Integration of renewable energy and optimization of energy use are key enablers of sustainable energy transitions and mitigating climate change. Modern technologies such the Internet of Things (IoT) offer a wide number of applications in the energy sector, i.e, in energy supply, transmission and distribution, and demand. IoT can be employed for improving energy efficiency, increasing the share of renewable energy, and reducing environmental impacts of the energy use. This paper reviews the existing literature on the application of IoT in in energy systems, in general, and in the context of smart grids particularly. Furthermore, we discuss enabling technologies of IoT, including cloud computing and different platforms for data analysis. Furthermore, we review challenges of deploying IoT in the energy sector, including privacy and security, with some solutions to these challenges such as blockchain technology. This survey provides energy policy-makers, energy economists, and managers with an overview of the role of IoT in optimization of energy systems.

Internet of Things (IoT) and the Energy Sector

Energy crisis and carbon emission have become two seriously concerned issues universally. As a feasible solution, Energy Internet (EI) has aroused global concern once proposed. EI is a new power generation developing a vision of evolution of smart grids into the Internet. The communication infrastructure is an essential component to the implementation of EI. A scalable and permanent communication infrastructure is crucial in both construction and operation of EI. In this paper, we present an introduction and the motivation to the evolution from smart grid to EI. We also introduce a representative EI architecture, i.e., the future renewable electric energy delivery and management system. Four critical EI features are emphasized. Then, we summarize the essential requirements that EI systems have to meet. With several key supporting technologies, EI shall realize the optimal utilization of highly scalable and distributed green energy resources, so that the situation of severe energy source crisis and carbon emission can be efficiently relieved. Since an EI system might have extensively distributed consumers and devices, the guarantee of its reliability and security is extremely significant. The further specific exploration for challenges, including reliability and security, will be stated in this paper.

A Survey on Energy Internet: Architecture, Approach, and Emerging Technologies

The present study explores the relationship between economic growth, electricity consumption, urbanization and environmental degradation in case of United Arab Emirates (UAE). The study covers the quarter frequency data over the period of 1975–2011. We have applied the ARDL bounds testing approach to examine the long run relationship between the variables in the presence of structural breaks. The VECM Granger causality is applied to investigate the direction of causal relationship between the variables. Our empirical exercise reported the existence of cointegration among the series. Further, we found an inverted U-shaped relationship between economic growth and CO 2 emissions i.e. economic growth raises energy emissions initially and declines it after a threshold point of income per capita (EKC exists). Electricity consumption declines CO 2 emissions. The relationship between urbanization and CO 2 emissions is positive. Exports seem to improve the environmental quality by lowering CO 2 emissions. The causality analysis validates the feedback effect between CO 2 emissions and electricity consumption. Economic growth and urbanization Granger cause CO 2 emissions.

Economic growth, electricity consumption, urbanization and environmental degradation relationship in United Arab Emirates

This paper conducts a comprehensive study on the application of big data and machine learning in the electrical power grid introduced through the emergence of the next-generation power system-the smart grid (SG). Connectivity lies at the core of this new grid infrastructure, which is provided by the Internet of Things (IoT). This connectivity, and constant communication required in this system, also introduced a massive data volume that demands techniques far superior to conventional methods for proper analysis and decision-making. The IoT-integrated SG system can provide efficient load forecasting and data acquisition technique along with cost-effectiveness. Big data analysis and machine learning techniques are essential to reaping these benefits. In the complex connected system of SG, cyber security becomes a critical issue; IoT devices and their data turning into major targets of attacks. Such security concerns and their solutions are also included in this paper. Key information obtained through literature review is tabulated in the corresponding sections to provide a clear synopsis; and the findings of this rigorous review are listed to give a concise picture of this area of study and promising future fields of academic and industrial research, with current limitations with viable solutions along with their effectiveness.

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Application of Big Data and Machine Learning in Smart Grid, and Associated Security Concerns: A Review

Global energy challenges have driven the adoption of renewable energy sources. Usually, an intelligent energy and battery management system is deployed to harness the renewable energy sources efficiently, whilst maintaining the reliability and robustness of the power system. In recent years, the battery-supercapacitor based hybrid energy storage system (HESS) has been proposed to mitigate the impact of dynamic power exchanges on battery's lifespan. This study reviews and discusses the technological advancements and developments of battery-supercapacitor based HESS in standalone micro-grid system. The system topology and the energy management and control strategies are compared. The study also discusses the technical complexity and economic sustainability of a standalone micro-grid system. A case study of a standalone photovoltaic-based micro-grid with HESS is presented.

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Battery-supercapacitor hybrid energy storage system in standalone DC microgrids: areview

Energy Internet: The business perspective

The invention and application of the electric power technology triggered the second industrial revolution in human history, which marked the human society entered the age of electricity. Electricity provides the sustainable power for economic and social development. With the rapid development of economy, the electricity consumption is also increasing. The increase of electricity consumption has further promoted the progress of the industrial economy. In order to achieve the goal of improving the level of economic development while reducing energy consumption, it is necessary to reveal the relationship between electricity consumption and economic growth. This study is an extensive overview of the literature surrounding this topic. In this paper, we focus on the relationship between electricity consumption and economic growth in China. We first analyze the general situation of China's electricity consumption and economic development. Then we explore the relationship between China's electricity consumption and economic growth from three dimensions, i.e., the time dimension, the regional dimension and the industrial dimension. Finally, we study the key issues in the research of the relationship between electricity consumption and economic growth, including variable selection, model construction and results discussion. This work suggests that the nature of the nexus in China should and can be explored from a wider perspective, by developing a suitable integrated methodological framework.

On electricity consumption and economic growth in China

Household monthly electricity consumption pattern mining: A fuzzy clustering-based model and a case study

Since the reforming and opening up in 1978, China has experienced a miraculous development. To investigate the transformation and upgrading of China’s economy, this study focuses on the relationship between economic growth and electricity consumption of the secondary and tertiary industry in China. This paper captures the dynamic interdependencies among the related variables using a theoretical framework based on a Vector Autoregressive (VAR)–Vector Error Correction (VEC) model. Using the macroeconomic and electricity consumption data, the results show that, for secondary industry, there is only a unidirectional Granger causality from electricity consumption to Gross Domestic Product (GDP) from 1980 to 2000. However, for the tertiary industry, it only occurs that GDP Granger causes electricity consumption from 2001 to 2014. All these conclusions are verified by the impulse response function and variance decomposition. This study has a great significance to reveal the relationship between industrial electricity consumption and the pattern of economic development. Meanwhile, it further suggests that, since China joined the World Trade Organization (WTO) in 2001, the trend of the economic transformation and upgrading has gradually appeared.

Exploring the transformation and upgrading of China's economy using electricity consumption data: A VAR-VEC based model

The invention discloses a charging and discharging scheduling method for electric vehicles connected to a micro-grid. The method comprises the steps of: (1) determining a system structure of the micro-grid and the characteristics of various units; (2) building a micro-grid optimal scheduling target function of considering the battery depreciation cost of the electric vehicles under time-sharing electrovalence; (3) determining constraints of various distributed power supplies, batteries of the electric vehicles and the like and forming a micro-grid optimal scheduling model by the constraints and the micro-grid optimal scheduling target function; (4) determining the number, the start-stop time, the start-stop charged states and other basic calculated data of the electric vehicles connected to the power grid under the time-sharing electrovalence; and (5) solving the micro-grid optimal scheduling model through a particle swarm algorithm and determining the charge and discharge power when the electric vehicles are connected to the power grid. The batteries of the electric vehicles are connected to the micro-grid as mobile distributed energy storage devices to achieve the peak load shifting effect; the operation safety and stability of the micro-grid in the time-sharing electrovalence environment are improved; and the energy utilization efficiency and the power grid operation efficiency are simultaneously improved.

Zhen Shao

Papers

Energy Internet: The business perspective

On electricity consumption and economic growth in China

Household monthly electricity consumption pattern mining: A fuzzy clustering-based model and a case study

Exploring the transformation and upgrading of China's economy using electricity consumption data: A VAR-VEC based model

Charging and discharging scheduling method for electric vehicles connected to micro-grid