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In traditional electricity generation plants, large powerful synchronous, induction, and direct current generators were used. With the proliferation of microgrids focused on electricity generation from renewable energy sources in today's power grids, studies have been conducted on different types of generators. Instead of the traditional generator architecture, generators with brushless structures, particularly those utilizing magnets for excitation, have found broad applications. Flux-switching generators (FSGs) are innovative types owing to their robust structure, active stator design, and high power density capabilities. However, designs have typically relied on rare-earth element magnets. Rare-earth magnets possess negative characteristics such as price uncertainty, the potential risk of scarcity in the future, and limited geographical production, leading to research on FSGs that do not depend on rare-earth magnets. This study comprehensively examines FSGs that do not use rare-earth element magnets. The study delves into the usage areas, operational mechanisms, structural diversities, and counterparts in the literature of these generators.

A proportional-integral-derivative (PID) control strategy based on desired dynamics equation is proposed to solve the problem of load frequency control in micro-grid integrated with a large proportion of renewable energy. Based on the analysis of the load frequency control model and control difficulties of the micro-grid, a PID control strategy based on desired dynamics equation is designed. By analyzing the influence of controller parameters on the control performance using the single variable method, a simple and practical parameter procedure is summarized and the proposed controller strategy is applied to the load frequency control of the micro-grid. The simulation comparison with various controllers under different conditions show that the proposed control strategy can obtain the best control performance with good robustness and have a significant value for engineering application.

For making the grid to be more intelligent, a unique design is required in this changing sphere. A better-automated grid is more important for controlling the variations in the entire system. This is possible by introducing secondary controllers where both power and voltage are shifted at necessary stages. Therefore, in this article, a Neural Fizzy Identification System with Partial Integral Controller (NFISPID) is introduced. Also, a secondary controller named Selective Voltage Pulse Width Modulation with Hybrid Artificial Bee Colony-Particle Swarm Optimization technique is incorporated for handling all false situations in the grid. The efficiency of the proposed system is checked with MATLAB environment where the results prove to be much efficient in terms of all parametric values, and the grid becomes stable within a short span of time than other existing controllers. [ABSTRACT FROM AUTHOR]

Renewable energy is a promising solution to combat the scarcity of electricity, particularly in isolated and rural areas. Microgrids (MG) can be employed for installing systems with different energy sources, such as renewable energy components and conventional energy sources like utility grids or grid-connected inverter systems. The amount of energy produced by renewable sources depends on their location, which has implications for energy production. This research aims to explore MG and their challenges for efficient operation. The study discusses various AI models used by researchers to mitigate problems associated with MG planning. Additionally, the paper presents a case study based on the most beneficial ML tool like clustering to gain insights into an existing MG system. The paper also delves into the issues related to PV, a connected distributed energy resource (DER), such as forecasting, and predictive management to reduce maintenance costs, and how AI tools can address them. Furthermore, forecasting methods such as LSTM and GRU models are discussed because of the stochastic nature of PV production. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Nowadays, electricity consumption is increasing rapidly which leads to conventional power systems exhaustion. Therefore, micro-grids (MGs) implantation can enhance the resilience of power systems by implication of new resources, such as renewable energy sources (solar panel and wind power systems), electric vehicles (EV), and energy storage systems (ESS). This paper proposes a new strategy for optimal power consumption inside one microgrid; then, the approach will be extended to optimize the power consumption to enhance the resilience in the case of multi-MGs systems. The system controller of each microgrid has been implemented using ESP32 microcontroller and Raspberry IP4. The proposed approach intends to enhance the resilience of the system to react to any contingency in the system such as loss of power linkage between MG and the network in case of any natural disaster, especially in the rural area. Two controllers are implemented; the first one ensures MG autonomy by the efficient use of its own sources. The second one handles the system resilience cases by demanding/delivering power from/into neighbor microgrids. Hence, this work enhances the system resilience with an optimal cost. Thus, the MG can offer ancillary services for the neighboring MGs. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the process of grid connection of irregular output micro‐grid systems, it is of great research significance to make micro‐grid scheduling operate more effectively and economically. To minimize the environmental and total operating costs of the micro‐grid intelligent scheduling system during grid connection, this study proposes a micro‐grid intelligent scheduling model based on an optimized particle swarm optimization algorithm. The optimized particle swarm algorithm mainly improves the problem of the particle swarm algorithm being prone to local optima, making it reduce the possibility of local convergence. Comparative tests were conducted on the improved IPSO algorithm, and the results showed that the non‐dominated solution concentration pollution emissions obtained by the improved IPSO algorithm were at least 18326 Ib, which was lower than the comparison algorithm. In the empirical analysis of the intelligent scheduling model based on the improved IPSO algorithm, the convergence and satisfaction of the model were 0.0054 and 0.922, respectively, which were superior to the comparison algorithm. The above results indicate that the intelligent scheduling model for micro‐grid based on the improved IPSO algorithm has good economy and stability. Compared with traditional energy dispatch methods, this energy dispatch mode has higher economic benefits and is of great significance for promoting the development of micro‐grid. [ABSTRACT FROM AUTHOR]

Hydrogen energy storage has many characteristics such as large energy storage capacity, long storage time, clean and pollution-free, and it realizes the interconnection and complementation of multiple energy networks and collaborative optimization. It is expected to become an important supporting technology to promote the development of distributed energy and improve the efficiency of terminal energy utilization. In order to improve the reliability and renewable energy utilization rate of islanded microgrid, the operation characteristics of typical electric, hydrogen and thermal devices are analyzed, and a multi-objective optimization configuration model of the islanded microgrid is proposed. Then, the target problem is solved based on simulated annealing particle swarm optimization (SAPSO) algorithm to obtain technical and economic indicators under different configuration schemes. Finally, based on the annual natural resource and electric heating load characteristic curve of a certain place in the north, the model built on MATLAB can effectively promote that the load loss rate of the proposed multi-energy complementary configuration scheme decreases by 3.18%, and the utilization rate of renewable energy increases by 8.37% compared with the traditional electric energy storage configuration scheme. Thus, the proposed configuration scheme can effectively promote the consumption of renewable energy and ensure the economy and power supply reliability of the independent micro-grid.

This paper proposes an advanced Load Frequency Contol (LFC) strategy, using a hybrid adaptive particle swarm-spiral dynamic optimization algorithm (HAPSSDOA). The optimization algorithms iteratively adjust the PID gains based on the evaluation of the cost function and eventually find the optimal values that minimize the cost function and result in a satisfactory frequency value of 50Hz. The P-I-D parameters for the APSO, SDA, and HAPSSDOA algorithms were 3.5112, 2.9691 and 1.1972; 2.3712, 2.8479 and 0.9827; 2.3519, 1.7989 and 0.8864 respectively. The system’s performance showed the rise time, settling time, percentage overshoot and ITAE of 0.28s, 2.33s, 42.10%, and 0.1076 for APSO; 0.324s, 3.67s, 38.4%, and 0.3590 for SDA; 0.36S, 2.2S, 33.5%, and 0.2430 for HAPSSDOA respectively. The LFC strategy using the hybrid HAPSSDOA achieved better performance, improved robustness, and the best response. The optimized PID controllers’ values brought more stability to the designed microgrid network, by ensuring that the frequency remained at 50Hz. [ABSTRACT FROM AUTHOR]

In the restructured electricity market, microgrid (MG), with the incorporation of smart grid technologies, distributed energy resources (DERs), a pumped-storage-hydraulic (PSH) unit, and a demand response program (DRP), is a smarter and more reliable electricity provider. DER consists of gas turbines and renewable energy sources such as photovoltaic systems and wind turbines. Better bidding strategies, prepared by MG operators, decrease the electricity cost and emissions from upstream grid and conventional and renewable energy sources (RES). But it is inefficient due to the very high sporadic characteristics of RES and the very high outage rate. To solve these issues, this study suggests non-dominated sorting genetic algorithmII (NSGA-II) for an optimal bidding strategy considering pumped hydroelectric energy storage and DRP based on outage conditions and uncertainties of renewable energy sources. The uncertainty related to solar and wind units is modeled using lognormal and Weibull probability distributions. TOU-based DRP is used, especially considering the time of outages along with the time of peak loads and prices, to enhance the reliability of MG and reduce costs and emissions. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Engineering Technology is the property of Editorial Department of Electric Power Engineering Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Developing countries are facing many electricity crises such as low production, worst power outages, blackouts, and poor inadequate supplies. Most of their rural areas have no basic power infrastructure as it requires long transmission lines, which causes high installation, operation, maintenance costs and line losses issues. The people of these unconnected rural areas belong to low-income families and use kerosene oil, candles, small solar panels, and batteries to meet their basic electricity needs. As an alternative, renewable-based hybrid energy systems have enormous potential. It is also environmentally friendly and low-cost, sustainable, and promising solution to electrify rural off-grid areas. This research work focuses on the economic and optimal design of a renewable-based hybrid energy system for the rural off-grid area of Pakistan. HOMER (Hybrid Optimization of Multiple Energy Resources) software is used to perform techno-economic and environmental analysis. The optimization results show that the most economical and optimal configuration is PV/diesel generator with batteries having lower harmful emissions. Furthermore, the sensitivity analysis is performed to refine the results which show that the most optimal system will remain sustainable if variations occur in the sensitivity variables in the future. Moreover, the results and findings of this study can help the government to make effective policies and planning to provide reliable and affordable electricity to the off-grid areas, to improve the electrification rate in the country. [ABSTRACT FROM AUTHOR]

This paper proposes an accurate harmonic identification strategy for microgrids and distributed power systems. The harmonic identification strategy is one of the complex tasks in microgrids due to the need of high computational burden in terms of memory and computational time. The complexity of the considered problem is resulted from solving the transcendental nonlinear equations that characterize harmonics especially in real time is considered as a highly challenged problem. The proposed identification strategy aims at detecting individual and total harmonic distortion levels that is generated from several harmonic sources. In the current paper, the Machine Learning Regression Analysis (MLRA) including location-specific data and the Artificial Neural Networks (ANNs) are proposed to identify the harmonic distortion. To enhance the identification and prediction performance, the standard IEEE 34-bus test feeder with verified harmonic sources power system is emulated for various scenarios using Electric Transient Analysis Program (ETAP). An extracting procedure for individual and total harmonic components are employed. The higher reduction level of error with approximately maximum median of 3.7127e $-^{15}$ % assures the accurate prediction of harmonic components. In addition, this work investigates the impact of several practical cases when the voltages of the renewable clean energy source arrays vary (e.g., solar cell, wind turbine, and EV), and a double-stage topology is needed to have the same amount of voltage at the input of the inverter before inversion.

DC micro-grids are gaining more attention and becoming more predominant due to its robustness and ease of control. To confront the stability issues of the DC Micro-grid system, a model free intellectual Control approach is proposed and recognized with a deep Q-learning controller to influence the functions of DC–DC boost converter. This self-reliant model of deep Q-learning controller establish a suitable communication link among the agent-environment by applying the reward/penalty algorithm for attaining the stable output voltage with its training statistics. Upon learning the state of agent, an optimal proposal is defined by the controller to attain the stabilized voltage at the output for all possible operating scenarios. The simulation and experimental studies validate the performance of the controller with higher accuracy, reduced overshoot, and faster settling time upon the transient states. [ABSTRACT FROM AUTHOR]

The primary goal is to enhance the PSN by maintaining stable and consistent MGS operation and reestablishing stable operating conditions after generational interruptions. The artificial neural network is created using a bio-inspired optimization algorithm, such as particle swarm optimization, second generation particle swarm optimization, and new model particle swarm optimization., which directs the evolutionary learning process to determine the most optimal solution. For the best result, the ANN and bio-inspired algorithm (BIANN) are coupled. The suggested BIANN-based controller is made comprised of an internal current and an external power loop. The proper PI gain parameter is tuned using BIANN, allowing the MGS to be stable. Three PSOs are used to investigate the suggested method, and the Matlab Simulink platform is used to create the fitness functions. The results are examined and contrasted. The new model's particle swarm optimization provides MGS functioning and stability that is largely accurate and reliable. [ABSTRACT FROM AUTHOR]

False data injection attacks are executed in the electricity markets of smart grid systems for financial benefits. The attackers can maximize their profits through modifying the estimated transmission power and changing the prices of market electricity. As a response, defenders need to minimize expected load losses and generator trips through load and power generation adjustments. The selection of strategies of the attacking and defending sides turns out to be a symmetric game process. This article proposes a hybrid game theory method for analyzing the attack–defense confrontation: firstly, a micro-grid-based power market model considering false data injection attacks is established using the Nash equilibrium method; secondly, the attack–defense game function is constructed and solved via the Stackelberg equilibrium algorithm. The Markov game algorithm and distributed learning algorithm are used to update equilibrium function; finally, a dynamic game behavior model of the two players is constructed through simulating the attack–defense probability. The evolutionary game method is used to select the optimal defense strategy for dynamic probability changes. Modified IEEE standard bus systems are illustrated to certify the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

This paper proposed a time-varying gain-basedpredetermined time distributed optimization algorithm for the economic dispatch problem in micro grids.Different from the existing distributed dispatch algorithms with asymptotical exponential convergence rates, our proposed algorithm ensured that the output power of each generator converged to the global optimal point within a given time.Moreover, this predetermined time can be set in advance by the designer according to the task requirements and was independent of the initial conditions of the system or the control parameters.By exploiting the convex optimization theory and Lyapunov stability theory, the predefined-time convergence performance was analyzed.Finally, several numerical examples wereprovided to show the effectiveness of the presented distributed predefined-time economic dispatch algorithm. [ABSTRACT FROM AUTHOR]

One way to solve the shortage in power supply and the rapid load growth is by operating power systems that could improve power supply reliability. The study aims to carry out a holistic evaluation by identifying the several reliability variables that could influence the micro-grid power system's reliability which is vital in electricity generation. Thirty-three reliability variable factors that are commonly observed to influence power systems reliability were chosen for the micro-grid power systems study and examined using the principal component analysis (PCA). The system reliability key variables were evaluated using the StatistiXL software. A structured questionnaire was crafted considering thirty-three reliability variables, harvested from literature, and administered to respondents in the micro-grid power system industry. The respondent size was determined at a level of confidence of 95% and an error margin of 5% was deployed to corroborate an adequate population size representation which validated the study data. StatistiXL software was deployed to analyze the (mxn) data matrix, collated from the respondents' scores. The matrix was used as the input variable for the model deployed for the factor analysis. Nine factors with eigenvalues (λ>1) were mined and labeled for the analysis, but all the trivial variables were discarded. The PCA result holistically pinpointed the key reliability variables that influence the micro-grid reliability, revealing that system availability represented by factor 1 (F1) loaded 24% of the total variables studied, with reliability cluster including Mean Time Between Failures (MTBF) = - 0.844, Mean Time to Repair (MTTR) = - 0.737, Demand Response (DR) technique = 0.752, Failure Rate = 0.647 among others. The failure rate and the frequency of outages in F1, were an indication that system availability would be influenced, thereby affecting the micro-grid performance. The study also extracted some weak factor loading, F8 and F9 indicting them as reliability variables whose influences do not impact negatively on the micro-grid reliability but should not be discarded in the study of the reliability of micro-grid power systems. Hence an attempt to improve the system's reliability, concentrating on the key variables factors, the weak variables should not be neglected. [ABSTRACT FROM AUTHOR]

The energy management issue of microgrids typically adopts demand response programs and reconfiguration of distribution networks to improve the technical and financial characteristics of microgrids. This manuscript proposes an energy management optimization in micro grids using IoT by applying the GBDT-JS technique to account for the uncertainty introduced by renewable energy sources and electric demands. The proposed technique is a combination of gradient boosting decision tree algorithm (GBDT) and jellyfish search (JS); hence, it is called GBDT-JS technique. The proposed method is to include maximizing the benefits of microgrids and reducing power fluctuations to the main grid. This proposal presents a GBDT-JS to develop the suitable adaptive day-ahead real-time energy dispatch under the occurrence of operating uncertainty based on the energy management system (EMS). Energy dispatch is achieved to satisfy the many operating criteria based on optimum solutions which are further implemented, for example minimum power fluctuation and operating cost. The main aim of the works contributions is shortened as below: (1) The optimum GBDT system is decided day-ahead depending on the predictive microgrid system levels consider a many operating objects. (2) JS technique is improved a day-to-day basis, adopted to execute the energy dispatch, and directly present in the uncertainty system. The GBDT-JS technique is done on MATLAB, and their performance is compared to different existing techniques, such as slime mould optimization algorithm, side-blotched lizard algorithm, and jellyfish search (JS). [ABSTRACT FROM AUTHOR]

Renewable solar energy is the future of all other resources because of its reliability and availability all over the earth. Optimization of the energy consumption and utilization of internet of things (IoT) devices deployed in such systems poses significant challenges. Axis tracker panel is the scope for the next decade to increase the performance of the existing panels. This research focuses on the development of intelligent energy optimization algorithms for IoT devices. The integration of renewable energy sources and IoT devices in solar-microgrid energy systems offers promising solutions for sustainable and efficient energy management. The proposed whale optimization algorithm (WOA) takes into account dynamic factors, including varying energy availability and fluctuating demand patterns, to optimize the overall performance. Leveraging real-time data from IoT sensors and smart meters, the algorithms balance energy generation and consumption, prioritize critical loads, and incorporate energy forecasting techniques to handle fluctuations in renewable energy production. Moreover, they integrate demand response mechanisms and dynamic pricing models to encourage flexible energy consumption patterns and minimize operational costs. The results of this study demonstrate the significant potential of the WOA algorithm in enhancing the sustainability of microgrid energy systems, paving the way for a greener and more reliable energy future. [ABSTRACT FROM AUTHOR]

In this paper, an innovative transactive energy approach is proposed as viable option for coordinated distribution system planning across a certain horizon. The proposed approach is evaluated across a multi-looped (meshed) test system and is implemented with load growth having prosumers participating in the electrical market in transactive energy system aiming at evaluation on techno-economic basis. Apart from prosumer sensitivity analysis, evaluations have been carried across reducing total production cost of energy, reduction in per unit price, active power losses. Whereas improving voltage profile, cost of scheduling and consumer per kWh purchase and sales in comparison with traditional counterpart. The proposed framework includes optimization algorithm aiming at sources scheduling and IEEE 69 system for validation. The algorithm minimizes cost, maximizes energy efficiency, increases renewable energy mix and reduces consumers cost of energy purchase. Reduction of 51.44 % in cost of energy is achieved, whereas loss reduction of 12.6% is achieved. The comparison of IEEE 69-bus base case with the 10 %, 15% and 20% transactive energy applied with simulations to evaluate performance parameters that will directly benefit both prosumers and utility alike in-terms of low bills and further reduction of stress on the grid amid load growth across multiple years.

Abstract Due to the advantages of micro‐grids including power losses and voltage drops reduction, reliability improvement and reduction in transmission network cost, numerous clean micro‐grids including renewable resources such as wind, tidal and solar are developed in different countries of world. The investment cost of the renewable energy‐based generation units especially tidal barrages is high, and to generate the cost‐effective electricity from the renewable resources, the optimization process should be performed. In this paper, optimal planning of a micro‐grid containing energy storage device and new mixture of renewable resources including tidal barrage and photovoltaic system is performed. For this purpose, optimal characteristics of barrage type tidal plant including number of turbines, sluices and hydro‐pumps, turbine tip and hub diameters and sluice width are determined to maximum energy of tidal plant is generated. Then, number of photovoltaic systems and batteries are obtained to supply the required load in minimum cost. To optimize the objective functions, different heuristic approaches including particle swarm, genetic and imperial competitive algorithms (ICAs) are applied, in the paper. To study the effectiveness of the proposed approach, optimal planning of a micro‐grid is performed. It is deduced from the numerical results that the particle swarm method has performed best in determining the optimal solution.

Recently, the danger of a long blackout is discussed in Europe. Blackouts can be caused by failures in the energy distribution, errors in large power plants or even cyber-attacks. This can lead to a chain reaction and a disintegration of the mains. Longer blackouts have an extreme impact on the economy as a whole and on local households. Therefore, a small local grid at home which can supply the most important loads over some time has garnered increasing interest. With a small direct current (DC) grid, critical loads such as for deep freezers and refrigerators can be supplied, and some LED lights can be used in the evening or at night. Solar generators (panels) can be used to charge energy storage devices, e.g., batteries. A DC grid can not only be used in the case of an emergency, but can also be used to reduce energy consumption out of the public mains and reduce energy bills. The architecture of the household emergency DC grid is discussed; suggestions for batteries are given; two simple chargers, based on DC-DC-converters like the Buck (step-down) and on the Boost (step-up) converters, are shown; dimensioning suggestions are given; and simple, robust controllers, a P-controller with disturbance feedforward and a hysteresis controller, are treated and tested via simulations. The goal of the paper is to show a simple autonomous home energy system without an external fieldbus, LAN or internet connection with special focus on simple charger topologies.

AbstractThis paper aims to provide a model that combines reconfiguration with Unit Commitment (UC) and analytically examine Storage Capacity Increment Effects (SCIEs) in Micro-Grid (MG) operation. The case study includes batteries as the storage system and a conventional 10-bus MG with Wind Turbine (WT) and Micro-Turbines (MTs) as energy sources. The load demand and energy estimation of the Wind Unit (WU) with respect to wind speed changes are considered uncertain parameters. Additionally, a newly introduced algorithm and an objective function based on MG’s day-ahead benefit are employed to tackle the problem. According to Monte Carlo Simulation (MCS), a few scenarios are created for modeling uncertainties, and the MG’s optimal operation is examined under these situations. This study is based on two cases: the first examines the MG’s scheme with just one battery, and the second investigate SCIEs. This paper seeks to maximize MG’s benefit and optimize power exchange with increasing storage capacity. The statistical analysis results show that the proposed strategy can offer more cost-effectiveness, reliability, and power quality, though challenges remain.

Energy resiliency plays an important role in the proper functioning of data centers as they heavily rely on an uninterrupted power supply to ensure smooth operation. In the case of a power outage, the data center's operation might be hampered, which results in system downtime, data, and economic loss. This issue is severe in developing countries where power supply infrastructures are inadequate and conventional. Microgrids can be an effective solution in this regard. Although several studies developed microgrids to observe the energy resilience benefit for some critical facilities, critical facilities like data centers are often overlooked. In addition, sustainability analysis of a microgrid is also scarce in the present literature. Therefore, one new resilience and sustainability indicator has been developed and implemented in this analysis to fill this gap. For this, new indicators, such as the resilience cost index (RCI) and renewable energy penetration (REP), were used in this study. This study used HOMER version 3.13.3 and REopt software to simulate a robust photovoltaic (PV) and battery microgrid for a hypothetical data center in Bangladesh. A random (48 h) outage was assigned to witness the adaptability of the modelled micro-grid. The suitable size of PV and battery was found to be 249,219 kW and 398,547 kWh, respectively. The system's USD 18,079,948 net present value (NPV) demonstrates the economic potential of utilizing PV and battery microgrids for data centers. The RCI of the system is found to be 35%, while the REP is 87%. The energy consumption saving of the system is USD 21,822,076. The system emits 652% less CO2 than the grid. The result of this system is also compared with a diesel-based system. After comparison, it is found that the developed PV/battery microgrid provides better environmental and economical service than the diesel generator. During blackouts, the system keeps the data center powered up without interruption while improving energy resilience and lowering carbon emissions. The outcome of this current analysis can serve as a blueprint for other microgrid projects in Bangladesh and other developing countries. By integrating PV/battery microgrids, data centers can cut costs, reduce emissions, and optimize energy use. This will make data centers less reliant on grid services and more flexible to forthcoming development. [ABSTRACT FROM AUTHOR]

Recently, the danger of a long blackout is discussed in Europe. Blackouts can be caused by failures in the energy distribution, errors in large power plants or even cyber-attacks. This can lead to a chain reaction and a disintegration of the mains. Longer blackouts have an extreme impact on the economy as a whole and on local households. Therefore, a small local grid at home which can supply the most important loads over some time has garnered increasing interest. With a small direct current (DC) grid, critical loads such as for deep freezers and refrigerators can be supplied, and some LED lights can be used in the evening or at night. Solar generators (panels) can be used to charge energy storage devices, e.g., batteries. A DC grid can not only be used in the case of an emergency, but can also be used to reduce energy consumption out of the public mains and reduce energy bills. The architecture of the household emergency DC grid is discussed; suggestions for batteries are given; two simple chargers, based on DC-DC-converters like the Buck (step-down) and on the Boost (step-up) converters, are shown; dimensioning suggestions are given; and simple, robust controllers, a P-controller with disturbance feedforward and a hysteresis controller, are treated and tested via simulations. The goal of the paper is to show a simple autonomous home energy system without an external fieldbus, LAN or internet connection with special focus on simple charger topologies. [ABSTRACT FROM AUTHOR]

Abstract Many progressed information scientific strategies, particularly Artificial Intelligence (AI) and profound learning methods, have been proposed and tracked down wide applications in our general public. This proposition creates information driven arrangements by utilizing the most recent profound learning and AI innovation, including outfit learning, meta‐learning and move learning, for energy the executives framework issues. Genuine world datasets are tried on proposed models contrasted and best in class plans, which exhibit the predominant presentation of the proposed model. In this proposition, the engineering of the Smart Grid testbed is additionally planned and created by using ML calculations and true remote correspondence frameworks to such an extent that constant plan necessities of Smart Grid testbed is met by this reconfigurable system with stacking of full convention in medium access control (MAC) and physical layers (PHY). The proposed engineering has the reconfiguration property in view of the organization of remote correspondence and trend setting innovations of Information and communication technologies (ICT) which incorporates Artificial Intelligence (AI) calculation. The fundamental plan objectives of the Smart Grid testbed is to make it simple to construct, reconfigure and scale to address the framework level prerequisites and to address the ongoing necessities.