Dynamic spillovers between clean energy and non-ferrous metals markets in China: A network-based analysis during the COVID-19 pandemic

Due to the close production link between clean energy and non-ferrous metals, their price and market dynamics can easily affect one another through production costs. Furthermore, with the increased financialization of clean energy and non-ferrous metals markets, investment risk can easily spread between them. Therefore, this paper intends to explore the risk contagion between the two markets through the spillover index model and the minimum spanning tree (MST) method. Employing the data collected in China, this paper quantifies the magnitude of risk transfer by the volatility spillovers of eight clean energy stock markets as identified in The Energy Conservation and Environmental Protection Clean Industry Statistical Classification 2021 and the eight corresponding non-ferrous metals futures markets, while fully considering the heterogeneity between sub-markets. First, we find that risk is mainly transmitted from clean energy to non-ferrous metals. Second, this paper identifies not only the most influential market but also the shortest path of risk contagion based on the MST topology analysis. Last, the empirical results show that the COVID-19 has increased the scale of risk transmission between the two markets and their connectivity. During the COVID-19 period, the shortest path between the two markets shifted from "hydropower–gold” to "smart grid–zinc”, and the systematically influential markets correspondingly become smart grid and zinc. The results obtained in this paper might have practical implications for policymakers seeking to achieve effective risk management, which could also facilitate investors for diversification benefits. © 2023 Elsevier Ltd

Exploring the Acceptance of Smart Grid Technology in India: Role of Government Policies and Regulatory Framework

In the last few decades, technological advancements in the power sector have accelerated the evolution of the smart grid to make the grid more efficient, reliable, and secure. Being a consumer-centric technology, a lack of knowledge and awareness in consumers may lead to consumer opposition, which could imperil the grid modification process. This research aims to identify and prioritize the factors that can be considered barriers to technology acceptance for smart grid development in India. This study follows an integrated approach of literature review, AHP, and FERA. In the present work, 17 barriers have been identified and ranked on the basis of the social, technical, and economic paradigm. This study finds the impact of government policies and stakeholders' involvement in consumers' acceptance of smart grid technology and its importance towards improving the quality of life of Indians. The government should play as the main proponent. The present work will contribute to developing and upgrading the basic framework for the smart grid in a developing country like India. Copyright © 2022, IGI Global.

Forecasting energy consumption demand of customers in smart grid using Temporal Fusion Transformer (TFT)

Energy consumption prediction has always remained a concern for researchers because of the rapid growth of the human population and customers joining smart grids network for smart home facilities. Recently, the spread of COVID-19 has dramatically increased energy consumption in the residential sector. Hence, it is essential to produce energy per the residential customers' requirements, improve economic efficiency, and reduce production costs. The previously published papers in the literature have considered the overall energy consumption prediction, making it difficult for production companies to produce energy per customers' future demand. Using the proposed study, production companies can accurately have energy per their customers' needs by forecasting future energy consumption demands. Scientists and researchers are trying to minimize energy consumption by applying different optimization and prediction techniques;hence this study proposed a daily, weekly, and monthly energy consumption prediction model using Temporal Fusion Transformer (TFT). This study relies on a TFT model for energy forecasting, which considers both primary and valuable data sources and batch training techniques. The model's performance has been related to the Long Short-Term Memory (LSTM), LSTM interpretable, and Temporal Convolutional Network (TCN) models. The model's performance has remained better than the other algorithms, with mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) of 4.09, 2.02, and 1.50. Further, the overall symmetric mean absolute percentage error (sMAPE) of LSTM, LSTM interpretable, TCN, and proposed TFT remained at 29.78%, 31.10%, 36.42%, and 26.46%, respectively. The sMAPE of the TFT has proved that the model has performed better than the other deep learning models. © 2023 The Author(s)

Exploring the Acceptance of Smart Grid Technology in India

In the last few decades, technological advancements in the power sector have accelerated the evolution of the smart grid to make the grid more efficient, reliable, and secure. Being a consumer-centric technology, a lack of knowledge and awareness in consumers may lead to consumer opposition, which could imperil the grid modification process. This research aims to identify and prioritize the factors that can be considered barriers to technology acceptance for smart grid development in India. This study follows an integrated approach of literature review, AHP, and FERA. In the present work, 17 barriers have been identified and ranked on the basis of the social, technical, and economic paradigm. This study finds the impact of government policies and stakeholders' involvement in consumers' acceptance of smart grid technology and its importance towards improving the quality of life of Indians. The government should play as the main proponent. The present work will contribute to developing and upgrading the basic framework for the smart grid in a developing country like India.

Lessons Learned from an Operational Smart Grid Through the Example of a Local Government in Hungary

The steadily growing global demand for electricity, sustainability expectations, the global Covid epidemic and the Russian-Ukrainian war are also affecting the electricity supply chain. In our study we will focus on the smart grid, the modern smart electricity network of the near future, from a Hungarian perspective, with management approach. Hungary's newest and most complex smart grid is analysed using the case study method. We investigated the actors of the smart grid and were interested in how the cooperation between the parties was, what learning processes they experienced, and what risks they saw and see now. Our results show that customers and contractors are mutually satisfied;in addition to the numerical benefits, each party also values the learning processes in different areas. The risks identified stem from modern technology, complexity, novel solutions and operational mechanisms, but geopolitical, global economic uncertainties and shortages also have an impact.

In Search of Deep Learning Architectures for Load Forecasting: A Comparative Analysis and the Impact of the Covid-19 Pandemic on Model Performance

In power grids, short-term load forecasting (STLF) is crucial as it contributes to the optimization of their reliability, emissions, and costs, while it enables the participation of energy companies in the energy market. STLF is a challenging task, due to the complex demand of active and reactive power from multiple types of electrical loads and their dependence on numerous exogenous variables. Amongst them, special circumstances-such as the COVID-19 pandemic-can often be the reason behind distribution shifts of load series. This work conducts a comparative study of Deep Learning (DL) architectures-namely Neural Basis Expansion Analysis Time Series Forecasting (N-BEATS), Long Short-Term Memory (LSTM), and Temporal Convolutional Networks (TCN)-with respect to forecasting accuracy and training sustainability, meanwhile examining their out-of-distribution generalization capabilities during the COVID-19 pandemic era. A Pattern Sequence Forecasting (PSF) model is used as baseline. The case study focuses on day-ahead forecasts for the Portuguese nationa115-minute resolution net load time series. The results can be leveraged by energy companies and network operators (i) to reinforce their forecasting toolkit with state-of-the-art DL models;(ii) to become aware of the serious consequences of crisis events on model performance;(iii) as a high-level model evaluation, deployment, and sustainability guide within a smart grid context. © 2022 IEEE.

Artificial Intelligence Assisted Smart Photovoltaics

The paper discusses research efforts in combining recent progress in Artificial Intelligence with automated management of solar energy generated in grid-connected photovoltaic (PV) systems along with their operation- and-maintenance (O&M) and their smart on-grid integration control. The outlined research aligns with the strategy of the European Union joining Digital and Green agendas as two major pillars for the COVID-19 economic recovery in the EU and is a part of the EU funded standardization action under the H2020 StandICT programme coordinated by the author and hosted by the Smart Energy Standards Group of the European Information Technologies Certification Institute (EITCI SESG) in cooperation with the European Solar Network. It also contributes to one of the four primary objectives of the European Green Deal, i.e. to achieve a fully integrated, interconnected and digitalized EU energy market by increasing research oriented towards technical reference standardization aimed at consolidation of the expert community and the technology uptake. © 2021. The Authors. Published by International Solar Energy Society Selection and/or peer review under responsibility of Scientific Committee.

Microclimate Monitoring System Design for the Smart Grid Analysis and Constructive Parameters Estimation

This paper describes a microclimate monitoring system consisting of a LoRaWAN network of wireless climate sensors, a data collector and analytical software. The system is a part of the ICS RAS SmartGrid Centre project for predicting building energy consumption. During the design phase, the authors considered the concept of comfort, which is involved in setting control objectives for HVAC plants. It was necessary to overcome some characteristics of the LoRaWAN protocol, such as floating data transmission period and limited intensity of sensor communication. These have been overcome by post-processing the data with Python software, using libraries numpy and scipy. The collected data was passed through an interpolation filter for synchronization, and the resulting data is freely available in dataset format on our website for all interested researchers. Additionally, weather data was collected using a local meteostation to be considered as external disturbances in analysis problems. This paper also considers an approach to passive identification of the thermal protection parameters of a building. The coronavirus lockdown period was chosen to assume the impact of visitors negligible. The parameters are supposed to be estimated by correlation analysis. The estimates obtained should be compared with the values calculated according to ISO and Russian construction standards for diagnostic reasons. © 2022 Elsevier B.V.. All rights reserved.

Household Electricity Consumer Classification Using Novel Clustering Approach, Review, and Case Study

There is an increasing demand for electricity on a global level. Thus, the utility companies are looking for the effective implementation of demand response management (DRM). For this, utility companies should know the energy demand and optimal household consumer classification (OHCC) of the end users. In this regard, data mining (DM) techniques can give better insights and support. This work proposes a DM-technique-based novel methodology for OHCC in the Indian context. This work uses the household electricity consumption (HEC) of 225 houses from three districts of Maharashtra, India. The data sets used are namely questionnaire survey (QS), monthly energy consumption (MEC), and tariff orders. This work addresses the challenges for OHCC in energy meter data sets of the conventional grid and smart grid (SG). This work uses expert classification and clustering-based classification methods for OHCC. The expert classification method provides four new classes for OHCC. The clustering method is employed to develop eight different classification models. The two-stage clustering model, using K-means (KM) and the self-organizing map (SOM), is the best fit among the eight models. The result shows that the two-stage clustering of the SOM with the KM model provides 88% of overlap-free samples and 0.532 of the silhouette score (SS) mean compared to the expert classification method. This study can be beneficial to the electricity distribution companies for OHCC and can offer better services to consumers.

A Cyber-Physical-Social Perspective on Future Smart Distribution Systems

An increasing number of distributed energy resources (DERs), such as rooftop photovoltaic (PV), electric vehicles (EVs), and distributed energy storage, are being integrated into the distribution systems. The rise of DERs has come hand-in-hand with large amounts of data generated and explosive growth in data collection, communication, and control devices. In addition, a massive number of consumers are involved in the interaction with the power grid to provide flexibility. Electricity consumers, power networks, and communication networks are three main parts of the distribution systems, which are deeply coupled. In this sense, smart distribution systems can be essentially viewed as cyber-physical-social systems. So far, extensive works have been conducted on the intersection of cyber, physical, and social aspects in distribution systems. These works involve two or three of the cyber, physical, and social aspects. Having a better understanding of how the three aspects are coupled can help to better model, monitor, control, and operate future smart distribution systems. In this regard, this article provides a comprehensive review of the coupling relationships among the cyber, physical, and social aspects of distribution systems. Remarkably, several emerging topics that challenge future cyber-physical-social distribution systems, including applications of 5G communication, the impact of COVID-19, and data privacy issues, are discussed. This article also envisions several future research directions or challenges regarding cyber-physical-social distribution systems.

Regional PV Energy Forecasting using Distributed Data and Deep Neural Networks

Despite the COVID-19 pandemic, the global Photovoltaic market once more grew significantly in 2020, mainly on Grid-Connected Systems. The exponential increase of these systems raises new challenges for Smart Grid operators trying to predict load demand. That occurs because of the panels' output uncertainty and the leak of regional models for solar energy prediction. In this paper, we propose a distributed data approach to predict solar energy generated by Photovoltaic Systems. As input, we combine data from a community of solar panel owners and a historical weather website to build our dataset. This paper evaluates two scenarios: predicting regional next-day generation by using weather forecasting and the impact of a new system in that region. We sort the results seasonally and achieved 7% of MAE weighted percentage for summer predictions. © 2022 IEEE.

The Development and Implementation of a Microgrid SCADA System Simulator

Shifting the paradigm to decarbonized, distributed renewable future implies changes to conventional principles of power systems operation and requires the implementation of smart grid concepts. Microgrids have been widely recognized as a decentralized approach to successfully integrating renewable energy sources and end consumer empowerment. However, their implementation requires significant improvements and transformation of the distribution system in terms of increased observability and controllability, especially in the context of (near) real-time operation. Supervisory, Control, and Data Acquisition Systems (SCADA) enable system and infrastructure automated monitoring and control and serve as a foundation for advanced management and application of optimization-driven operation. Moreover, the development and testing of the functions mentioned above is a complex task, and today there is still a lack of holistic simulation tools, even though well-established power system simulators exist. The main objective of this paper is to introduce a novel simulation tool developed to simulate the SCADA system used in the Smart Grid Laboratory of the Faculty of Electrical Engineering and Computing for control, integration, and interactions between a microgrid's components. This paper includes simulator system architecture design, implemented functionalities, and future directions. Simulator testing shows successful communication, measurement generation, and meaningful response to commands and reference signals, proving correct functionality. Besides significant value in testing SCADA functionality, designing such a simulator has been of great benefit during restricted access to real-world devices in the Smart Grid Laboratory during the COVID-19 pandemic lockdown. © 2022 Croatian Society MIPRO.

Survey of Simulation Tools to Assess Techno-Economic Benefits of Smart Grid Technology in Integrated T&D Systems

In order to succeed in the energy transition, the power system must become more flexible in order to enable the economical hosting of more intermittent distributed energy resources (DER) and smart grid technologies. New technical solutions, generally based on the connection of various components coupled to the power system via smart power electronic converters or through ICT, can help to take up these challenges. Such innovations (e.g., decarbonization technologies and smart grids) may reduce the costs of future power systems and the environmental footprint. In this regard, the techno-economic assessment of smart grid technologies is a matter of interest, especially in the urge to develop more credible options for deep decarbonization pathways over the long term. This work presents a literature survey of existing simulation tools to assess the techno-economic benefits of smart grid technologies in integrated T&D systems. We include the state-of-the-art tools and categorize them in their multiple aspects, cover smart grid technology, approach methods, and research topics, and include (or complete) the analysis with other dimensions (smart-grid related) of key interest for future power systems analysis such as environmental considerations, techno-economic aspects (social welfare), spatial scope, time resolution (granularity), and temporal scope, among others. We surveyed more than 40 publications, and 36 approaches were identified for the analysis of integrated T&D systems. As a relatively new research area, there are various promising candidates to properly simulate integrated T&D systems. Nevertheless, there is not yet a consensus on a specific framework that should be adopted by researchers in academia and industry. Moreover, as the power system is evolving rapidly towards a smart grid system, novel technologies and flexibility solutions are still under study to be integrated on a large scale. This review aims to offer new criteria for researchers in terms of smart-grid related dimensions and the state-of-the-art trending of simulation tools that holistically evaluate techno-economic aspects of the future power systems in an integrated T&D systems environment. As an imperative research matter for future energy systems, this article seeks to contribute to the discussion of which pathway the scientific community should focus on for a successful shift towards decarbonized energy systems.

Investigation and analysis of effective approaches, opportunities, bottlenecks and future potential capabilities for digitalization of energy systems and sustainable development goals

Taking into consideration of substantial role of energy system and sustainable development goals (SDGs) in modern society, it is critical to analyse current situation and forthcoming renewable energy development strategies under the impact of COVID-19. For this purpose, this paper provides significant new insights to assess effective approaches, opportunities, challenges and future potential capabilities for the development of energy systems and SDGs under on-going pandemic and in case of a future global crisis. The digital energy systems with Industry 4.0 (I4.0), which provide noteworthy solutions such as enhancing energy efficiency policy, providing clean, secure and efficient energy and achieving SDG targets, has been discussed and evaluated. Integration of the smart grid (SG) architecture with blockchain-Internet of Things (IoT)-based technologies is also offered. Alongside the various discussions, short-term, mid-term and long-term plans have been suggested in determining the well-defined renewable energy development and SDGs targets, struggling with climate change, transition to a more sustainable energy future and reaching global net-zero emissions. To achieve SDGs and provide more strong and sustainable energy systems under the continuing pandemic and in case of potential risk of forthcoming global crisis, this paper reveals significant perceptions that inform politicians and legislators in performing successful policy decisions.

Electric Mobility: An Overview of the Main Aspects Related to the Smart Grid

Electric mobility has become increasingly prominent, not only because of the potential to reduce greenhouse gas emissions but also because of the proven implementations in the electric and transport sector. This paper, considering the smart grid perspective, focuses on the financial and economic benefits related to Electric Vehicle (EV) management in Vehicle-to-Building (V2B), Vehicle-to-Home (V2H), and Vehicle-to-Grid (V2G) technologies. Vehicle-to-Everything is also approached. The owners of EVs, through these technologies, can obtain revenue from their participation in the various ancillary and other services. Similarly, providing these services makes it possible to increase the electric grid’s service quality, reliability, and sustainability. This paper also highlights the different technologies mentioned above, giving an explanation and some examples of their application. Likewise, it is presented the most common ancillary services verified today, such as frequency and voltage regulation, valley filling, peak shaving, and renewable energy supporting and balancing. Furthermore, it is highlighted the different opportunities that EVs can bring to energy management in smart grids. Finally, the SWOT analysis is highlighted for V2G technology.

Experimental Performance Analysis of Wi-SUN Channel Modelling Applied to Smart Grid Applications

The grid operation and communication network are essential for smart grids (SG). Wi-SUN channel modelling is used to evaluate the performance of Wi-SUN smart grid networks, especially in the last-mile communication. In this article, the distribution approximation of the received signal strength for IEEE 802.15.4g Wi-SUN smart grid networks was investigated by using the Rician distribution curve fitting with the accuracy improvement by the biased approximation methodology. Specifically, the Rician distribution curve fitting was applied to the received signal strength indicator (RSSI) measurement data. With the biased approximation method, the Rician K-factor, a non-centrality parameter (rs), and a scale parameter (σ) are optimized such that the lower value of the root-mean squared error (RMSE) is acheived. The environments for data collection are selected for representing the location of the data concentrator unit (DCU) and the smart meter installation in the residential area. In summary, the experimental results with the channel model parameters are expanded to the whole range of Wi-SUN’s frequency bands and data rates, including 433.92, 443, 448, 923, and 2440 MHz, which are essential for the successful data communication in multiple frequency bands. The biased distribution approximation models have improved the accuracy of the conventional model, by which the root mean-squared error (RMSE) is reduced in the percentage range of 0.47–3.827%. The proposed channel models could be applied to the Wi-SUN channel simulation, smart meter installation, and planning in smart grid networks.

Understanding Sustainable Energy in the Context of Smart Cities: A PRISMA Review

In the context of smart cities, sustainability is an essential dimension. One of the ways to achieve sustainability and reduce the emission of greenhouse gases in smart cities is through the promotion of sustainable energy. The demand for affordable and reliable electrical energy requires different energy sources, where the cost of production often outweighs the environmental factor. This paper aims to investigate the ways smart cities promote sustainability in the electricity sector. For this, a systematic literature review using the PRISMA protocol was employed as the methodological approach. In this review, 154 journal articles were thoroughly analyzed. The results were grouped according to the themes and categorized into energy efficiency, renewable energies, and energy and urban planning. The study findings revealed the following: (a) global academic publication landscape for smart city and energy sustainability research;(b) unbalanced publications when critically evaluating geographical continents’ energy use intensity vs. smart cities’ energy sustainability research outcomes;(c) there is a heavy concentration on the technology dimension of energy sustainability and efficiency, and renewables topics in the literature, but much less attention is paid to the energy and urban planning issues. The insights generated inform urban and energy authorities and provide scholars with directions for prospective research.

Global vs. Local Models for Short-Term Electricity Demand Prediction in a Residential/Lodging Scenario

Electrical load forecasting has a fundamental role in the decision-making process of energy system operators. When many users are connected to the grid, high-performance forecasting models are required, posing several problems associated with the availability of historical energy consumption data for each end-user and training, deploying and maintaining a model for each user. Moreover, introducing new end-users to an existing network poses problems relating to their forecasting model. Global models, trained on all available data, are emerging as the best solution in several contexts, because they show higher generalization performance, being able to leverage the patterns that are similar across different time series. In this work, the lodging/residential electricity 1-h-ahead load forecasting of multiple time series for smart grid applications is addressed using global models, suggesting the effectiveness of such an approach also in the energy context. Results obtained on a subset of the Great Energy Predictor III dataset with several global models are compared to results obtained with local models based on the same methods, showing that global models can perform similarly to the local ones, while presenting simpler deployment and maintainability. In this work, the forecasting of a new time series, representing a new end-user introduced in the pre-existing network, is also approached under specific assumptions, by using a global model trained using data related to the existing end-users. Results reveal that the forecasting model pre-trained on data related to other end-users allows the attainment of good forecasting performance also for new end-users.

Impact of Covid-19 Pandemic on Smart Natural Gas Grids and Infrastructure Companies

The global economy was adversely affected due to the contraction in international trade volume during the epidemic. The health systems of many countries are on the verge of collapse. In the study, phone calls were made with the managers of 5 companies providing natural gas and mechanical infrastructure services in Istanbul, and the multi-criteria decision-making method was used. As a result of the findings, it was determined that companies operating simultaneously in many sectors with different customer types were not affected by the epidemic. In contrast, companies serving individual customers in a single area were negatively affected by the Covid-19 global epidemic, and one of the companies stopped its activities. While there is no shrinkage in industrial customers’ demands, companies serving only individual customers faced great challenges. This study aims to determine how the Covid-19 global epidemic affects companies providing natural gas and mechanical infrastructure services in Turkey. In this context, from the study, it was concluded that companies operating in the mechanical and natural gas infrastructure sector, which concentrate on a single field, were adversely affected by the global epidemic. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Analysis of energy management schemes for renewable-energy-based smart homes against the backdrop of COVID-19

This article reviews energy management schemes for smart homes integrated with renewable energy resources in the context of the COVID-19 pandemic. The incorporation of distributed renewable energy system has initiated an acute transition from the traditional centralized energy management system to independent demand responsive energy systems. Renewable energy-based Smart Home Energy Management Systems (SHEMSs) play a vital role in the residential sector with the increased and dynamic electricity demand during the COVID-19 pandemic to enhance the efficacy, sustainability, economical benefits, and energy conservation for a distribution system. In this regard, the reviews of various energy management schemes for smart homes appliances and associated challenges has been presented. Different energy scheduling controller techniques have also been analyzed and compared in the COVID-19 framework by reviewing several cases from the literature. The utilization and benefits of renewable-based SHEMS have also been discussed. In addition, both micro and macro-level socio-economic implications of COVID-19 on SHEMSs are discussed. A conclusion has been drawn given the strengths and limitations of different energy scheduling controllers and optimization techniques in the context of the COVID-19 pandemic. It is observed that renewable-energy-based SHEMS with improved multi-objective meta-heuristic optimization algorithms employing artificial intelligence are better suited to deal with the dynamic residential energy demand in the pandemic. It is hoped that this review, as a fundamental platform, will facilitate the researchers aiming to investigate the performance of energy management and demand response schemes for further improvement, especially during the pandemic.