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The key advantage of power utility-owned smart meters over rotating-disc meters is the ability of transmitting electrical energy consumption data to power utilities' remote data centers. Besides enabling the automated collection of consumers' electrical energy consumption data for billing purposes, data gathered by smart meters and analyzed through Artificial Intelligence make plentiful consumer-centric use cases possible. One of the plentiful consumer-centric use cases is detection and classification of household activities of daily livings (ADLs). A smart meter can acquire composite/circuit-level electrical energy consumption, but it cannot disaggregate its acquired composite/circuit-level electrical energy consumption into appliance-level electrical energy consumption. In this paper, a non-context-aware human life-pattern identification mechanism based on non-intrusive load monitoring (NILM) is presented for detection and classification of household ADLs. Here, NILM disaggregates composite/circuit-level electrical energy consumption into appliance-level electrical energy consumption with no intrusive deployment of networked plug-level power meters as the fundamental constituents of a traditional home energy management system, and it automatically characterizes physical characteristics of relevant electrical appliances for detection and classification of household ADLs without user intervention. [ABSTRACT FROM AUTHOR]

With the development of distribution generation (DG) technology, large amount of renewable energy connected to the microgrid, which has a significant impact on the consumption of renewable energy. The nonlinear load connected to the microgrid leads to the reduction of power quality, and the line impedance between the distribution generation and the point of common coupling (PCC) is different, resulting in the harmonic current cannot be reasonably shared, and the transmission of redundant information brings enormous pressure to communication system. To solve these problems, this paper proposed a microgrid harmonic power-sharing method based on an event-triggered consensus algorithm. Firstly, the hierarchical coordination control architecture of the microgrid is established. Then, when designing distributed frequency and harmonic power controllers based on event-triggered consensus, the impact of communication network time delay on system performance is considered. The status information is exchanged between distributed controllers only when the event is triggered, and the actual value is replaced by the estimated value at other times, which can effectively reduce the transmission of redundant information. Finally, this paper uses the Lyapunov method to prove the stability of the proposed control method, analyzes the event-triggered interval, and avoids the Zeno phenomenon. The effectiveness of the proposed power-sharing method is verified by the hardware-in-the-loop experiment. [ABSTRACT FROM AUTHOR]

With the increase in energy demand and carbon emission requirements, energy management and CO2 emission reduction on the user side are significant for integrated energy system (IES) scheduling. This paper constructs a two-stage optimal dispatching model combining active/passive demand response (DR) and reward and punishment ladder-type carbon trading. In the first stage, the electricity/gas load was optimized by adding a logistics function to the active DR model. A passive DR model is built based on the optimized electric load and the heat/cooling–electricity ratio of the cogeneration unit to optimize the heat/cooling load. In the second stage, a multi-objective IES model based on the optimized load and reward and punishment ladder carbon trading was solved by the golden eagle multi-objective optimization (MOGEO). Analyzing the scheduling results from different scenarios, the active/passive DR model can reduce the load peak-to-valley difference and improve user satisfaction and grid revenue. At the same time, the combination of reward and punishment ladder carbon trading makes the system gain more carbon revenue, reduces system operating costs and carbon emissions, and improves energy utilization. [ABSTRACT FROM AUTHOR]

This paper presents the control strategies and performance analysis of doubly fed induction generator (DFIG) for grid-connected wind energy conversion system (WECS). The wind power produces environmentally sustainable electricity and helps to meet national energy demand as the amounts of non-renewable resources are declining. The development of the WECS can be seen with development in power electronics technology. Currently, two types of major generator used in WECS these are DFIG and the permanent magnet synchronous generator (PMSG). Both variable oprational speed are used in wind turbine. Because its high performance, reliability, controllability and high-power and voltage capabilities, multilevel converters have become a popular option for multi-MW WECSs. This paper focuses on the application of back-to-back modular multilevel converters in a DFIG and PMSG-based WECS. A comparative performance analysis of the two systems based on power quality, active, reactive power, and modular multilevel converter dynamics is presented. The comparison is based on simulation results created in MATLAB/Simulink software. [ABSTRACT FROM AUTHOR]

This paper presents a three-port isolated hybrid converter (3PIHC) with extended phase-shift modulation (EPM) to reduce voltage and current stress in the converter for DC microgrid applications. A high-frequency transformer with three independent windings is employed for isolating three ports in the converter. A wide variation in voltage transformation ratio is one of the main concern in DC microgrid, which need to be addressed for effective power conversion and utilization of the energy generation systems. Traditional phase-shift modulation (TPM) in 3PIHC leads to high conduction and core losses in the converter under wide variation in voltage transformation ratio. This paper proposes an extended phase-shift modulation in 3PIHC for different sequence of voltage transformation ratios between port 1 and port 2 (m 12) and between port 1 and port 3 (m 13) , which is a cost-effective solution for reducing losses in the converter. The peak current at all ports and power equations in 3PIHC are deduced, and the computational method for obtaining the phase-shift values with minimum current stress is explained in detail. A comparative evaluation study proves that the proposed three-port converter with EPM outperforms to TPM in the aspects like low current stress, reduced semiconductor switching losses and transformer losses for wide variation in voltage transformation ratios. A hardware prototype of the proposed converter is built, and the results are evaluated and presented. [ABSTRACT FROM AUTHOR]

Under the umbrella of a smart grid environment, demand response (DR) is a comprehensive way to make the best use of household energy consumption. DR refers to the rescheduling of household energy consumption in response to the pricing signal received from the utility. Interoperability enables the DR loads to communicate with the utility through a smart meter to plan the energy consumption according to the pricing signal received. This paper presents an exclusive tri-objective model that considers real-time data of different parameters like peak, off-peak demand, and time of use pricing signal and performs optimal scheduling to reduce the overall energy expenses, peak-to-average power ratio (PAPR), and improve the load factor (LF) of the system over the entire horizon. Proposed planning considers the least, and highest priority loads for an optimal implementation of proposed planning. The proposed planning is implemented for a time horizon of 30 days. A price-responsive DR model is effectively implemented based on time of use tariff regulation. Based on predefined parameters, a proposed algorithm shifts the least priority load from peak hours to off-peak hours without disrupting the highest priority. Proposed planning is a Heuristic problem tackled as Mixed integer linear programming, mathematically solved in MATLAB. The results demonstrate that; the proposed planning effectively reduces the cumulative energy expenses by 13.36% for the end customer, improves the LF of the system by 5.11%, and reduces PAPR by 15.22% to improve the stability margin of the system. Finally, sensitivity analysis has been performed based on varying peak tariffs which shows the effectiveness of the proposed approach. The results clarify the effectiveness of the proposed model for both customer and utility ends. [ABSTRACT FROM AUTHOR]

Energy optimization is one of the major issues for electrical distribution companies to meet demand with minimum generation costs and power losses. In this paper, the optimal operation of the smart electrical distribution grid is modeled by three-stage multi-objective optimization with optimal consumers' participation and electrical storage systems (ESSs) under electrical price and load demand uncertainties at day ahead. The three strategies of demand response programs (DRPs) such as demand shifting (DS), demand curtailment (DC), and onsite generation (OG) to consumers' participation are considered with attention to electrical price traffic. In the first and second stages, electrical demand is optimized using DS and DC strategies, respectively. Thus, optimized electrical demand is applied to the third stage optimization for minimization of the multi-objective functions including (1) minimum generation costs and (2) power losses. Also, OG is implemented by ESSs in the third stage optimization as a local resource. All stages of the proposed approach are optimized by the shuffled frog leaping (SFL) algorithm. On the other side, the TOPSIS decision-making method is used to choose the best trade-off solution in the third stage optimization. Eventually, the proposed optimization by the numerical simulation is carried out considering four case studies in IEEE 33-bus test system to demonstrate validation of the strategies. The case studies are implemented based on the participation of the consumers by using DS, DC, and OG strategies. In the first case study, the participation of the consumers is not considered, and this case is assumed as a base case study. The participation of the consumers with the DS strategy is implemented in the second case study. The DS and DC strategies are used by consumers in the third case study, simultaneously. In the fourth case study, OG with DS and DC strategies is considered for optimal participation in energy optimization. The participation of all strategies by consumers leads to minimizing energy generation costs and power loss by 5.52% and 14.68% in comparison with non-participation of the consumers. [ABSTRACT FROM AUTHOR]

The topology of grid-connected seven-switch boost-type current source inverter (CSI7) is a promising alternative to the conventional six-switch current source inverter (CSI) due its superiority in terms of reliability and energy efficiency. It is a simple single-stage boost-type converter that allows the injection of high quality sinusoidal AC-currents with controllable power factor into the grid with a DC-link voltage lower than the grid voltage amplitude. This paper evaluates the performance of grid-connected photovoltaic (PV) CSI7 in terms of power losses and efficiency considering distinct configurations of the voltage reverse blocking power switches, i.e., silicon IGBTs (Si-IGBTs) in series with hyper-fast recovery silicon diodes (Si-HFRDs) and Si-IGBTs in series with wide band-gap silicon-carbide Schottky-barrier diodes (SiC-SBDs). The proposed investigation is based on experimental tests and combined electrical-thermal computer co-simulations using PLECS and simulink. The obtained results empathize the features of SiC-SBDs based power switches in terms of reducing the inverter's power losses and improving the overall system efficiency. They also demonstrate the high performance of the CSI7 topology in terms of power quality in grid-connected PV applications. [ABSTRACT FROM AUTHOR]

In the growing world, the utilization of electrical energy is increasing rapidly. Excessive use of fossil fuels will drain them and also invite hazardous pollution. Integrating renewable energy resources as distributed generators (DGs) can fulfill the rapidly increasing electrical energy demand and promote green energy generation to a large extent. The intermittent nature of renewable energy and higher penetration of DG may adversely affect the operation of the distribution network (DN). As a result, power disparity, reverse power flow to the grid, and voltage instability may exist. One key solution is to optimally integrate the renewable energy-based DG and battery energy storage system (BESS) in the coordination of demand response (DR). This paper proposes a multilevel particle swarm optimization technique to synchronize the distributed energy resources (DER) and DR in the DN. The proposed approach is implemented on the IEEE 33 bus system energized by coal power plant (CPP). The first level of optimization finds the sizes and locations of DER (DG and BESS), and the next level determines the optimal power dispatch in the coordination of DR. The outcomes of this framework exhibit effectiveness in the optimal utilization of renewable energy resources and the enhancement of power quality parameters in DN so that CO2 emissions are reduced by 32.71% from CPP. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has caused fluctuations in electricity demand, altering people's lifestyles and electricity usage patterns, thereby affecting the accuracy of demand predictions. However, existing studies on electricity forecasting have not adequately considered the incorporation of COVID-19-related features and the analysis of electricity usage characteristics across different regions of the UK. Therefore, this paper, based on data of the UK's national electricity demand, conducts an analysis around the scenario of a large-scale health emergency in society. We explore the changing patterns and regional characteristics of electricity consumption during the COVID-19 pandemic, comparing the forecast results before and during the pandemic to illustrate its impact on the UK's electricity demand. By introducing COVID-19-related features into the models, we compare the forecast results before and after their inclusion. The results indicate that the COVID-19 pandemic has had a certain impact on the electricity prediction in the UK, leading to a 22.8% decrease in prediction accuracy. However, the models' correlation improved with the inclusion of COVID-19-related features, resulting in a 13.2% enhancement in prediction accuracy compared to the previous models. Additionally, the study summarizes other factors influencing electricity demand, such as power imports/exports and clean energy usage, as considerations for electricity distribution planning. This contributes to improving the accuracy of predicting the UK's electricity demand during COVID-19 pandemic, enabling the government to adjust power dispatching plans reasonably based on relevant factors, achieving rational distribution and efficient scheduling of power resources. [ABSTRACT FROM AUTHOR]

Unintentional islanding, defined as the inadvertent separation of distributed generators from the main grid, is a significant issue for wind power and synchronous-based microgrid. Islanding events must be detected to ensure safety and protect utility workers and equipment. The improper detection islanding may lead to power quality degradation, grid protection interference, and out-of-phase reclosing. To overcome these issues, researchers developed the islanding detection technique. This paper proposes a method, for accurately discerning between instances of islanding and those that are not islanding-related events, that employs two new criteria: (1) superimposed positive sequence impedance (SPSI) and (2) superimposed negative sequence impedance (SNSI). The islanding detection indices (IDI1 and IDI2) are calculated using the SPSI and SNSI. If the values of IDI1 and IDI2 exceed a predefined threshold, islanding is detected. The performance of the proposed approach is tested using the MATLAB/Simulink platform. The simulation results show that the proposed technique detects islanding in 20 ms at nearly zero power mismatch and under balanced conditions. Therefore, it limits unwanted tripping caused by various types of non-islanding events. The proposed technique is compared with existing schemes to demonstrate its superiority. [ABSTRACT FROM AUTHOR]

Renewable energy sources (RES) are currently being substantially integrated into the power grid because of the recent liberalization of the energy market and associated benefits to both the environment and the economy. A significant amount of renewable energy sources are stochastic and weather dependent, being integrated into the power system that causes a mismatch between the generation of power and the load demand, which creates major issues like variations in frequency and power transmission between the regions. So it is necessary to maintain the frequency and tie-line power variations within a defined range. This is known as automatic generation control (AGC) or load frequency control (LFC). This paper uses brown bear optimization algorithm (BOA) to tune the PID and cascaded PI-PDN controllers for LFC of the renewable integrated two-area power system. The ITAE objective function is utilized to obtain optimal gains of the PID controller. The performance of LFC with BOA-tuned PID controller is compared with other optimization techniques named grey wolf optimization technique (GWO) and particle swarm optimization (PSO) on a RES integrated two-area power system under various scenarios. Further, the effectiveness of the BOA is verified by comparing performance of the PID and cascaded PI-PDN controllers. According to simulation results, BOA offers superior performance in comparison with the GWO and PSO technique. [ABSTRACT FROM AUTHOR]

This article proposed the architecture of a stand-alone photovoltaic connected system (SPVS) with energy storage. An SPVS with energy storage requires power management for various operating modes. A coordinate controller is often necessary to manage the change in control architecture depending on the operating mode. This proposed system contains a boost converter (BC) and a bidirectional switched quasi-Z-source DC-DC converter (BSQZSDC). The BC is used for two purposes. The first is extracting the maximum power from the PV. The second is to control the DC link voltage when the SPVS is not in MPPT mode; dual-loop PI controllers are used for this. The BSQZSDC is used for energy management and regulates DC link voltage when SPVS operates in MPPT mode. This paper addresses about the effective control approach that BSQZSDC used, i.e., the double-integral sliding mode control, to regulate the DC link voltage and battery current in various operating modes. Experimental results were carried out to depict that the controller reasonably achieves the control objectives and proper energy management. [ABSTRACT FROM AUTHOR]

Lithium-Ion battery powered electric vehicles (EVs) offer many benefits, such as having high energy efficiency, requiring lower maintenance, and being cheaper to run. Besides, they play a crucial role in decarbonization. These advantages will probably make it an indispensable option for both drivers and governments in the future decades. However, the bottleneck of them is the batteries. In particular, accurate estimation of state of charge (SoC) of batteries, which refers to the remaining driving range, is one of the most notable challenges for EVs. With this in mind, in this paper, a novel Kalman filter-based estimation method is proposed to increase the accuracy of the SoC. The proposed method considers not only the current but also the previous covariance matrices since abrupt changes in the nonlinear dynamics of the battery may lead to incorrect estimation. Consequently, smoother state transitions are provided, and more accurate SoC estimation is possible. The improved method is supported by the 2-RC Thevenin equivalent circuit model, whose parameters are described as a function of the SoC and temperature. The battery model and proposed method are tested with three different driving cycles to prove the efficiency. According to the results, the proposed method can minimize the RMSE of SoC estimation up to at least 10% and provides better SoC estimations for compact EVs. [ABSTRACT FROM AUTHOR]

The drawbacks of the "take-or-pay" conception led to the search for alternatives to mitigate its effects. This paper proposes an intelligent multistage control system consisting of two-phased to control the energy sources' operation in a connected-mode microgrid consisting of renewable energy sources and a diesel generator. The first phase is the forecasting process to predict the day's required fuel amount, thus dispensing with the take-or-pay method that forces the purchase of redundant quantities of fuel periodically. This stage depends on the deep neural network long short-term memory method. The second phase is the energy management to find optimum energy source scheduling relying on a model-free strategy using reinforcement learning to achieve minimum energy cost consumption. The proposed methodology is verified using improved PSO. Simulations and theoretical calculations reveal that the proposed scheme is very successful at decreasing energy consumption costs, in addition to meeting the preferences of users. [ABSTRACT FROM AUTHOR]

This paper provides a high-accuracy assessment of domestic demand-side management (DSM) approach in the context of distributed renewable energy sources (RES). To determine the potential of domestic DSM for households, a microgrid model of a typical UK residential estate was developed to simulate the impact of RES. The microgrid model comprises 15 UK households with appropriate allocation of washing machines, tumble dryers and dishwashers in accordance with the statistical data. In order to obtain a high-accuracy result, the power consumption of the microgrid model utilises real historical high-resolution data of household energy consumption and RES generation. Thereafter, 40% of distributed wind and solar energy is implemented in the model to produce two individual scenarios. The operation of the white appliances in the model is controlled using a domestic DSM based on a load shifting algorithm. The primary criterion of the DSM considered in this paper is the reduction in energy feedback to the grid in order to decrease the utilisation of the grid and to reduce the transmission losses. The results obtained from the model simulation are compared to the baseline model and discussed with respect to the possible benefits of implementation of domestic DSM under the impact of RES. It has been shown that the self-consumption ratio of the microgrid operating under the DSM is increased by 3% for both scenarios. The model analysis provides highly realistic results which can be used for efficiency assessment of various load shifting methods. [ABSTRACT FROM AUTHOR]

The forecast of electricity demand in recent years is becoming increasingly relevant because of market deregulation and the introduction of renewable resources. To meet the emerging challenges, advanced intelligent models are built to ensure precise power forecasts for multi-time horizons. The use of intelligent forecasting algorithms is a key feature of smart grids and an effective tool of resolving uncertainty for better cost and energy efficiency decisions like scheduling the generations, reliability and power optimization of the system, and economic smart grid operations. However, prediction accuracy in forecasting algorithms is highly demanded since many important activities of power operators like load dispatch depend upon the short-term forecast. This paper proposes a model for the estimation of the consumption of electricity in Agartala, Tripura in India, which can accurately predict the next 24 h of load with and estimation of load for 1 week to 1 month. A number of specific characteristics in the city have been analysed in order to extract variables that could affect the pattern of electricity consumption directly. In addition, the present paper shows the way to significantly improve the accuracy of the prediction through ensemble machine learning process. We demonstrated the performance of individual Random forest and XGBoost along with their ensemble. The RF and XGBoost ensemble obtained an accuracy with an improvement of 15–29%. The analyses or findings also provide interesting results in connection with energy consumption. [ABSTRACT FROM AUTHOR]

Intelligent load scheduling for the smart city is essential due to the growth of industrialization, which directly consumes energy from the grid. The energy management system in industrialization is enhanced using the Internet of things in the same domain. The general need in a smart city is to maintain the energy utilization and cost that the industrial Internet of things could achieve for industry application. Researchers have also found a solution to this problem, but every time the generator cannot sell energy, the industry purchases the same. There is a chance for the industry to supply its energy to other industries or the grid. By keeping this constraint, an intelligent approach to load scheduling based on optimized Q-Net is proposed in this paper, comprising the supplies as renewable energy, battery, and industrial energy. Also, the reactive power flow of smart cities during scheduling is mitigated by classifying the energy available in the bus network. The proposed method scheduled demand power with 99% accuracy with the lowest cost of 13.7041$/KW for peak hr. scheduling with a demand of 6000KW/hr. [ABSTRACT FROM AUTHOR]

In this paper, an optimization approach is formulated to determine the optimal power split in a proton exchange membrane fuel cell-battery-hybrid energy system (PEMFC-battery-HES) to meet the dynamically varying locomotive power demand. The optimization approach aims to determine the power references for the PEMFC at each loading point of the locomotive power demand with the objective function of minimization of the PEMFC fuel consumption over the driving period. The constraints of optimization include the instantaneous power balance between the source and the load, the limits on the variation of battery state-of-charge and PEMFC output power, and the durability constraints in terms of the maximum rate of change in PEMFC output power, and maximum allowable degradation in the state-of-health of the battery over a specified planning period. The optimization model is solved using the CONOPT solver of General Algebraic Modeling System. The performance of the proposed energy management optimization approach is tested through comparison with some of the already available approaches for different drive cycle scenarios. Simulation results confirm that the proposed approach results in 1–5.5% reduction in the PEMFC fuel consumption with optimum utilization of the capacities of the energy sources. [ABSTRACT FROM AUTHOR]

Today, microgrids are used increasingly in different types because of its several financial and environmental benefits for customers, societies and nations. Its implementation, however, makes significant theoretical and practical challenges, such as fault detection that could make crucial damage to the utility grid and microgrids. This paper, accordingly, developed a novel, fast and accurate method based on Support Vector Machines (SVM) approach for High Impedance Fault (HIF) detection. The proposed method applied to a typical distributed generation system for detecting single line, double line and triple line HIFs. Also, the behavior of current signals of the other phases are investigated during faults occurrence. The simulation results show how this algorithm can separate faults condition among the other fault-like phenomena and gets a better response in comparison to present methods like Wavelet Transformation (WT) and Artificial Neural Networks (ANN). This method will boost the development of renewable energy usage by reducing fault detection delays and operational risks. [ABSTRACT FROM AUTHOR]

Increasing the penetration level (PL) of renewable energy in the distribution power grids has the potential of fulfilling growing energy demands and curtailing fossil fuel usage along with its economic and technical benefits. Nevertheless, the quantity of electricity supplied to customers cannot be permitted to take precedence over its quality. This is because the high penetration of the renewable-based power generation comes at the cost of power quality deterioration, especially at the point of common coupling (PCC), owing to nonlinear characteristics of renewable-based distributed generation (DG) systems and pre-contamination of modern distribution grids. So, the role of power quality (PQ) improvement technologies inevitably comes into the picture when the required level of renewable energy penetration is constrained by any of the grid PQ performance indices. Passive power filters (PPFs) have proven to be a very successful solution for increasing the harmonic-constrained penetration level (HC-PL) of renewable energy in power systems. However, there is a scarcity of information on how to choose between various passive filters to meet high penetration objective. The purpose of this paper is to look into the problem of filter type selection. The designing of different PPF types is formulated as an optimization problem with an objective to enhance the penetration of a renewable DG system in a distorted distribution power grid and solved by the firefly algorithm (FA). A comprehensive assessment of common PPF types, in terms of effectiveness in increasing renewable energy penetration in a harmonically polluted distribution grid along with their economic aspects, is performed. The simulation results show that the filters under consideration can be ranked from best to worst as composite type, third-order damped, C-type, second-order damped and single-tuned (ST) ones in terms of their performance on the percentage penetration level enhancement of renewable energy. While in terms of per-unit cost, the highest percentage enhancement in penetration is obtained by the ST filter. [ABSTRACT FROM AUTHOR]

This paper presents a photovoltaic (PV)-interfaced dual-drive hybrid electric vehicle (PV-DDHEV) based on dual boost three-port DC–DC converter to increase the fuel efficiency of the electric vehicle by increasing the conversion efficiency of the converters. HEV designed with the photovoltaic (PV) power system requires a DC–DC converter to enhance the PV array output voltage to the level demanded by the drive inverters. Normally, the high gain of DC conversion is obtained by connecting two or more DC–DC converters in series with each other. The series connection of the converters reduces the conversion efficiency and increases the cost of the unit. In this paper, a three-port DC–DC converter designed with a two-winding high-frequency transformer is proposed to offer high voltage gain with reduced number of active and passive elements. In addition to this, the power management algorithm incorporated with the proposed PV-DDHEV reduces the power consumption of the drive motor by estimating the drive torque requirement. The MATLAB simulink and experimental prototype results are presented to validate the proposed PV-DDHEV. [ABSTRACT FROM AUTHOR]

A large number of power transactions are possible in competitive power systems due to the open-access transmission networks and the wide deployment of distributed generations (DGs). Such power transactions render system operations complex and create security, reliability and congestion problems in transmission networks. In the perspective of future energy demands as well as in the assessment of secure and economically profitable power market models, the placement of DGs and the selection of optimal and feasible transactions are of great importance. In this paper, a market dispatch model is introduced for the optimal dispatch of multilateral transactions with minimization of generation cost and transmission cost of transactions. The loading capability factor is proposed for the identification of suitable locations for the placement of DGs. Zones and their load buses corresponding to each DG unit and each combination of two DG units are identified based on the loading capabilities of the load buses. The performance of the proposed dispatch model is validated using a modified IEEE 30-bus system. The proposed approach efficiently mitigates congestion and enhances both the economic benefits and the power transfer capability of the system. [ABSTRACT FROM AUTHOR]

Integration of renewable energy resources (RERs) along with demand-side management (DSM) is almost inevitable in the present scenario to meet the growing energy demand with minimum environmental pollution. This paper proposes a combined model of dynamic economic and emission dispatch (DEED) and DSM with the integration of renewable energy resources (RERs). In this analysis, the DSM load shifting scheme is incorporated with the DEED problem to obtain the generation-side operational benefits as well as the reduction in environmental pollution level. In this study, various smart home appliances and their complex constraints are included in the DSM load shifting process. The variability and stochastic nature of the load demand and RERs such as solar and wind are modeled with Normal, Beta, and Weibull distribution functions, respectively. The proposed model is implemented in both deterministic and stochastic approaches with the help of non-dominated sorting genetic algorithm (NSGA-II) and Monte Carlo Simulation (MCS) approach. In the stochastic model, the MCS approach appropriately handles the uncertainties of system load demand and RERs. Four different case studies are carried out in the simulation analysis to show the impacts of RERs and DSM integration on the traditional DEED problem. [ABSTRACT FROM AUTHOR]

The control strategy is an essential element in the improvement of electric motor energy efficiency through flux adaptation, according to the operating conditions. Therefore, the goal of this paper is to present an energy-efficient field-oriented control structure for squirrel-cage induction motors (IM), improving their energy efficiency and taking into consideration the core loss effect. For this purpose, optimal flux is computed in off-line calculations based on an improved IM dynamic model including core losses. These optimal values are stored in a torque-speed look-up table used to provide the flux reference of the control structure. Simulation and experimental results validate the proposed optimized field-oriented control showing the improvement in energy efficiency and the reduction in power losses. Experimental tests also confirm the improvement of the proposed method, compared to an industrial optimization technique and a state-of-art approach. [ABSTRACT FROM AUTHOR]

This paper presents a generalized structure of a new three-phase multilevel inverter (MLI) which ensures better performance with the minimum number of components for different applications including DC power supply-based renewable energy sources. The proposed MLI topology is developed in the form of several basic blocks which are individually made of a DC voltage source and a semiconductor switch. All the balanced DC sources required for the proposed inverter are made through a high-frequency magnetic link. The single–phase and three-phase five level (5-L) inverters with the rating of 3.5 and 100 kVA, respectively, are simulated, while a five level (5-L) single-phase inverter is implemented for demonstrating the switching strategy and different operational modes. The rigorous calculations are presented to determine switching losses and conduction losses which are minimum because only three conducting switches are responsible for generating each voltage level during every operating mode. The structure of the aforementioned inverter provides optimized value of total standing voltage (TSV = 5.5Vdc with 5-L single-phase leg) of the system. The proposed inverter offers energy conversion efficiency of 94.84% for 5-L single-phase leg which is significantly enhanced and it also minimizes the equipment ratings as well as overall costs of the entire system. [ABSTRACT FROM AUTHOR]

The Republic of Serbia must make significant efforts to promote and exploit renewable energy sources and increase energy efficiency in all energy sectors to ensure energy security and economic competitiveness, reduce the negative impact on the environment from energy production and use, and contribute to global efforts to reduce greenhouse gases. Within the paper several issues of integration of recently realized CHP plant are introduced and discussed. Firstly, the legal and energy policy issues in the Republic of Serbia regarding connecting CHP to the grid are presented. The challenges and technical solutions for CHP connection to the grid, as well as power quality issues and the role of the CHP plant during the restoration of power supply during the maintenance of the substation and unplanned loss of high voltage supply, are presented and discussed. Finally, the impact of prospective massive integration of CHP on the energy balance and CO2 emission reduction in the province of Vojvodina in Serbia is investigated and discussed. Since it is the first CHP plant realized in Serbia, it is crucial that experience be shared to all potential stakeholders in the future energy efficiency projects. [ABSTRACT FROM AUTHOR]

Accurate estimation of wind speed distributions is a challenging task in wind power planning and operation. The selection of convenient functions for describing wind speed distribution is a crucial requisite. In this paper, remarkable bi-parameter Weibull function is presented to estimate the wind energy potential. Weibull parameters based on different six estimation methods, namely graphical, method of moment, energy pattern factor, mean standard deviation, power density methods, and genetic algorithm are evaluated. Besides, the goodness of fit of the estimation methods is investigated via mean absolute error, root mean square error, normalized mean absolute error, Chi-square error, and regression coefficient. To plainly identify the best matching estimation method, Net Fitness test is also presented. Catalca in the Marmara region in Istanbul, Republic of Turkey, is selected to be the underlying site. The experimental results show the effectiveness of the estimation methods in modeling wind distribution but with relatively small differences in terms of performance. However, the genetic algorithm and energy pattern factor accomplish the best and worst matching estimation methods, respectively. [ABSTRACT FROM AUTHOR]

The paper presents the development of a downscaled hardware-in-the-loop (HiL) setup for a wave energy converter heaving point absorber. HiL simulation involves the buoy motion coupled to a gearbox system that converts the buoy's vertical movement into rotational. The dynamic movement of the heaving buoy is simulated and converted into rotation. A process is employed to determine the resultant torque based on the generator's rotational speed. The use of an online derivative technique allows the system to implement an inertia emulation. Consequently, without modifying physical components, it is possible to change the system dimension. The demanded torque is then applied using an electric machine torque control loop. The technique is developed for real-time implementation and allows the reproduction of different wave conditions. Froude's law is used to determine the scale down factor considering the machine limitations and the wave conditions. Experimental results validate the methodology. [ABSTRACT FROM AUTHOR]

Operating complementary metal oxide semiconductor (CMOS) circuits in subthreshold mode of operation is proved to be energy efficient for many applications ranging from biomedical implantable devices to wireless sensors which require ultra-low power consumption. In particular, operating CMOS circuits in subthreshold mode minimizes the energy per operation which has been widely considered as the metric for measuring the power efficiency of the circuit. In this paper, the value of the supply voltage that minimizes the energy per operation in CMOS circuits is determined analytically considering the activity factor, the logic depth and the technology parameters. In addition, another analytical closed-form model is developed to predict the least value of the supply voltage for CMOS circuit to function properly. The developed models target the nano-regime CMOS technology. The simplicity of these models provides a good insight into the factors that affect the subthreshold operation. Despite their simplicity, the produced models show a very good agreement with the simulation results considering 16-nm PTM HP process parameters and different CMOS circuits. [ABSTRACT FROM AUTHOR]

Encouraged by the increasing electric energy consumption, the distributed generation has been widely included in the electrical power system. However, renewable energy resources have intermittent characteristics. When the distributed generation is high at certain times of the day, the energy generation at distribution systems can become higher than the required power. The surplus energy is returned to the transmission system, giving rise to active distribution networks. This paper proposes an approach to optimally managing the existing distributed generation and voltage control devices. The management is performed by solving an optimal power flow problem, aiming to find an operating condition for the system in which the losses are minimized, and the voltage profiles are improved. A combined transmission and distribution system is proposed. Some scenarios are considered to assess the impact of distributed generation insertion and the contribution of these sources in support of reactive power. The results indicate the need for adequate active distribution networks management, which depends on the distribution generation insertion level and the generation sources' power factor. [ABSTRACT FROM AUTHOR]

The current trend puts emphasis on the efficiency of the all electronics devices. For this reason, it is necessary to maximize the efficiency of the wireless energy transfer system as well as on other electronic devices. Therefore, this article deals with the most suitable working point analysis of the serial-serial WET system. The core of the article contains the simulation analysis at the various loads and various coupling coefficients. Experimental measurements were made on the existing prototype and the results were compared with the results from the simulation analysis. The main outcome of this paper is thus detailed investigation of operational point of WET system in order to meet the highest possible system efficiency together with the highest possible level of transferred power. [ABSTRACT FROM AUTHOR]

The electricity is generated in a traditional way extensively using diesel in off-grid areas. However, it increases the consumption of fossil fuels and carbon emissions and therefore it is essential to find alternative solutions to help reaching sustainability goals. The case study is Stewart Island, where the electricity is provided by a distribution network powered by up to five diesel generators at a central diesel power station. Local residents believe that reducing the consumption of diesel and having a renewable source of electricity generation are two of the island's highest priorities. HOMER PRO software is used to compare current diesel generators system with a hybrid system merging a tidal energy source (predictable) with wind (unpredictable) and diesel (back-up), through a microgrid. Using two wind and four tidal turbines, plus one diesel generator for back-up, produces the highest renewable fraction (61.1%) and reduces the fuel consumption and green gases emissions (GHGs) 199,176 l and 534,061 kg/yr, respectively, in comparison with using diesel station. Its levelized cost at 21 c/kWh is also more cost-effective rather than 23 c/kWh in the present scenario. [ABSTRACT FROM AUTHOR]

In this paper, optimal power flow problem has been implemented and evaluated simultaneously with the emergency demand response program (EDRP), considering the economic and environmental aspects, as well as the voltage stability enhancement of power system. Moreover, employing DRPs is a way to control customers' electric energy consumption and to improve network reliability. The EDRP is one of the various demand response programs that provides incentives for participants in the program to reduce their consumption during peak hours or shift to off-peak hours. In order to solve the multi-objective function and achieve optimal compromise, the multi-objective Non-Dominated Sorting Genetic Algorithm II was used in this study. The proposed model was implemented on a 30-bus IEEE system. The results show that the proposed model is very effective in reducing fuel costs, emission and improving voltage stability. [ABSTRACT FROM AUTHOR]

Rotational speed sensor is one of the most important components used in wind energy conversion system control strategies. The latter is very expensive and may be faulty due to the harsh environment working, causing by that a low efficiency operation of the system. The use of a speed estimator or an observer may be a good alternative solution for the use either in sensorless control or detecting the sensor failure or degradation (FDI and FTC), provided that the observer is robust and ensures high rotational speed estimation accuracy. This paper presents a comprehensive study to solve the optimization problem of a model reference adaptive speed (MRAS) observer in a typical wind energy generation system based on permanent magnet synchronous generator using five adaptation mechanisms: The first one is the classical MRAS observer; it is based on proportional–integral (PI) controller. The second one uses a fuzzy logic controller (FLC) with two inputs. The third one uses the single-input fuzzy logic controller which represents the simplification of the conventional FLC. The fourth one uses the sliding mode controller, and the last one uses the super-twisting algorithm. A detailed comparison between the five adaptation mechanisms is carried out. The obtained results show a good estimation stability as well as a fast speed estimation at dynamic regime for the improved adaptation mechanisms compared with the conventional PI controller. [ABSTRACT FROM AUTHOR]

Nowadays, by increasing the amount of greenhouse gases (GHGs) emitted from electricity generation sector, substantial challenges will be forced to the power system scheduling problems. However, under the smart environment, the amount of GHGs can be declined by handling the consumers' demand. In this regard, this paper presents a two-stage framework concentrating on demand side management including energy efficiency programs and demand response programs as significant aspects of smart power system to handle system expenditures as well as pollutants. Therefore, in the first stage, investment rate on energy efficiency is specified over the midterm horizon time, and in the second one, a formulation of cost-and-emission-based generation scheduling in the presence of demand side management programs has been performed. Finally, a novel index the so-called emission mitigation index is nominated to investigate the impacts of demand side management on evaluation of GHGs emissions' level. The IEEE 10 unit standard test system is conducted to evaluate the capability of demand side management in reduction of GHGs emissions and financial burden. Results indicate that by efficient utilization of demand side management programs, significant improvement is obtained. [ABSTRACT FROM AUTHOR]

The increasing complexity of modern power systems due to the integration of prosumers, renewable energy sources, and energy storage, has significantly complicated system organization and planning. Traditional centralized power plants are being replaced by decentralized structures, making the power flow more complex to predict. As a result, alternative methodologies for power system planning are imminent. This paper introduces a novel approach using a combination of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models for forecasting system states. Here, ANN model predicts energy consumption, while the ANFIS model forecasts thermal and hydro power plant production as well as CO2 emissions. The accuracy of these models results from leveraging the collective expertise of power system planning professionals, utilizing extensive databases containing hourly data from measurements in Serbian power systems. These datasets encompass hourly production data from various energy sources, energy consumption patterns, and relevant environmental parameters (such as temperature, wind speed, and solar irradiation). To underscore the effectiveness of the proposed ANN model, predictions of power consumption from ANN are compared with predictions from ARIMA (autoregressive integrated moving average) model. The developed forecasting models are employed to predict annual and daily energy consumption, seasonal variations in thermal and hydro production, and annual CO2 emissions. The dependencies between power consumption/production and ambient parameters are visually depicted in three-dimensional representations. Model accuracy is evaluated through graphical, numerical, and error-based analyses across four distinct error metrics. By utilizing historical data and expert insights from previous production scheduling, these models enhance the precision of future production scheduling decisions. This approach minimizes human error, maximizes the utilization of human expertise, and establishes a framework for effectively planning large-scale power systems. The primary contribution of this research lies in the integration of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) methodologies. This combined approach minimizes the errors inherent in each individual methodology while leveraging their respective advantages. Specifically, the consumption prediction error achieved is 5.64%. When ANFIS is utilized with a training database based on ANN consumption prediction, the prediction error for CO2 emissions is 1.27%. [ABSTRACT FROM AUTHOR]

This article addresses the pressing issue of reducing heat energy and conserving energy resources by exploring new energy sources, specifically wind and photovoltaic electricity generation in distribution systems. The study highlights the expanding use of electric devices in apartment houses, particularly the adoption of DC smart homes. A key highlight is the development of a smart grid optimization method to tackle power fluctuations, a critical challenge in integrating renewable energy sources and loads into the power system. The paper proposes controllable distributed loads, like batteries and heating pumps, to balance energy production and demand, forming an efficient network with a solar collector and photovoltaic generator. A breakthrough is identifying the best performance for a group of DC smart houses in the network, paving the way for potential future advancements that benefit consumers. The concept of a smart network with controllable loads in smart homes is thoroughly investigated, offering valuable insights into its potential benefits. Simulation results validate the system’s effectiveness, providing high-quality electricity, cost reduction, and improved network stability while mitigating greenhouse gas effects, contributing to sustainability efforts. The study’s findings are reinforced by simulations using MATLAB, further validating the novel approach’s practical implications for real-world application. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative demand response energy management system tailored for smart homes, aimed at optimizing appliance usage in real time. The system considers dynamic pricing tariffs, device characteristics, usage patterns and user behavior to achieve efficient energy management. Unlike conventional systems, the proposed approach integrates a novel fuzzy logic-based pricing system that combines real-time pricing, multi-time pricing and load-dependent inclining block rate coefficients. This integration enhances cost reduction effectiveness for both homeowners and grid operators. Furthermore, appliance runtime optimization is achieved through linear programming, enhancing consumer behavior and domestic energy efficiency. By merging mathematical optimization methods with AI-enabled smart pricing coefficients, practical applications in real-world energy management scenarios are demonstrated. Moreover, a user-friendly interface is designed to facilitate real-time multitasking optimization steps using MATLAB, thus advancing the application of Internet of things (IoT) beyond data storage and communication to include intelligent real-time optimizations. The effectiveness of the proposed system is evaluated in various usage scenarios, including an analysis of the impact of comfort parameters and user behaviors. Additionally, savings effectiveness is compared with existing pricing systems. Results show that the proposed system optimizes energy usage effectively, leading to significant cost savings for consumers and improved grid management for operators. The analysis highlights the system’s adaptability to various usage scenarios and its potential to enhance user comfort and energy efficiency, thus presenting a robust solution for demand response in residential settings. [ABSTRACT FROM AUTHOR]

In order to improve energy efficiency, reduce dependence on fossil fuels, and enhance the sustainable development capability of the power system, this paper proposes a virtual grid and thermal power joint peak shaving optimization model based on the power Internet of Things. Establish an objective function to optimize the charging and discharging loss cost of energy storage equipment, the interruption compensation cost of interruptible loads, and the operating cost of thermal power units, in order to improve the optimization scheduling effect of the power system. Set power balance constraints, interruptible load constraints, energy storage constraints, and power constraints for thermal power units to maximize their operational efficiency, and construct a virtual grid and thermal power joint peak shaving optimization model. Using ant colony algorithm to solve the model and obtain the optimal peak shaving value that meets the development needs of the power system. The simulation results show that the mean r2 of our method is 0.97, and the average RMSE is 0.17. It has been proven that the model has good joint shaving optimization ability and high optimization accuracy. It can ensure the output of thermal power units and lower the peak shaving pressure of the power system, thereby promoting the sustainable development and stable operation of the power system. [ABSTRACT FROM AUTHOR]

Frequency stability, as one of the most important issues in the modern power grids, requires more efficient control methods due to the increasing complexity of the power system, high penetration of distributed generation sources as well as high electrical energy consumption. The challenges become more critical in the case of islanded microgrids (MGs), due to existing no traditional ancillary services of the upstream electric power network. Thus, the modern power grids, such as MGs, need advanced regulation methods to keep the generation-consumption balancing. Demand response (DR) is the recently introduced control approach which guarantees continuous contribution of controllable loads in the system frequency control. In this paper, a new online droop-based DR, generalized droop control (GDC), is introduced to apply in islanded MGs frequency control. An artificial neural network is used for online tuning of droop coefficients in the presented GDC framework. The proposed control approach changes controllable active and reactive loads, using a set of equations based on satisfying dynamics. To evaluate the effectiveness of the proposed control method, several scenarios are simulated in which changes of the system frequency and voltage are studied. Results show significant damping of power–frequency fluctuation and a desirable performance of the closed-loop system. [ABSTRACT FROM AUTHOR]

An experimental investigation of a magneto hydrodynamics power generator using a low-melting-point Gallium alloy at high Reynolds numbers is presented in this paper. The power output is found to increase with increasing the flow Reynolds number. By considering the non-uniform distribution of the magnetic field in the test channel, theoretical evaluation gives a more accurate prediction to the experimental data. The present generator demonstrates the maximum power generation efficiency of $$8.3\times 10^{-4}$$ % and the maximum output electric power of 1.5 mW. [ABSTRACT FROM AUTHOR]

Abstract  This paper describes the Differential Pass Transistor Pulsed Latch (DPTPL) which enhances D-Q delay and reduces power consumption using NMOS pass transistors and feedback PMOS transistors. The proposed flip-flop uses the characteristic of stronger drivability of NMOS transistor than that of transmission gate if the sum of total transistor width is the same. Positive feedback PMOS transistors enhance the speed of the latch as well as guarantee the full swing of internal nodes. Also, the power consumption of the proposed pulsed latch is reduced significantly due to the reduced clock load and smaller total transistor width compared to conventional differential flip-flops. DPTPL reduces E  D by 45.5% over ep-SFF. The simulations were performed in a 0.13 um CMOS technology at 1.2 V supply voltage with 1.25 GHz clock frequency. [ABSTRACT FROM AUTHOR]

The standard motor for variable-speed main drives is the induction motor (IM). In comparison to synchronous machines (SYM), induction motors show the disadvantage of additional ohmic losses in the rotor. Therefore, especially low-power induction motors are not able to reach energy efficiency levels as high as IE4. The transition of motor technology from induction motors to synchronous motors with ferrite excitation provides the opportunity to increase motor efficiency without using expensive rare-earth magnets. Therefore, this paper deals with the steady-state modeling, simulation and design of PM synchronous motors with ferrite excitation in respect of reaching the highest possible motor efficiency. Due to the comparatively poor magnetic parameters of the ferrite magnet, a motor design using the flux concentration effect has to be used. An analytical approach based on a magnetic network is developed to describe the steady-state operating behavior of the ferrite excited PM synchronous motor. The non-linear network equations are solved by applying the Newton-Raphson Method. Due to short calculation times and a high flexibility in respect of the design of a whole product series, the analytical approach is preferred to a numerical approach. To validate the simulation results, a prototype is designed based on a fundamental induction motor. Finally, the test of the prototype in laboratory allows a comparison between simulation and measurement as well as between ferrite excited PM synchronous motor and induction motor. [ABSTRACT FROM AUTHOR]

In the October 2009 issue of this Journal, we introduced a special pot type, which could improve the energy efficiency of induction cooking, make the boiling process faster and the cooking more comfortable. The dissipated power in the new pot was significantly higher than in the traditional ones at any inductor current. In this paper we show another pot type with similar advantages, but having a simpler structure. The main model parameters were the same as in our previous work: we used the same material properties, including the B-H curve of the ferromagnetic pot material, the same arrangement of the coil-pot system, and the same formula for calculating the hysteresis loss. We applied similar inductor current with $$f = 25~\hbox {kHz}$$ frequency in the finite element analyses, but instead of 2D, we had to use 3D models because of the pot structure. The non-linear magnetization curve necessitated the use of transient simulations, and we calculated the steady-state rms results from the exact time variations of the field quantities. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-carrier energy system or energy hub in which natural gas and electricity resources are used as inputs. The output demand profile includes electrical, heating and cooling energy, which is evaluated in four seasons. The significant differences in simulations and evaluation of the considered energy hub in different seasons have led to climate variation, resulting in higher electrical energy consumption in warmer seasons than thermal energy, while it is contrariwise in the cold season. When the customers are willing to participate in the demand response programs, total energy consumption decreases, and this can only change the pattern of customer consumption in the warmer seasons. A mixed-integer linear programing (MILP) formulation for this optimization problem is proposed and solved using the CPLEX solver in general algebraic modeling system (GAMS). Simulations of the energy hub system, including renewable wind and solar sources, will confirm and verify that the model provided represents a growth in energy hub profit, reducing the cost of purchased power from electricity grid as well as decreasing cost of social welfare. [ABSTRACT FROM AUTHOR]

In recent era, the reduction of greenhouse gas emission and fuel consumption is accompanied by adopting photovoltaic (PV) and wind turbine-based hybrid renewable energy sources (HRES). In nature, an intermittent characteristic of the wind speed and solar irradiation makes these sources unpredictable, and hence, energy produced by wind and PV system generates uncertain conditions in operation of microgrid. In such cases, the security and reliability of microgrid are enhanced by integration of energy storage system (ESS). This work deals with an energy management in a hybrid system incorporating PV source and permanent magnet synchronous generator (PMSG)-based wind energy system. The PV output is enhanced with a help of switched trans-quasi-Z-source (TQZS) boost converter in which cuckoo search-assisted radial basis function neural network (RBFNN) approach is used as maximum power point tracking (MPPT) technique for tracking maximum photovoltaic power. The proposed approach results in high-power tracking efficiency with reduced power loss and settling time. A battery is incorporated to address an intermittent nature of RES. Artificial neural networks (ANN), which are capable of self-learning battery dynamics, keep track of the state-of-charge (SOC) of the battery. The system thus framed is implemented using MATLAB software, and promising results are generated in terms of power management with improved efficiency of 92%, gain ratio of 1:10 and total harmonic distortion (THD) value of 2.33%, respectively. [ABSTRACT FROM AUTHOR]

To meet the energy demand and increase the reliability, DC microgrids are mainly preferred in the present era. Particularly, photovoltaic-based DC microgrids are considered due to ease of use, availability and economic operation. These DC microgrids face protection issues mainly due to absence of several parameters such as reactive power and frequency. Because of this many algorithms applied in ac networks are not valid. This study introduces a novel protection mechanism of proposed DC ring microgrid for islanding and non-islanding disturbance detection. The extracted DC signals are processed with improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) for accurate detection. An ICEEMDAN method gives the intrinsic mode functions (IMFs) from which the best IMF is chosen by sparse kurtosis (SKI). The best IMF is passed through different indices like energy, mean, variance, etc., for retrieving the data. The acquired data are input to the multi-class AdaBoost approach for classification, which recognizes faults by modifying the distribution of data and iteratively adjusting the weight of each instance. The proposed system efficacy is tested using the MATLAB/Simulink platform under various operating situations such as load variation, irradiation and fault resistance changes. The proposed algorithm superiority is demonstrated by comparing it to existing approaches using confusion matrix (CM) parameters. [ABSTRACT FROM AUTHOR]

This paper introduces a shuffled frog-leaping algorithm based method to approximate the equivalent circuit parameters of induction machines from the manufacturer data, such as nameplate data and motor performance characteristics. The steady-state equivalent circuit is applied for the simulations. The circuit parameters are found as the result for the error minimization function between the estimated and maker data. The suggested algorithm solves the parameter estimation problem and surpasses the solutions reached by differential evolution, particle swarm optimization and genetic algorithms. Therefore, this algorithm can be employed in motor energy management system for bettering the overall energy savings in industry. [ABSTRACT FROM AUTHOR]

This article presents an efficient home energy management system for residential household within a microgrid. The smart meter's database initially stores the consumer's solar generation, battery's state of charge, and appliance-level information. Both the appliance's consumption and its state of operation are gathered. The end-user's energy use habits are then investigated using this data. As a consequence, accurate data on the frequency of usage, preferred operation interval, and average power consumption of the appliances were gathered using a time-of-day schedule. The outcomes were integrated with a Competitive Price Tracking algorithm to produce a smart home energy management solution that is efficient and user-focused. In addition to providing the optimum energy management strategy for a smart home in a microgrid, the developed model planned the controllable loads by taking into account consumer comfort and a separate waiting factor for each Appliances. The suggested model is demonstrated via simulation in the Typhoon HIL Real-Time Simulator. The operating cost recorded from the simulation is 155.08 INR, 150.56 INR, and 130.54 INR for case 1, 2, and 3, respectively. However, the proposed method is compared to other method, where operating cost from NILM-based method is recorded as 155.08 INR,153.12 INR, and 142.98 INR for case 1,2, and 3, respectively, and 151.38 INR for all cases, recorded from traditional Method. [ABSTRACT FROM AUTHOR]