Traditional Electrical Grid Research Articles

A bidirectional gated recurrent unit based novel stacking ensemble regressor for foretelling the global horizontal irradiance

The rapid expansion of solar power generation has led to new challenges in solar intermittency, requiring precise forecasts of Global Horizontal Irradiance (GHI). Accurate GHI predictions are crucial for integrating sustainable energy sources into traditional electrical grid management. The article proposes an innovative solution, the novel Enhanced Stack Ensemble with a Bi-directional Gated Recurrent Unit (ESE-Bi-GRU), which uses machine learning (ML) boosting regressors such as Ada Boost, Cat Boost, Extreme Gradient Boost, and Gradient Boost, and Light Gradient Boost Machine acts as a base learner and the deep learning (DL) algorithms such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) for both directions are taken as a meta-learner. The predictive performance of the proposed ESE-Bi-GRU model is evaluated against individual models, showing significant reductions in mean absolute error (MAE) by 86.03 % and root mean squared error (RMSE) by 66.43 %. The model's ability to minimize prediction errors, such as MAE and RMSE holds promise for more effective planning and utilization of sporadic solar resources. By improving GHI forecast accuracy, the ESE-Bi-GRU model contributes to optimizing the integration of sustainable energy sources within the broader energy grid, fostering a more sustainable and environmentally conscious approach to energy management.

Integration of smart grid technologies: Implementing advanced procurement strategies leveraging AI and machine learning

Integrating smart grid technologies represents a pivotal advancement in modernizing electrical grids and enhancing efficiency, reliability, and sustainability. This publication explores the role of advanced procurement strategies, specifically leveraging artificial intelligence (AI) and machine learning (ML), in streamlining the sourcing and integration of smart grid components. By examining current challenges, potential solutions, and the impact on national security and economic stability, this paper aims to provide a comprehensive framework for the effective implementation of smart grid technologies. The traditional electrical grid, characterized by its centralized generation and unidirectional power flow, faces numerous challenges, such as inefficiency, susceptibility to outages, and inability to integrate renewable energy sources effectively (Brown et al., 2020). The transformation to a smart grid infrastructure, which incorporates digital communication technology and advanced sensors, promises significant improvements. However, the transition is fraught with challenges, particularly in the procurement and integration of the necessary components. One of the primary challenges in smart grid integration is the complexity of supply chains. The procurement of smart grid components involves multiple stakeholders, including manufacturers, suppliers, and regulatory bodies. This complexity often leads to delays, increased costs, and inefficiencies (Smith & Wang, 2019). Additionally, the high initial costs of smart grid technologies pose a barrier to widespread adoption. Traditional procurement methods struggle to balance cost-efficiency with the need for high-quality, durable components, often resulting in budget overruns or compromised quality.

Smart Grid Monitoring: Enhancing Reliability and Efficiency in Energy Distribution

Smart grid has been replaced with traditional electrical power grid with its various technologies. In today’s world it has emerged in as solution of increasing demand. This technology has changed the way electricity is produced, transferred, and consumed. This technology benefits in advanced monitoring systems to improve the reliability and efficiency of energy distribution. This abstract provides an overview of smart grid monitoring, highlighting its key components, benefits, and challenges. Smart grid monitoring comprises of different technologies and techniques. Information communication technology helps the grid in collection of data from various consumers. These technologies involve advanced sensors, Advanced Metering Infrastructure (AMI), data analytics. It has multidimensional benefits. It enhances grid reliability by enabling rapid detection and response to the faults. It provides consumers with detailed information about their energy usage, adapting more awaking consumption habits. This contributes to the overall reduction of greenhouse gas emissions and environmental impact. In conclusion, it represents a crucial advancement in the energy sector, transforming the traditional grid into an intelligent grid. The continued development and deployment of smart grid monitoring technologies promise a brighter and more sustainable energy future.

Comprehensive Bibliometric Analysis on Smart Grids: Key Concepts and Research Trends

Over the years, a rapid evolution of smart grids has been witnessed across the world due to their intelligent operations and control, smart characteristics, and benefits, which can overcome several difficulties of traditional electric grids. However, due to multifaceted technological advancements, the development of smart grids is evolving day by day. Thus, smart grid researchers need to understand and adapt to new concepts and research trends. Understanding these new trends in smart grids is essential for several reasons, as the energy sector undergoes a major transformation towards becoming energy efficient and resilient. Moreover, it is imperative to realize the complete potential of modernizing the energy infrastructure. In this regard, this paper presents a comprehensive bibliometric analysis of smart grid concepts and research trends. In the initial search, the bibliometric data extracted from the Scopus and Web of Science databases totaled 11,600 and 2846 records, respectively. After thorough scrutiny, 2529 unique records were considered for the bibliometric analysis. Bibliometric analysis is a systematic method used to analyze and evaluate the scholarly literature on a particular topic and provides valuable insights to researchers. The proposed analysis provides key information on emerging research areas, high-impact sources, authors and their collaboration, affiliations, annual production of various countries and their collaboration in smart grids, and topic-wise title count. The information extracted from this bibliometric analysis will help researchers and other stakeholders to thoroughly understand the above-mentioned aspects related to smart grids. This analysis was carried out on smart grid literature by using the bibliometric package in R.

Localized management of distributed flexible energy resources

The integration of distributed energy resources continues to be a challenge for the traditional electricity grid. Aside from additional variability in the net power demand, there is often a temporal discrepancy between generation and consumption schedules which can exacerbate demand peaks and troughs, leading to system overloading, voltage violations, and a low system load factor. This study proposes a control system approach to unify the management of the different types of flexible energy resources through Localized Demand Control. In this system, all flexible energy resources are coordinated using a power line carrier frequency signal to follow a preferred demand curve at the distribution transformer level. The system’s functionality has been confirmed in an actual 5-house microgrid without adverse effects on end-user comfort. Meanwhile, scaled-up simulation studies have shown improvements in the system load factor by as much as 189%, and increased hosting capacity by 40% for electric vehicles and 16% for solar photovoltaic panels.

A Reinforcement Learning controller optimizing costs and battery State of Health in smart grids

Smart Grids are the evolution of traditional electric grids and allow two-way flows of electricity and information between different actors. At the edge of this network, customers can both produce and consume energy. Due to the intermittent nature of renewable energy sources, customers are characterized by moments of energy surplus and deficit. To solve this problem, customers are connected to the power grid, and, usually, they are also provided with Lithium-Ion battery packs positioned near the energy source used to store energy in excess for later use, reducing expensive energy exchanges with the grid. On the one hand, using the battery at its full capabilities produces significant economic savings. On the other hand, massive use of the battery leads to degradation and consequently to a more frequent substitution of the battery. Therefore, depending on the cost of energy and batteries, one should carefully choose when it is favorable to use it. To avoid inefficiencies, it is common to design controllers that regulate the energy flow within the battery packs, deciding whether to exchange energy with the network or store it in the battery. In this work, a Reinforcement Learning controller optimizing energy flow is developed. The controller’s goal is to balance the costs of exchanges with the power grid and those derived from the degradation due to battery usage. A synthetic experimental campaign conducted using real-world data demonstrates that the policy learned shows an improvement in the worst-case of 3% w.r.t. state-of-the-art baselines.

Balancing urban energy considering economic growth and environmental sustainability through integration of renewable energy

This paper delves into the critical intersection of urban energy management, economic growth, and environmental sustainability through the integration of renewable energy sources in smart urban homes. The escalating demand for power has created an energy scarcity, necessitating innovative solutions to balance supply and consumption. In this context, the transition from traditional electric grids to smart grids (SG) is gaining prominence. To harness the potential of smart urban homes, particularly in the context of renewable energy, solar panels on rooftops have become indispensable. Smart grid technologies enable users to engage in demand response strategies, offering incentives and financial benefits for flexible device usage. This study introduces a novel optimization algorithm, the Polar Bear Optimization Algorithm (PBOA), designed to schedule domestic device usage patterns efficiently. To evaluate the effectiveness of PBOA, this paper conducts comparative analyses with other optimization algorithms such as Differential Evolution (DE) and Particle Swarm Optimization Algorithm (PSOA). Energy cost metrics, including Critical Peak Price (CPP) and Real-Time Price (RTP), are employed to assess cost reduction strategies. The primary focus of this study is to minimize energy costs and reduce the Peak to Average Ratio (PAR), thereby promoting economic growth and environmental sustainability in smart urban homes. It should be noted that neural network (NN) machine learning technique has been used for forecasting the weather and renewable energies output power.

DATAEMS: Design and development of a data analysis-based energy monitoring system

Electrical load forecasting is now a key component of planning and running a smart grid due to the ongoing integration of green energy sources (RES) into the energy mix and the transformation of the traditional electric grid into a more intelligent, flexible, and interactive system. Either when it comes to the distribution system or a single household, predicting the electric load is a difficult task due to its high volatility and uncertainty. In this paper, the study suggests a method for monitoring electrical energy that is based on long short-term memory (LSTM). The research for the suggested method is based on real data of electricity use from four distinct clients during a week. The analysis is made more effective and produces better findings owing to the use of a customized meter that transmits data to the server every minute. Modeling irregular trends in electric power consumption is made possible by the LSTM's aptitude for examining recurrent patterns in time series components. The suggested method calculates the dataset on individual household power usage with the lowest root mean square error (RMSE) as compared to existing forecasting methods. The simulation results highlight the parameters that have the biggest impact on forecasting power consumption.The suggested LSTM model substantially reduced time series forecasting errors when compared to previous techniques.

Optimal design and sizing of a multi-microgrids system: Case study of Goma in The Democratic Republic of the Congo

Nowadays, renewable energy generation has gained a lot of attention in the global electrical energy production. This has led to an increased integration of distributed generators into the traditional electrical grid, standalone generations as well as the formation of microgrids. The continuous increase in microgrid penetration has resulted in the microgrids becoming neighbours to one another, thus, favouring the interconnection of these microgrids to form the multi-microgrids. The multi-microgrids infrastructure has the potential to improve energy availability, reliability and reduction in the net cost of energy through sharing of resources. The notion of unidirectional power flow that normally applies to the traditional electrical grid is no longer applicable in this context. This paper investigates the advantages of several microgrids’ interconnection on the system reliability within the town of Goma in the Democratic Republic of the Congo (DRC) using the Homer Grid software for optimal sizing of components considering technical and economic aspects. From the potential assessment of this case study, it is realized that the methane gas present in Kivu Lake and solar PV are potential distributed sources that can be used. In addition, it was observed that when connected to the Virunga private grid, the system with PV can be sold to the grid with a payback period of about seven years. The system made of Generator, PV and battery was found to be the most reliable. This led to the conclusion that, the interconnection of several microgrids produces a more stable and reliable system, especially when different sources of energy are used.

Enhancing smart grids with a new IOT and cloud-based smart meter to predict the energy consumption with time series

More energy-consuming devices such as household electronics and more production facilities worldwide are causing increases in electricity demand and energy prices. The public service has difficulty maintaining the energy balance. These problems can be overcome by rapidly transforming the traditional electricity grid into a smart grid (SG) infrastructure. Smart meters (SMs) are an essential component of SGs and have vital tasks. This study has developed an Internet of Things (IoT) based SM that can reach high data rate of 38,400 bps or frequency of 160 MHz, using SQL Server for data storage and bidirectional data transmission with 174 W total load. In this way, consumers can track their energy consumption hourly, daily, and monthly, learn how much they spent on consumption, and receive warnings for a power outage. A fuzzy system and mobile application software are integrated into the SM structure for all these purposes. The designed device is believed to contribute significantly to the spread of SGs due to all these features.

Review of Methods for Reducing Power Losses in Electric Grids Containing Renewable Energy Sources

In recent decades, there has been an increase in the use of renewable energy sources, which serve as the basis for the concept of distributed generation, that is, facilities that produce electricity at the location of consumers. The large-scale introduction of distributed generation affects the reliability and stability of electric power systems, including by reducing power losses in them by reducing the current in the lines. Taking into account the forecasts of many experts on the growth of distributed energy resources, the issue of studying methods for reducing electrical energy losses in distribution grids containing renewable energy sources is relevant. (Research purpose) The research purpose is determining methods for reducing electricity losses in electric grids containing renewable energy sources. (Materials and methods) Presented an information search, including an overview of Internet sources and sources of scientific literature devoted to methods of reducing electricity losses in electric grids. (Results and discussion) They used Yandex and Google search engines, Scopus, Web of Science and Google Scholar scientific databases, Cyberlenink electronic libraries as sources of information, eLIBRARY.ru , Elsevier, Springer, IEEE Xplore. It was shown that the literary analysis included 59 sources most suitable for the research topic, of which 8 sources of information are given in Russian, 51 in English. In the course of the information search, we determined methods for reducing electricity losses both in traditional electric grids and in electric grids containing renewable energy sources. (Conclusions) It was found out that the considered methods inherent in traditional power supply grids are also relevant for networks with renewable energy sources. It was revealed that the use of renewable energy sources in itself also allows to significantly reduce electricity losses in electric grids due to distributed generation

Optimization and simulation of a grid-connected PV system using load forecasting methods: A case study of a university building

Distributed generation represents a paradigm shift from the traditional electric grid to localized generation of electric power along with the capability of incorporating renewable energy (RE) sources into the energy mix. Responding to the need for sufficient analysis, simulation, and study of feasibility of distributed generation, this study aims to design a hybrid energy system for a university building and analyze its economic benefits. The viability of existing load forecasting methodologies for energy systems is also presented in this paper. The energy system design was determined through predictive modeling of the load profile of the building using historical data and optimization using machine learning methods, namely auto-regressive integrated moving average (ARIMA) and long short-term memory (LSTM). Simulations were run in HOMER Pro. Results show that a grid-connected solar photovoltaic (PV) system installed on the roof coupled with an energy storage system (ESS) will provide the most economic benefits because it yields a reduced cost of energy (COE) per kilowatt-hour (kWh) for the building. This study shows that in line with efforts to transition towards clean energy, hybrid energy systems using RE not only have economic benefits, but can also ensure energy security and environmental sustainability.

An IOT based smart grid system for advanced cooperative transmission and communication

In the Internet of Things (IoT) ecosystem, devices will predominate, using it in a manner similar to how people used it. Devices will cooperating in a multicast network to collect, share, and forward information way while interacting with one another autonomously and without centralised supervision. The building of an intelligent environment the capability of real-time collection of data, which is crucial for maximising the value of the IoT, will make this possible. A typical electric grid is made up of many power plants that use various power generating units, such as coal-based units, gas-based units, hydro units, etc. The majority of the infrastructure and wires that make up the conventional electricity grid have been in existence for a long time, it should be mentioned. They require significant investments, so providing them could take years. As a result, many grid components are outmoded and must be maintained and monitored on a regular basis to keep power flowing. A sophisticated technology is the smart grid (SG) system that makes it easier to integrate green technology and environmental considerations. The SG cyber–physical system was implemented thanks to the advancement and use of communication technologies in the conventional power system. The Internet of Things (IoT) and essential devices are both present in the complicated architecture of the SG systems. The traditional electric grids are been transformed into smart and efficient grid known as “Smart grid”. The Internet of Things’ smart grid allows for two-way communications among connected devices and technology that can recognise and respond to human needs. The cost and reliability of a smart grid are superior to that of conventional power infrastructure. Through use and data maintenance, smart grid technology will assist in reducing energy use and costs. One of the primary contributions made to grids is the integration of IoT with producing facilities using sustainable energy at various levels. To enhance the smart grid for bidirectional information exchange, improve power quality, and increase reliability Internet of Things (IOT) devices are becoming an important part of smart electric grid. IOT Infrastructure (IOTI) provide a flexible, efficient and secure platform providing strategic management for monitoring and controlling of different operations under different working conditions. This paper discusses cyber security on IOT based infrastructure for electric power systems. A comprehensive study is highlighted which includes type of IOTs, architecture used for smart grid, and future challenges.

Neuro-Fuzzy Logic Applications for Grid Energy Management

Grid management is becoming increasingly complex due to continuous growth in consumer demand and distributed energy resources (DERs). These two rapidly changing areas present a unique challenge when taking into consideration the mission of the United States Department of Energy being to ensure energy security through efficient, environmentally friendly, reliable, and affordable electric energy [1]. As consumer demand and DERs continue to saturate the electric grid, the problem that arises is data measurement and processing. The traditional electric grid had a one-way flow of power, whereas the modern electric grid can have bi-directional power flow via DERs and other load resources such as curtailment, load shifting programs, energy storage, to name a few. This new and rapidly developing electric grid has many more data points that will need to be processed due to the exponentially growing amount of energy resources throughout it. This means that more sophisticated management software is needed to help balance an increasingly complex electric grid. This paper will expand on traditional load management technology compared to the new concepts of neuro-fuzzy logic system managing the grid. A neuro fuzzy logic-based energy management system could be a new option to use for grid management and planning. This would allow grid operators to add additional decision processing capabilities. In this manner, the grid would be able to make complex decisions across the network rather than in specifically localized areas. This capability would allow grid operators the ability to make more efficient and reliable decisions since the system is able to process more data. This additional data could open options for the network to utilize complex responses to grid scenarios by utilizing more resources available to the network. The critical concept here is to allow more data to be processed, therefore opening more resources to be used creatively for complex grid response.

Exploring The Feasibility Of Residential Solar Panel Adoption In Algeria’s Arid And Hot Regions: A Cost-Benefit Analysis Of An On-Grid System

This research aims to explore the economic and technical feasibility of residential solar panel adoption in Algeria, specifically in its arid and hot regions. By analyzing the potential for solar energy generation and assessing the economic viability of solar panel systems, this study aims to evaluate the financial impact on energy bill savings and the economic feasibility of solar panel adoption. A case study of a residential building located in the city of Biskra is used as a representation of the potential for solar energy in these specific areas, along with real data of energy consumption over the last five years. A simulation of an on-grid system using Skelion plugin and PVsyst software was performed to evaluate the financial impact on energy bill savings. The study also evaluated the economic feasibility through the calculation of costs and benefits, including the payback period, net present value (NPV), and the internal rate of return (IRR). Results showed that solar energy has great potential in Algeria and that residential solar panel systems can provide a positive net present value and internal rate of return, indicating that they are economically viable. Additionally, the payback period for these systems was found to be reasonable. Homeowners who install solar panels on their roof can reduce their dependence on the traditional electricity grid by generating their own electricity, which can lead to significant savings on energy bills and reduce their carbon footprint.

Deep Learning-Based FOPID Controller for Cascaded DC-DC Converters

Smart grids and their technologies transform the traditional electric grids to assure safe, secure, cost-effective, and reliable power transmission. Non-linear phenomena in power systems, such as voltage collapse and oscillatory phenomena, can be investigated by chaos theory. Recently, renewable energy resources, such as wind turbines, and solar photovoltaic (PV) arrays, have been widely used for electric power generation. The design of the controller for the direct Current (DC) converter in a PV system is performed based on the linearized model at an appropriate operating point. However, these operating points are ever-changing in a PV system, and the design of the controller is usually accomplished based on a low irradiance level. This study designs a fractional-order proportional-integrated-derivative (FOPID) controller using deep learning (DL) with quasi-oppositional Archimedes Optimization algorithm (FOPID-QOAOA) for cascaded DC-DC converters in micro-grid applications. The presented FOPID-QOAOA model is designed to enhance the overall efficiency of the cascaded DC-DC boost converter. In addition, the proposed model develops a FOPID controller using a stacked sparse autoencoder (SSAE) model to regulate the converter output voltage. To tune the hyper-parameters related to the SSAE model, the QOAOA is derived by the including of the quasi-oppositional based learning (QOBL) with traditional AOA. Moreover, an objective function with the including of the integral of time multiplied by squared error (ITSE) is considered in this study. For validating the efficiency of the FOPID-QOAOA method, a sequence of simulations was performed under distinct aspects. A comparative study on cascaded buck and boost converters is carried out to authenticate the effectiveness and performance of the designed techniques.

Localized Demand Control of Flexible Devices for Peak Load Management

The increased adoption of electric vehicles poses a challenge for the traditional electricity grid, especially at the local residential network. The impact is primarily seen in terms of higher peak demands, system overloads, and voltage violations. This study proposes a non-wire solution to these challenges using a network-oriented demand response technology called Localized Demand Control. In this technology, the local grid is enabled to follow a preferred demand curve at the distribution transformer level through a distributed but coordinated actions of various flexible loads. The system functionality has been validated in a real-world setup using a 5-house microgrid facility. Realistic simulations of a 70-house representative network were also done to create recommended rates for gradual technology adoption in the next 20 years. Results show that the system is a practical solution for reducing peak demand, enhancing load factor, and improving network efficiency.

Electrificación por microrredes en zonas rurales de la provincia del Azuay, Ecuador

The electrification of rural areas is an increasingly relevant issue, since in some cases they do not have access to the traditional electricity grid. For this reason, the use of renewable energy microgrids applied to these areas is being studied. It is important to consider the available energy resource in order to adequately size the microgrid system, since certain systems may not be viable. In this study, two renewable energy generation microgrid systems are proposed: the first is a wind/photovoltaic system that works with the indirect radiation of the sun [kWh/m^2/day] and the average wind speed [m/s]; the second is a purely photovoltaic system that uses the indirect radiation and the temperature of the sun, both systems are isolated and have an energy storage system (battery bank), also have as load a rural residential consumer of 160 kWh/month. The systems are simulated and modeled in HOMER Pro software. The results indicate that the photovoltaic microgrid system is the most viable, since the site has low wind speeds and therefore a wind system is not viable.

Status, Challenges and Future Directions of Blockchain Technology in Power System: A State of Art Review

Intermittent distributed energy resources (DERs) add challenges to the modern power system network. On the other hand, information and communication technology (ICT) is changing traditional electricity grids into smart grids, which facilitates a decentralized system in which prosumers may participate in energy trading. Smart grids, DER integration, and network connectivity are adding complexity to the power system network day by day; Blockchain technology might be a great tool to manage the network’s operational complexity. The Blockchain provides for quicker, frictionless, secure, and transparent transactions. With the addition of smart contracts, it may be utilized to manage the expanding complexity of the contemporary power system. In this study, the authors focus on the scope, challenges, and potential future direction of Blockchain technology application in the power system. Blockchain has received interest and has been used for decentralized power system applications in recent years, but it is still young and has scalability, decentralization, and security concerns. This article discusses the interfaces and the possibilities that can assure trust, security, and transparency in decentralized power system applications and make a decentralized power system and power market possible.

Designing a hybrid renewable energy source system to feed the wireless access network

Today, our society mainly relies on the energy generated by burning fossil fuels, which provides a reliable supply at an affordable price. However, this energy is not renewable and will eventually be depleted in the future. To address sustainability issues, we need to take action in all layers of our society, including our wireless access networks, which are still large power consumers. A possible solution in this field is the integration of RESs (Renewable Energy Sources) for the network supply. Nevertheless, since the production of these RESs is characterized by randomness, which is strictly dependent on the weather conditions, the network service may be compromised because of lack of energy for its supply. In this paper, we investigate the network’s power performance i.e., how much power should be bought from the traditional electricity grid, when using either solar, wind, and geothermal energy or a combination of these three to feed the network (this is here called a multiple RES system). Furthermore, we propose a novel algorithm optimizing the (multiple) RES system accounting for the related CAPEX (Capital Expenditures) and OPEX (Operational Expenditures) costs. Our study shows that geothermal energy is the most reliable one, but also extremely expensive to invest in. Wind energy is the most appropriate choice – even for summer – since it is a rather cheap RES to invest in. The optimized multiple RES system performs the best as only between 0.4% and 11% (depending on the season) of the power required by the network should be bought from the traditional electricity grid.