Smart Grid Context Research Articles

A Study the optimisation of smart grid power flow

Optimal Power Flow (OPF) is a fundamental concept in the management and operation of modern electrical power systems, particularly within the context of smart grids. As the demand for electricity continues to grow and the energy landscape evolves towards greater sustainability, the need for efficient, reliable, and adaptive power systems has become more critical than ever. The primary objective of OPF is to determine the most efficient way to operate the power system while satisfying various technical and operational constraints. This involves the optimization of several variables, including generator outputs, voltage levels, and power flows, to minimize costs, reduce losses, and enhance system reliability. Smart grids can enhance their resilience to disturbances, support the efficient use of renewable energy, and provide reliable electricity to consumers.

Integration of distributed PV into smart grids: A comprehensive analysis for Germany

Modern smart grids typically combine physical and communication networks for efficient information exchange and innovative applications. Aligned with digitalization and advancements in smart grids, the integration of photovoltaic (PV) systems comprises a variety of regulatory and technological aspects. However, no previous study has conducted an extensive and systematic analysis of the PV-grid integration framework, particularly for one country. To fill this gap, this paper uses Germany as an example to present a comprehensive, state-of-the-art analysis of integrating distributed PV systems into smart grids, focusing on the regulation and technical implementation of the German Smart Meter Infrastructure and PV control interfaces. Starting from a standardization perspective, this analysis utilizes the Smart Grid Architecture Model to identify crucial roles, components and processes specifically in Germany. Furthermore, it outlines the current implementation of PV integration into distribution networks at a national level. The results of this study show the overall complexity of PV integration in the smart grid context, confirm the feasibility of the German integration approach, and highlight the necessity of deploying standardized information models and communication technologies. These key findings can help market participants with different roles to identify potential technical bottlenecks or other critical points in the regulation and technical implementation. For instance, the proposed in-depth analysis framework provides an orientation for characterizing the PV integration or, more generally, the grid integration scenario of renewables in other countries.

Optimizing solar power efficiency in smart grids using hybrid machine learning models for accurate energy generation prediction

The fourth energy revolution is characterized by the incorporation of renewable energy supplies into intelligent networks. As the world is shifting towards cleaner energy sources, there is a need for efficient and reliable methods to predict the output of renewable energy plants. Hybrid machine learning modified models are emerging as a promising solution for energy generation prediction. Renewable energy generation plants, such as solar, biogas, hydropower plants, wind farms, etc. are becoming increasingly popular due to their environmental benefits. However, their output can be highly variable and dependent on weather conditions, making integrating them into the existing energy grid challenging. Smart grids with artificial intelligent systems have the potential to solve this challenge by using real-time data to optimize energy production and distribution. Although by incorporating sensors, analytics, and automation, these grids can manage energy demand and supply more efficiently, reducing carbon emissions, increase energy security, and improve access to electricity in remote areas. However, this research aims to enhance the efficiency of solar power generation systems in a smart grid context using machine learning hybrid models such as Hybrid Convolutional-Recurrence Net (HCRN), Hybrid Convolutional-LSTM Net (HCLN), and Hybrid Convolutional-GRU Net (HCGRN). For this purpose, this study considers various parameters of a solar plant such as power production (MWh), irradiance or plane of array (POA), and performance ratio (PR). The HCLN model demonstrates superior accuracy with the RMSE values of 0.012027 for MWh, 0.013734 for POA and 0.003055 for PR, along with the lowest MAE values of 0.069523 for MWh, 0.082813 for POA, and 0.042815 for PR. The obtained results suggest that the proposed machine learning models can effectively enhance the efficiency of solar power generation systems by accurately predicting the required measurements.

Analysis of Real-time Processing Approaches for Large Data Volumes in Metering Infrastructure

Smart grid systems and communication technologies, such as Advanced Metering Infrastructure (AMI), have revolutionized utility service management and monitoring. AMI leverages smart meters equipped with advanced communication capabilities, facilitating bidirectional communication between utilities and consumers. The increasing deployment of smart meters and the adoption of sub-hourly data collection requirements by utility companies highlight significant data volume growth. Thus, there is a need for efficient real-time data processing solutions as existing approaches may not meet previously established Service-Level Agreements (SLAs) concerning performance, accuracy, and scalability metrics. This research aims to comprehensively review the latest publications relevant to distributed real-time data processing methods for smart grid applications and outline problems for further research. Specifically, the study delves into the effectiveness and application of reviewed approaches in managing the constant stream of data from smart meters and IoT devices within the smart grid context. By analysing existing methodologies and advancements, this study seeks to identify challenges and opportunities in real-time data processing for smart grid infrastructures, focusing on addressing the complexities of processing, managing, and storing large volumes of real-time data. The literature review revealed two primary applications of real-time data processing: optimization of data streaming performance and data analysis. The review encompasses various studies, each presenting distinct methodologies and technologies applied to address the challenges of processing large volumes of real- time data from smart meters and IoT devices. Future research should address the challenges and limitations discovered in this study.

Empowering the Future : The Vision of Smart Transmission Grids

In the quest for an adaptable, dependable, and sustainable energy supply, the modern power grid necessitates a transformative upgrade towards intelligence. While substantial efforts have been devoted to envisioning and advancing smart power grids in the United States and Europe, the focus has predominantly centered on the distribution grid and demand-side management, leaving the holistic perspective of the transmission grid within the context of smart grids somewhat obscured. This paper proposes a distinct vision for the future of smart transmission grids, delineating their pivotal attributes. In this envisioned framework, each smart transmission grid emerges as a cohesive, integrated system comprising three interrelated smart components: smart control centers, smart transmission networks, and smart substations. The paper meticulously outlines the characteristics and functionalities of each of these three essential components, elucidating the enabling technologies necessary to realize their envisioned capabilities. By conceptualizing smart transmission grids as multifaceted systems driven by intelligent components, this vision underscores the potential for enhancing grid efficiency, reliability, and sustainability. Through the seamless integration of advanced control centers, transmission networks, and substations empowered by cutting-edge technologies, smart transmission grids aim to optimize grid operations, facilitate dynamic energy management, and foster resilience against emerging challenges and disruptions.

ICT Scalability and Replicability Analysis for Smart Grids: Methodology and Application

The essential role of Information and Communication Technologies (ICT) in modern electricity grids makes it necessary to consider them when evaluating the scalability and replicability capabilities of smart grid systems. This paper proposes a novel step-by-step methodology to quantitatively perform an ICT scalability and replicability analysis (SRA) in a smart grid context. The methodology is validated and exemplified by applying it to two real case studies that are demonstrated in the EU-funded RESPONSE project and comprise solutions relying on different communication technologies. The results of the proposed methodology are summarised through ICT scalability and replicability maps, which are introduced in this paper as a quick way of obtaining an overview of the scalability and replicability capabilities of an ICT system and as an efficient way of estimating the feasibility of scenarios not covered in the SRA.

Discussion on Reliability and Power Quality in the Smart Grid: a prosumer approach of a time scalable index

This article presents a discussion related to the measurement techniques of the electric energy quality within the smart grid (SG) context, and considering the producer-consumer (prosumer) approach. It establishes the basis where new power quality (PQ) indexes should be proposed, in order to complement the current recommendations regarding consumer dissatisfaction indexes (CDI) on which the network should be assessed and planned. Indeed, the current indices only consider the number of interruptions and the time during which they occur, and do not take into account neither the type nor the non-linear loads (distributed energy resources, DER) connected to the SG, which are giving rise to new kinds of transients coupled to the electrical power supply signal. In this sense, the paper provides reasons on why temporal evolution of energy and its deviation from the contractual specifications should be monitored and assessed, with the goal of making real-time decisions. Based on advanced statistical signal processing, forecasting methods and artificial intelligence, this focus conveys the conclusive need for a dual measurement index of PQ reliability, on the basis of dynamic temporal scalability, and time-space compressions. A practical example is outlined.

Enhancing smart grid security: A novel approach for efficient attack detection using SMART framework

Smart Grids are constructed using control, networking, and advanced computing technologies. Malicious attacks are still a possibility for the grid, although there are now insufficiently fast and accurate detection methods. Cybercrimes affecting the security of the smart grid include the compromise of vital client data by attackers, the spread of viruses, cybersecurity mistakes, and vulnerabilities in distributed systems. In order to provide security in a smart grid context, a SMART (Smart Attack detectoR Technique) framework is developed in this paper. The attacker first sends the request to the risk identification, which will identify the request's original source. Risk estimation calculates the degree of risk and gives processing decisions. Following that, word embedding is used to complete the pre-processing. Information will be prohibited if the process is found to be too vulnerable according to CNN-BiLSTM categorization criteria. The data will be sent to the smart grid automatically if the procedure is low vulnerable and there is no assault. A MATLAB simulator is used to implement the suggested approach. In comparison to the presently used CNN-LSTM [10], SCADA [13], and GOOSE [14] approaches, which have success rates of 19.09 %, 32.22 %, and 57.47 %, respectively, the experimental findings indicate that the suggested SMART strategy has a 99 % success rate, which is very high.

Demand Side Management In Smart Grid Optimization Using Artificial Fish Swarm Algorithm

The demand side management and their response including peak shaving approaches and motivations with shiftable load scheduling strategies advantages are the main focus of this paper. A recent real-time pricing model for regulating energy demand is proposed after a survey of literature-based demand side management techniques. Lack of user’s resources needed to change their energy consumption for the system's overall benefit. The recommended strategy involves modern system identification and administration that would enable user side load control. This might assist in balancing the demand and supply sides more effectively while also lowering peak demand and enhancing system efficiency. The AFSA and BFO algorithms are combined in this study to handle the optimization of difficult problems in a range of industries. Although the BFO will be used to exploit the search space and converge to the optimum solution, the AFSA will be used to explore the search space and retain variation. In terms of reduction of peak demand, energy consumption, and user satisfaction, the AFSA-BFO hybrid algorithm outperforms previous techniques in the field of demand side management in a smart grid context, using an AFSA. According to simulation results, the genetic algorithm successfully reduces PAR and power consumption expenses.

Optimal Extraction and Conditioning of Historical Information to Support the Operational Decisions in a Smart Grid Context

Optimal Extraction and Conditioning of Historical Information to Support the Operational Decisions in a Smart Grid Context

Cybersecurity Vulnerabilities in Smart Grids with Solar Photovoltaic: A Threat Modelling and Risk Assessment Approach

Cybersecurity is a growing concern for smart grids, especially with the integration of solar photovoltaics (PVs). With the installation of more solar and the advancement of inverters, utilities are provided with real-time solar power generation and other information through various tools. However, these tools must be properly secured to prevent the grid from becoming more vulnerable to cyber-attacks. This study proposes a threat modeling and risk assessment approach tailored to smart grids incorporating solar PV systems. The approach involves identifying, assessing, and mitigating risks through threat modeling and risk assessment. A threat model is designed by adapting and applying general threat modeling steps to the context of smart grids with solar PV. The process involves the identification of device assets and access points within the smart grid infrastructure. Subsequently, the threats to these devices were classified utilizing the STRIDE model. To further prioritize the identified threat, the DREAD threat-risk ranking model is employed. The threat modeling stage reveals several high-risk threats to the smart grid infrastructure, including Information Disclosure, Elevation of Privilege, and Tampering. Targeted recommendations in the form of mitigation controls are formulated to secure the smart grid’s posture against these identified threats. The risk ratings provided in this study offer valuable insights into the cybersecurity risks associated with smart grids incorporating solar PV systems, while also providing practical guidance for risk mitigation. Tailored mitigation strategies are proposed to address these vulnerabilities. By taking proactive measures, energy sector stakeholders may strengthen the security of their smart grid infrastructure and protect critical operations from potential cyber threats.

ДОСЛІДЖЕННЯ ЕФЕКТИВНОСТІ ВИКОРИСТАННЯ РОЗПОДІЛЕНОЇ МАТРИЧНОЇ ФАКТОРИЗАЦІЇ В ПРОМИСЛОВИХ СИСТЕМАХ

The processing of big data is an exceedingly urgent challenge in the functioning of modern information systems. The latest information technologies must be employed to collect, store, and analyze vast amounts of information. Intelligent data processing systems were implemented in numerous fields, particularly in the industry. Smart industrial systems also utilize data from various devices, enabling automated management processes and network component analysis. A prime example of an intelligent industrial system is the smart grid, which efficiently distributes electricity to users by considering demand, network parameters, load, etc. Processing large amounts of information necessitates the use of machine learning methods and mathematical data analysis. Matrix factorization serves as an exemplary technique for transforming information into a more convenient form for further processing, establishing relationships between elements, and optimizing outcomes. In particular, the SVD (Singular Value Decomposition) and Funk-SVD algorithms are employed to address big data processing challenges, and they were discussed in this work. The key features of processing large data volumes in industrial smart grid systems were analyzed in the paper. The advantages of distributed computing for more efficient information analysis were identified. The recommendation algorithms that enable faster and more accurate processing of extensive data were explored in the study. Specifically, the SVD and Funk-SVD algorithms, used in recommendation systems for large data processing, were examined. A method of distributed matrix factorization to provide recommendations to smart grid system users was proposed in the paper. This approach involves the exchange of public data between devices and the local processing of private data. The advantages of this distributed model include flexibility in adjusting parameters, improved calculation accuracy through result exchange between nodes, high data processing speed, and scalability were identified. The conclusion that the proposed method can be effectively used in recommendation systems within the smart grid context, enhancing automated management processes and resource distribution was exclaimed.

Personalized Services for Smart Grids in the framework of Society 5.0: A Smart University Campus Case Study

The evolution of the power grid towards a Smart Grid is a crucial aspect of the transformation of society towards the so-called Society 5.0, where human-centered technology is the key to address social challenges. This paper proposes a personalized service approach for Smart Grids within the Society 5.0 framework, which aims to provide a personalized energy management service to consumers through advanced sensing technologies, intelligent algorithms, and social and contextual data. Furthermore, the paper highlights the potential of personalized services in the Smart Grid context and their relevance in the Society 5.0 framework and proposes an Architecture for the design and deployment of personalized energy management services in other Smart Grid contexts. In this approach end-users play an active role in the design and deployment of personalized energy management services. The proposed methodology is tested and evaluated in a Smart University Campus case study. The results indicate energy management improvement and energy consumption reduction.

Data Analysis for Smart Grid and Communication Technologies

Smart grids and communication technologies are among the significant advancements in theenergy sector. These technologies improve energy efficiency by increasing the use of renewable energysources. However, the integration of these technologies also creates a vast amount of data that requiresanalysis to optimize energy management and improve system performance. In this context, data analysisplays a crucial role in identifying patterns, predicting energy demand, and managing energy supply. Thispaper provides an overview of the importance of data analysis in smart grids and communicationtechnologies and discusses various data analysis techniques used in energy management systems.Additionally, it highlights the challenges and opportunities associated with data analysis in the context ofsmart grids and communication technologies.

Data Analysis Techniques Applied to Distribution Systems: A Systematic Mapping Study

The increasing electricity demand has led the conventional distribution system to evolve into the smart grid context. One of the advantages of this intelligent system is the ability to estimate the states of a grid. Implementing a state estimator in distribution networks requires much analysis – such as the observability of an extensive system, big data techniques for data processing, and the use of artificial intelligence to generate predictions and protect the control system against cyber-attacks. This systematic mapping study covers 15148 articles, selecting 37 for further research and discussion. The reports offer different state estimators and techniques to improve performance, for instance, solutions against malicious data, optimal meter allocation, and voltage control. In addition to these topics, this article discusses different approaches to generate pseudo measurements. Probabilistic and deterministic, machine learning, and neural network models appear as a trend. The studies show the main methods used for data collection, offering a trend toward smart meters.

Distribution network topology planning and optimization: A brief review

This article investigates distribution network topologies, with an emphasis on Mesh, Radial, and Ring designs, within the context of smart grids. With the primary goal of decreasing power losses using optimization techniques. The ZIP model is used to precisely capture load behaviour in load flow. This research adds to our understanding of topology selection, optimization approaches, and load behaviour modelling, improving in this way increases the efficiency and reliability of electrical distribution networks.

Explicit reliability incorporated switches planning of smart distribution system with high flexibility

Different types of switches play different roles in the fault management procedure in particular context of smart grid, which have different impact on system reliability. The motivation behind this paper is to develop an explicit reliability assessment method adapting to flexible deployment of circuit breakers (CBs), remote-controlled switches (RCSs), and manual switches (MSs), which can be further integrated into switch planning models analytically. The peculiar of this paper is to use the propagation constraints of fictitious power flows to model three stages of fault management procedures. Based on the fictitious power flows, the placement and action for CBs, RCSs, and MSs are modeled in detail as linear constraints, considering power flow constraints in fault management procedure. Therefore, this MILP model can be easily integrated into an exact mathematical optimization model. Finally, the proposed method is tested in 37, 85, 137, 417 node distribution network systems to verify the efficiency of the algorithm. Besides, the necessity of considering flexible location of three types of switches and its application on switch planning are verified.

A Trust-Influenced Smart Grid: A Survey and a Proposal

A compromised Smart Grid, or its components, can have cascading effects that can affect lives. This has led to numerous cybersecurity-centric studies focusing on the Smart Grid in research areas such as encryption, intrusion detection and prevention, privacy and trust. Even though trust is an essential component of cybersecurity research; it has not received considerable attention compared to the other areas within the context of Smart Grid. As of the time of this study, we observed that there has neither been a study assessing trust within the Smart Grid nor were there trust models that could detect malicious attacks within the substation. With these two gaps as our objectives, we began by presenting a mathematical formalization of trust within the context of Smart Grid devices. We then categorized the existing trust-based literature within the Smart Grid under the NIST conceptual domains and priority areas, multi-agent systems and the derived trust formalization. We then proposed a novel substation-based trust model and implemented a Modbus variation to detect final-phase attacks. The variation was tested against two publicly available Modbus datasets (EPM and ATENA H2020) under three kinds of tests, namely external, internal, and internal with IP-MAC blocking. The first test assumes that external substation adversaries remain so and the second test assumes all adversaries within the substation. The third test assumes the second test but blacklists any device that sends malicious requests. The tests were performed from a Modbus server’s point of view and a Modbus client’s point of view. Aside from detecting the attacks within the dataset, our model also revealed the behaviour of the attack datasets and their influence on the trust model components. Being able to detect all labelled attacks in one of the datasets also increased our confidence in the model in the detection of attacks in the other dataset. We also believe that variations of the model can be created for other OT-based protocols as well as extended to other critical infrastructures.

A Human-Machine Reinforcement Learning Method for Cooperative Energy Management

The increasing penetration of distributed energy resources and a large volume of unprecedented data from smart metering infrastructure can help consumers transit to an active role in the smart grid. In this article, we propose a human-machine reinforcement learning (RL) framework in the smart grid context to formulate an energy management strategy for electric vehicles and thermostatically controlled loads aggregators. The proposed model-free method accelerates the decision-making speed by substituting the conventional optimization process, and it is more capable of coping with the diverse system environment via online learning. The human intervention is coordinated with machine learning to: 1) prevent the huge loss during the learning process; 2) realize emergency control; and 3) find preferable control policy. The performance of the proposed human-machine RL framework is verified in case studies. It can be concluded that our proposed method performs better than the conventional deep Q-learning and deep deterministic policy gradient in terms of convergence capability and preferable result exploration. Besides, the proposed method can better deal with emergent events, such as a sudden drop of photovoltaic (PV) output. Compared with the conventional model-based method, there are slight deviations between our method and the optimal solution, but the decision-making time is significantly reduced.

Robust Energy Resource Management Incorporating Risk Analysis Using Conditional Value-at-Risk

The energy resource management (ERM) problem in today’s energy systems is complex and challenging due to the increasing penetration of distributed energy resources with uncertain behavior. Despite the improvement of forecasting tools, and the development of strategies to deal with this uncertainty (for instance, considering Monte Carlo simulation to generate a set of different possible scenarios), the risk associated with such variable resources cannot be neglected and deserves proper attention to guarantee the correct functioning of the entire system. This paper proposes a risk-based optimization approach for the centralized day-ahead ERM taking into account extreme events. Risk-neutral and risk-averse methodologies are implemented, where the risk-averse strategy considers the worst scenario costs through the conditional value-at-risk ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$CVaR$ </tex-math></inline-formula> ) method. The model is formulated from the perspective of an aggregator that manages multiple technologies such as distributed generation, demand response, energy storage systems, among others. The case study analysis the aggregator’s management inserted in a 13-bus distribution network in the smart grid context with high penetration of renewable energy and electric vehicles. Results show an increase of nearly 4% in the day-ahead operational costs comparing the risk-neutral to the risk-averse strategy, but a reduction of up to 14% in the worst-case scenario cost. Thus, the proposed model can provide safer and more robust solutions incorporating the CVaR tool into the day-ahead management.