Future Of Smart Grid Research Articles

Probabilistic modeling of renewable energy sources in smart grids: A stochastic optimization perspective

Machine learning (ML) techniques have emerged as powerful tools for optimizing renewable energy management in smart grids. This paper focuses on the application of ML algorithms to enhance the efficiency and reliability of renewable energy integration within smart grid systems. By leveraging predictive analytics, ML models can forecast energy production and consumption patterns, facilitating proactive decision-making for grid operators and energy stakeholders. The abstract explores various ML methodologies such as supervised learning, unsupervised learning, and reinforcement learning, tailored to address specific challenges in renewable energy management. Supervised learning algorithms enable accurate prediction of renewable energy generation, aiding in resource allocation and demand-response strategies. Unsupervised learning techniques facilitate anomaly detection and clustering of energy consumption patterns, contributing to grid stability and optimization. Reinforcement learning algorithms optimize control strategies, enabling autonomous decision-making in dynamic grid environments. The abstract highlights case studies and real-world implementations where ML techniques have demonstrated significant improvements in renewable energy integration, grid reliability, and cost-effectiveness. Through a synthesis of research findings and practical insights, this abstract elucidates the transformative potential of machine learning in shaping the future of smart grids and renewable energy management.

Game Theory in Smart Grids: Strategic decision-making for renewable energy integration

This article explores the application of Game Theory in the context of Smart Grids, focusing on its role in strategic decision-making for the effective integration of renewable energy sources. As the demand for sustainable energy solutions rises, the complexities of managing renewable resources within power grids become increasingly challenging. Game Theory provides a valuable framework for understanding and addressing the strategic interactions among various stakeholders in Smart Grids. The discussion encompasses the fundamentals of Smart Grids, the challenges associated with renewable energy integration, and the crucial role of strategic decision-making. By examining real-world case studies and evaluating the benefits and limitations, this article aims to shed light on the practical implications of employing Game Theory models in optimizing Smart Grids for the seamless integration of renewable energy. Looking forward, it explores future trends and developments, emphasizing the continuing significance of Game Theory in shaping the future of sustainable energy infrastructure. The results underscore the transformative potential of game theory in shaping the future of smart grids and advancing the transition towards a more sustainable and resilient energy infrastructure.

THE IMPACT OF SMART GRIDS ON ENERGY EFFICIENCY: A COMPREHENSIVE REVIEW

Smart grids have emerged as a key technology in the quest for energy efficiency and sustainability. This review provides a comprehensive analysis of the impact of smart grids on energy efficiency, highlighting key findings, challenges, and future directions. Smart grids leverage advanced sensing, communication, and control technologies to optimize the generation, distribution, and consumption of electricity. By enabling two-way communication between utilities and consumers, smart grids improve grid reliability, reduce energy losses, and facilitate the integration of renewable energy sources. One of the key findings of this review is the significant impact of smart grids on energy efficiency. Studies have shown that smart grids can reduce energy consumption by enabling real-time monitoring and control of energy usage. This not only helps consumers reduce their electricity bills but also reduces the overall carbon footprint of the electricity sector. However, the deployment of smart grids faces several challenges, including high upfront costs, interoperability issues, and data privacy concerns. Addressing these challenges will be crucial for the widespread adoption of smart grid technologies. Looking ahead, the future of smart grids holds great promise for further improving energy efficiency. Emerging trends such as the Internet of Things (IoT) integration, artificial intelligence (AI) algorithms, and distributed energy resources (DERs) management are expected to drive further innovation in smart grid technologies. In conclusion, smart grids have a transformative impact on energy efficiency, offering significant benefits for consumers, utilities, and the environment. However, addressing challenges such as cost and interoperability will be crucial for realizing the full potential of smart grids. By embracing emerging trends and technologies, stakeholders can further enhance the efficiency and sustainability of the electricity sector.
 Keywords: Impact, Smart Grid, Energy, Efficiency, Review.

Advancements in Home Energy Management Systems: A Review of Energy Optimization Strategies and Algorithmic Approaches

Most of the energy generated globally is used by residential and commercial structures. Due to the expansion of the world economy and population, there is a growing need for energy. Due to in Effective energy management, this has resulted in increased unit electricity prices, frequent strain on the primary electrical system, and carbon emissions. The Home Energy Management System optimizes electricity usage, reduces costs, and limits CO2 emissions by using advanced algorithms to improve the peak-to-average ratio (PAR) and dynamically adjust energy consumption. By using the Services Category (Load Shifting, Valley Filling..., households significantly improve their energy efficiency, resulting in substantial savings. Integrating smoothly with smart grids, HEMS enables real-time monitoring, empowering owners to do informed energy consumption choices. The incorporation of renewable energy sources further enhances their impact, reducing dependence on traditional grids and contributing to a collective reduction in CO2 emissions. HEMS are crucial tools for sustainable and efficient residential energy management, especially in a future of smart grids and environmentally friendly lifestyles. In this article, we are carried out a comparative study of the different algorithms used in the field of Home Energy Management (HEMS) to promote sustainable residential energy consumption. We highlight the goals, results, and limitations of each algorithm.

An ultimate peak load shaving control algorithm for optimal use of energy storage systems

Peak load shaving is one of the applications of energy storage systems (ESS) that will play a key role in the future of smart grid. Peak shaving is done to prevent the increase of network capacity to the amount of peak demand and also increase its reliability. Although the development of diverse ESS with high round-trip efficiency is very important and is considered a necessary condition for optimal peak load shaving, its sufficient condition is to follow an appropriate control algorithm. In this study, a new control algorithm called ultimate peak load shaving (UPLS) is developed for the optimal use of ESS for the peak shaving and valley filling purposes. The UPLS control algorithm is not only able to guarantee peak load shaving in any condition considering any load profile but also can ingeniously control ESS charging and discharging rates. This feature distinguishes it from other algorithms developed in the field.In this study, first, the UPLS control algorithm was explained in detail. Then, by comparing it with two other common control algorithms, its four unique features were investigated. After that, by applying these three control algorithms on different hypothetical load demand profiles A, B, C, and D, these features were demonstrated. Through sensitivity analysis and changing the allowed range for ESS charging and discharging rates (other parameters are assumed constant), how to design the grid power, Pg(t) by the UPLS control algorithm is described. It was found that the mentioned parameters are completely under the control of this algorithm. The optimization results show that peak demand can be reduced by 28.12 % and 27.82 % for load profiles B and C, respectively and the ESS discharging rate is significantly reduced to 55.28 % and 77.93 %, respectively compared to their corresponding values in basic design.

A Review of Modeling and Applications of Energy Storage Systems in Power Grids

As the penetration of variable renewable generation increases in power systems, issues, such as grid stiffness, larger frequency deviations, and grid stability, are becoming more relevant, particularly in view of 100% renewable energy networks, which is the future of smart grids. In this context, energy storage systems (ESSs) are proving to be indispensable for facilitating the integration of renewable energy sources (RESs), are being widely deployed in both microgrids and bulk power systems, and thus will be the hallmark of the clean electrical grids of the future. Hence, this article reviews several energy storage technologies that are rapidly evolving to address the RES integration challenge, particularly compressed air energy storage (CAES), flywheels, batteries, and thermal ESSs, and their modeling and applications in power grids. An overview of these ESSs is provided, focusing on new models and applications in microgrids and distribution and transmission grids for grid operation, markets, stability, and control.

Hosting capacity enhancement of hybrid AC/DC distribution network based on static and dynamic reconfiguration

AbstractThe hybrid AC/DC distribution networks will have a crucial role in the future of smart grids due to their adaptiveness to a variety of DC loads and energy resources. Thus, it is necessary to assess and enhance the Hosting Capacity (HC) of these hybrid networks for the integration of Renewable Energy Sources (RESs). One way to increase the HC without further expansion in the grid is using the potential of Network Reconfiguration (NR). This paper proposes an HC enhancement model that includes AC/DC distribution NR. Two types of NR are considered, namely static, that is, embedding the NR capabilities at the planning stage, and dynamic, that is, using remotely controlled switches as an Active Network Management (ANM) scheme. Besides, two unique potential sources of hybrid AC/DC distribution networks that could be used for the HC enhancement are included, that is, reactive power control of Voltage Source Converters (VSCs) and the mesh operation capability. The problem is formulated as a mixed‐integer linear optimal power flow that aims to maximize the RESs HC under thermal and voltage constraints. Also, stochastic programming is utilized for managing the RESs uncertainties. The simulation results show the accuracy and efficiency of the proposed formulation from various perspectives.

Design and implementation of a PI-PBC to manage bidirectional power flow in the DAB of an SST

The interest of moving towards more sophisticated power grids and the possibility of having more efficient and reliable power semiconductor devices have enabled the introduction of new concepts in power systems, such as solid-state transformers (SSTs). SSTs are considered to be one of the most relevant elements in the future of smart grids since their functionalities allow for the appropriate integration of distributed generation, renewables, energy storage systems, among others, with traditional power grids. One of the main stages of an SST (a DC-DC stage) includes a dual active bridge (DAB) that enables bidirectional power flow, which is regarded as one of the main advantages over traditional power transformers. This work focuses on proposing a current control strategy based on PI passivity in order to regulate the power flow in a DAB. The proposed controller guarantees the system's stability in a closed loop, keeping its passive properties. In addition, it preserves the simplicity of a PI controller, with high performance and robustness, and it does not depend on the converter's parameters. The performance and effectiveness of the proposed controller is validated through time-domain simulations and experimental results under different power flow conditions, including bidirectional power flow. The results show that the proposed controller has a better performance than classical PI controllers.

The Estimation of the Influence of Household Appliances on the Power Quality in a Microgrid System

This article presents the analysis of the influence of household appliances on the quality of the energy consumed by the end-user. The results of the research, then, concern the final consumer (the lowest level of the power grid). The research was conducted on 120 combinations of electrical appliances connected into a grid. Each combination consisted of three devices working simultaneously in a micro-grid. The obtained and statistically analyzed data proved that there are several types of appliances that have a great influence on the power quality (PQ) parameters changes. The results of the conducted experiments indicate the devices which influenced significantly the total harmonic distortion of voltage (THDV), the voltage frequency (FREQ) and the voltage fluctuation (V). Specific features of particular devices were examined in terms of their significance for the power quality deviation. This showed the most important features which should be considered while working out the prediction model. The future of smart grids resides in data analysis, predictive models and real-time optimization. One of the key characteristics is the reducing energy consumption generated by renewable energy sources. This phenomenon, namely looking for problems connected with sustainable power quality and their appropriate solution, is described in this article. We performed an extended analysis of the smart home appliances influence of individual quantities on a real model. Furthermore, we explored devices with a high impact on chosen power quality indicators. In the end, we discuss their specific behavior and relevance to the above-described phenomenon to improve the predictive model utility.

Short-Term Load Forecasting Using Modified Sequence-to-Sequence Deep Learning Framework

The future of smart grids promises unprecedented flexibility in energy management. Therefore, it is crucial to accurately predict the energy load demand of individual power grid stations and overall levels. In this paper, a Sequence to Sequence learning method based on long short-term memory neural network (Seq2Seq) forecast model is proposed to predict the load of the community microgrid. In particular, this paper studies the effectiveness of Seq2Seq in community microgrid load forecasting, avoiding overfitting through weight attenuation regularization, and reducing the impact of interference and noise on training data. The proposed method is implemented on the base load data set of multi-user. The results of Seq2Seq were compared with the results of two well-known methods, one is traditional LSTM and the other is Support Vector Machine (SVM) on the same datasets. Experimental results show that compared with SVM and traditional LSTM, Seq2Seq has better learning effect. Finally, we calculate the performance index. When using load data to train and test the model, it was noted that compared with traditional LSTM and SVM, the root mean square error (RMSE) of Seq2Seq was decreased by 44.07% and 64.06%, respectively. Our research offers a decent reference for the application of deep learning in the field of energy management.

A survey on smart grid technologies and applications

The Smart Grid is an advanced digital two-way power flow power system capable of self-healing, adaptive, resilient and sustainable with foresight for prediction under different uncertainties. In this paper, a survey on various Smart Grid enabling technologies, Smart Grid metering and communication, cloud computing in Smart Grid and Smart Grid applications are explored in detail. Opportunities and future of Smart Grid is also described in this paper. For Smart grid enabling technologies Smart meters, smart sensors, vehicle to grid, plug in hybrid electric vehicle technology, sensor and actuator networks are explored. Advanced metering infrastructure, intelligent electronic devices, phasor measurement units, wide area measurement systems, local area network, home access network, neighborhood area network, wide area networks and cloud computing are explored for Smart Grid metering and communication. Home and building automation, smart substation, feeder automation is explored for smart grid applications. Associations of initial studies for the next step in smart grid applications will provide an economic benefit for the authorities in the long term, and will help to establish standards to be compatible with every application so that all smart grid applications can be coordinated under the control of the same authorities. Therefore, this study is expected to be an important guiding source for researchers and engineers studying the smart grid. It also helps transmission and distribution system operators to follow the right path as they are transforming their classical grids to smart grids.

Users Empowered in Smart Grid Development? Assumptions and Up-To-Date Knowledge

Active involvement of users in smart grids is often seen as key to beneficial development of smart grids. In this paper, we investigate the diverse assumptions about how and why users should be active and to what extent these assumptions are supported by experiences in practice. We present the findings of a systematic literature review on four distinctive forms of user involvement in actual smart grid projects: demand shifting, energy saving, co-design, and co-provision. The state-of-the-art knowledge reflects the preoccupation with demand shifting in the actual smart grid development. Little is known about the other user roles. More diversity in types of projects regarding user roles would improve the knowledge base for important decisions defining the future of smart grids.

Research on Operation and Control of Micro-Grid System

Micro-grid is the significant part of the distribution network in the future of smart grid, which has advanced and flexible operation and control pattern, and integrates distributed clean energy. It is a study of the direction of the grid in the future. In the paper, it gives an account of the operation and control of micro-grid. First, the concept of micro-grid, the structure and characteristics of the typical micro-grid and the significance of the development of micro-grid are analyzed. Second, it introduces typical control strategy which contains the integrated control strategy of Micro-grid and a typical control mode of Micro-power. At last, based on a comprehensive control strategy, it proposed the operation mode of Micro-grid.

Cost-Optimal Consumption-Aware Electric Water Heating Via Thermal Storage Under Time-of-Use Pricing

Time-of-use electricity pricing is touted as one of the solutions toward optimal load distribution in the future of smart grid. However, the benefits of such an approach are limited as human electricity usage can often be described as inelastic in nature, or not sensitive to pricing in economic theory. Water heating constitutes one of the largest components of such usage. This paper proposes technology to enable residential water heating to be converted from an inelastic to an elastic demand, which would respond to pricing incentives. This is accomplished by utilizing consumer hot water use patterns combined with thermal storage to calculate a user-specific temperature profile. Results demonstrated that such an approach could not only reduce electrical water heating costs significantly and allow for realization of large-scale grid benefits, but also improve customer experience as thermal storage capability would enable more hot water to be delivered at times of peak consumption.

A Game Theoretic Model for Smart Grids Demand Management

Demand-side management (DSM) plays a key role in the future of smart grids. Recently, DSM researchers have developed various mathematical models to optimize the demand response. Most of these works ignore the channel impairments' impact on the optimization process. In this paper, we propose a new noncooperative game theoretic model for the management of smart grid's demand considering the packet error rate in our formulation. We set the Nash equilibrium conditions for the proposed model. Under an assumption on the form of the utility functions, we develop a 0-1 mixed linear programming approach to compute nondominated extreme Nash equilibria. Results on a numerical example are provided and some useful insights are presented. Under some assumptions and a fully proven proposition, a feasible nondominated Nash equilibrium solution is found. Finally, we report and comment on computational experiments on randomly generated smart grid DSM game instances with different characteristics.

Microgrids Frequency Control Considerations Within the Framework of the Optimal Generation Scheduling Problem

Microgrids are perceived to play an important role in the future of smart grids. Cost reduction and frequency regulation are among the major concerns in this context. This paper aims to study the mutual impacts of these issues and to propose an integrated mathematical framework in order to achieve minimum costs while maintaining frequency-regulation requirements. Inspired by the operational practices and requisites of the real-world microgrid applications, different methods of frequency control are reviewed and discussed. This is followed by developing adequate models for the different types of frequency-regulation mechanisms in the framework of the microgrid generation scheduling problem. The application of the proposed methodology to the Bella Coola microgrid in British Columbia, Canada, is explained, and several numerical results are presented and discussed.

The Internet of Energy: Smart Sensor Networks and Big Data Management for Smart Grid

Abstract Smart sensor networks provide numerous opportunities for smart grid applications including power monitoring, demand-side energy management, coordination of distributed storage, and integration of renewable energy generators. Because of their low cost and ease-of-deployment, smart sensor networks are likely to be used on a large scale in future of smart power grids. The result is a huge volume of different variety of data sets. Processing and analyzing these data reveals deeper insights that can help expert to improve the operation of power grid to achieve better performance. The technology to collect massive amounts of data is available today, but managing the data efficiently and extracting the most useful information out of it remains a challenge. This paper discusses and provides recommendations and practices to be used in the future of smart grid and Internet of things. We explore the different applications of smart sensor networks in the domain of smart power grid. Also we discuss the techniques used to manage big data generated by sensors and meters for application processing.

Electric field analysis on the insulation design of the stop joint box for DC HTS power cable

DC HTS power cable is a promising electric power transmission line for the future of smart grid, and it has been competitively investigated at many research institutes all over the world. For the commercialization of DC HTS cable, higher power transmission capacity and longer length for long distance transmission line should be prepared. In order to meet the needs of long distance DC HTS cable, a joint box should be developed for the connection of cable components. As for AC HTS cable, a number of patents of nominal joint box have been already reported. However, any conceptual designs of the joint box for DC HTS cable have not been suggested yet. One of the reasons is that the cryogenic high voltage insulation design, especially in DC environment is not fully investigated yet. Conventional normal joint box for AC HTS cable could not be directly applied to DC HTS cable because different electric field distributions compared to AC electric field which requires totally different electrical insulation design concepts. In this paper, in order to establish the basic insulation design of the stop joint box (SJB) for DC HTS cable, three kinds of SJB models were designed and electric field analyses have been conducted both considering AC and DC environment. And the critical factors affecting the DC insulation design of the stop joint box were analyzed. From the simulation results, it was observed that the electric field distribution was totally different both in AC and DC operating conditions. And it was possible to find the weakest regions in the insulation design of the SJB. Consequently, based on the DC electric field analysis, the insulation design criteria and the desirable configurations were suggested for the insulation design of the stop joint box for DC HTS cable.

Emerging communication technologies and security challenges in a smart grid wireless ecosystem

The scope of this paper is to present contemporary communication aspects regarding the employment in future of smart grid as a wireless ecosystem. A wide rigorous survey on wireless access and network technologies ideal for smart grid networks is conducted, and we present applicable technologies like Internet of Things IoT and Machine-to-Machine M2M communications. Moreover, we present key topics of security, privacy and cryptography. Indeed, multimedia services will be provided in a wireless communications environment of a smart grid, while Quality of Service QoS as well as security audit shall always be crucial dimensions during a communication network modelling and planning. More specifically, the objective of our work is concentrated on the study of smart grid communications topics and security evaluation using a simulation environment and to demonstrate some interesting results.