels to global climate change is particularly controversial. Another dichotomy is between those who see government intervention as a positive force and those who view it as a necessary evil at best. The 112th Congress did not take up comprehensive energy legislation, but numerous bills were considered on specific energy issues. The most significant action was extension of energy tax incentives, including the Production Tax Credit (PTC) for wind energy, to January 1, 2014, as part of P.L. 112-240, The American Taxpayer Relief Act of 2012. With the expiration of the extension, the issue of the PTC is once again an issue before Congress. In the 113th Congress, a number of issues, some of which drew attention previously, have been taken up in proposed legislation. The Energy Savings and Industrial Competitiveness Act of 2013, S. 1392 and H.R. 1616, would promote energy efficiency in buildings and industry by encouraging adoption of uniform building codes and authorize a grant program for state energy efficiency programs. The bill was reported out by the Senate Energy and Natural Resources Committee on May 13, 2013, but attempts to bring it to the Senate floor failed. The bill was reintroduced with some amendments as S. 2074 in March 2014, and on March 5 the House passed H.R. 2126, which contains some of the provisions in S. 2074, by a vote of 375-36. H.R. 3, the Northern Route Approval Act, would declare that a presidential permit would not be required for construction of the Keystone XL pipeline from Canada to the Gulf of Mexico. The bill passed the House on May 22, 2013, by a vote of 241-175. The issue of approving the Keystone XL pipeline is deeply involved in the question of global climate change, since opponents argue that Canadian oil sands production contributes excessive amounts of greenhouse gas emissions. In November 2013 the House passed three energy related bills. H.R. 2728 would prohibit the Bureau of Land Management from enforcing rules to regulate hydraulic fracturing in any state that has its own regulations on the activity. H.R. 1900 would require the Federal Energy Regulatory Commission to respond to permit applications for new natural gas pipelines within one year. H.R. 1965 would be aimed at increasing on-shore leasing of oil and gas resources on federal land. On March 6, 2014, the House passed H.R. 3826, a bill to block the Environmental Protection Agency from limiting power plant carbon emissions.
In this study, the implementation of cloud based smart community management and control system was undertaken. SmartComunity.in is a flexible platform to manage and control the affairs of a condominium or society with thorough participation, visibility and transparency. Our research is the first attempt to study one such real life system of cloud based control and management in a smart housing community in India. There is a dearth of exploratory studies that explain the diffusion and adoption of cloud computing in different contexts and from a multiple stakeholder perspective. So, the main contribution of our research is to understand the framework of cloud computing based smart community services in India and the emerging cloud computing ecosystems. This research has wide ranging implications on the future of Internet of Things, and can be extended to elderly health and support, energy efficient systems and smart cities.
This paper presents a co-simulator framework for wide-area Smart Grid monitoring systems based on phasor measurement units (PMU). The communication network framework is based on the North American Synchrophasor Initiative Network (NASPInet) specification. To understand the performance and to facilitate design of wide-area monitoring based Smart Grid applications, there is a need for a simulator that has both power system and communication system capabilities. In this paper, we present a co-simulation framework developed using two existing public-domain simulators (OpenDSS and OMNET++ for power systems and communication networks simulation respectively). This framework has been implemented at IIT Madras for public use. A hybrid state estimation application, a voltage monitoring application and a renewable energy integration application are implemented over this framework, as example of simulator usage. The simulator software has been made available for public download on http://sourceforge.net.
The Smart Grid (SG) is an emerging concept, which is in a testing phase in many countries. It is a relatively new perception that integrates electricity and communication on power system networks, which enable SG applications, mainly for the use of Renewable Energy Sources (RES). This paper presents a review of the existing studies on SG systems, the use of SG technologies and the modeling techniques for SG available in the literature. Furthermore, the paper focuses on SG features when connected at a distribution grid and its active distribution management capability. It also identifies the current practice as provided in technical standards and potential industry investments that can facilitate a pathway towards SG development in Australia. The paper also has highlighted the possible areas of research that would be required to implement practical SG facilities.
The transformation of traditional energy networks to smart grids revolutionizes the energy industry in terms of reliability, performance, and manageability by providing bi-directional communications to operate, monitor, and control power flow and measurements. However, communication networks in smart grid bring increased connectivity with increased severe security vulnerabilities and challenges. Smart grid can be a prime target for cyber terrorism because of its critical nature. As a result, smart grid security is already getting a lot of attention from governments, energy industries, and consumers. There have been several research efforts for securing smart grid systems in academia, government and industries. This article provides a comprehensive study of challenges in smart grid security, which we concentrate on the problems and proposed solutions. Then, we outline current state of the research and future perspectives.With this article, readers can have a more thorough understanding of smart grid security and the research trends in this topic.
The power losses reduction is one of the main targets for any electrical energy distribution company. This paper studies the applicability of a control system based on a Genetic Algorithm (GA) on a portion of the actual Italian electric distribution network located in Rome and surroundings, managed by the ACEA Distribuzione S.p.A. The joint optimization of both power factor correction (PFC) and distributed feeder reconfiguration (DFR) is faced. The PFC is performed tuning the phases of the distributed generators (DGs) and the output voltage of the Thyristor Voltage Regulator (TVR). The DFR is performed by opening and closing the available breakers according to a graph based algorithm that is able to find all the possible radial configurations of the network. The joint PFC and the DFR optimization problem are faced by solving a suitable optimization problem, defining the fitness function that drives the GA. In order to have the opportunity to study a realistic future scenario, the actual network has been modified by introducing a few extra distributed generators. Aiming to validate the applicability of the proposed algorithm to an operative scenario, two different tests have been performed. The first one, referred to as time-unconstrained optimization, represents an ideal scenario where there are no constraints on time available for optimization. The second one, referred to as time-constrained optimization, represents a real scenario where the optimization must be completed within a time slot of one hour. Both tests have been performed by feeding the developed simulation tool with real data concerning dissipated and generated active and reactive power values. The comparison between results obtained in the two tests campaigns furnishes the opportunity to evaluate the effectiveness of the proposed control algorithm in real time, relying on the computational performances of an entry-level workstation. The obtained results encourage the use of derivative free methods in a real-time control scenario, showing that the performances achieved by the time-constrained optimization procedures are very close in terms of objective function values to the ones obtained by the time-unconstrained procedure.
Toward efforts to improve sustainability of energy supply and to achieve worthwhile reduction in greenhouse gas emissions, in addition to the increased use of renewable energy sources in buildings, more emphasis is being placed on the need for improved control, management and coordination of building operations and its interaction with the smart-grid. Multi-agent system (MAS), due to its distributed and autonomous properties is considered a viable tool for improving the coordination and management of interactions between buildings and components of the smart-grid. This paper provides an overview of the application of multi-agent systems in building operations for coordination of various building processes and buildings interaction with the smart-grid. By first reviewing the notions of agents, its potentials and key attributes, the paper identifies key areas in building operation where its application provides improved performance. Furthermore, the paper provides a discussion on the knowledge gaps, challenges with its uptake in practical building application as well as opportunity for concentrated research.
Street lighting is a ubiquitous utility, but sustaining its operation presents a heavy financial and environmental burden. Many schemes have been proposed which selectively dim lights to improve energy efficiency, but little consideration has been given to the usefulness of the resultant street lighting system. This paper proposes a real-time adaptive lighting scheme, which detects the presence of vehicles and pedestrians and dynamically adjusts their brightness to the optimal level. This improves the energy efficiency of street lighting and its usefulness; a streetlight utility model is presented to evaluate this. The proposed scheme is simulated using an environment modelling a road network, its users, and a networked communication system - and considers a real streetlight topology from a residential area. The proposed scheme achieves similar or improved utility to existing schemes, while consuming as little as 1-2% of the energy required by conventional and state-of-the-art techniques.
Abstract Electric vehicles (EVs) represent one of the most promising technologies to green the transportation systems. An important issue is that high penetration of EVs brings heavy electricity demand to the power grid. One effective way to alleviate the impact is to integrate local power generation such as renewable energy sources (RESs) into charging infrastructure. Because of the intermittent and indispatchable nature of RESs, it becomes very challenging to coordinate EVs charging with other grid load and renewable generation. In this paper, EVs charging problem in the presence of smart grid technologies is investigated, and the interaction with renewable energy is reviewed. An overview about EVs and RESs is first presented, which mainly introduces major types of EVs and renewable energy estimation methods. Then, according to the objectives, the existing research works are divided into three categories: cost-aware, efficiency-aware, and emissions-aware interactions between EVs and RESs. Each category's discussion includes the description of core ideas, summarization of solutions, and comparison between different works. Finally, some key open issues about EVs interacting with RESs are given and some possible solutions are also discussed.
This paper presents a new framework considering decentralized energy coordination and generation, and flow control for supply-demand balance in distributed grid networks. Consensus schemes using only local information are employed to produce energy coordination, generation, and flow control signals. For the supply-demand balance, it is required to determine the amount of energy needed at each distributed resource. Also, due to the different generation capacities of each energy resource, coordination of energy flows among distributed energy resources is essentially required. Thus, this paper proposes a new framework which gives decentralized energy coordination scheme, generation, and flow control method considering these constraints based on distributed consensus algorithms. The proposed framework in this paper can be nicely utilized in energy dispatch or energy flow scheduling. Furthermore, it can be applied to various engineering problems including water irrigation systems, traffic networks, and building automation systems since it deals with attributed distribution and resource allocation in large scale distributed systems. Through illustrative examples, the effectiveness of the proposed approaches is illustrated. A possible application to power dispatch problem in the IEEE-14bus is also addressed for more detailed and realistic evaluation.
This paper presents a new optimization framework to optimize the bidding strategy of a smart distribution company (SDC) in a day-ahead (DA) energy market. This SDC contains wind farms as stochastic DG units as well as plug-in electric vehicles (PEVs) as responsive loads. The intermittent nature of wind power may result in significant imbalance penalty costs for the SDC participated in the DA energy market. The proposed optimization framework uses the potential of plug-in electric vehicles (PEVs) and battery energy storage (BES) to manage possible imbalances of wind farms. In order to modify the charging pattern of PEVs, hourly electricity prices are calculated in the optimization framework and sent to PEV owners via smart communication system. PEV owners change their charging pattern in response to these hourly prices with the aim of reducing their electricity bills. In addition to responsive loads, BES and wind farms, the SDC also contains dispatchable distributed generators (DGs), distribution network and non-responsive loads. The two-point estimate method (TPEM) is used to model the uncertainties associated with wind farms power generation. Moreover, Benders decomposition technique (BDT) is implemented to simplify the optimization procedure. Finally, the effectiveness of the proposed framework is evaluated on several case studies.
The smart grid (SG) integrates the power grid and the Information and Communication Technology (ICT) with the aim of achieving more reliable and safe power transmission and distribution to the customers. Integrating the power grid with the ICT exposes the SG to systems security threats and vulnerabilities that could be compromised by malicious users and attackers. This paper presents a SG systems threats analysis and integrated SG Systems Security Threat Model (SSTM). The reference architecture of the SG, with its components and communication interfaces used to exchange the energy-related information, is integrated with the results of SG systems security threat analysis to produce a comprehensive, integrated SG SSTM. The SG SSTM in this paper helps better depict and understand the vulnerabilities exploited by attackers to compromise the components and communication links of the SG. The SG SSTM provides a reference of the systems security threats for industrial security practitioners, and can be used for design and implementation of SG systems security controls and countermeasures.
Buildings are ecosystems that influence and are influenced by internal and external factors concerning both Distributed Generation (DG) and Distributed Energy Systems (DES). Existing communication and integration standards, tools and platforms for buildings and power systems still show several limitations in terms of data integration and correlation, applicability and vendor independence. These are required to support adequate online forecasting, management and decision, in the context of DG and DES. To overcome this drawbacks a holistic view is necessary, combining streamed (real-time) and asynchronous data communication of relevant sources of information influencing both the building and the distributed energy system, such as weather, occupancy, energy system characteristics and associated devices, among others. This work proposes a communication infrastructure supported by communication standards and by a Service-Oriented Architecture (SOA). This Standard-based Service-oriented Infrastructure (SSI) addresses the communication between buildings and power system management tools and each of the building systems, devices and sources of information. Open communication standards are used, such as IEC 61850 and Device Profile for Web Services (DPWS). The SSI becomes an interoperability enabler regarding complex systems’ management where occupants and energy production, distribution and consumption are considered, thus encouraging the development of Plug and Play and vendor independent operation.
Effective and efficient matching of supply and demand of materials and energy with consideration of spatial allocation is essential to realize low-carbon city planning with high material- and energy-use efficiency. Implementing real-time action management can contribute to smart system designs in a long-term plan. Integrated databases will need to be established by using information about regional spatial characteristics, such as land use, infrastructure installation, production, and livelihood. Moreover, an appropriate methodology for operating and designing the energy–material circulation system will also be required. Here, we examined the possibility of collecting various types of spatial information on cities and considered the use of existing databases for designing an energy circulation system in one region of Japan. We introduced an innovative information network system integrating a regional geographical information system and information and communication technology application and evaluated its performance and future outlooks. The proposed system is critical to support better management, reporting, and verification of material and energy network construction.
Energy needs increases day by day in those areas of the world where the electrical distribution lines are not available. This situation limits the industrial and economic development of these areas as well as the establishment of the population, so that it facilitates the exodus and the uprooting of people. At the moment the electrical energy supply in remote areas is based on solutions with generator sets, consuming great amounts of fuel with a great environmental damage. The technological development of renewable energies and the new concepts for their management, make possible proposing off-grid generation solutions for providing great amount of energy with high quality and reliability. Along this paper, a smart-grid configuration is proposed which is developed with products of proved reliability and which uses renewable energies as main generation source. This configuration allows having “standard” grid services anywhere, without the need of connection to the traditional grid. Environmental, economic and operation benefits of using these technologies against traditional solutions are also presented in this paper. This proposed solution allows the economical development and exploitation of areas where due to the lack of energy or to the energy cost it has not been possible until now.
In recent years, power grids have been evolving to decentralised production and control. Direct Load Control (DLC) methods are oriented to manage loads on the demand side. A DLC method based on Multi-Objective Particle Swarm Optimisation (MOPSO) algorithm is described. This method sets up the operation of the appliances when a power restriction must be accomplished. Since the method must operate in real-time, the calculations are distributed. The operation of appliances is obtained by dividing the power restriction among neighbourhoods and calculating multiple local optimisations. The method has been experimentally evaluated through simulations.
State Grid Corporation of China and China Southern Power Grid Corporation put forward the vision of Smart Grid in 2009. Then China launched the second round of distribution automation system (DAS) project. This paper summarizes the DAS in this round, including the project planning, current development and related technical standard. The development situation and trend of feeder automation (FA) technology are focused. The various realization modes and test verification technology of FA are presented in details. With the integration of distributed generation to distribution grid, the trend of Advanced Distribution Automation (ADA) and other techniques such as optimizing operation and practicability of FA in China are introduced.
In last years, the power system operators are tackling many challenges for the renewable energies integration on the grid. Further, the expected increase of electrical demand due to the uncoordinated contemporary charging of a huge number of Electric Vehicles (EVs) can create chaotic phenomena with a negative impact especially on the distribution network. Help can be offered by the deployment of Smart Grid technologies, such as Smart Metering Systems (SMSs), Information and Communications Technology (ICT) and Energy Storage Systems (ESSs). In particular, in Micro-Grids, Battery ESSs (BESSs) can play a fundamental role and can become fundamental for the integration of EV fast charging stations and distributed generations. In this case the storage can have peak shaving, load shifting and power quality functions. The ESSs can provide ancillary services also on the grid as the reactive control to adjust the power factor. In the present paper, a monitoring control program to manage the reactive power of a real ESS in a Micro-Grid has been implemented. The system is a prototype, designed, implemented and now available at ENEA (Italian National Agency for New Technologies, Energy and Sustainable Economic Development) labs. A wide experimental activity has been performed on the prototype system in order to test this functionality for the integration in a bigger Smart Grid available at the same ENEA labs including the Micro-grid. The integration has been possible, thanks to the free ICT protocols used by the researchers and which are described here. The results of the experimental tests show that the system can have good performance to adjust the power factor in respect to the main distribution grid and an EV charging station.
In a smart grid context, the increasing penetration of embedded generation units leads to a greater complexity in the management of production units. In this paper, we focus on the impact of the introduction of decentralized generation for the unit commitment (UC) problem. Unit commitment problems consist in finding the optimal schedules and amounts of power to be generated by a set of generating units in response to an electricity demand forecast. While this problem has received a significant amount of attention, classical approaches assume that these problems are centralized and deterministic. However, these two assumptions are not realistic in a smart grid context. Indeed, finding the optimal schedules and amounts of power to be generated by multiple distributed generator units is not trivial since it requires to deal with distributed computation, privacy, stochastic planning, etc. In this paper, we focus on smart grid scenarios where the main source of complexity comes from the proliferation of distributed generating units. In solving this issue, we consider distributed stochastic unit commitment problems. We introduce a novel distributed gradient descent algorithm which allows us to circumvent classical assumptions. This algorithm is evaluated through a set of experiments on real-time power grid simulator.
Smart metering systems generally referred to as the next-generation power measurement system, is considered as a revolutionary and evolutionary regime of existing power grids. More importantly, with integration of advanced computing and communication technologies, the smart meter (SM) is expected to greatly enhance efficiency and reliability of future power systems with renewable energy resources, as well as distributed intelligence and demand response. Different electrical energy metering standards are point of concern for power/energy measurements. As measurement standards are formed, systems built around them can become interoperable from a standards point of view but still have incompatible configurations or different maturity levels, or include non-standardized functions. Even in areas that are standardized, there are sometimes implementation decisions that can result in different measurement and security behavior. With this paper we make three contributions: firstly, we identify various 1-channel and 3-channel metrology integrated circuits (ICs), which are mandatory for the standard measurement of distributed and renewable electricity generation. Secondly, we describe harmonics effect on metrology, which impacts on reliability of widespread smart metering infrastructure. Finally, we develop and describe a comprehensive set of security issues for SMs. Specifically, we focus on reviewing and discussing smart metrology meter (SMM) applications (i.e. metrological functions and real-time monitoring functions), security requirements, network vulnerabilities, attack countermeasures, secure communication protocols required in smart grid (SG) architectures. This review will enable the researchers, public policy makers and stakeholders to open the mind to explore possible in an evolving energy domain as well as beyond this area.
The intermittent nature of renewable energy sources (RESs) and unpredictable variable load demands have necessitated the inclusion of energy storage devices in the smart grid environment. Electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs), with vehicle-to-grid capability, referred to as “gridable vehicles” (GVs), have become an option as storage devices. However, unsupervised use of GVs as storage devices presents new challenges due to concerns over battery lifetime and cost effectiveness of this two-way power transfer. These issues reduce the participation rate of GVs in the vehicle-to-grid discharge program. In this study, we present a system model, for GVs to act as distributed storage devices, which mitigates concerns over battery lifetime, and provides GV owners with a transparent cost-benefit analysis of their participation in the vehicle-to-grid discharge program. With this model in place, fuel and emissions cost has been reduced, as shown using particle swarm optimization. Simulation results show that up to 52% of GVs might be discharging at a net loss. In contrast, our proposed system ensures that no GVs are at loss when discharging to the grid. This factor alone is expected to increase the participation rate of GVs by a significant margin, and ensure economic load dispatch.
In view of recent attacks on smart grid surveillance is of vital importance to enforce disaster recovery management operations to ensure seamless energy generation and distribution. The reliability of disaster recovery management depends on availability and privacy preservation of data. In this paper we propose a privacy preserving smart grid surveillance architecture over cognitive radio sensor networks. Cognitive radio sensor networks are capable of facilitating reliable communications through reliable spectrum sensing capabilities as opposed to fixed radio terminal networks based surveillance architectures. The main privacy preserving feature is a novel power expenditure aware physical unclonable function (PUF) based key generation. The proposed solution determines the encryption key length depending on the remaining energy reserve to facilitate secure data transmission over an expected period of time over minimum channel interferences. Based on the experimental evaluation we recommend the PUF pattern matching based key generation is viable for 32 bits pattern length over a cognitive radio sensor with optimum power utilization and with a probability of reproducibility of a bit pattern . We have also performed experiments to validate the reliability model using real-world data. In conclusion, our proposed cognitive radio sensor based solution provide more pragmatic insights in reliability assurances for surveillance in smart grid.
Smart grid has diverse stakeholders that often require varying levels of access to grid state and measurements. At the distribution level (i.e., MAN), smart grid provides two way communication between households and utilities. At the transmission level (i.e., WAN), multiple organizations need to share the transmission lines and cooperate with participants in their region. In this paper, we propose secure communication and computation services for smart grid to transform the current “closed cyber architecture” to an “open cyber architecture”. In order to ensure the privacy and integrity of communicating parties at the distribution level, we propose to utilize the smart meters as a gateway between intra-network (i.e., HAN) and inter-network (i.e., WAN) communications, and manage incoming and outgoing traffic and mediate household devices based on the instructions from the electric utility or contracted service providers. To enhance data sharing between operators at the transmission level, we propose an open cyber architecture that utilizes blind processing service, in which sensitive data is transmitted through the secured channel and used in computations running in an isolated environment while the outcome is rendered only to a dedicated user or process. The “open” communication between the smart substructures and “blind” computation at operation centers will increase data sharing, minimize human intervention, and mitigate cascading events. In the paper, we provide and discuss underlying mechanisms to achieve an open cyber architecture.
This paper is aimed to analyze design criteria, setting up, control strategies and experimental tests related to a power configuration of DC micro-grid for fast charging of full electric and plug in hybrid vehicles. The proposed DC fast charging architecture is derived by an analysis comparing the main characteristics of well known architectures, mainly based on AC and DC bus, taking also into account the integration of renewable energy sources (RESs) with stationary energy storage systems and fleets of road electric/hybrid vehicles. On the base of the proposed architecture a laboratory prototype of charging station has been realized by means of a 20 kW AC/DC bidirectional grid tie converter interconnected with two different power DC/DC converters of similar rated power. In this micro-grid architecture the AC/DC converter realizes a conversion stage at 790 V DC, whereas other two converters allow either the electric vehicle battery packs to be charged or an energy storage buffer to save electric energy and support the main grid during the fast charging operations. The laboratory tests described in this paper are mainly devoted to characterize the laboratory demonstrator, in different operative conditions, such as vehicle-to-grid (V2G), charging/discharging operations of different types of storage systems and fast charging operations of road electric vehicles. Then the study of the proposed power conversion architecture is focused on the evaluation of charging/discharging power, efficiency, energy flux management and its impact on the main grid. In addition proper control strategies are evaluated and implemented, allowing the proposed architecture to follow the required operations. The obtained experimental results demonstrate real advantages in terms of charging times and power requirements from the main grid, when adopting DC buffer architecture for fast charging operations. Finally, these results support the identification of a knowledge base, useful to evaluate energy management and control strategies to be adopted for DC charging stations and each one of their power converters in a smart grid scenario with distributed generation systems.
Deployment of Phasor Measurement Units (PMU) in the United States transmission grid has brought a new data stream to be processed and an opportunity to improve situational awareness on the grid. This new data stream offers opportunity for a faster detection and response algorithm to minimize wide spread outages. High rate of data collection of PMU systems has also brought a challenge on how to extract information from fast moving PMU data stream in real time to improve situational awareness inside a control room. Despite the fact that mathematical and probabilistic methods are the most accurate methods of stability analysis, online decision making algorithms cannot afford the latency brought by those methods. Traditional batch processing Artificial Intelligence (AI) techniques have been extensively studied as potential replacements for these approaches, however conventional AI techniques do not deal with continuous streams of fast moving phasor data. This paper presented a novel application of the stream mining algorithms for synchrophasor data to meet quick decision making requirement of future situational awareness applications in power systems. To prove that the proposed methods are efficient and capable of handling huge amounts of data with reasonable accuracy and within limited resources of memory and computational power, four different experiments with different conditions (changing/unchanging the load conditions of Real Power and Reactive Power, fixing the size of memory, and comparing the performance of non-adaptive Hoeffding tree with traditional decision tree algorithms) were conducted. The algorithms discussed in this paper support decisions inside the control rooms helping stakeholders make informed decisions to improve reliability of the future smart grid.
Smart grid is the direction of power system development and it has aroused wide attention. It is also the physical infrastructure to integrate renewable energy into the power system. In China power grid companies are the pioneer in developing smart grids. Propelled by strong demand, China has made encouraging progress in smart grid development, especially in the aspect of ultra-high voltage transmission system. However, in other aspects as distributed generation, microgrid and intelligent demand management etc., the progress is slow and limited. In this paper we analyze the policy, pilot projects, achievements and barriers of developing smart grids in China. We find that lack of a clear national strategy is one main institutional barrier. The current industrial structure of the electric power sector, or the vertical integration of power transmission with distribution and supply, is another institutional barrier. Finally we provide an outlook on smart grid development in China.
Electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEV) with vehicle-to-grid capability, referred to as “gridable vehicles” (GVs), have become a useful choice for storage devices in the smart grid environment. The success of a smart grid system with GVs heavily depends on the market penetration of GVs and their participation rate in the vehicle-to-grid (V2G) program. Battery lifetime and revenue earning potential are influential factors for GV owners considering whether to participate in the V2G program. Second use of GV batteries, after their automotive life, can generate some revenue for the owners to recover a part of their battery purchase cost. However, this is dependent on the remaining capacity of the battery and the capacity degradation rate both in the automotive and second life. In this paper, capacity degradation and the remaining energy of a GV battery at different operating cycles have been quantified in both their automotive and second lives. Cost of battery energy both in automotive and second life have also been modelled that informs the owners of the revenue potentials, especially from the second life use. Finally, an economic load dispatch model with the inclusion of second life revenue has been developed to establish that using GV batteries in this way would earn extra revenue thus contributing to the initial buying price and encouraging more GV participation in the smart grid. Simulation results show that the proposed model can contribute up to 19.56% of the initial battery purchase cost, while still ensuring economic load dispatch.
This paper presents a heuristic approach to Active Demand Side Management in the Off-Grid systems with a set of specific requirements. The tests were performed on the smart house platform developed at VSB – Technical University of Ostrava campus, Czech Republic in order to accomplish the effective design, testing, operation and analysis of the proposed system. These results consequently consist of a prerequisite for the development of new leading-edge power distribution systems in the Off-Grid environment and for improvement of the efficiency, security and reliability of the existing systems.