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This paper presents optimized energy storage and distribution network model under uncertainty of the power generation problem, based on an improved gravitational search algorithm (IGSA). Throughout the search phase, the IGSA uses a trial-and-error strategy to update the best agent. It also modifies the orbit of the poor agent in the late stages of the search and uses the coordinate descent approach to search for the optimal agent's position. A dual-stage optimization (DSO) model is employed for energy storage and uncertainty of energy generation. The hybrid IGSA-DSO used an adoptive speed inertia coefficient which ensures global and local searchability. The features of the proposed model were validated using IEEE-RTS 24 bus test system with various cases to realize the coordinated planning of the power grid and energy storage. Finally, the simulation shows interesting results of the proposed approach (IGSA-DSO), which reduces the investment cost by 0.248% comparatively.

With the rapid development of clean energy, the combined cooling and heating power (CCHP) and hybrid energy storage system (HESS) have become matured significantly. However, further optimizing the configuration of the energy supply system and adjusting the output of distributed micro-sources and energy storage units are still attractive issues. This paper focuses on the two-stage optimization strategy of the microgrid system, including CCHP and HESS. The details of the operating characteristics and mathematical models of distributed micro-sources in the system are presented. The energy storage architecture is used for mathematical modeling, and the optimization model is analyzed from the two perspectives of energy supply and demand, which explores the feasibility of improving the economic operation of the micro-energy system. The two-stage optimization model in which the first stage is to determine the optimal installation capacity of various equipment and the second stage determines the optimal operation plan of the system by obtaining the system’s capacity configuration. The simulation results show that the CCHP system can reduce the operation cost by 4.61% and 6.48% for winter and summer, respectively, also reduce fuel cost consumption by 3.01% and 3.68% for winter and summer, respectively.

Torque ripple and harmonics in permanent magnet synchronous motor (PMSM) cause negative impacts on its speed control and overall efficiency. In this article, an Elman neural network (ENN)-controlled feedback linearization-based direct torque control strategy has been proposed to mitigate torque ripples and undesirable harmonics of PMSM. In this approach, the stator flux, stator flux position, and developed torque are used to generate a switching pulse to the impedance source inverter employing ENN controller. By changing the switching pattern of the impedance source inverter through modifying the pulse width modulation using the proposed control strategy. It is possible to control the armature current of the PMSM at various orders and corresponding time harmonics under different operating conditions. The outstanding aspect of the proposed control strategy is that it boosts the inverter operation and reduces the requirement of the DC-DC converter in boosting. For evaluating the performance of the developed control strategy, an extensive simulation study has been conducted in the MATLAB/Simulink environment, and experimental analysis is carried out using the Xilinx-FPGA kit.

Discover the major issues, solutions, techniques, and applications of active electrical distribution networks with this edited resource Active Electrical Distribution Network: A Smart Approach delivers a comprehensive and insightful guide dedicated to addressing the major issues affecting an often-overlooked sector of the electrical industry: electrical distribution. The book discusses in detail a variety of challenges facing the smart electrical distribution network and presents a detailed framework to address these challenges with renewable energy integration. The book offers readers fulsome analyses of active distribution networks for smart grids, as well as active control approached for distributed generation, electric vehicle technology, smart metering systems, smart monitoring devices, smart management systems, and various storage systems. It provides a treatment of the analysis, modeling, and implementation of active electrical distribution systems and an exploration of the ways professionals and researchers from academia and industry attempt to meet the significant challenges facing them. From smart home energy management systems to approaches for the reconfiguration of active distribution networks with renewable energy integration, readers will also enjoy: •A thorough introduction to electrical distribution networks, including conventional and smart networks •An exploration of various existing issues related to the electrical distribution network •An examination of the importance of harmonics mitigation in smart distribution networks, including active filters •A treatment of reactive power compensation under smart distribution networks, including techniques like capacitor banks and smart devices •An analysis of smart distribution network reliability assessment and enhancement Perfect for professionals, scientists, technologists, developers, designers, and researchers in smart grid technologies, security, and information technology, Active Electrical Distribution Network: A Smart Approach will also earn a place in the libraries of policy and administration professionals, as well as those involved with electric utilities, electric policy development, and regulating authorities.

The hybridization of two or more energy sources into a single power station is one of the widely discussed solutions to address the demand and supply havoc generated by renewable production (wind-solar/photovoltaic (PV), heating power, and cooling power) and its energy storage issues. Hybrid energy sources work based on the complementary existence of renewable sources. The combined cooling, heating, and power (CCHP) is one of the significant systems and shows a profit from its low environmental impact, high energy efficiency, low economic investment, and sustainability in the industry. This paper presents an economic model of a microgrid (MG) system containing the CCHP system and energy storage considering the energy coupling and conversion characteristics, the effective characteristics of each microsource, and energy storage unit is proposed. The random forest regression (RFR) model was optimized by the gravitational search algorithm (GSA). The test results show that the GSA-RFR model improves prediction accuracy and reduces the generalization error. The detail of the MG network and the energy storage architecture connected to the other renewable energy sources is discussed. The mathematical formulation of energy coupling and energy flow of the MG network including wind turbines, photovoltaic (PV), CCHP system, fuel cell, and energy storage devices (batteries, cold storage, hot water tanks, and so on) are presented. The testing system has been analysed under load peak cutting and valley filling of energy utilization index, energy utilization rate, the heat pump, the natural gas consumption of the microgas turbine, and the energy storage unit. The energy efficiency costs were observed as 88.2% and 86.9% with heat pump and energy storage operation comparing with GSA-RFR-based operation costs as 93.2% and 93% in summer and winter season, respectively. The simulation results extended the rationality and economy of the proposed model.

With the conversion of the global power economy and energy structure, access to a large amount of renewable energy has led to a decrease in power system inertia. The slight abnormal disturbance in the distribution network may have a significant impact on social and economic development. Aim at enhancing power stability and system resiliency; this study focuses on the detection and location of multiple abnormal sources in the distribution network. Most traditional methods use models relying on precise line parameters, subject to poor adaptability to the distribution network with a large number of nodes, and rapidly changing topology. Therefore, this study proposes a novel random matrix model, driven by monitoring data from phasor measurement units distributed on the overhead transmission lines. In this model, linear shrinkage (LS) theory, and Marchenko-Pastur law are combined for noise reduction to ensure the dynamic character and anti-noise ability. Moreover, data dimensions and sample points may be at the same level in an extensive scale network. The LS and standard condition number rule (SCN) are used for estimating the number of abnormal sources. Finally, the effectiveness of this paper’s model is verified in PSCAD. The results indicate that the method has specific dynamic performance and anti-noise ability.

The universal power demand is increasing every day because of population growth, industrial revolution and high standards of living. The renewable energy technologies (RETs) are environmental and cost-effective solutions to satisfy dynamic load profile based on the application of multiple components of a standalone microgrid system that encompasses photovoltaic (PV), diesel generator (DG), electric storage system and wind turbine generator (WTG). This study reveals more insight into various research questions that have not been completely addressed in past investigations. The contributions of the study are expressed: (i) analyse the effects of RETs on the total cost, (ii) test the total benefits, (iii) estimate the market benefit share of the unit that reflects the benefit of one generation unit in proportion to the whole power system, (iv) maximise and minimise the yearly average power generated by (PV and WTG) and DG. This research work presents the application of fmincon optimising tool to accomplish the objectives of the study owing to some significant characteristics. The outcomes of the work show the validity of the model and can assist the utilities to minimise the costs that are identified with the optimal operation of their power systems.

This study introduces a type of solid-state transformer (SST) for solar power station design and an energy management strategy (EMS) for the SST. The purpose of this study is to design a real efficient EMS for the photovoltaic-assisted charging station in smart grid ancillary services and apply the optimal decision method. Also, the energy bound calculation (EBC) model is proposed to find the upper and lower bounds of flexible sources. Also, taking into account the EBC results and the power order from the aggregator, a charge power allocation algorithm is designed to distribute the power of flexible electric vehicles (EVs). With the help of a case study and laboratory analysis, the proposed EMS strategy is effective in real-time energy management and is suitable for practical applications. The obtained results show the stable performance in the calculation of the energy range and real-time power allocation which improves the efficiency of the photovoltaic-based charging station. Also, the SST improves the operation of charge stations for supplying the sustaining power.

Two asymmetrical embedded switched-inductor Z-source inverters (AESL-ZSI) are introduced in this paper. It divides proposed Inverters into two types; ripple input current asymmetrical embedded switched-inductor Z-source inverter (rAESL-ZSI) and continuous input current asymmetrical embedded switched-inductor Z-source inverter (cAESL-ZSI). In both topologies, insert two DC voltage sources into impedance network to attain smooth current across the sources using no further passive filter, which reduces complications of resonant considerations in the proposed system. Proposed topologies provide higher voltage gain with a lower number of components compared to similar previous works that reduce the cost and size of the inverters. In comparison with conventional switched-inductor ZSI, presented AESL-ZSI can suppress start-up inrush current that prevents damage to the devices. As a result, high voltage boost ability, continuous input current with no inrush current and embedded DC sources make the proposed inverters appropriate for renewable energy resources such as PV and fuel cells. Also, embedded DC-sources give flexible control features to the proposed inverters to attain the required output voltage. Simulation results validate the working principle and theoretical analysis of the proposed inverters.

This paper presents the design of a new single-Phase 9- and 17-level inverters with sinking the components. The 17-level inverter topology is designed by cascading the two nine-step inverters. The topology produces the complex number of output voltage levels with high reliability and reduced total harmonic distortion. We develop numerical modeling of the converters using MATLAB/Simulink block sets, and we realize experimental prototype in a laboratory-scale model using digital processor dSPACE RTI 1104. Set of experimental and numerical results are provided in this paperwork to ensure and validate the feasibility as per IEC standards. Proposed inverters are well apt for low/high voltage renewable and hybrid electric vehicle applications.

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Photovoltaic (PV) systems based on multistring configuration are the best effective solution, given its advantages in terms of system availability, reliability, and energy efficiency. In this particular configuration each substring has its own dc–dc converter and a dedicated maximum power search algorithm which increase the cost and complexity. In this article, an efficient centralized global maximum power tracking (GMPPT) algorithm for multistring PV array subject to partial shading conditions is proposed. The algorithm is based on artificial bee colony (ABC) as an optimization approach to provide the optimal duty cycles allowing the extraction of the optimal global maximum power from each substring. In particular, the proposed approach allows significant reduction of the required sensors to only one pair of current and voltage sensors, at the common point of connection of the overall PV strings. The simulation study has been carried out under Cadence/Pspice and MATLAB/Simulink platforms on the I–V curves to confirm the effectiveness of the proposed algorithm when several shading patterns occur. In addition, complex shading pattern of a daily profile has been also carried out to demonstrate the GMPPT finding in dynamically variable conditions. Performance comparison against particle swarm optimization based maximum power point tracking algorithm and the traditional perturb and observe method has also been carried out. The obtained simulation and experimental results have shown the effectiveness and a good tracking capability of the proposed ABC algorithm in a multistring PV array configuration under uniform and nonuniform irradiance.

The paper focuses on optimal operation of a smart neighbourhood with houses comprising of Photovoltaic array (PV), Electric Vehicle (EV), flexible/ non-flexible loads and Energy Storage System (ESS). EVs are also used as a power generation source apart from PV and ESS. For this, House-to-self load (H2H), House-to-Neighbour (H2N) and House-to-Grid (H2G) modes of transmission are proposed. Markov Chain model addresses uncertainties in EV availability. In this, a transition matrix is developed based on historical travel data of consumers. The minimizing energy cost is taken as the main objective. Hybrid Ant colony-Cuckoo search (ACO-CS) algorithm is proposed to optimize the charging/discharging schedule of EV to attain this objective.

This paper aims at proposing a complete mathematical analysis and derivation of the dynamic and steady-state models for current-source inverter (CSI) to be extended in the Green Energy Applications. The basic equations of the switch state functions are used to develop the mathematical model for current-source inverter (CSI). Also, the steady-state equivalent model for the adopted converter is derived and investigated by assuming the no losses operation condition to realize the target of output voltage control. Furthermore, the modulation scheme with carrier-based PWM technique is applied for the adopted converter to ensure simplicity and easy implementation. For verify the effectiveness of the presented analysis, some of the obtained results were discussed.

Nowadays, power quality becomes an essential concern in the field of power system networks due to the use of sensitive devices. The problem associated with power quality is voltage sag, damage to power equipment, product quality degradations, voltage swell, etc. But one of the severe disturbances in the power system is the problem of voltage sag. To mitigating these disturbances, the use of custom power devices or FACTS devices with the help of power electronic devices is presented. Hence an economical and proficient power device called as the Series Compensator Circuit (SCC) is employed in power distribution networks. This device helps in injecting the voltage in synchronism with the standard system voltage to compensate the voltage influences in the situation of voltage disturbance. This article deals with modelling, methodology and simulation of a Series Compensator Circuit (SCC). A simulation is modelled using MATLAB/Simulink. The simulation results are presented, which validates the functionality and the concept effectively.