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In this paper, an interleaved DC–DC step-up converter with improved characteristics based on a voltage multiplier rectifier is presented. The proposed converter is presented and analyzed for two different operating duty regions including operating region 1 (0 < D ≤ 0.5), and operating region 2 (0.5 ≤ D < 1). This converter can be used in various applications such as energy storage, electric vehicles, and renewable energy systems. This converter is composed of two stages: an interleaved boost stage and a voltage multiplier rectifier stage, which collectively forms its general structure. The interleaved boost stage is a type of two-phase boost converter that transforms the input DC voltage into a high-frequency AC square waveform. This waveform can be readily filtered using smaller capacitors. The square-shaped voltage waveform from the interleaved boost stage is rectified and converted to a high DC voltage by the Voltage Multiplier Rectifier (VMR) stage. The operating regions, the evaluation of the steady-state condition, the voltage gain of the proposed converter's parasitic and ideal models as well as its losses and efficiency analysis have been evaluated. The proposed converter has an efficiency of 97% at the output power of 150 W. The proposed converter is simulated to convert a voltage of 25–159.5 V and to validate the mathematical relationships and simulation results, a laboratory prototype has been developed. The simulation and experimental results show the precision of the performance of the proposed interleaved boost converter. [ABSTRACT FROM AUTHOR]

The optimal operation of power systems is one of the most critical issues for operators. Optimizing the operation of power systems while accommodating the increasing penetration of RES, owing to the intermittent and variable nature of these resources, stands as one of the most critical challenges for power systems. Numerous primary challenges, including fluctuations in load and nonlinear constraints, further complicate the issue, given its substantial dimensions. Therefore, this paper introduces an approach for improving the management of optimal generation and the associated carbon emissions costs of traditional power plants, which is achieved through integrating wind farms and incorporating battery energy storage. This enhancement is achieved by integrating wind farms and utilizing battery storage systems while considering the costs associated with traditional units using fossil fuels and the expenses related to carbon emissions. The simulation incorporates seven load customer types and five traditional generation sources integrated with wind farms and battery storage devices. The simulation results demonstrate that the new problem formulation has significantly improved the ability and performance of optimizing the scheduling of power plant generations. [ABSTRACT FROM AUTHOR]

A micro-grid consisting of distributed generation resources (DGRs) with a hybrid energy storage system (HESS) composed of batteries and super-capacitors was studied. A control strategy based on the particle swarm optimization (PSO) and energy management algorithms was proposed to facilitate power distribution in the micro-grid and to improve the reliability, control levels, and penetration of micro-grids in the current electrical grids. The proposed operational strategy is based on the power predicted using the load profile and power generation resources. Energy management strategies were then presented by solving a multi-objective problem by the PSO algorithm and submitting the optimization results to the fuzzy controller and power distribution management (PDM) unit. The optimizer, the PDM unit, and the fuzzy controller provide a comprehensive operating procedure for the islanded and grid-connected micro-grids, taking into account their stability against grid fluctuations. In another part of this strategy, an auxiliary power control unit (APCU) was proposed for supporting the HESS and increasing the reliable performance of this unit. The proposed structure was applied to the net power (P net) of the islanded and grid-connected micro-grids. The net power was divided into high-frequency (super-capacitor) and low-frequency (battery and APCU) components. The proposed algorithm and simulation results were analyzed using MATLAB/Simulink • The optimization unit and fuzzy controller generate optimal values for the HESS units. • The PDM unit sends control signals to storage system and APC unit. • Calculation of optimal charge and discharge ( T c & T d ) thresholds of HESS unit. • The proposed model compensates the power estimation error by the optimization unit. [ABSTRACT FROM AUTHOR]

Development in utilising the energy storage system (ESS) has led to increasing flexibility in the planning of energy networks. This study presents optimal day‐ahead scheduling for multi‐carrier energy networks in the presence of ESS. To achieve this purpose, a new economic approach for ESS is proposed that aims to utilise for generation management in the multi‐carrier networks. Also, the proposed economic approach presents a novel pricing policy that reduces the total cost of the system at each time interval. Therefore, the proposed pricing policy results in obtaining the charging and discharging pattern of ESS adaptively. To solve the multi‐period optimal energy flow problem in multi‐carrier energy networks, this study utilises the well‐known teaching‐learning based optimisation algorithm. The investigated multi‐carrier energy system consists of electrical, natural gas and district heating sub‐networks in which an ESS is included in the electrical sub‐network. The performance of the proposed approach is validated by comparing the ESS daily charging and discharging pattern and the daily load demand curve. The results show that the proposed method could be utilised for load shedding and tracking the load demand curve more effectively. [ABSTRACT FROM AUTHOR]

Natural gas is increasingly becoming the preferred choice of fuel for flexible electricity generation globally resulting in an electricity system whose reliability is progressively dependent on that of the natural gas transportation system. The cascaded relationship between the reliabilities of these system necessitates an integrated approach to planning both systems. This paper presents a chance constrained programing approach to minimize the investment cost of integrating new natural gas-fired generators, natural gas pipeline, compressors, and storage required to ensure desired confidence levels of meeting future stochastic power and natural gas demands. The proposed model also highlights the role of natural gas storage in managing short-time uncertainties in developing a long-term expansion plan for both the electric and natural gas systems. A two-stage chance constrained solution algorithm is employed in solving the mix-integer nonlinear programing optimization problem and illustrated on a standard IEEE 30 bus test system superimposes on the Belgian high-calorific gas network. [ABSTRACT FROM AUTHOR]

Highlights: [•] In this paper an expert energy management system (EEMS) is presented. [•] A power forecasting module for wind generation capacity is presented. [•] The objective functions that must be minimized are operating cost and net emission. [•] A smart energy storage system (EES) for electrochemical batteries is presented. [•] A new modified Bacterial Foraging Optimization (MBFO) algorithm is presented. [Copyright &y& Elsevier]

In this paper, dynamic voltage restorer (DVR) compensation methods are compared to each other for the load side connected shunt converter topology of z-source inverter based DVR to choose the best method. Four different topologies are recognized for DVR that two of them have energy storage devices, and two topologies have no energy storage that take energy from the grid during the period of compensation. Here the load side connected shunt converter topology that takes necessary energy from the grid is used. Pre-sag compensation, in-phase compensation, energy-optimized methods are the three DVR compensation methods that studied and compared. A deep analysis through different diagrams would show the advantages or disadvantages of each compensation method. Equations for all methods are derived and the characteristics of algorithms are compared with each other. The simulation results done by SIMULINK/ MATLAB shows compensating by this topology based on the compensation methods. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, an IEMS (intelligent energy management system) is proposed for optimal operation of a CHP-based micro-grid over a 24-hour time interval. The IEMS aims to find the optimal set points of DERs (distributed energy resources) and thermal and electrical storage systems, in such a way that the total operation cost and the net emission are minimized, simultaneously. For optimal operation of the micro-grid, a smart ESS (energy storage system) is introduced which consists of electrical and thermal storage systems. A comprehensive mathematical model for the ESS and also the economic and environmental objective functions are formulated. To solve the problem, we propose an efficient MBFO (modified Bacterial Foraging Optimization) algorithm. An interactive fuzzy satisfying method is also used to simulate the trade-off between conflicting objectives (cost and emission). To validate the performance of the proposed algorithm, we compare the MBFO algorithm with some other previously proposed analytical methods. Finally, the proposed IEMS is applied to a micro-grid which consists of various DERs, CHP (combined heat and power) system, ESS and thermal and electrical loads. The results show that the proposed IEMS can smartly cover the total electrical and thermal load demands with respect to economic and environmental criteria. [Copyright &y& Elsevier]

• Achieving the independence of the voltage of photovoltaic panels in the downstream network (DC) by using the DMPPT algorithm and battery and converter topology. • Voltage and power control in the upstream network (AC) by considering the SOC limitations/ weight factors of each storage. • Multilevel control by using hybrid energy storage units and considering voltage fluctuations (local and global voltages) and Droop factor/curve. • Applying the droop control structure and synchronization of injected power of HESS based on SOC of each hybrid energy storages. This paper presents a multilevel control method in two layers of micro-grids. Downstream grid (Battery-Converter Structure utilizing the distributed maximum power point tracking (DMPPT) for photovoltaic (PV) systems) and upstream grid (distributed generation (DG) sources with hybrid energy storage system (HESS)). In the downstream grid, each PV includes a DC/DC battery-converter with DMPPT. DMPPT converters can be used for voltage amplification and maintaining a constant PV voltage in the event of partial shading. To control the power and voltage in the upstream grid, hybrid energy storage systems (HESSs) distributed among AC/DC micro-grids are introduced. Each storage unit has two basic tasks. First, it controls the voltage of the node which as storage unit is connected (local node). Second, it controls the node identified as the global reference node on the grid. The output power of the HESS is controlled in such a way that the node voltage fluctuates in a predefined permissible range. This is due to fluctuations like load changes, reduced power of DGs or power constraints in DGs. To determine the power reference for each HESS, the state of charge (SoC) is considered matched to the dead-band droop control. Simulations were performed by MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

• Calculating the CCT in each step of simulation considering network preserving model. • Proposing an algorithm for considering contribution of the generator during fault. • Calculating PDF of CCT based on recursive formulation during fault. • Calculating the PDF of CCT for transient stability of the whole power grid. • Proposing new objective function for optimal location of energy storage system. Due to the high penetration of renewable energy resources leading to the enhancement of uncertainty, the probabilistic assessment of the transient stability has become more important than deterministic assessment. In a previous method published by the authors, an algorithm to find the probability density function of the transient stability margin of the power system was published. This present paper presents a modified algorithm to find the probability density function of critical clearing time for each generator for the whole power grid for a certain contingency and during the fault-on period. First, the critical corrected kinetic energy is calculated based on a modified sensitivity-based algorithm at each step of the simulation during the fault-on period. Since the total kinetic energy of the generators does not contribute in the critical kinetic energy of the whole system, a new criterion - based on the rate of change of kinetic energy - is utilized to classify severely disturbed generators from less disturbed generators. Utilizing critical corrected kinetic energy of the whole power grid, an index based on the critical clearing time, so-called network critical clearing time, is calculated. Eventually, the proposed algorithm is applied on the optimal placement of a superconducting magnetic energy storage unit. It is shown that utilizing the proposed objective function for locating superconducting magnetic energy storage units effectively results in the improvement of the transient stability margin of the power system. [ABSTRACT FROM AUTHOR]

• Increase speed and accuracy in compensating for voltage and frequency fluctuations. • Intelligent power and energy management based on MPC and INN algorithms. • Optimal performance of primary and secondary controllers based on uncertainties. • Optimal control of power generation sources and energy storage units. This paper presents an intelligent energy management method to control the voltage and frequency at the primary and secondary control levels of micro-grids. The proposed model is based on the model predictive control (MPC) at the primary and intelligent neural network (INN) at the secondary. The purpose of this paper is to control the voltage and frequency based on the uncertainties of power generation resources and loads. In order to validate, the results of the proposed model are compared in different scenarios with other methods such as Fuzzy and PID controllers. The performance of the proposed method in primary and secondary as well as the coordination of this method with a high degree of sensitivity along with HESS and diesel generator units, in addition to increasing stability and reliability, protects the operation of the micro-grid against fluctuations based on uncertainties. The results show that this method, in addition to accuracy, has a higher operating speed than other controllers. As a result, the voltage and frequency restoration in the primary and the compensation of deviations from the reference in the secondary are associated with acceptable results. In order to validate the simulation results and experimental results are also presented. [ABSTRACT FROM AUTHOR]