Smart Grid Framework Research Articles

Smart Grids based on Quantum Computing in the present-day Energy Systems for Fault Diagnosis

This paper's goal is to investigate how quantum computing might be used to optimize power systems and to address some of the difficulties that quantum computers may encounter, along with solutions. The fundamental ideas of quantum computing are also covered, along with how they differ from classical computations. Within the framework of smart grids, quantum computing (QC) presents itself as a next-generation alternative approach to address impending computational difficulties. QC is a relatively new but exciting technology that uses the special properties of quantum mechanics to analyze data and perform calculations. This new paradigm can solve optimization, simulation, and machine learning issues more effectively and quickly than ever before by overcoming the obstacle of computational constraints. Recent significant advancements in the development of sophisticated quantum hardware and software techniques have increased the viability of applying QC in a variety of research fields, including smart grids. It is clear that a great deal of research has already been done, and that research is remarkably ongoing. As a result, this article further defines the prospective smart grid applications and presents the research findings of the most current articles, emphasizing their recommendations for applying QC approaches for diverse smart grid applications. It states their plans, methods, and outcomes. The limits of the most recent quantum computers are also discussed in this research, along with how they might significantly affect the optimization of energy systems.

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.

Corrigendum: A data-driven ensemble technique for the detection of false data injection attacks in the smart grid framework

Corrigendum: A data-driven ensemble technique for the detection of false data injection attacks in the smart grid framework

A data-driven ensemble technique for the detection of false data injection attacks in the smart grid framework

The major component of the smart grid (SG) is the advanced metering infrastructure (AMI), which monitors and controls the existing power system and provides interactive services for invoicing and electricity usage management with the utility. Including a cyber-layer in the metering system allows two-way communication but creates a new opportunity for energy theft, resulting in significant monetary loss. This article proposes an approach to detecting abnormal consumption patterns using energy metering data based on the ensemble technique AdaBoost, a boosting algorithm. Different statistical and descriptive features are retrieved from metering data samples, which account for extreme conditions. The model is trained for malicious and non-malicious data for five different attack scenarios, which are analyzed on the Irish Social Science Data Archive (ISSDA) smart meter dataset. In contrast to prior supervised techniques, it works well even with unbalanced data. The efficacy of the proposed theft detection method has been evaluated by comparing the accuracy, precision, recall, and F1 score with the other well-known approaches in the literature.

Demand side management using ant colony optimization algorithm in renewable energy integrated smart grid

The increasing demand for electrical energy is a result of advancing technologies and changing lifestyles worldwide. Meeting this escalating energy need poses a substantial challenge, especially the difficulty in constructing new conventional power plants due to limited fossil fuel resources. To address this, demand-side management (DSM) in smart grid (SG), integrated with solar photovoltaic energy (SPE) have emerged as a crucial tool for effectively managing electricity demand, ensuring flexibility and reliability. DSM achieves optimal electricity utilization by rescheduling the operation schedules of consumer appliances and carefully adjusting their demand profiles. Integrating DSM into a smart grid framework is highly advantageous for the power industry’s pursuit of sustainable energy goals. While various heuristic-based optimization techniques have been employed for DSM, the focus on SPE has been limited to small-scale residential loads. This study utilizes the Ant Colony Optimization (ACO) algorithm to tackle a day ahead DSM minimization problem, considering SPE in areas with large number of appliances. The DSM minimization problem falls into the category of discrete combinatorial problems, making it well-suited for ACO optimization. The self-healing, self-protection, and self-organizing attributes of ACO make it particularly effective for DSM solutions. Residential, commercial, and industrial loads, with and without SPE integration, are considered to demonstrate the efficacy of the proposed ACO algorithm. Simulation results are compared with other studies in the literature, including Evolutionary Algorithm (EA), Moth Flame Optimization (MFO), and Bacterial Foraging Optimization (BFO), in terms of reducing consumer’s cost of energy (CCE) and utility peak load (UPL). The findings indicate that the proposed ACO algorithm outperforms the other algorithms considered in the current context.

Optimizing Electric Vehicle Charging Infrastructures in the Smart Grid with a Multi-Objective Approach

This paper introduces a tool designed to optimize electric vehicle (EV) charging infrastructures within the smart grid framework. The tool utilizes a multi-objective approach and is programmed in Python. It enables dynamic management of energy distribution among different EV charging infrastructures, addressing scenarios where surplus photovoltaic (PV) power generation exceeds charging demands but faces challenges due to storage costs and electric energy transmission rates to alternative infrastructures. In instances of low PV production relative to charging demand, the algorithm strategically selects the optimal procurement strategy, either purchasing electric energy from neighboring infrastructures or utilizing surplus PV energy for direct charging. The tool empowers stakeholders to make informed decisions by facilitating comparisons between the cost of storing electric energy locally and the expense of procuring it from external sources, thereby enhancing the efficiency and cost-effectiveness of EV charging infrastructures in the smart grid ecosystem. Extensive simulations and case studies demonstrate the efficacy of the proposed approach, showcasing its potential to optimize energy distribution and promote sustainable practices within the EV charging domain.

Energy Management System Technology in Smart Grid

The smart grid, envisioned as the future of the power industry, aims to revolutionize energy supply with efficiency and sustainability. This paper extensively examines the critical role of Energy Management System (EMS) technology within the smart grid framework. By harnessing advanced technologies like distributed energy storage, intelligent energy management, electric vehicle charging, intelligent energy dispatching, and fault perception diagnosis, the smart grid promises heightened power system efficiency, reliability, and environmental consciousness. In today's dynamic energy landscape, the integration of renewable sources introduces a unique challenge due to their inherent volatility. EMS emerges as the linchpin, offering real-time monitoring and data-driven optimization to ensure grid stability amid fluctuating supply. The smart grid, with advanced information technology and intelligent control strategies, leans heavily on EMS for seamless energy production, transmission, and consumption. However, challenges persist, encompassing stable energy supply, security, infrastructure upgrades, and cross-border standardization. Yet, with sustained technological innovation and collaborative efforts, a future of intelligent, sustainable, and reliable power systems is attainable. In conclusion, EMS integration into the smart grid not only signifies technological progress but also heralds a monumental shift towards a greener, more sustainable energy landscape. This paper provides a comprehensive exploration of how EMS, as the backbone of the smart grid, empowers us to navigate the complexities of modern energy demands, paving the way for a brighter, more efficient future.

Distributed fixed‐time economic dispatch over directed graphs: An event‐triggered approach

AbstractThis paper introduces a novel distributed fixed‐time algorithm that employs an event‐triggered strategy to solve the economic dispatch problem within the framework of smart grids. Distinguishing from existing fixed‐time and finite‐time algorithms of distributed economic dispatch applied to undirected graphs, the proposed distributed optimization algorithm in this paper has been proven effective for directed graphs. The proposed algorithm ensures the continual satisfaction of the supply and demand balance constraints. Additionally, the incorporation of the event‐triggered strategy not only conserves actuator updates but also avoids Zeno behavior. Finally, simulation cases verify the effectiveness of the proposed algorithm.

A novel non-intrusive load monitoring technique using semi-supervised deep learning framework for smart grid

A novel non-intrusive load monitoring technique using semi-supervised deep learning framework for smart grid

Development of Design Optimization for Smart Grid (DOfSG) Framework for Residential Energy Efficiency via Fuzzy Delphi Method (FDM) Approach

The smart grid revolution has benefited many sectors but the potential for design optimization among residential units has yet to be explored. Despite some researchers having negative perception of house design's association with the smart grid system, there is in fact potential for investigating design attribute optimisation aligned with the smart grid system. As electricity becomes a necessity of the 21st century society, residential dwellers are becoming more dependent on this indispensable source of energy. As such, this paper explains the development of a framework focusing on design optimization for residential units aligned to the smart grid system using the Fuzzy Delphi Method approach. It focuses on the significant smart grid components linked to the residential sector incorporating key design attributes for energy optimization purposes. The proposed framework denoted two main components of residential design optimization, depicted as indoor and outdoor parameters with its subsequent attributes further categorised into main and detailed components. Twelve design parameters were found to be substantial for the DOfSG development, intended to provide useful guide for aligning residential design towards the smart grid system in Malaysia.

Solution to secure data and decision at sensor nodes and communication links of smart grid networks using blockchain and IPFS

Smart grid is proposed as a solution to the problems of production, distribution, monitoring, and control of the electricity in traditional power grids. Smart grid networks place IoT sensor nodes at various grid lines and collect large volume of data about power flow, usage etc. The collected data are analyzed for various applications like demand forecasting, fault diagnosis and fault prediction etc. The sensor nodes and the communication links can be compromised affecting the privacy of consumers. False data can be propagated with malicious intentions. This work proposes a secure and privacy preserving framework for smart grid IoT networks to secure the data and decision at sensor nodes and communication links. The work proposes a novel Data and Decision rules Secure Efficient Smart Grid (DDSESG) framework integrating secure compressive sensing technique with blockchain and interplanetary file system (IPFS) for securing both data and decision. Through experimental analysis, the proposed solution is found to provide higher resiliency against data security attacks at comparative 12.4% lower computation cost, 15% lower communication cost, 19.9% lower storage cost. Forecasting on transformed data in proposed solution had only a marginal 1.08 % difference in accuracy compared to forecasting on original data.

A Machine Learning Model Ensemble for Mixed Power Load Forecasting across Multiple Time Horizons

The increasing penetration of renewable energy sources tends to redirect the power systems community’s interest from the traditional power grid model towards the smart grid framework. During this transition, load forecasting for various time horizons constitutes an essential electric utility task in network planning, operation, and management. This paper presents a novel mixed power-load forecasting scheme for multiple prediction horizons ranging from 15 min to 24 h ahead. The proposed approach makes use of a pool of models trained by several machine-learning methods with different characteristics, namely neural networks, linear regression, support vector regression, random forests, and sparse regression. The final prediction values are calculated using an online decision mechanism based on weighting the individual models according to their past performance. The proposed scheme is evaluated on real electrical load data sensed from a high voltage/medium voltage substation and is shown to be highly effective, as it results in R2 coefficient values ranging from 0.99 to 0.79 for prediction horizons ranging from 15 min to 24 h ahead, respectively. The method is compared to several state-of-the-art machine-learning approaches, as well as a different ensemble method, producing highly competitive results in terms of prediction accuracy.

Energy Aware Load Balancing Framework for Smart Grid Using Cloud and Fog Computing.

Data centers are producing a lot of data as cloud-based smart grids replace traditional grids. The number of automated systems has increased rapidly, which in turn necessitates the rise of cloud computing. Cloud computing helps enterprises offer services cheaply and efficiently. Despite the challenges of managing resources, longer response plus processing time, and higher energy consumption, more people are using cloud computing. Fog computing extends cloud computing. It adds cloud services that minimize traffic, increase security, and speed up processes. Cloud and fog computing help smart grids save energy by aggregating and distributing the submitted requests. The paper discusses a load-balancing approach in Smart Grid using Rock Hyrax Optimization (RHO) to optimize response time and energy consumption. The proposed algorithm assigns tasks to virtual machines for execution and shuts off unused virtual machines, reducing the energy consumed by virtual machines. The proposed model is implemented on the CloudAnalyst simulator, and the results demonstrate that the proposed method has a better and quicker response time with lower energy requirements as compared with both static and dynamic algorithms. The suggested algorithm reduces processing time by 26%, response time by 15%, energy consumption by 29%, cost by 6%, and delay by 14%.

Framework for smart grid to implement a price elasticity-based peak time rebate demand response program

The smart grid model is developed with some changes to help in implementing a demand response program which was initially developed for a Pecan Street project. Correspondingly, the real-time solar and load data are collected from the data port for the city of Austin. A single day is selected for our analysis of all four seasons of the year. The flat rate, and real-time and day-ahead pricing information are collected from ComEd. The key challenge for addressing business problems is the flexibility of consumption. However, without considering the properties of loss aversion, the system would not be a practical solution. So, in this article, a dynamic demand response program based on price elasticity that integrates loss aversion characteristics is proposed. The proposed system is compared for all pricing schemes and all seasons. Four scenarios are created for peak time rebate with different combinations of loss aversion factor values and all the possible combinations of rebates. This article directs how these combinations could change the demand curve and how the utility can make a decision about the specific importance of the criteria, such as the total demand carrying capacity, peak demand reduction, and in obtaining optimum profit for utility and the consumer.

An Intelligent Intrusion Detection System in Smart Grid Using PRNN Classifier

Typically, smart grid systems enhance the ability of conventional power system networks as it is vulnerable to several kinds of attacks. These vulnerabilities might cause the attackers or intruders to collapse the entire network system thus breaching the confidentiality and integrity of smart grid systems. Thus, for this purpose, Intrusion detection system (IDS) plays a pivotal part in offering a reliable and secured range of services in the smart grid framework. Several existing approaches are there to detect the intrusions in smart grid framework, however they are utilizing an old dataset to detect anomaly thus resulting in reduced rate of detection accuracy in real-time and huge data sources. So as to overcome these limitations, the proposed technique is presented which employs both real-time raw data from the smart grid network and KDD99 dataset thus detecting anomalies in the smart grid network. In the grid side data acquisition, the power transmitted to the grid is checked and enhanced in terms of power quality by eradicating distortion in transmission lines. In this approach, power quality in the smart grid network is enhanced by rectifying the fault using a FACT device termed UPQC (Unified Power Quality Controller) and thereby storing the data in cloud storage. The data from smart grid cloud storage and KDD99 are pre-processed and are optimized using Improved Aquila Swarm Optimization (IASO) to extract optimal features. The probabilistic Recurrent Neural Network (PRNN) classifier is then employed for the prediction and classification of intrusions. At last, the performance is estimated and the outcomes are projected in terms of grid voltage, grid current, Total Harmonic Distortion (THD), voltage sag/swell, accuracy, precision, recall, F-score, false acceptance rate (FAR), and detection rate of the classifier. The analysis is compared with existing techniques to validate the proposed model efficiency.

Analytical Design of Synchrophasor Communication Networks with Resiliency Analysis Framework for Smart Grid

The advent of synchrophasor technology has completely revolutionized the modern smart grid, enabling futuristic wide-area monitoring protection and control. The Synchrophasor Communication Network (SCN) is a backbone that supports communication of synchrophasor data among Phasor Measurement Units (PMUs) and Phasor Data Concentrators (PDCs). The operator at the control center can visualize the health of the smart grid using synchrophasor data aggregated at PDCs from several PMUs. Since the core of the SCN is the existing IP network as an underlying communication infrastructure, the synchrophasor data is subjected to attacks that can compromise its security. The attacks, such as denial-of-service (DoS), can result in degradation of performance and even can disrupt the entire operation of the smart grid, if not controlled. Thus, a resilient SCN is a pertinent requirement in which the system continues to operate with accepted levels of performance even in response to the DoS. This article endeavors to propose a comprehensive resiliency framework for the SCN with enhanced resiliency metrics based on hardware reliability and data reliability. The proposed framework is deployed for a SCN pertaining to a practical power grid in India for its resiliency analysis. The proposed work can be regarded as a significant contribution to smart grid technology, as it provides a framework for resiliency analysis covering different aspects such as hardware reliability, data reliability, and parameters validation using the QualNet network simulator. Nevertheless, an analytical design of the hybrid SCN proposed in this work can even be extended to other topological designs of SCN.

Comparative Study of Performance About the Integrated Power Quality and Optimized Framework for Smart Grid

As global warming becomes more serious in the 21st century, efforts are being made around the world to reduce greenhouse gas emissions. The greenhouse gas reduction objective is set in 2009 to be a 30% emission forecast and set to low carbon emissions. There are several grids which are using coal for generating electricity. As a result, the environment has got more pollutants and the world needs reducing such type of pollution for a green world. Then the concept of integration of renewable energy sources is coming to force. Their integration of such sources is providing the opportunity of availability of power with less pollution of the environment. A smart grid was developed and applied to the field as a system that can manage the energy in a smart environment. In this paper, a comparative study is performed about the performance of Integrated Technology in a smart environment.

The Performance Analysis of Optimized Integrated Framework for Smart Grid

Supervisory Control and Data Acquisition system are monitoring and controlling system. It focuses on the supervisory level, not the full control permissions. It is gathering real-time data with the help of several kinds of sensors. Internet of Things (IoT) is a three-dimension any time, any place anything connectivity for anything. This paper performing the comparison between the SCADA and optimization framework for smart grid. A power quality model is integrated with the optimization framework. Moreover, this paper focused on the comparison of optimized framework with SCADA.

A Blockchain-based Security Framework for Secure and Resilient Smart Grid

Smart grid integrates the information and communication technologies with the physical grid. It can not only improve security and efficiency of traditional power grids, but also has advantages in promoting environmental protection and sustainable development. However, the existing smart grid systems suffer some security concerns such as privacy leakage and data tampering. In order to address these security issues, we propose a security framework for smart grid using Blockchain. It protects data privacy through cryptographic tools, and it applies Blockchain technologies to prevent data tampering. Compared with the existing smart grid systems, our proposed system enjoys better security and resilience.

Fine-grained Access Control Method for Blockchain Data Sharing based on Cloud Platform Big Data

Blockchain technology has the advantages of decentralization, de-trust, and non-tampering, which breaks through the limitations of traditional centralized technology, so it has gradually become the key technology of power data security storage and privacy protection. In the existing smart grid framework, the grid operator is a centralized key distribution organization, which is responsible for sending all the secret credentials, so it is easy to have a single point of failure, resulting in a large number of personal information losses. To solve the problems of inflexible access control in smart grid data-sharing framework and considering the limitation of multi-party cooperation among grid operators and efficiency, an attribute-based access control scheme supporting privacy preservation in smart grid is constructed in this paper. A fine-grained access control scheme supporting privacy protection is designed and extended to the smart grid system, which enables the system to achieve fine-grained access control of power data. A decryption test algorithm is added before the decryption algorithm. Finally, through performance analysis and comparison with other schemes, it is verified that the performance of this system is 7% higher than the traditional method, and the storage cost is 9.5% lower, which reflects the superiority of the system. Full optimization of the access policy is achieved. It is proved that the scheme is more efficient to implement the coordination and cooperation of multiple authorized agencies in the system initialization.