Smart Grid System Research Articles

A Survey on Key Management and Authentication Approaches in Smart Metering Systems

The implementation of the smart grid (SG) and cyber-physical systems (CPS) greatly enhances the safety, reliability, and efficiency of energy production and distribution. Smart grids rely on smart meters (SMs) in converting the power grids (PGs) in a smart and reliable way. However, the proper operation of these systems needs to protect them against attack attempts and unauthorized entities. In this regard, key-management and authentication mechanisms can play a significant role. In this paper, we shed light on the importance of these mechanisms, clarifying the main efforts presented in the context of the literature. First, we address the main intelligent attacks affecting the SGs. Secondly, the main terms of cryptography are addressed. Thirdly, we summarize the common proposed key-management techniques with a suitable critique showing their pros and cons. Fourth, we introduce the effective paradigms of authentication in the state of the art. Fifth, the common two tools for verifying the security and integrity of protocols are presented. Sixth, the relevant research challenges are addressed to achieve trusted smart grids and protect their SMs against attack manipulations and unauthorized entities with a future vision. Accordingly, this survey can facilitate the efforts exerted by interested researchers in this regard.

Minimization of Low Frequency Current Oscillation in Resonant Link of a Solid State Transformer by Passive Filters

Solid State Transformer (SST) is expected to be a key component in future smart grid systems, which integrates MVAC grid with LV DC/AC grid. For a three stage SST, resonant converter is a potential candidate as the DC-DC stage due to its high conversion efficiency. However, it exhibits an oscillation of 100 Hz (double line frequency) in the high frequency (HF) link current, which increases device current stress and conduction loss. This paper proposes an analytical model which determines the magnitude of this oscillation at different power factor operations. The obvious solution to minimize this oscillation is to keep a high value of capacitor at each MVDC bus, but it reduces the power density of the system. Therefore, this paper proposes an improved solution with a combination of inductor and capacitor at the LVDC bus. The proposed filter reduces the RMS value of the HF link current by 29% at unity power factor operation. The dynamics of the proposed filter is also included in the control system design by deriving a dynamic equivalent circuit in this paper. The design is verified in a laboratory prototype of 8 kVA, 1.65 kV/300 V solid state transformer.

Smartgrid System Integration In Development Potential Energy Of Salt Water In The Riau Islands

electricity demand projection. Law Number 30 of 2009 concerning In electricity, it is determined that the primary energy source found in the country and/or originating from abroad must be utilized optimally in accordance with the policy national energy and ensure sustainable electricity supply to integrate the smartgrid system as a source of ignition (lamp) with a voltage source from seawater, to determine the effect of the number of plates on the value of voltage and electric current, so that it can generate electricity for lamps. A Research for types of electrodes that meet the standards for powering alternative lamp prototypes with seawater voltage sources are using zinc (Zn) and copper (Cu) electrodes. The highest current produced from zinc (Zn) and copper (Cu) electrodes is 3.8Volt, research, seawater contains the percentage of the mixture is 96.5% pure water and 3.5% is a composition in the form of salt which is influenced by the Equivalent Salt Deficit Density test dissolved gases, organic matter and inorganic particles. dissolved. The compound contained in seawater is Sodium Chloride (NACL) and by H2O is decomposed into Na+ and Cl−. The interaction of two free particles Na+ and Cl− combined with the voltaic cell method will result in a change/conversion into an electric current. The value of the electric voltage created which will affect the value of the electric voltage. The greater the number of electrodes used, the more electrons will be decomposed by the anode, which is also known as an oxidation reaction. This paper will cause a change in the voltage generated by the electrolyte solution (sea water). Effectiveness in the use of brine can be produced the highest current energy that can not be used for two hours to produce energy in lamps.

Artificial Intelligence-based Control Techniques for HVDC Systems

The electrical energy industry depends, among other things, on the ability of networks to deal with uncertainties from several directions. Smart-grid systems in high-voltage direct current (HVDC) networks, being an application of artificial intelligence (AI), are a reliable way to achieve this goal as they solve complex problems in power system engineering using AI algorithms. Due to their distinctive characteristics, they are usually effective approaches for optimization problems. They have been successfully applied to HVDC systems. This paper presents a number of issues in HVDC transmission systems. It reviews AI applications such as HVDC transmission system controllers and power flow control within DC grids in multi-terminal HVDC systems. Advancements in HVDC systems enable better performance under varying conditions to obtain the optimal dynamic response in practical settings. However, they also pose difficulties in mathematical modeling as they are non-linear and complex. ANN-based controllers have replaced traditional PI controllers in the rectifier of the HVDC link. Moreover, the combination of ANN and fuzzy logic has proven to be a powerful strategy for controlling excessively non-linear loads. Future research can focus on developing AI algorithms for an advanced control scheme for UPFC devices. Also, there is a need for a comprehensive analysis of power fluctuations or steady-state errors that can be eliminated by the quick response of this control scheme. This survey was informed by the need to develop adaptive AI controllers to enhance the performance of HVDC systems based on their promising results in the control of power systems. Doi: 10.28991/ESJ-2023-07-02-024 Full Text: PDF

LACP-SG: Lightweight Authentication Protocol for Smart Grids.

Smart grid (SG) recently acquired considerable attention due to their utilization in sustaining demand response management in power systems. Smart meters (SMs) deployed in SG systems collect and transmit data to the server. Since all communications between SM and the server occur through a public communication channel, the transmitted data are exposed to adversary attacks. Therefore, security and privacy are essential requirements in the SG system for ensuring reliable communication. Additionally, an AuthentiCation (AC) protocol designed for secure communication should be lightweight so it can be applied in a resource-constrained environment. In this article, we devise a lightweight AC protocol for SG named LACP-SG. LACP-SG employs the hash function, "Esch256", and "authenticated encryption" to accomplish the AC phase. The proposed LACP-SG assures secure data exchange between SM and server by validating the authenticity of SM. For encrypted communication, LACP-SG enables SM and the server to establish a session key (SEK). We use the random oracle model to substantiate the security of the established SEK. Moreover, we ascertain that LACP-SG is guarded against different security vulnerabilities through Scyther-based security validation and informal security analysis. Furthermore, comparing LACP-SG with other related AC protocols demonstrates that LACP-SG is less resource-intensive while rendering better security characteristics.

Ensemble-Learning-Based Decision Support System for Energy-Theft Detection in Smart-Grid Environment

Theft of electricity poses a significant risk to the public and is the most costly non-technical loss for an electrical supplier. In addition to affecting the quality of the energy supply and the strain on the power grid, fraudulent electricity use drives up prices for honest customers and creates a ripple effect on the economy. Using data-analysis tools, smart grids may drastically reduce this waste. Smart-grid technology produces much information, including consumers’ unique electricity-use patterns. By analyzing this information, machine-learning and deep-learning methods may successfully pinpoint those who engage in energy theft. This study presents an ensemble-learning-based system for detecting energy theft using a hybrid approach. The proposed approach uses a machine-learning-based ensemble model based on a majority voting strategy. This work aims to develop a smart-grid information-security decision support system. This study employed a theft-detection dataset to facilitate automatic theft recognition in a smart-grid environment (TDD2022). The dataset consists of six separate electricity thefts. The experiments are performed in four different scenarios. The proposed machine-learning-based ensemble model obtained significant results in all scenarios. The proposed ensemble model obtained the highest accuracy of 88%, 87.24%, 94.75%, and 94.70% with seven classes including the consumer type, seven classes excluding the consumer type, six classes including the consumer type, and six classes excluding the consumer type. The suggested ensemble model outperforms the existing techniques in terms of accuracy when the proposed methodology is compared to state-of-the-art approaches.

Modelling and real time performance evaluation of a 5 MW grid-connected solar photovoltaic plant using different artificial neural networks

Renewable energy sources have gained greater importance in the past decade owing to the continuous growth in energy demand of developing nations. The modular nature of solar energy has made it possible to utilize it in a variety of applications without any adverse economic impact. In this research, a 5 MW grid-connected solar PV plant was modelled using various ANN networks namely Cascade forward backdrop, ELMAN back prop, Feed forward back prop, Feed forward Distribute Time delay, Layer current and Regression Analysis to identify the impact of various environmental parameters on the PV power generation. Comparatively the Lavenberg- Marquardt model is obtained as a reliable model for smart grid system especially for solar PV power prediction with high R value of 0.9966. Also, the plants real time annual energy production, performance ratio (PR), and capacity utilization factors (CUFs) were analysed under various geometrical condition. The shading losses of the entire PV plant was evaluated using PV SYST software at different tilting conditions (seasonal and fixed tilt). Further, the annual PV generation and energy consumption of the plant is calculated under various load conditions. Out of the total energy consumption of the plant, only 11.2 % of the load will be handled by the 5 MW solar PV plant. Compared with fixed tilt, the proposed optimized tilt increased PR and CUF values to 48.5 % and 9.3 % for the months of November and December, respectively (excluding Sunday maintenance). During Sunday, energy loss was nearly 5248 kWh/day due to improper shutdown of power control units. With proper supply management schemes, the average power saving was nearly 48,000 kWh/year and the total cost saving was nearly INR 38 lakh per year. Owing to proper cleaning (twice in a month), the average power gain was increased to 5.4 %.

Power Optimization of a Three-Node Two-Way Relay-Assisted Power-Line Communication System

A smart grid system requires two-way communication links. Because power-line infrastructure is widely available, power-line communication (PLC) is preferred as the communication technology in smart grid systems. A power-line, on the other hand, was not designed to carry data and thus suffers from disturbances that attenuate the transmitted signal. Therefore, it is beneficial to provide spatial diversity to improve the system performance. Optimal power allocation has always been an issue to be solved in relay-aided communication systems. Using a two-way multicarrier relay configuration, this paper investigates power optimization of a relay-assisted PLC system with the consideration of the quality of service (QoS) constraints. Further, we investigate the combination of half-duplex and full-duplex nodes. Since the QoS constrained power allocation problem is highly non-convex, an alternating optimization (AO) algorithm is proposed to decompose the optimization problem into sub-problems. The AO algorithm is an iterative algorithm where it uses the newly obtained parameters (e.g., power) to find a new set of other parameters. Simulation results show that the proposed system successfully reduces the total power required by the system compared to the conventional bidirectional direct transmission (BDT) system and the relay-assisted two-way information exchange (R2WX) system under the same QoS requirement.

DisastDrone: A Disaster Aware Consumer Internet of Drone Things System in Ultra-Low Latent 6G Network

Internet of Things application in disaster responses and management is a predominant research domain. The introduction of the consumer drones, flying ad-hoc networks, low latency 5G, and beyond 5G produce significant acceleration of this research. The work proposed the implementation of the Consumer Internet of Drone Things (CIoDT) framework for emergency message transfer among smart grid systems and other power sources and enables stable network connectivity in a disaster scenario. A real-life mobility model has been engineered, and the edge-enabled opportunistic MQTT message transfer mechanism is implemented. Also, a dedicated network slice is devised to examine routing performance. A real-life prototype test-bed emulator has been designed to evaluate mobility, message delivery, and flight attitude performance. The results show a message delivery probability of nearly 0.99 in Quality of Service 2(QoS2), and 1.19 seconds of end-to-end latency in Quality of Service level 1(QoS1). An experiment also shows a 94% of coverage ratio in a network slice for QoS2, 84% of bandwidth utilization per network slice, and a minimum of 100ms latency per network slice. The proposed system is highly suitable for mass production of light weight drone network for disaster management.

An access control scheme in IoT-enabled Smart-Grid systems using blockchain and PUF

IoT-enabled Smart Grid (IoT-SG) is an emerging paradigm that enables the bi-direction communication of IoT devices and hardware to efficiently collect and transmit the consumer’s information over the Internet. However, the underlying open communication network and resource-constrained capabilities of devices invite manifold challenges and threats in terms of security and privacy. To alleviate such issues, we designed a private blockchain-based access control protocol for IoT-SG using Physically Unclonable Function (PUF). Our protocol allows the Service Provider (SP) and smart meters to transfer the data in an efficient and secure manner. The participating SPs form the Peer-to-Peer (P2P) network, and each peer node is responsible for securely creating the blocks from the gathered data. Thereafter, all the peer nodes employ a voting-based consensus mechanism to verify and add the recently created block into the blockchain network. We have rigorously validated the security of our protocol under the Random or Real (RoR) model. The testbed results and security feature analysis show that our protocol is more efficient than competing ones while it also provides significant security properties.

Robust multi-objective load dispatch in microgrid involving unstable renewable generation

The microgrid improve productivity and performance by rational load balancing and intelligent energy management schemes. This paper addresses a robust multi-objective load dispatch problem to tactically schedule various resources for an microgrid equipped with controllable distributed generators and unstable renewable energy (RE) sources. Power generation cost and emissions are considered as the key indicators for the load dispatching. In such smart grid system, the indeterminate renewable energy generation (REG) imposes intractable dispatching challenges. To properly cope with the uncertain RE, a model with uncertain distribution is developed to depict the fluctuation in a flexible yet detailed manner. Chance constraint (CC) approximations and robust optimization (RO) methods are involved to change the prime issue to a more solvable form. We further explore a framework based on deep reinforcement learning (DRL) to deal with multi-objective optimization which is generally computational intractable. Real-world data-based simulation has verified the superiority of the proposed uncertain model in handling unstable REG. Furthermore, the DRL based framework is time-efficient in solving the multi-objective load dispatch problem and surpasses the state-of-art methods by having 56.74% and 15.62% better efficiencies on average.

Short term load forecasting based on ARIMA and ANN approaches

Forecasting electricity demand requires accurate and sustainable data acquisition systems which rely on smart grid systems. To predict the demand expected by the grid, many smart meters are required to collect sufficient data. However, the problem is multi-dimensional and simple power aggregation techniques may fail to capture the relational similarities between the various types of users. Therefore, accurate forecasting of energy demand plays a key role in planning, setting up, and implementing networks for the renewable energy systems, and continuously providing energy to consumers. This is also a key element for planning the requirement for storage devices and their storage capacity. Additionally, errors in hour-to-hour forecasting may cause considerable economic and consumer losses. This paper aims to address the knowledge gap in techniques based on machine learning (ML) for predicting load by using two forecasting methods: Auto Regressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN); and compares the performance of both methods using Mean Absolute Percentage Error (MAPE). The study is based on daily real load electricity data for 709 individual households were randomly chosen over an 18-month period in Ireland. The results reveal that the (ANN) offers better results than ARIMA for the non-linear load data.

Advanced optimizer for maximum power point tracking of photovoltaic systems in smart grid: A roadmap towards clean energy technologies

With the increased load demand and generation cost of electricity, renewable energy sources (RES) provide cost effective and environment-friendly solution. Integration of solar energy includes the challenges of finding the Maximum Power Point (MPP) to achieve the maximum power from the solar. Combining RESs with distributed generation (DG) comes with the challenges of voltage regulation and frequency mismatch. In this work, a novel approach of Robust Antlion Optimizer (ALO) algorithm is implemented for MPP tracking in solar photovoltaic (PV) system and charge controllers are designed for storage system while the DC to AC converters have been designed to match RESs’ frequency with that of DG. Power scheduling is done to decrease operational cost while keeping the voltage and frequency in the allowable range. Research findings provide useful policy implications for all stakeholders to meet energy needs in a consistent and coherent manner.

K-means clustering of electricity consumers using time-domain features from smart meter data

Smart meter stores electricity consumption data of every consumer in the smart grid system. A better understanding of consumption behaviors and an effective consumer classification based on the similarity of these behaviors can be helpful for flexible demand management and effective energy control. In this paper, we propose an implementation of unsupervised classification for categorizing consumers based on the similarity of their typical electricity consumption behaviors. The main goal is to group similar observations together in order to easily look at the dataset. Hence, we go through pattern identification in households’ consumption with the K-means clustering algorithm. K-means clusters consumption behaviors based on extracted temporal features into k groups. The result from the algorithm helps power suppliers to understand power consumers’ better and helps them make better informed decision based on the information available to them. The dataset used in this paper is a real data from the London Data Store energy consumption readings for a sample of 5567 London Households that took part in the UK Power Networks Led Low Carbon London project between November 2011 and February 2014 available at: https://data.london.gov.uk/dataset/smartmeter-energy-use-data-in-london-households.

Wild Horse Optimization with Deep Learning-Driven Short-Term Load Forecasting Scheme for Smart Grids

Energy is a major driver of human activity. Demand response is of the utmost importance to maintain the efficient and reliable operation of smart grid systems. The short-term load forecasting (STLF) method is particularly significant for electric fields in the trade of energy. This model has several applications to everyday operations of electric utilities, namely load switching, energy-generation planning, contract evaluation, energy purchasing, and infrastructure maintenance. A considerable number of STLF algorithms have introduced a tradeoff between convergence rate and forecast accuracy. This study presents a new wild horse optimization method with a deep learning-based STLF scheme (WHODL-STLFS) for SGs. The presented WHODL-STLFS technique was initially used for the design of a WHO algorithm for the optimal selection of features from the electricity data. In addition, attention-based long short-term memory (ALSTM) was exploited for learning the energy consumption behaviors to forecast the load. Finally, an artificial algae optimization (AAO) algorithm was applied as the hyperparameter optimizer of the ALSTM model. The experimental validation process was carried out on an FE grid and a Dayton grid and the obtained results indicated that the WHODL-STLFS technique achieved accurate load-prediction performance in SGs.

A Comprehensive Review on Smart Grid Ecosystem

A smart grid is an intervention technology for the massive energy demand of the world today. It combines cyber-physical technologies, information communication technology, and electrical power networks from the generating company stations to the end-users while ensuring bidirectional communication among the actors. The smart grid is a complex growing technology that is yet to reach its maturity state. This paper seeks to examine the literature on the state of the art of smart grid technology both from the industry perspective and from academia. To this end, a literature review with a qualitative deductive approach built on the National Institute of Standards and Technology (NIST) guideline and the simplified International Telecommunication Union Telecommunication Standardization Sector (ITU-T) five-domain model were used as a guide to this research. Furthermore, the paper reviewed Smart Grid data centre topologies and identified prospects in spine-leaf architecture as a promising architecture that can be adapted in a smart grid ecosystem data centre design. The literature was searched from the databases: IEEE Xplore Digital Library, Springer Link Digital Library, and Google Scholar, IET Digital Library, Frontiers Library, ACM Digital Library repositories resulting in 151 papers after several exclusions. The work reviewed relevant literatures published from 2002 to 2021 and grouped the reviewed papers according to the key domains of the NIST/ITU-T model. Based on the evaluated literature, the need for more built-in predictive learning curves in smart grid systems and robust Smart grid architecture with enhanced data centre design for Smart grid systems is observed and recommended Keywords: Smart Grid, Smart Metre, Data Centre Computing, Distributed Energy, Protocols

Federated Learning in IoT: A Survey on Distributed Decision Making

The proliferation of Internet of Things (IoT) devices has ushered in an era of unprecedented data generation, necessitating innovative approaches to extract valuable insights while respecting privacy and resource constraints. Federated Learning (FL) has emerged as a promising paradigm, enabling decentralized model training across a network of edge devices. This paper presents a comprehensive survey on the application of Federated Learning in IoT, with a specific focus on the challenges and solutions related to distributed decision making. The survey begins by elucidating the foundational concepts of Federated Learning and IoT, highlighting their convergence and the potential benefits of leveraging FL in decentralized IoT ecosystems. The paper explores the diverse applications of FL in various IoT domains, including smart cities, healthcare, industrial IoT, and smart grid systems. It investigates how FL can address the challenges posed by the distributed nature of IoT data, such as data heterogeneity, privacy concerns, and communication constraints. A significant portion of the survey is dedicated to examining the methodologies and algorithms employed in federated learning for distributed decision making in IoT. This encompasses a discussion on federated optimization techniques, communication-efficient algorithms, and privacy-preserving mechanisms. The survey also delves into the role of edge computing in facilitating efficient FL in IoT, considering the resource constraints inherent in edge devices. Furthermore, the paper reviews the current state-of-the-art federated learning frameworks and platforms tailored for IoT environments. It evaluates their capabilities in handling real-world challenges and providing scalable solutions for distributed decision making. The survey concludes by identifying open research challenges and potential avenues for future developments in federated learning for IoT, emphasizing the need for novel algorithms, robust security measures, and standardized frameworks. In summary, this paper offers a comprehensive overview of Federated Learning in the context of IoT, shedding light on its potential, challenges, and solutions with a specific emphasis on the critical aspect of distributed decision making. The insights provided aim to guide researchers, practitioners, and policymakers in navigating the complex landscape of FL in IoT and fostering advancements in this rapidly evolving field.

Exploring Energy Trading Markets in Smart Grid and Microgrid Systems and Their Implications for Sustainability in Smart Cities

Smart cities are aimed at connecting urban infrastructures to enhance the efficiency of their operation and services while taking sustainability goals into consideration. As a result of the intermittency associated with renewable generation, smart city systems such as smart grids and microgrids may not be able to ensure the security of supply. This can be mitigated by allowing these systems to trade surplus energy with other neighboring systems through local energy markets based on peer-to-peer schemes. Such an approach can play an important role on achieving sustainability due to the positive impacts at the economic, social, and environmental level. Therefore, this work explores the design of local energy markets to help determine how they are relevant to smart grid and microgrid applications and what their contributions are to sustainability in smart cities. Essentially, this is achieved by performing a literature review to address key characteristics related to the design of local energy markets while considering their relationship with urban sustainability. In addition, the concept of game theory and its potential to evaluate market designs are also introduced and discussed. Finally, the suitability of centralized, decentralized, and distributed market designs for each dimension of sustainability is estimated based on their design characteristics.

Lightweight and dynamic authenticated key agreement and management protocol for smart grid

With the development of IoT and 5G, the smart grid, as one of the key component for the smart city, can provide the uninterrupted and reliable electricity service by properly adjusting the electricity supply according to the consumption of users. The advanced metering infrastructure (AMI), as an important part of smart grid system, is a complete network and system for measuring, collecting, storing and analyzing the electricity consumption information of users. The security of AMI plays a vital role in the smooth operation of smart grid. In this paper, we study how to establish the secure communication between two entities in AMI, namely the smart meter and the electricity service provider. Although, there are many authentication and key management protocols for AMI, the high complexity and computation overhead of these protocols hinder their application in the smart grid environment. Based on identity cryptosystem and elliptic curve cryptography (ECC), we put forward a lightweight and dynamic authenticated key agreement and management protocol, which can significantly reduce the computation overhead of the resource-constrained smart meters. In addition, we utilize a one-way key tree technique to efficiently generate and update the group key in the multicast communication. We give a systematic proof to show that our designed protocol not only guarantees the confidentiality and integrity of transmitted messages, but also resists various attacks from an adversary. Finally, we carry out some simulated experiments to demonstrate the high efficiency of our designed protocol.

High Penetration of Solar Photovoltaic Structure on the Grid System Disruption: An Overview of Technology Advancement

Solar photovoltaic (PV) power generation is distinct from conventional power generation systems. It is vital to comprehend the effect of an expanded control system on solar PV generation. This article discusses the advancement made to the module, which is critical to PV and electric power systems, to achieve a high PV penetration in the smart grid system. The first zone initiates the solar power energizing transformation, which transfers a controlled energy load to a grid system. The descriptive subsections consider the accessibility of electronic inverters, solar PV energies, and grid concepts, as well as their realizability. As a result, a case study was considered, where various scientists around the world participated, discussion ensued, and future suggestions were made. Finally, practical conclusions were drawn from the investigations. This paper infers that the improvement of appropriate methods is fundamental to the viability and effectiveness of overseeing a high infiltration of PV inside low-voltage (LV) conveyance systems. This review provides an overview of the current state, effects, and unique difficulties associated with PV penetration in LV appropriation systems. Nonetheless, grid innovation is not well developed, and it requires continuous research from various rational aspects.