Smart Grid Era Research Articles

Exploring the convergence of Metaverse, Blockchain, Artificial Intelligence, and digital twin for pioneering the digitization in the envision smart grid 3.0

The ongoing evolution of the Metaverse, digital twin (DT), artificial intelligence (AI), and Blockchain technologies is fundamentally transforming the utilization of sustainable energy resources (SERs) within smart grids (SGs), ushering in the era of smart grid 3.0 (SG 3.0). This paradigm shift presents unprecedented opportunities to establish robust and sustainable energy trading frameworks between consumers and utilities within the SG 3.0 environment. The integration of DT, AI, and Blockchain technologies in the context of the Metaverse’s smart grids, facilitated by optimized communication protocols, represents a transformative approach. AI models play a pivotal role in predictive energy trading analysis, while DT assumes a crucial role in effectively managing diverse SERs within SGs. Simultaneously, Blockchain technology holds the potential to create a trusted and decentralized environment, laying the foundation for an energy trading system within the SG 3.0 universe of the Metaverse. This visionary convergence of DT and Blockchain sets the stage for a futuristic paradigm in SGs, establishing a robust energy trading and management foundation. This paper embarks on exploring uncharted research paths and unlocking new dimensions by surveying and delving into the convergence of the Metaverse, Blockchain, AI, and DT. Its primary objective is to drive the digitization of SERs within SGs, to revolutionize energy systems. The envisioned SG 3.0 represents a significant leap by amalgamating cutting-edge technologies in sustainable energy, paving the way for revolutionary advancements in SGs’ digitization, which aligns with and contributes to achieving the sustainable development goals outlined by the United Nations.

Protection strategy for fault detection in AC microgrid based on MVMD & differential CUSUM

Abstract In the era of smart grids and microgrids, the transformation of the traditional grid system brings many operational, technical, and economic benefits. However, the complexity of the network due to the integration of various distributed generations (DGs), continuous change of topology, and non-linear load make fault detection a major issue that forces power engineers to focus on. In this paper, a novel fault detection scheme is suggested based on the multivariate variational mode decomposition mode (MVMD) and differential cumulative sum (DCUSUM). As a generalized extension of the original variational mode decomposition (VMD) algorithm for multivariate data residing in multidimensional spaces, the main goal of MVMD is to decompose the input signal into different band-limited intrinsic mode functions (IMFs). Due to the inherent characteristics of being insensitive to noise and very effective in decomposing the local features even with similar frequencies, it is very effective for fault detection in microgrid distribution systems. The proposed DCUSUM algorithm computes the differential cumulative energy for the remaining significant modes. A fault detection index is considered in this approach and applied for fault detection by adaptively through the threshold setting to accurately result in fault detection. To justify the proposed approach, a standard AC microgrid test system is considered and the approach is verified for fault detection under various fault conditions and resistances. The obtained results and the comparative analysis with other methods reflect the better accuracy, robustness, and reliability of the proposed approach.

Mitigating Missing Rate and Early Cyberattack Discrimination Using Optimal Statistical Approach with Machine Learning Techniques in a Smart Grid

In the Industry 4.0 era of smart grids, the real-world problem of blackouts and cascading failures due to cyberattacks is a significant concern and highly challenging because the existing Intrusion Detection System (IDS) falls behind in handling missing rates, response times, and detection accuracy. Addressing this problem with an early attack detection mechanism with a reduced missing rate and decreased response time is critical. The development of an Intelligent IDS is vital to the mission-critical infrastructure of a smart grid to prevent physical sabotage and processing downtime. This paper aims to develop a robust Anomaly-based IDS using a statistical approach with a machine learning classifier to discriminate cyberattacks from natural faults and man-made events to avoid blackouts and cascading failures. The novel mechanism of a statistical approach with a machine learning (SAML) classifier based on Neighborhood Component Analysis, ExtraTrees, and AdaBoost for feature extraction, bagging, and boosting, respectively, is proposed with optimal hyperparameter tuning for the early discrimination of cyberattacks from natural faults and man-made events. The proposed model is tested using the publicly available Industrial Control Systems Cyber Attack Power System (Triple Class) dataset with a three-bus/two-line transmission system from Mississippi State University and Oak Ridge National Laboratory. Furthermore, the proposed model is evaluated for scalability and generalization using the publicly accessible IEEE 14-bus and 57-bus system datasets of False Data Injection (FDI) attacks. The test results achieved higher detection accuracy, lower missing rates, decreased false alarm rates, and reduced response time compared to the existing approaches.

A Simple Method for Detection of Voltage Sags and their Mitigation using a Dynamic Voltage Restorer

Power quality is the major focus of progressive nations.In the era of smart grid,power quality receives a lot of thrust at all voltage levels namely,medium ,high and low.The objective of this paper is to present a simple and cost effective method of sag detection and mitigation in three phase systems at the consumer's end. The use of the concept of space vector representation of three phase quantities, effectively in the detection ,quantification and mitigation of short duration voltage disturbances such as sags in three phase supply systems under both balanced and unbalanced conditions of operation is presented.Three phase balanced quantities can be represented by a synchronously rotating space vector of constant magnitude .The two phase equivalent of which on a stationary reference frame are orthogonal to each other.When balanced sags occur,the magnitude of the rotating space vector obtained as a resultant of its orthogonal components is sensed for magnitude deviations from its constant nominal value.A trigger signal of varying amplitude is generated in proportional response to the detection of the deviation in its magnitude . In case of sags occurring on one or two phases of the supply, (unblanced),the positive sequence components of the unbalanced three phase quanities are determined using mathematical transformations and the magnitude of the resultant is found out. This resultant is sensed and its deviations from nominal value are measured and used to generate the trigger after processing.The trigger thus generated, initiates the control system of a Dynamic Voltage Restorer(DVR).When a sag is detected,the trigger signal generated, switches on the control circuit of the inverter which uses the space vector pwm control using sampled phase amplitudes of the control signal to generate the modulating wave in proportion to the deviation of the sag voltage from the reference value.The fundamental component of the output voltage which in proportion to the deviation from the nominal value is injected in series with the three phase disturbed signal through a transformer of suitable gain .The results show the successful operation of the DVR in maintaining nominal value of the voltage across the load through out the duration of the disturbance for all cases of balanced and unbalanced operation. The paper presents the detection algorithm as well as the control algorithm for restoration of voltage which are simple and easy to implement .

A new iterative mixed integer linear programming based real time energy efficient management of AC-DC distribution networks

In the smart grid era, AC-DC distribution networks are evolving for integrating massive amounts of distributed energy resources, especially battery associated solar power generation units, into distribution networks in most energy efficient ways. Performing real time energy management for such networks is challenging due to presence of different power electronic converters, grid-edge devices and voltage controlling equipment. Existing studies either focused on developing offline day ahead methods, or shortsighted greedy strategies by neglecting the offline beneficial attributes in real time optimization frameworks. On the contrary, this article proposes a novel real time AC-DC distribution network energy management portfolio by merging load control and conservation voltage reduction techniques to achieve energy efficient operation of the network by regulating the operation of the controllable resources. Initially, the overall optimization problem is developed as a mixed integer non-convex programming by modelling the offline benefits as time coupled stochastic expressions in real time optimization platform. The non-convex expressions are later simplified by adopting different linearization techniques and the entire energy management framework is revised as a simple mixed integer linear programming (MILP). The original problem is decoupled for each time step by simplifying the time coupled expressions using virtual queues and Lyapunov functions. Additionally, a new iterative MILP algorithm is proposed to solve the revised energy management problem with less computation complexity and time. Unlike the previous solution methods, the proposed algorithm initializes the variables by their median values and handles the infeasibilities by adding tangential cutting plane constraints and penalty variable minimization objective. Demonstrating on 33-bus AC-DC distribution network, the superiority of the proposed real time energy management process and iterative MILP algorithm is established after comparing with standard energy management portfolios and existing solution methods.© 2017 Elsevier Inc. All rights reserved.

Enhancing Power Grid Resilience Against Cyber Threats in the Smart Grid Era Using Bi-LSTM Model

Enhancing Power Grid Resilience Against Cyber Threats in the Smart Grid Era Using Bi-LSTM Model

Enhancing Power Grid Resilience against Cyber Threats in the Smart Grid Era

Enhancing Power Grid Resilience against Cyber Threats in the Smart Grid Era

Protection Challenges on Integration of Distributed Sources to Power system network: A Review

— Electricity generation has to be increased to meet various type of future load demand like LED lightning, Electrical 
 Vehicles, mobiles, communication network, non linear load etc. Automation is essential requirement now a days in each sector. 
 To provide automation at residential, commercial and industrial sectors electrical energy is required. To meet energy 
 requirement of various sector utility grid is integrated with DG Sources, which mainly include renewable energy sources like 
 solar, wind and other environment friendly sources either in large scale or small scale depends on the location and availability. 
 This type of technology leads power system to era of smart grid. Smart grid technology provides features like digitization, two 
 way communication, distributed generation, adaptive islanding, self monitoring and self mitigating, fully controllable compare to 
 conventional Grid. Though smart grid technology is advantageous but due to integration of distributed generation at 
 transmission and distribution system will increase protection challenges in the power network. This paper gives an insight of 
 different protection issues which occur at transmission, distribution and Micro grid network. The paper also focuses on the 
 techniques and solution of the various protection challenges.

Multi-objective optimal scheduling of household appliances for demand side management using a hybrid heuristic algorithm

In the era of smart grids and the Internet of Things, demand side management, which aims to reduce electricity bills while increasing user satisfaction by scheduling appliances properly, becomes imperative for residential consumers. As a result of the conflict between the two objectives, it is impossible to optimize them simultaneously. Nevertheless, using multi-objective optimization approaches, trade-off solutions can be obtained. In this paper, a novel demand-side management method is presented to manage the operation of residential appliances. In the beginning, appliances are divided into interruptible, non-interruptible, and power-shiftable types according to their operating characteristics and the user’s preferences. And the mathematical models are built accordingly. Then, a multi-objective optimization problem is formulated to minimize the electricity cost and user dissatisfaction, in which residents’ tolerance to discomfort is considered. Since it is a multi-objective mixed integer nonlinear programming problem, a hybrid meta-heuristic algorithm is proposed to solve it efficiently. The experiment results have confirmed the effectiveness of the optimization model and the higher efficiency of the hybrid algorithm. Furthermore, a case study has been performed to demonstrate the effectiveness of the scheduling method.

Prosumers Flexibility as Support for Ancillary Services in Low Voltage Level

The prosumers flexibility procurement has increased due to the current penetration of distributed and variable renewable energy sources. The prosumers flexibility is often able to quickly adjust the power consumption, making it well suited as a primary and secondary reserve for ancillary services. In the era of smart grids, the role of the aggregator has been increasingly exploited and considered as a player that can facilitate small prosumers' participation in electricity markets. This paper proposes an approach based on the use of prosumers flexibility by an aggregator to support ancillary services at a low voltage level. An asymmetric pool market approach is considered for flexibility negotiation between prosumers and the local marker operator (aggregator). From the achieved results it is possible to conclude that the use of flexibility can bring technical and economic benefits for network operators.

Photovoltaic-storage energy system management considering wireless data communication

In the era of smart grids, wireless communication technology improves the flexibility of data transmission for home appliances. Home energy management systems facilitate the maintenance and updates of hardware in the home while coordinating the direction of energy flow between photovoltaic (PV) storage and energy storage, and their various loads. This study designs an energy management system for PV and energy storage devices of ordinary household users to achieve optimal economic energy dispatching within the household and energy trading between the household and the power grid. Considering the network communication between home devices, this study establishes the Zigbee network by hardware devices to realize the data transmission link of the energy management system. Communication interference experiments with communication data loss that occur at different times are designed to study the influence of water wave effect on the economic operation of users’ home load. Experimental results show that when the positive difference between the PV power generation and load is larger, communication failure occurs, and users bear a higher operation cost.

Flexible Boundary Multi-Microgrids Power Distribution Systems with Internet of Thing for System Efficiency Enhancement

Multi-microgrid power distribution systems are gaining attention in the smart grid era. Distributed energy resources, energy storage, as well as energy sharing and scheduling has a great potential to enhance multi-microgrid systems’ performance. This research develops an algorithm for optimal operation of various distributed energy resources in a flexible boundary multi-microgrid power distribution network, considering internet of things (IoT). The proposed algorithm used in this research can reduce power system operating costs, power, and energy losses and emissions, and ultimately increase the systems’ efficiency. A hybrid Particle Swarm Optimization-Tabu Search algorithm is developed for optimization purposes.

Towards Optimal and Executable Distribution Grid Restoration Planning With a Fine-Grained Power-Communication Interdependency Model

Distribution service restoration (DSR) under natural disasters is always a critical and challenging problem for utility companies. An effective solution must not ignore the power-communication interdependency as various systems are getting increasingly connected in the Smart Grid era. In this paper, we propose a two-layer distribution system model with both power and communication components. Based on this model, we formulate the restoration process as a routing problem that schedules the path and action sequence of utility crews that involves repairing damaged components, closing power switches, and enabling communication paths between the control center and remote field devices. We develop a simulation-based method to quantitatively evaluate the restoration process with public reference models of large-scale power systems. The experimental results show that our method improves the total restored energy up to 57.6% and reduces the recovery time up to 63% by considering the power-communication interdependency.

Improved MQTT Secure Transmission Flags in Smart Homes.

In the current era of smart homes and smart grids, complex technical systems that allow for the automation of domestic functions are rapidly growing and becoming more widely available. A wide range of technologies and software applications are now available for use in smart homes, and many of them are free to use. They allow for communication between home appliances and their users, as well as the automation, monitoring, and remote-control capabilities of home appliances themselves. Unfortunately, a lot of previous research ignored security issues involving the great attention to detail of the data in a transmission session within the devices in smart home architectures, which is why this study proposed smart grid secured transmission flags suitable for preventing every bit of data transmission in a smart home. Secure Message Queueing Transport Protocol (MQTT) in Internet of Things (IoT) Smart Homes protocols was utilized; an experimental testbed was designed with a prototype involving the process of a smart home system and the sequences of the data transmission. The evaluation of the proposed strategies has shown an improved bi-directional secure resource constraint strategy for the smart home within data packet transmission at 70 to 80 mbps over secure MQTT. A number of concerns, including technological barriers, difficulties, challenges, and future trends, as well as the role of users, have been presented in this study, among others.

Green manufacturers’ power strategy in the smart grid era

Conventional electricity generation from coal and natural gas is one of the largest greenhouse gas sources and using wind power is an effective way to achieve climate neutrality. Realizing this, green manufacturers such as SC Johnson and Silk decide to use 100% wind power for their manufacture. This enables the manufacturers to receive green certifications and hence, attract more customers. However, wind power can be unstable, so some green manufacturers like New Belgium Brewing purchase power from both the regular power supplier and the wind power supplier, resulting in a discount of market expansion. Modelling this complex system can be challenging that requires the use of meta-heuristic algorithms, so we build a game-theoretic model comprising of a wind power supplier, a regular power supplier and a green manufacturer to examine whether the green manufacturer is more benefited under all-wind power strategy. This helps clarify the strategic perdition about the firms’ equilibrium decisions. We find that the green manufacturer prefers all-wind power strategy when (a) the discount effect is significant and the market potential is greatly expanded; (b) both the discount effect and the market expansion are moderate. We further study the impact of the environmental effect, the improvement of wind power stability and the production cost uncertainty, finding that the main results are qualitatively unchanged.

Controlling a large constant speed centrifugal chiller to provide grid frequency regulation: A validation based on onsite tests

High penetration of intermittent renewables may cause safety and stability problems to the electricity grid. Building thermal loads could contribute to the grid stability in the era of renewable energy and smart grid, because they are high and flexible. This study proposes a model predictive control strategy to control a large constant speed centrifugal chiller to follow grid frequency regulation signals while providing sufficient cooling to the building. Onsite tests were conducted to identify critical parameters in the process including time delay and ramping speed of chiller power consumption. A dynamic platform was built based on the studied system and fine-tuned using onsite tests data. Validation tests were conducted using the 40 min Regulation A and Regulation D test signals provided by PJM (Pennsylvania - New Jersey - Maryland Interconnection). According to the onsite tests, the total delay time and the maximum ramping speed of chiller power were 20–25 s and 2.53 kW/s, respectively. Validation based on the 40 min test signals shown that the composite performance scores were 0.901 and 0.885 for Regulation A test and Regulation D test, respectively. Continuous 12 h validation tests in a working day shown that the composite scores were 0.917 and 0.893 for Reg A and Reg D tests, respectively. And the studied constant speed centrifugal chiller could provide a regulation capacity of 5–7.5% of its nominal power.

Data Consistency for Data-Driven Smart Energy Assessment.

In the smart grid era, the number of data available for different applications has increased considerably. However, data could not perfectly represent the phenomenon or process under analysis, so their usability requires a preliminary validation carried out by experts of the specific domain. The process of data gathering and transmission over the communication channels has to be verified to ensure that data are provided in a useful format, and that no external effect has impacted on the correct data to be received. Consistency of the data coming from different sources (in terms of timings and data resolution) has to be ensured and managed appropriately. Suitable procedures are needed for transforming data into knowledge in an effective way. This contribution addresses the previous aspects by highlighting a number of potential issues and the solutions in place in different power and energy system, including the generation, grid and user sides. Recent references, as well as selected historical references, are listed to support the illustration of the conceptual aspects.

A methodology for energy key performance indicators analysis

With the advent of the smart grid era, the electrical grid is becoming a complex network in which different technologies coexist to bring benefits to both customers and operators. This paper presents a methodology for analyzing Key Performance Indicators (KPIs), providing knowledge about the performance and efficiency of energy systems, focusing on the demand side. In the first stage of the methodology, the baseline KPIs are calculated. In the second stage, all KPIs are updated to be compared with the baseline ones. In fact, due to the dynamic nature of players in a smart grid, this methodology plays a crucial role in the performance assessment. Moreover, the proper definition and selection of KPIs is usually a challenging task since KPIs can be applied to evaluate diverse areas within a smart grid. Such areas include building energy efficiency, home communications, and smart metering deployment, just to mention a few. In the proposed methodology, the information obtained from a KPI can be driven to distinct aspects such as efficiency, environment, reliability, power quality, safety, security, or cost reduction. Through a case study from a real implementation of an energy system, we show how to assess energy consumption efficiency, thus improving energy management.

5G-Wireless Sensor Networks for Smart Grid-Accelerating technology’s progress and innovation in the Kingdom of Saudi Arabia

The constant energy requirement was a crucial concern in today’s smart grid era, which needs tremendous attention. New wireless networking technologies should be integrated into the grid to achieve more distributed generation and energy storage. Advancements in wireless sensor networks (WSN) and embedded systems have enabled the implementation of smart grid monitoring and automation systems at low cost. The incorporation of fifth-generation networks (5G) in a smart grid would create novel business models of "edge" and "fog" technology at the utility side, accompanying with smart-control and automation. Nonetheless, WSN’s increased implementation also introduces impediments to safety, particularly relevant to confidentiality, accessibility, and safety management, creating both utilities and customers’ unpredictable expenditure and catastrophe. In this paper, a comprehensive overview of the communication and computing aspects of 5G network infrastructure is provided and discussed how they can be beneficial in promoting advanced smart grid systems in the Kingdom of Saudi Arabia. Besides, this paper discusses the smart grid, current security issues surrounding machine-to-machine information on the smart grid, and solutions available to identify and avoid cyber threats.

RSAM: Robust Self-Attention Based Multi-Horizon Model for Solar Irradiance Forecasting

With the widespread adoption of renewable energy sources in the smart grid era, there is an utmost requirement to develop prediction models that can accurately forecast solar irradiance. The stochastic nature of solar irradiance considerably affects photo-voltaic (PV) power generation. Since weather conditions have a high impact on solar irradiance; therefore, we need weather-conscious forecasting models to boost predictive accuracy. Although Recurrent Neural Networks (RNNs) has shown considerable performance in time-series forecasting problems, its sequential nature prohibits parallelized computing. Recently, architectures based on self-attention mechanism have shown remarkable success in natural language programming (NLP), while being computationally superior. In this paper, we propose an RSAM (Robust Self-Attention Multi-horizon) forecasting architecture, which mainly works in two parts: First, multi-horizon forecasting of solar irradiance using multiple weather parameters; Second, prediction interval analysis for model robustness using quantile regression. A self-attention based Transformer model belonging to the family of deep learning models has been utilized for multi-variate solar time-series forecasting. Using the National Renewable Energy Laboratory (NREL) benchmark datasets of two different sites, we demonstrate that the proposed approach exhibit enhanced performance in comparison to RNN models in terms of RMSE, MAE, MBE, and Forecast skill at each forecasted interval.