Smart Grid Energy Research Articles

Integrated Architecture for Smart Grid Energy Management: Deep Attention-Enhanced Sequence-to-Sequence Model with Energy-Aware Optimized Reinforcement Learning for Demand Response

Integrated Architecture for Smart Grid Energy Management: Deep Attention-Enhanced Sequence-to-Sequence Model with Energy-Aware Optimized Reinforcement Learning for Demand Response

Distributed Secondary Control of DC Microgrid with Power Management Based on Time-of-Use Pricing and Internal Price Rate

This paper presents a novel approach to manage distributed DC microgrids (DCMG) by integrating a time-of-use (ToU) electricity pricing scheme and an internal price rate calculation mechanism. The proposed power-management system is designed to effectively handle uncertainties such as utility grid (UG) availability, fluctuating electricity prices, battery state of charge (SOC) levels, and frequent plug-ins and plug-outs of electric vehicles (EVs). Uncertainties in DCMG systems often lead to inefficiencies, power imbalances, and inexact voltage regulation issues within DCMGs. In addition, to maintain the power balance and constant voltage regulation under various operational states, the proposed scheme also incorporates secondary control into the DCMG power-management system. Unlike the existing approaches that often fail to adapt dynamically to changing conditions, the proposed method is the first approach to consider the concept of internal price rate in designing the DCMG power management. To address this challenge, this approach proposes a more resilient power-management strategy to enhance the efficiency and adaptability of DCMG systems. Extensive simulations and experimental validations demonstrate the practicality and adaptability of the proposed control strategy under diverse test conditions, including operation transitions between grid-connected mode (GCM) and islanded mode (IM), low battery SOC condition, operation transition from the current control mode (CCM) to distributed secondary control mode (DSCM), and EV plug-in scenarios. The test results confirm that the proposed method enhances the reliability, efficiency, and economic viability of DCMG systems, making it a promising solution for future smart grid and renewable energy integrations.

Time Synchronized Power Meters for Advanced Smart Distribution of Energy in Smart Grids

Time Synchronized Power Meters for Advanced Smart Distribution of Energy in Smart Grids

Distributed robust [formula omitted]asso-[formula omitted] based on [formula omitted]ash optimization for smart grid: Guaranteed robustness and stability

The integration of variable renewable energy supplies into smart grid energy management poses several obstacles to system operation. An efficient solution for resource management is essential to ensuring reliable operation. This research presents distributed robust Lasso-model predictive control (D − RLMPC) as a way to handle energy problems in a multi-layer and multi-time frame optimization method. The D − RLMPC is a hierarchical system that integrates a centralized supervisory management (SM) layer for long-term optimization with a distributed coordination management (CM) layer for short-term adaptation to high power fluctuations. The higher layer, known as the SM, is responsible for providing the grid operator with specific operating plans and offering guidance to the bottom layer, known as the CM. The CM is responsible for coordinating the interaction between the centralized optimization goals and the physical power system layer. Furthermore, a distributed extended Kalman filter (DEKF) is used to ascertain the inter-dependencies among subsystems. Next, an iterative approach based on Nash optimization is proposed to get the globally optimum solution of the whole system in a partly distributed manner. The simulation results demonstrate the effectiveness of the proposed control approach, which combines the advantages of centralized and distributed control to provide a comprehensive solution for the grid operating issue. To verify and assess the effectiveness of the suggested approach, the acquired outcomes are compared to those of the centralized robust, distributed robust, and distributedMPC approaches. The simulation findings confirm the practicality of using the suggested system to manage future smart grid assets.

Optimal energy management via day-ahead scheduling considering renewable energy and demand response in smart grids

The energy optimization in smart power grids (SPGs) is crucial for ensuring efficient, sustainable, and cost-effective energy management. However, the uncertainty and stochastic nature of distributed generations (DGs) and loads pose significant challenges to optimization models. In this study, we propose a novel optimization model that addresses these challenges by employing a probabilistic method to model the uncertain behavior of DGs and loads. Our model utilizes the multi-objective wind-driven optimization (MOWDO) technique with fuzzy mechanism to simultaneously address economic, environmental, and comfort concerns in SPGs. Unlike existing models, our approach incorporates a hybrid demand response (HDR), combining price-based and incentive-based DR to mitigate rebound peaks and ensure stable and efficient energy usage. The model also introduces battery energy storage systems (BESS) as environmentally friendly backup sources, reducing reliance on fossil fuels and promoting sustainability. We assess the developed model across various distinct configurations: optimizing operational costs and pollution emissions independently with/without DR, optimizing both operational costs and pollution emissions concurrently with/without DR, and optimizing operational costs, user comfort, and pollution emissions simultaneously with/without DR. The experimental findings reveal that the developed model performs better than the multi-objective bird swarm optimization (MOBSO) algorithm across metrics, including operational cost, user comfort, and pollution emissions.

Enhancing Smart Grid Efficiency through Machine Learning-Based Renewable Energy Optimization

Managing renewable energy in smart grids poses a significant challenge due to the inherent uncertainty and variability of energy sources like solar and wind power. To address this issue, we propose a novel approach that leverages the strengths of both Extreme Learning Machine (ELM) and Particle Swarm Optimization (PSO) algorithms. Our method utilizes ELM to model and predict renewable energy generation, enabling more accurate forecasting and planning. Meanwhile, PSO optimizes the parameters of the ELM algorithm, ensuring optimal performance and efficiency. We evaluated our approach using a dataset of solar energy production and compared its performance to existing optimization techniques. The results show that our ELM-PSO approach significantly improves the accuracy of renewable energy predictions and reduces energy costs in smart grids. The implications of our research are far-reaching, as our approach can be applied to various renewable energy systems, including wind turbines, solar panels, and hydroelectric power plants. By enhancing the efficiency and reliability of renewable energy utilization, we can create a more sustainable and resilient energy future.

Smart Grid Energy Reduction and QoS Maximization through Renewable Energy and Data Buffering

The integration of renewable energy sources and advanced data management strategies is pivotal for the evolution of smart grids. This research focuses on optimizing energy consumption and enhancing Quality of Service (QoS) in smart grids by leveraging renewable energy and implementing data buffering techniques. The proposed data buffering integrated renewable smart grid framework reduces the dependance on conventional energy sources, minimizes energy costs, and improves service consistency. Through a detailed analysis and experimental validation, the proposed study demonstrates significant improvements in energy efficiency and QoS metrics, showcasing the potential of the proposed approach in modern smart grid environments.

Research on Industry 4.0 smart grid monitoring and energy management based on data mining and Internet of Things technology

With the increasing global focus on sustainability and energy efficiency, the concept of Industry 4.0 is becoming an important driver of smart grid technology advancement. Traditional power grids are faced with the challenges of high energy consumption and low management efficiency, and it is urgent to realize intelligent transformation through emerging technologies. This study aims to explore the application of data mining and Internet of Things technology in smart grid monitoring and energy management, with a particular focus on thermal energy management, in order to improve energy utilization efficiency and achieve dynamic regulation. A smart grid monitoring platform integrating data mining and Internet of Things technology is constructed. The data of power and heat energy are collected by sensors, and the data mining algorithm is used to analyze the energy consumption behavior of users and the running state of equipment. A prediction model is also introduced to realize dynamic prediction and optimal scheduling of heat demand. The experimental results show that the thermal energy management system based on the platform can significantly improve the energy utilization rate, data mining technology effectively identifies the peak energy use and potential energy saving opportunities, and the Internet of Things technology realizes real-time monitoring and feedback, enhancing the flexibility and response ability of the system. Through accurate energy monitoring and effective management strategies, it can not only realize the optimal allocation of resources, but also promote the development of enterprises in the direction of low-carbon environmental protection.

Intelligent Control Strategy for Optimal Utilization of Energy in Smart Grid

Amidst the relentless surge in global energy demand, the optimal utilization of intelligent grid energy has emerged as a pivotal avenue for energy conservation, emission reduction, and sustainable development. The deployment of sophisticated control strategies within intelligent grids aims to elevate energy utilization rates and mitigate power losses. Drawing upon an extensive analysis of data, this study delves into the impact and significance of intelligent control strategies in the optimal utilization of smart grid energy. Our data analysis reveals that the implementation of intelligent control strategies enhances the energy utilization rate of smart grids by approximately 25%, significantly mitigating energy waste compared to traditional grids. Furthermore, power losses are effectively curbed and reduced by approximately 18%. This implies that, while fulfilling the same power demand, smart grids can reduce substantial energy losses and conserve vast resources for society. Furthermore, the intelligent control strategy boasts the capability to carry out real-time scheduling and control, tailored to the evolving energy demand and supply scenarios. This approach ensures the stability and reliability of power supply, subsequently elevating both the operational efficiency of the power system and the quality of power service delivered. Ultimately, the intelligent control strategy occupies a crucial position in optimizing the utilization of intelligent grid energy, serving as a catalyst for enhanced efficiency and sustainability within the global energy landscape. Our data analysis unequivocally demonstrates the remarkable impact achieved by sophisticated control strategies. Looking ahead, with the continuous advancement and refinement of technology, smart grids are poised to play an even more significant role in energy optimization, contributing meaningfully towards global energy conservation, emission reduction, and sustainable development.

Preface

It is my honor to present the proceedings of the 2024 3rd International Conference on Smart Grid and Green Energy (ICSGGE 2024). ICSGGE 2024 is an event that brings together the world’s leading experts and scholars in the fields of smart grid and green energy, and is also an excellent platform for sharing the latest research results and discussing the application of cutting-edge technologies in related research domains. With the increasing severity of global climate change and energy crisis, smart grid and green energy, as the key means to solve the energy problem, have received extensive attention from the international community. In order to further promote technological innovation, academic exchanges and cooperation in the fields of smart grid and green energy, ICSGGE 2024 was held from April 19th to 21st, 2024 in Chengdu, China (virtual conference). ICSGGE 2024 brought together top scholars and researchers from all over the world, who shared their latest research results and practical experiences in the fields of smart grid and green energy in this conference. Such topics as The Role of Smart Grids to Drive Cities Towards Carbon Neutrality (by Prof. Steivan Defilla, Tianjin University, China), Role of Energy Conservation & Management in Energy Transition (by Prof. Muhammad Asif, King Fahd University of Petroleum & Minerals, Saudi Arabia), among others were discussed during the keynote speech part. Through exchanges and discussions, we have jointly explored the development trends and future research direction of smart grid and green energy, stimulated innovative thinking, and promoted academic exchanges and cooperation. This proceedings of ICSGGE 2024 contains excellent academic papers from all around the world, which not only cover the basic theories, teaching methods, and evaluation systems of smart grid and green energy, but also discuss in depth the applications and innovations in many fields. Topics include but are not limited to Smart Grid Security, Energy Transfer and Interactive Technology, Communication-Based Control and Protection, Photovoltaic Systems and Solar Energy Engineering, Power Electronics and Drives, Wind Energy Generation, etc. Each paper has been strictly reviewed and screened to ensure its academic value and practical application value. We would like to express my heartfelt thanks to all the experts and scholars, organizers, sponsors and participants who have contributed to and supported this conference. And we believe that all the experts and scholars have harvested fruitful results and inspired new inspirations in this event. Through our joint efforts and wisdom, the ICSGGE 2024 has become a grand event in the fields of smart grid and green energy, and made a new contribution to the promotion of electronic and energy engineering. The Committee of ICSGGE 2024 List of Committee Member is available in this Pdf.

A Full Calibration Approach on a Drone-Borne Platform for HF Antenna Measurements in Smart Grid Energy Facilities

A Full Calibration Approach on a Drone-Borne Platform for HF Antenna Measurements in Smart Grid Energy Facilities

Novel cyber-physical collaborative detection and localization method against dynamic load altering attacks in smart energy grids

Owing to the integration of energy digitization and artificial intelligence technology, smart energy grids can realize the stable, efficient and clean operation of power systems. However, the emergence of cyber-physical attacks, such as dynamic load-altering attacks (DLAAs) has introduced great challenges to the security of smart energy grids. Thus, this study developed a novel cyber-physical collaborative security framework for DLAAs in smart energy grids. The proposed framework integrates attack prediction in the cyber layer with the detection and localization of attacks in the physical layer. First, a data-driven method was proposed to predict the DLAA sequence in the cyber layer. By designing a double radial basis function network, the influence of disturbances on attack prediction can be eliminated. Based on the prediction results, an unknown input observer-based detection and localization method was further developed for the physical layer. In addition, an adaptive threshold was designed to replace the traditional precomputed threshold and improve the detection performance of the DLAAs. Consequently, through the collaborative work of the cyber-physics layer, injected DLAAs were effectively detected and located. Compared with existing methodologies, the simulation results on IEEE 14-bus and 118- bus power systems verified the superiority of the proposed cyber-physical collaborative detection and localization against DLAAs.

A review of asset management using artificial intelligence‐based machine learning models: Applications for the electric power and energy system

AbstractPower system protection and asset management present persistent technical challenges, particularly in the context of the smart grid and renewable energy sectors. This paper aims to address these challenges by providing a comprehensive assessment of machine learning applications for effective asset management in power systems. The study focuses on the increasing demand for energy production while maintaining environmental sustainability and efficiency. By harnessing the power of modern technologies such as artificial intelligence (AI), machine learning (ML), and deep learning (DL), this research explores how ML techniques can be leveraged as powerful tools for the power industry. By showcasing practical applications and success stories, this paper demonstrates the growing acceptance of machine learning as a significant technology for current and future business needs in the power sector. Additionally, the study examines the barriers and difficulties of large‐scale ML deployment in practical settings while exploring potential opportunities for these tactics. Through this overview, insights into the transformative potential of ML in shaping the future of power system asset management are provided.

Analytic Hierarchy Process Assessment Framework for Blockchain in Renewable Energy

The urgency to reach net-zero emissions until 2050 has created a vast interest in innovative technologies for renewable energy sector. At the same time the need for trusty and unfailing solutions will rise considering the increasing amount of decentralized and decarbonized energy systems. It has previously been described in the literature that the aforementioned characteristics could be combined by blockchain technology. Therefore, this study focuses on the practical side – the opinion of industry experts about the use of blockchain technology in renewable energy. By using analytic hierarchy process analysis, the goal of this study is to develop a methodology for the selection of the most appropriate blockchain applications in renewable energy and to identify and evaluate the possible types of use taking into account socio-economic, political and legal, technological and environmental factors. The results of this study highlights the important influence of political, legal and technological factors like involvement of government institutions and possible attacks to the system, but considers the economic factors as the least significant for the introduction and use of blockchain technology in the renewable energy sector. The most promising use cases for blockchain would be associated with reliable and immutable certificates of origin for renewable energy and use in smart grid (including smart metering), smart homes and relevant Internet of Things applications. The use of crypto-assets and initial coin offerings for renewable energy development should be viewed with precaution and willingness to inform and educate considering social factors like public opinion and societies’ knowledge on crypto-assets.

Whale Optimization for Cloud-Edge-Offloading Decision-Making for Smart Grid Services.

As IoT metering devices become increasingly prevalent, the smart energy grid encounters challenges associated with the transmission of large volumes of data affecting the latency of control services and the secure delivery of energy. Offloading computational work towards the edge is a viable option; however, effectively coordinating service execution on edge nodes presents significant challenges due to the vast search space making it difficult to identify optimal decisions within a limited timeframe. In this research paper, we utilize the whale optimization algorithm to decide and select the optimal edge nodes for executing services' computational tasks. We employ a directed acyclic graph to model dependencies among computational nodes, data network links, smart grid energy assets, and energy network organization, thereby facilitating more efficient navigation within the decision space to identify the optimal solution. The offloading decision variables are represented as a binary vector, which is evaluated using a fitness function considering round-trip time and the correlation between edge-task computational resources. To effectively explore offloading strategies and prevent convergence to suboptimal solutions, we adapt the feedback mechanisms, an inertia weight coefficient, and a nonlinear convergence factor. The evaluation results are promising, demonstrating that the proposed solution can effectively consider both energy and data network constraints while enduring faster decision-making for optimization, with notable improvements in response time and a low average execution time of approximately 0.03 s per iteration. Additionally, on complex computational infrastructures modeled, our solution shows strong features in terms of diversity, fitness evolution, and execution time.

Generating Synthetic Electricity Load Time Series at District Scale Using Probabilistic Forecasts

Thanks to various European directives, individuals are empowered to share and trade electricity within Renewable Energy Communities, enhancing the operational efficiency of local energy systems. The digital transformation of the energy market enables the integration of decentralized energy resources using cloud computing, the Internet of Things, and artificial intelligence. In order to assess the feasibility of new business models based on data-driven solutions, various electricity consumption time series are necessary at this level of aggregation. Since these are currently not yet available in sufficient quality and quantity, and due to data privacy reasons, synthetic time series are essential in the strategic planning of smart grid energy systems. By enabling the simulation of diverse scenarios, they facilitate the integration of new technologies and the development of effective demand response strategies. Moreover, they provide valuable data for assessing novel load forecasting methodologies that are essential to manage energy efficiently and to ensure grid stability. Therefore, this research proposes a methodology to synthesize electricity consumption time series by applying the Box–Jenkins method, an intelligent sampling technique for data augmentation and a probabilistic forecast model. This novel approach emulates the stochastic nature of electricity consumption time series and synthesizes realistic ones of Renewable Energy Communities concerning seasonal as well as short-term variations and stochasticity. Comparing autocorrelations, distributions of values, and principle components of daily sequences between real and synthetic time series, the results exhibit nearly identical characteristics to the original data and, thus, are usable in designing and studying efficient smart grid systems.

A spatio‐temporal graph convolutional approach to real‐time load forecasting in an edge‐enabled distributed Internet of Smart Grids energy system

SummaryAs the edge nodes of the Internet of Smart Grids (IoSG), smart sockets enable all kinds of power load data to be analyzed at the edge, which create conditions for edge calculation and real‐time (RT) load forecasting. In this article, an edge‐cloud computing analysis energy system is proposed to collect and analyze power load data, and a combination of graph convolutional network (GCN) with LSTM, called KGLSTM is used to achieve mid‐long term mixed sequential mode RT forecasting. In the proposed edge‐cloud framework, distributed intelligent sockets are regarded as edge nodes to collect, analyze and upload data to cloud services for further processing. The proposed KGLSTM network adopts a double branch structure. One branch extracts the data characteristics of mid‐short term time‐series data through an encoding–decoding LSTM module; the other branch extracts the data features of long term timing data through an adapted GCN. GCN is used to extract spatial correlations between different nodes. In addition, by combining a dynamic weighted loss function, the accuracy of peak forecasting is effectively improved. Finally, through various experimental indicators, this article shows that KGLSTM and weighted KGLSTM have achieved significant performance improvement over recent methods in mid‐long term time‐series forecasting and peak forecasting.

Smartgrid-based hybrid digital twins framework for demand side recommendation service provision in distributed power systems

Electricity consumers face challenges in selecting an optimal energy-saving plan, and this is a sustainability problem. To set consumers focus on sustainable energy management, developments around ”Industry 4.0” are needed to achieve an optimal balance between cost and energy consumption with a focus on cutting-edge machine-learning models and smart services introduction. Energy modelling is crucial for confronting the challenges introduced by the energy transition. This research is to promote digital twin (DT) technology in smart energy grids. The hybrid digital twin model is developed based on smart grid end-users’ electrical components. This approach emulates the component specification of the reference smart grid system and encourages a reduction in net energy consumption. Additionally, the application of AI connects smart meters, IoT devices, and the assets of the smart grids to the DT recreation of demand-side end-users for energy efficiency recommendation provision; this setup improves energy management, energy efficiency, and the usage of renewable energy resources. The novelty of this study is that the recent scope of demand-side recommendation schemes has been extended in the direction of cutting-edge Industry 4.0 hybrid DTs. Additionally, a prototype system and its functional characteristics have been proposed with the potential to pave the path for utilization in microgrid environments. Simulation results show a unique level of parallelism between the reference system and the developed model, along with a 36.8% reduction in net energy consumption following implementation of the recommendation advice.

Fabrication of cathode composite Co3O4/CC and study on electrochemical properties of zinc cobalt battery

Purpose Aqueous zinc-ion battery has broad application prospects in smart grid energy storage, power tools and other fields. Co3O4 is one of the ideal cathode materials for water zinc-ion batteries due to their high theoretical capacity, simple synthesis, low cost and environmental friendliness. Many studies were concentrated on the synthesis, design and doping of cathodes, but the effect of process parameters on morphology and performance was rarely reported. Design/methodology/approach Herein, Co3O4 cathode material based on carbon cloth (Co3O4/CC) was prepared by different temperatures hydrothermal synthesis method. The temperatures of hydrothermal reaction are 100°C, 120°C, 130°C and 140°C, respectively. The influence of temperatures on the microstructures of the cathodes and electrochemical performance of zinc ion batteries were investigated by X-ray diffraction analysis, scanning electron microscopy, cyclic voltammetry curve, electrochemical charging and discharging behavior and electrochemical impedance spectroscopy test. Findings The results show that the Co3O4/CC material synthesized at 120°C has good performance. Co3O4/CC nanowire has a uniform distribution, regular surface and small size on carbon cloth. The zinc-ion battery has excellent rate performance and low reaction resistance. In the voltage range of 0.01–2.2 V, when the current density is 1 A/g, the specific capacity of the battery is 108.2 mAh/g for the first discharge and the specific capacity of the battery is 142.6 mAh/g after 60 charge and discharge cycles. Originality/value The study aims to investigate the effect of process parameters on the performance of zinc-ion batteries systematically and optimized applicable reaction temperature.

The Impact of the Presence of a High Number of Electric Vehicles in Future Smart Cities – A Review

Attention to the increasing growth of electric vehicles (8.3% in light type and 39% in heavy type) in smart grid structures, it is essential to consider bottlenecks like proper energy management, charging infrastructure, ICT and cybersecurity problems, and economic and environmental challenges. Tariffs for the production of distributors and charging stations for electric vehicles in smart cities are definitely in a state of smart changes. The correct response to the consumption of electric vehicles will make energy distribution completely intelligent and structured with a new framework. In this paper, the structures of future smart cities will be discussed in the presence of a significant number of electric vehicles. The benefits that smart energy grids bring to future smart cities are explored and discussed in detail. A complete review of past research in the field of potential and capacities and challenges faced by future smart cities in the presence of electric vehicles has been demonstrated and the latest achievements and methods in this field have been reviewed. In the end, the bottlenecks and basic challenges that microgrids face in future smart cities are described.