Smart Grid Energy Research Articles

Online distributed neurodynamic optimization for energy management of renewable energy grids

An online distributed neurodynamic optimization-based energy management method is proposed to accommodate the integration of intermittent renewable energy in smart grids. The proposed approach takes advantage of the online prediction method to deal with uncertainties from renewable energy generators, the distributed consensus algorithm for information exchange, and neurodynamic optimization to manage coupling constraints. The proposed distributed optimization neurodynamic approach utilizes a one-layer neural recurrent network without auxiliary variables and enables parallel computation, significantly alleviating the data calculation burden compared with the traditional centralized methods and requiring no auxiliary variables, in contrast to most of the existing distributed methods. The convergence, optimality and robustness to communication failures of the proposed method are verified by various case studies with a modified IEEE 33-bus distribution system and a modified IEEE 123-bus distribution system.

Smart grid energy district based on the integration of electric vehicles and combined heat and power generation

The issues dealing with climate change also caused by the private transport sector are attracting more and more attention in European Countries. This work aims to simultaneously address the issue related to the management of the energy demand of the private transport sector and building sector in the framework of the smart grid energy districts.The investigated smart grid is designed for meeting the energy demands of a district, including the energy demand for space heating and cooling, as well as the electric energy of a lot of buildings, occupied by people who only use electric vehicles. The system is equipped with a cogenerator, which is expected to operate according to the base-load operation strategy, constantly producing the rated power for whatever power demanded. Two charging strategies are analyzed and compared with the aim of detecting which strategy better exploits the power produced by the cogenerator. The performed dynamic simulations of the whole system are carried out by means of TRNSYS tool. TRNSYS allows one to use a detailed library of components which carefully model and simulate the devices included in the proposed system model. Suitable control strategies are also developed in order to improve the energy, environmental and economic performance of the smart grid. The results show that this layout allows one to considerably reduce the primary energy consumption and carbon dioxide emissions of the investigated district. As matter of fact, the proposed smart energy grid achieves a primary energy saving index of about 32%, with a payback period of about 6 years. These promising results suggest that the proposed smart energy grid may be useful for addressing the issue related with environmental impact of building sector and private transport sector.

Guest Editorial: Industrial Cyber–Physical Systems—New Trends in Computing and Communications

The papers in this special section focus on industrial cyber-physical systems (CPS), with an emphasis on computing and communications applications. CPS systems are defined by integrating computation and communication facilities on the one hand and the monitoring and control of physical processes on the other hand. Industrial cyber–physical systems (ICPS) refer to the science and art of designing and using cyber–physical systems for industrial and process control applications, for example in smart factories, smart energy grids, smart transportation systems, smart cities, and several other areas. Many of these applications are time- and mission-critical and hence often require low latency and high reliability. In parallel, there has been a strong growth in integrating intelligence, often in the form of machine/deep learning, into applications, which also leads to vastly increasing computational requirements. These innovations then will be integrated into complex systems, which need to be properly engineered to become safe, reliable, trustworthy, and secure while at the same time being cost-efficient.

IoT based smart energy grid for sustainable cites

Abstract In present scenario, each and every activity in basic lifestyle of people is becoming smarter with the day-to-day flourishing development of technology such as Internet of Things, Artificial Intelligence, Machine Learning and Blockchain. Among the smart improvements, smart city holds the highest rank due to the initiatives provided by the administration. Smart city mission has influenced the citizen’s life with respect to the automation on fundamental needs. Energy resources are considered as one of the important requirements for the survival of people. With the advent of Internet of Things, smart grid systems are designed in order to meet the sustainable demands. It also provides information about the energy resources consumed and alert the consumers about the demand prevails on the resources. Technological improvement is more essential for a country, only if it can be considered and adaptable for sustainable development. Therefore these papers outline the Internet of Things based smart grid which helps in the energy conservation process to meet the future demands. Energy management processes has to be well-versed among the citizens as it helps in understanding the possible ways on storage of energy resources. However, when energy storage is concerned, security issues are the counterpart which has to be focused at higher level. Therefore, blockchain techniques applied for energy storage systems are also outlined along with the applications and future directions of smart grid.

Research on Power Equipment System of Knowledge Graph under Electric Energy in Smart Grid

In order to realize technological innovation in the electric power field, the article uses artificial intelligence, big data analysis and mining, knowledge graphs, natural language processing and other technologies to construct Chinese professional dictionaries and knowledge graphs in the electric power field. And on this basis, the thesis puts forward the technical architecture of a comprehensive energy service support system based on the concept of “a picture of the power grid”, a spatiotemporal data management platform and a graph database query application implementation mechanism. Finally, research and design the visualization of the hierarchical organization of the data, use the association relationship between the data to organize the data into multi-topic classification and hierarchical organization, and provide a friendly interactive man-machine interface.

Preface

As Covid-19 spreads around the world, people have to make some change to avoid being infected. And due to the outbreak of COVID-19, the participants were not able to meet each other at the 5th International Conference on New Energy and Future Energy System (NEFES 2020) supposed to be held in Xi’an during July 14th-17th, 2020. Even though the conference was postponed to November 3rd-6th, some of the participants from other countries were still not able to visit China to attend NEFES2020 for COVID-19. So, the conference organizer located in Wuhan made a decision to hold it as an online conference (virtual conference without any physical participation and held by Microsoft Teams) during November 3rd-6th. Microsoft Teams Meeting provides a more effective way for us to hold an online conference via some function like sharing the presenters’ Power Point or video, discussing in the chat box or taking pictures. However, there were also some shortcomings about online conference regarding time differences and internet conditions. Therefore, it is a way to hold an international conference online.NEFES 2020 was sponsored by Harbin Institute of Technology, Weihai. It aims to provide a platform for researchers and practitioners in both industry and academia to share their latest achievements and discuss the possible challenges in terms of new energy and future energy system. This volume records the proceedings of NEFES 2020 and contains 85 rigorously selected manuscripts submitted to the online conference. All the manuscripts were selected depending on their quality and relevance to the conference.One hundred and nine participants including presenters and listeners from a broad geographical coverage of counties including China, Australia, Switzerland, Italy, Spain, Romania, Indonesia, Iraq, Poland, Japan, Mexico, India, Russia, Germany and Iran joined NEFES2020 and made it a successful conference. The conference program included welcomes speech, 3 keynote speeches, 25 invited presentations, 11 oral presentations and 16 poster presentations. About 5 minutes were left for each oral presentation, the audience could raise up their hands before the session chair allowed them to ask questions or left message in the chat box as well as wrote an email to the presenters. The conference program was outlined as follows:Welcome speech was delivered by Prof. Fuqiang Wang, School of New Energy, Harbin Institute of Technology (Weihai) of China for five minutes.Three keynote speeches were from Prof. Mattheos Santamouris, University of New South Wales, Australia; Prof. Hui Jin, Xi’an Jiaotong University, China; Dr. Eric Johnson, Editor-in-chief emeritus of Environmental Impact Assessment Review and Managing Director of Atlantic Consulting, Switzerland. Each keynote speech lasted for 40 minutes and delivered on November 4th.The other online presentations were delivered by researchers, scholars and experts such as Prof. Riyi Lin, Prof. Zehui Chang, Dr. Liqiang Zhang, Dr. Yushi Liu, Prof. Chongchong Qi, Dr. Diana Enescu, Dr. Luigi Costanzo, Dr. Claudia Masselli, Dr. Jan Kazak. Each oral presentation lasted for 15-25 minutes. The topics of the online presentations covered New energy resources, New energy device, smart grid load and energy management, optical technologies for smart grid, Solar cell materials and lithium-ion batteries.List of NEFES2020 Scientific Committee Members, General Chair, Chair of Technical Program Committee and Technical Program Committee are available in the pdf.

Molybdenum‐based materials for sodium‐ion batteries

AbstractSodium‐ion batteries are considered one of the most promising candidates for affordable and scalable energy storage as required in smart grid and renewable energy. One of the principal challenges sodium‐ion batteries being faced is to search suitable anode materials that can accommodate and store large amounts of Na+ ions reversibly and sustainably at reasonable galvanostatic rates. Molybdenum‐based materials such as oxides and sulfides might meet this challenge as they afford a capacity much greater than those of the carbonaceous materials and exhibit rich Na‐reaction chemistry. However, these materials are facing several technical issues, such as multiple‐phase transformation, particle pulverization as the result of volume swelling, and low surface activity during sodiation/desodiation. To tackle these issues, materials design and engineering are of indispensability. In this brief review, we present a state‐of‐the‐art overview of the research progress of molybdenum‐based materials for sodium storage, and highlight materials engineering strategies that are capable of addressing the mentioned challenges. We also offer valuable insights into their further development direction and discuss their potentiality in practical batteries.image

IoT driven framework based efficient green energy management in smart cities using multi-objective distributed dispatching algorithm

Nowadays, the concept of smart cities evolves from conceptual models to various stages of development. The smart services of cities like smart buildings, smart hospitals, smart traffic, smart factories, and smart transportation are vital to energizing resilient, reliable, seamless energy and electrical flow. One of these smart services' critical challenges is anticipated to run without disruptions using smart energy and smart electrical grids. Hence, in this research, the Internet of Things (IoT) role in green energy resources incorporation into smart electrical grids has been introduced using the Multi-Objective Distributed Dispatching algorithm (MODDA). Efficient energy management includes a tradeoff between the cost linked with utility function and energy consumption. The utility function can be the living comfort of the consumers or the utility firm's gross income. The interchanges between utility and energy consumption must, therefore, be recognized. The algorithm is intended to transmit the renewable green energy available to the battery, thermal, and load storing while preserving a thermal and battery storage limit. The experimental results at the lab-scale show the proposed dispatching algorithm minimizing the system cost and energy consumption. It is shown that substantial energy waste can be reduced by equipping a smart building with the proposed IoT system for effective green energy management.

Dynamic capabilities for ecosystem orchestrationA capability-based framework for smart city innovation initiatives

Firms are faced with increased dynamism due to rapid technological development, digitalization, and sustainability requirements, creating novel opportunities for ecosystem innovation. This is particularly prevalent in smart city contexts where initiatives concerning, for example, energy efficient buildings and smart energy grids drive new kinds of ecosystem formation. Orchestrating emerging innovation ecosystems can offer a path to sustained competitive advantage for ecosystem leaders. Yet, it calls for the development of new capabilities to sense, seize, and reconfigure digitalization opportunities in a highly dynamic ecosystem environment. Yet, prior research lacks insights into the dynamic capabilities and routines required for ecosystem innovation. Therefore, this study investigates how firms can develop dynamic capabilities to orchestrate ecosystem innovation and, thus, gain from it. Through a multiple case study of smart city initiatives, we offer insights into the specific micro-foundations or sub-routines underlying the ecosystem leader's sensing, seizing, and reconfiguring capabilities, which are necessary to orchestrate ecosystem innovation. We develop a capability-based framework demonstrating three orchestration mechanisms – namely, configuring ecosystem partnerships, value proposition deployment, and governing ecosystem alignment. Our findings carry implications for the literature on innovation ecosystems and dynamic capabilities, as well as for managers.

A new fuzzy logic based approach for optimal household appliance scheduling based on electricity price and load consumption prediction

ABSTRACT Smart Home Energy Management Systems (HEMS) constitute a vital necessity for optimizing electricity usage and saving energy in smart grids. However, these systems rely on dynamic factors that are stochastic and difficult to predict, such as the load consumption and electricity prices. Therefore, constructing an efficient control system for residential buildings requires an accurate prediction process of the associated parameters. This paper proposes an integrated predictive control system that consists of both predictive model and Demand Response (DR) scheme to predict and control the daily electricity usage in the residential sector. First, a Long Short-Term Memory-based (LSTM) optimized predictive model is implemented for predicting both the hourly load consumption and electricity price for a typical smart home. Then, the predicted data are transmitted to a DR fuzzy logic-based controller that can optimally schedule the home appliances usage. In comparison with the state-of-the-art prediction techniques for the residential load consumption and electricity price, the proposed LSTM predictive model outperforms Linear Regression (LR), Decision Tree (DT), Support Vector Regression (SVR), and Ensembled Boosted Trees (EBT). Moreover, the proposed DR-FIS controller has shown good results in terms of reducing the electricity cost by selecting the optimal time schedule.

RETRACTED: A smart grid incorporated with ML and IoT for a secure management system

The economy, national safety, and health care are tremendously dependent on the faithful supply of power. The communication technology integration and sensors in power systems have been authorized as a smart grid (SG) that is revolutionizing the model of power generation, distribution, monitoring, and control. To know the Smart Grid compatibility, many problems are required to be directed. The safety of the smart grid is the most challenging function and very crucial difficulties. This paper proposed, a safe demand-side management machine deploying machine learning for the Internet of Things authorized phase is recommended. The propounded demand-side management (DSM) machine protects the effective energy use based on their preferences. A particular flexibility sample was proposed to manage incursion into the smart grid. Anelastic agent prognosticates swindling companies, the ML classifiers are utilized. Promoted power management and intermediate control companies are recommended for processing power data to improve energy usage. The proposed project's effective simulation is implemented to examine the efficiency. The outcome of the analysis discloses that the planned demand-side management (DSM) machine is less susceptible to the incursion and it is sufficient to decrease the smart grid's energy consumption.

Estimating Energy Forecasting Uncertainty for Reliable AI Autonomous Smart Grid Design

Building safe, reliable, fully automated energy smart grid systems requires a trustworthy electric load forecasting system. Recent work has shown the efficacy of Long Short-Term Memory neural networks in energy load forecasting. However, such predictions do not come with an estimate of uncertainty, which can be dangerous when critical decisions are being made autonomously in energy production and distribution. In this paper, we present methods for evaluating the uncertainty in short-term electrical load predictions for both deep learning and gradient tree boosting. We train Bayesian deep learning and gradient boosting models with real electric load data and show that an uncertainty estimate may be obtained alongside the prediction itself with minimal loss of accuracy. We find that the uncertainty estimates obtained are robust to changes in the input features. This result is an important step in building reliable autonomous smart grids.

Mitigating global warming in smart energy grids via energy supply security for critical energy infrastructures

Mitigating global warming in smart energy grids via energy supply security for critical energy infrastructures

IoT-Enabled Smart Energy Grid: Applications and Challenges

The Internet of Things (IoT) is a rapidly emerging field of technologies that delivers numerous cutting-edge solutions in various domains including the critical infrastructures. Thanks to the IoT, the conventional power system network can be transformed into an effective and smarter energy grid. In this article, we review the architecture and functionalities of IoT-enabled smart energy grid systems. Specifically, we focus on different IoT technologies including sensing, communication, computing technologies, and their standards in relation to smart energy grid. This article also presents a comprehensive overview of existing studies on IoT applications to the smart grid system. Based on recent surveys and literature, we observe that the security vulnerabilities related to IoT technologies have been attributed as one of the major concerns of IoT-enabled energy systems. Therefore, we review the existing threat and attack models for IoT-enabled energy systems and summarize mitigation techniques for those security vulnerabilities. Finally, we highlight how advanced technologies (e.g., blockchain, machine learning, and artificial intelligence) can complement IoT-enabled energy systems to be more resilient and secure and overcome the existing difficulties so that they become more effective, robust, and reliable in operation. Precisely, this article will help understand the framework for IoT-enabled smart energy system, associated security vulnerabilities, and prospects of advanced technologies to improve the effectiveness of smart energy systems.

ANALYSIS OF THE PRECONDITIONS FOR THE DEVELOPMENT OF ENERGY NETWORKS IN UKRAINE

In many countries, the economy's energy sector is on the path of radical change and radical transformation. There are processes of mergers, acquisitions, changes in the structure of government, the boundaries of the sphere of activity, and territorial presence, forcing many former energy monopolies to use new models of value creation. In the EU, the USA, Japan, China, and other countries, smart energy networks and smart meters are positioned as priority areas for energy development shortly. Smart energy grids can empower consumers by making them more aware of energy consumption, forecasting, and demand management. Such networks contain tools to respond quickly to energy market price signals, flexibly increasing or decreasing energy consumption. According to the International Energy Agency forecast, a significant increase in global demand for energy resources (electricity, oil, and gas) is expected in the future. Under the pressure of this factor, a new sustainable business model of energy consumption is being formed, which is being scaled to consumers in all countries of the world. For Ukraine, the benefits of smart grids are particularly relevant, as energy infrastructure and systems are severely worn out both physically and morally. Quickly and flexibly managing infrastructure is crucial for balancing energy flows. With this in mind, economic agents of the energy market are faced with the need to implement new standards ratified by Ukraine in the framework of energy packages to continuously improve the efficiency and reliability of energy supply to meet the requirements for greenhouse gas emissions. Such changes include an analysis of the development of networks in view of existing requirements and prerequisites: replacement of fossil fuels with renewable energy sources; taking advantage of digital energy management technologies (e.g. IoT, blockchain); development of sustainable energy communities interested in improving the energy efficiency of both urban infrastructure as a whole and individual buildings; transition from centralized to decentralized energy generation.

APPROACHES TO INTENSIFICATION SMART GRIDS’ IMPLEMENTATION

Smart energy is an evolutionary stage in developing the economy's energy sector. The existing energy infrastructure of many countries worldwide is based on outdated technologies and principles of energy generation. The availability of advanced technologies capable of producing and distributing energy more efficiently was to guarantee the rapid development of the industry. However, the experience of implementing energy projects and the difficulties with which the modernization of the energy sector is taking place show significant organizational and financial problems that hinder investment in the industry. This is true for both public and private investment in large-scale energy projects and relatively small investments in local projects, including household investments. Today, there is a clear need to develop approaches that will make the latest energy-efficient technologies, particularly electricity, which is associated with smart grids, available to a wide range of consumers. We are talking about creating financial mechanisms and business models that will turn investments into smart energy grids, regardless of their size, into projects with a short payback period and high profitability. Objective technical and technological factors hamper this, but at the same time, it is clear that the available opportunities for the commercialization and dissemination of new energy technologies are not used enough. The article analyzes the current state of development of the energy sector and prospects for applying the latest highly efficient innovative energy technologies. The organizational and economic aspects of the spread of energy innovations belong to the field of smart energy, which is considered broader than usual in the scientific literature. Although the article does not offer clearly defined mechanisms for energy innovation transfer, the analytical work can be helpful for researchers in the field to develop their ideas to find ways to improve domestic and global energy in accordance with existing socio-economic and political challenges.

Mitigating global warming in smart energy grids via energy supply security for critical energy infrastructures

Problems in energy supply security threaten smart grids and cities, critical infrastructures and industrial control systems, and directly affect the economy. This study aims to examine the increase...

Retracted] Cascading Failure Dynamics against Intentional Attack for Interdependent Industrial Internet of Things

The emerging Industrial Internet of Things (IIoT) provides industries with an opportunity to collect, aggregate, and analyze data from sensors, including motion control, machine‐to‐machine communication, predictive maintenance, smart energy grid, big data analysis, and other smart connected medical systems. The physical systems and the cyber systems are organically integrated, forming an interdependent IIoT. This system provides us with enormous advantages, but at the same time, it also introduces the main safety challenges in the design and operation phase. To exploit the security threats of IIoT systems, in this paper, we propose a novel security‐by‐design approach for interdependent IIoT environments across two different levels, namely, theory modeling and runtime simulation. Our method theoretically analyzes the cascading failure dynamics of the intentional attack network. Simultaneously, we verified the theoretical results through simulations and gave the risk factors that affect the system’s security to mitigate potential security attack threats. Besides, we prove its applicability through comparative simulation experiments to study application environments that rely on IIoT, which shows that our method helps identify risk factors and mitigate IIoT attacks’ mechanism.

Household-Level Energy Forecasting in Smart Buildings Using a Novel Hybrid Deep Learning Model

Forecasting of energy consumption in Smart Buildings (SB) and using the extracted information to plan and operate power generation are crucial elements of the Smart Grid (SG) energy management. Prediction of electrical loads and scheduling the generation resources to match the demand enable the utility to mitigate the energy generation cost. Different methodologies have been employed to predict energy consumption at different levels of distribution and transmission systems. In this paper, a novel hybrid deep learning model is proposed to predict energy consumption in smart buildings. The proposed framework consists of two stages, namely, data cleaning, and model building. The data cleaning phase applies pre-processing techniques to the raw data and adds additional features of lag values. In the model-building phase, the hybrid model is trained on the processed data. The hybrid deep learning (DL) model is based on the stacking of fully connected layers, and unidirectional Long Short Term Memory (LSTMs) on bi-directional LSTMs. The proposed model is designed to capture the temporal dependencies of energy consumption on dependent features and to be effective in terms of computational complexity, training time, and forecasting accuracy. The proposed model is evaluated on two benchmark energy consumption datasets yielding superior performance in terms of accuracy when compared with widely used hybrid models such as Convolutional (Conv) Neural Network-LSTM, ConvLSTM, LSTM encoder-decoder model, stacking models, etc. A mean absolute percentage error (MAPE) of 2.00% for case study 1 and a MAPE of 3.71% for case study 2 is obtained for the proposed forecasting DL model in comparison with LSTM-based models that yielded 7.80% MAPE and 5.099% MAPE for two datasets respectively. The proposed model has also been applied for multi-step week-ahead daily forecasting with an improvement of 8.368% and 20.99% in MAPE against the LSTM-based model for the utilized energy consumption datasets respectively.

Technical Economic and Environmental analysis of Chemical Looping versus oxyfuel combustion for NGCC power plant

The Power Sector is undergoing a rapid technological change with respect to implementation of low carbon technologies. The IEA Energy Outlook 2017 shows that the investments in Renewables for the first time are equal to those on the fossil sources. It is likely that the conventional gas turbines and internal combustion engines will need to be integrated in systems employing biofuels and/or CCUS (Carbon Capture Usage and Storage). Also, the European Union is moving rapidly towards low carbon technologies (i.e. Energy Efficiency, Smart Grids, Renewables and CCUS), see the Energy Union Strategy. Currently 28% of the installed power capacity in Europe is based on natural gas plants. Gas-based power capacity has reached 418 GW in 2016 and is likely to continue to grow in the future. To efficiently capture the carbon dioxide emissions generated by the combustion of natural gas in the combustion chamber a possible solution could be to adopt new combustion processes, like Chemical Looping Combustion. The combination of CLC and GTs can decrease the efficiency of a combined cycle power plant from 60% to about 40.34%. These performances influence costs and environmental burdens and this is also the same for oxyfuel combustion, which is a competing technology to realize CCS. This paper, starting from literature mass and energy balances of a conventional combined cycle, a combined cycle coupled with chemical looping combustor and a combined cycle coupled with oxyfuel combustion, calculates the reduction of CO2 emissions which can be achieved during the whole life cycle of the power plant and then identifies the value of the carbon credit which is needed to have an interesting payback period for such kind of investment.