The application of mobile battery energy storage systems (MBESSs) in microgrids offers a transformative solution for efficient, flexible, and sustainable energy management. This paper develops a comprehensive optimization framework for the techno-economic operation of MBESSs using self-driving electric trucks. The proposed approach simultaneously minimizes power generation costs, active and reactive power losses, and voltage deviations—an aspect that has not been explored together in prior research. It incorporates detailed and dynamic transportation cost modeling, robust stochastic analysis of uncertainties in renewable generation and loads, and—for the first time—models the stochastic energy consumption of electric trucks. A sensitivity-based strategy is used for optimal placement and sizing of MBESS units. Simulation results highlight the effectiveness of the proposed method in bridging existing research gaps, particularly in the simultaneous optimization of multiple operational parameters, and providing a scalable, realistic, and environmentally sound energy solution for modern smart grids.

As natural disasters and human-induced threats become increasingly frequent, enhancing the resilience of Integrated Energy Distribution Systems (IEDSs) is essential for maintaining reliable energy services. This paper presents a novel framework for resilient expansion planning of IEDSs, incorporating Energy Hubs (EHs) alongside conventional measures such as line hardening and Distributed Generation (DG) placement. The proposed framework aims to deliver a cost-effective and robust solution through the strategic deployment of EHs. To address the inherent complexity of multi-carrier energy systems, a bi-level mixed-integer linear programming (MILP) model is developed. The upper-level addresses resilience-oriented expansion planning of the IEDS, while the lower level focuses on the optimal planning and operation of EHs. These hubs integrate DG, Combined Heat and Power (CHP) units, batteries, and thermal storage to minimize investment and operational costs while limiting infrastructure expansion and improving overall economic efficiency. A risk-averse (RA) approach is employed to handle uncertainties such as load variability, wind power fluctuations, and the impact of earthquakes, ensuring a robust and conservative planning outcome. Simulation results on a large-scale IEDS consisting of 54 electricity nodes and 50 gas nodes reveal that EH-based strategies outperform conventional resilience measures, achieving substantial improvements in system resilience at lower costs. Specifically, the utilization of EHs leads to an 8.47 % reduction in total expansion costs and an 18.59 % increase in the resilience index. Furthermore, EHs enhance operational efficiency under both normal and emergency conditions. These findings highlight EHs as a highly cost-effective solution for improving the resilience of integrated energy distribution systems.

PMU applications have increased rapidly in recent years. Even though PMUs may improve state estimation quality, a limited number of PMUs may not allow the system to be fully observed. This paper presents a combined linear and robust hybrid state estimator that preserves dynamic tracking of algebraic variables. It also improves state estimation accuracy and execution time. Based on PMU data in observable mode, an LSE estimator is proposed to track algebraic states’ dynamic behavior. Additionally, an algorithm is presented to track algebraic states’ dynamic behavior in nearly real-time for PMU’s incomplete observability. A fast decoupled GM-estimator minimizes residual norms to achieve robustness in this case. This approach improves traditional state estimation software by requiring less memory (running faster) and higher accuracy. Various test systems have been successfully implemented, including IEEE-6 bus, 14 bus, 30 bus, and 118 bus.

Power converters have many applications in different industry due to their wide advantages. High step-up DC-DC converters work as an interface to improve output voltage in electric vehicles, medical equipment, servo-motors and renewable energies. The main challenges in fuel-cell (FC) vehicles are the inconstant input voltage and output power, so the efficiency analysis of DC-DC converter in a new topology is vital for an optimal operation. This paper analyzes the power loss and efficiency of a new non-isolated boost converter designed for FC vehicles considering different variables such as frequency, duty-cycle, and input voltage. The performance of the proposed converter is evaluated using dynamic analysis. The proposed converter is analyzed in Continuous Conduction Mode (CCM), and an efficiency analysis is performed in different situations. Moreover, the proposed converter is compared with some recent topologies to show its superiority. The validity of the analysis was confirmed by using a simulation in Matlab software. The results shown, the proposed converter has a low voltage stress on components, high efficiency, and wide voltage gain range.

The distributed generation interconnection to the power system can have impact not only on the power flow, but also on power quality of customers, and utilities.This paper proposes a multi-function nonlinear link for distribution generation systems.The main function of this link is to regulate the active power supplied from distribution generation to the utility.The link can also supply reactive power to the utility to enable unity power factor operation or to regulate the voltage at the point of common coupling.This link can also be controlled to mitigate power quality problems.Genetic algorithm is also used for the optimization of parameters of the power factor correction controller.The simulation performed in MATLAB/Simulink validates the effectiveness of the proposed power factor scheme.

To enable the integration of renewable energy sources into smart grid distribution systems and ensure a continuous energy supply, the utilization of energy storage systems has become critical. Energy storage provides numerous benefits, including energy time shifting, capacity backup, outage management, transmission congestion relief, and power quality improvements, thereby supporting system operators. Nevertheless, smart grid applications encounter various challenges regarding energy storage, such as charge/discharge cycle issues, safety concerns, size limitations, and cost factors. Therefore, the development of energy storage systems that enhance storage performance through improved energy capacity, control, and protection mechanisms is essential. Battery-based storage systems are commonly employed to address the intermittent nature and fluctuations of renewable energy sources like wind and solar power. Additionally, mechanical storage methods are gaining prominence as they contribute to the widespread adoption of clean energy and ensure uninterrupted energy supply. Extensive research endeavors have been undertaken to enhance storage efficiency, reduce costs, and optimize storage duration. This study aims to investigate different energy storage methods, classify them based on their specific purposes, and explore various applications of energy storage. Furthermore, a detailed discussion is provided on the advantages and disadvantages of different energy storage technologies

Transactive energy management (TEM) in smart grids is a promising approach for enhancing the participation of consumers in managing their energies and developing decentralized energy market models using peer-to-peer (P2P) transactions. The present paper designs and models a fully decentralized P2P energy market considering the technical constraints of the physical electricity network to redesign electricity markets as consumer-centric markets. As the sellers and buyers trade energy via the electricity grid, allocating power losses and transaction fees to players in P2P transactions is of utmost significance. The proposed market allows prosumers to engage directly in bilateral energy trading without requiring intermediaries. In addition to local P2P markets, the buyer prosumers can purchase energy from the upstream market and participate in the demand response (DR) program during hours of local generation shortage. A fairness index is applied to evaluate the players’ satisfaction with fairness and participation in the proposed market. Finally, an alternating direction method of multipliers (ADMM) is utilized to clear the proposed decentralized market. Numerical studies are performed on a standard IEEE 13-bus distribution network with multiple prosumers. The simulation results verify the effectiveness and feasibility of the proposed decentralized market and its clearing approach.

With large scale integration of micro-generation into low voltage grids, stability becomes an important issue for a Microgrid (MG). The unique nature of the MG requires that it becomes stable in both grid-connected and islanded modes. In grid-connected mode, the main grid guarantees the stability of MG but in islanded mode, the stability is related to all elements of the system and their control units. In this paper, the changes in droop coefficient influencing the frequency stability of the MG are investigated in islanded MG. The simulation results determine the new constraint for controllers in MG.

In this paper analysis results of a cooperation of the unified power quality conditioner with the fuel cell units are presented.A comprehensive control strategy is introduced to extract the compensating signals for the control of the proposed system.The used control strategy can extract the reference currents and voltages of UPQC fast and accurately in the presence of harmonics and/or frequency oscillation.The proposed scheme can compensate voltage sag, voltage interruption and harmonics at the Point of Common Coupling (PCC) on power distribution system.The simulation results prove the efficiency of using the presented method on power quality improvement.

The operation and control strategies for interconnection of DC microgrids have not been studied in previous researches.In this paper, an electrical model has been developed for DC microgrids and a suitable control system has been proposed for a DC/DC converter connecting two DC microgrids.Simulation results indicate the effectiveness of the proposed control strategy in power flow control and the cancellation of interactions between two DC microgrids.

The motivation to develop microgrids (MG), as a particular form of active networks is explained and presented as an effective solution for the control of grids.In MGs, with high level of penetration of Distributed Energy Resources, the stability becomes an important issue.Some control characteristics of elements such as droop coefficients in VSI highly affect voltage and frequency stability.In this paper, the changes in droop coefficient influencing the voltage stability of the microGrid are under investigation in islanded MG.The simulation results show the importance of the proposed limitation for the droop controller.

The proposed system consists of a series inverter, a shunt inverter, and a Distributed Generation (DG) connected in the dc link.It focuses on improving the power quality and ensuring the continuity of the electric power supply.The function of the scheme has been investigated in islanding mode which is a challenging mode of UPQC.The formulation of the proposed control scheme which is based on the instantaneous power theory is described.The proposed system can improve the power quality at the point of installation on power distribution systems or industrial power systems.Compensation of reactive power at the Point of Common Coupling (PCC) is examined.The effectiveness of the proposed scheme has been verified by simulation.

Supplying stable and sustainable energy for power grids is one of the most important challenges in the power industry, due to its importance, power generation systems, must connect their production power to the grid in a controlled manner, which is necessary to maintain quality and stability. system, high power quality in terms of voltage, stable frequency, safety and efficiency are very important. In general, maintaining stability and balance between electricity supply and demand in the electricity network plays an important role in providing stable and sustainable energy. This paper uses a controlled battery-fuel cell assembly to power a two-feeder system. It has been shown that when the power exceeds the capacity of the fuel cell many times, the battery enters the scene and supplies power to the system in a controlled manner. The values of each feeder are non-linear, and in order to reduce the current harmonics caused by the presence of electronic power converters such as inverters and boost converters, a low-pass filter has been used. The system design and simulations were done using MATLAB/Simulink ver. 2021b software.

Optimal service restoration in emergency cases is considered one of the most essential features of a smart distribution system. In the present study, a new IGDT-based scheme for risk-based system restoration in the form of a two-layer method is proposed. In the first layer, the binary variables related to the status of the closed switches and tie switches are obtained by a new strategy. Also, the optimal islands are obtained online after the fault. In the second step, the continuous variables, including reprogramming the distributed generation resources (DGR), storage, and amount of load shedding in the optimal island or islands that have been created in the first layer, are solved by a nonlinear model. In a fault event, to establish the power equilibrium, reprogramming the generation resources (GRs) and storage must be associated with the load shedding in the system. The load shedding must be done so that the sensitive load undergoes minimum outage. Thus the loads are prioritized by placing the loads into three residential, industrial, and sensitive categories. The risk-based problem is formulated in two operation modes: risk-averse and risk-seeker. In the risk-averse mode, the robustness function is used under high market prices. Moreover, in the risk seeker mode, an opportunity function is used under lower market prices towards lower-cost results. The simulation process is performed on an IEEE 33-bus system to evaluate the proposed model. To validate the proposed method, it is compared with one of the existing methods, and the proposed method is considerably better.

State estimation is one of the essential processes in controlling and monitoring a power system. State estimation with conventional and low precision measurements is now replaced by fast and direct measurements provided by Phasor Measurement Units (PMUs). However, the high cost of PMU requires engineers to place these meters optimally. In this article, depending on the desired observability depth and considering the conventional measurements, various methods for combining PMU measurements to increase the accuracy in power system state estimation have been investigated. The key idea of this method is to maintain full observability of the system and reduce the installation costs of the meter. Also, the results of two scenarios using MATLAB software on IEEE standard systems, 14 and 30 buses, have been investigated regarding PMU placement and its effect on the estimation error. A seasonal load profile is considered to measure the accuracy of the state estimation and make the simulation more realistic. The simulation results show that the effect of the optimal placement of the phasor measurement unit, even in the state of incomplete observability of the power system, has increased the accuracy and speed of the estimation.

Abstract Network control, optimization, and security analysis are facilitated by accurate microgrid state estimation. By utilizing phasor measurement units (PMUs), a combined robust centralized dynamic algebraic approach is proposed in this paper for estimating algebraic states (voltage phasors) as well as dynamic states (associated with synchronous generators and wind turbines). Assuming that a load is placed on each bus, an innovative PMU placement method is also presented to provide measurements for all dynamic and algebraic state variables. As a robust link between algebraic state estimation and dynamic state estimation, the output results of the least absolute value (LAV) estimator, which is robust against bad data, are fed to dynamic state estimators as pseudo measurements. Next, to address the nonlinearity of synchronous generator and wind turbine models, an unscented Kalman filter (UKF) is applied to estimate dynamic states precisely. The numerical tests on a microgrid test system show that the suggested approach is suitable for both algebraic and dynamic state estimation. Synchronous generators and wind turbines are modeled using nonlinear models of the 9th and third orders, respectively, and static loads are modeled as voltage‐frequency dependent ones.

All major developing nations have started to invest in renewable energy to promote a cost-effective and low emission way of power production. Policy planning and financial aspects are the key issues for renewable power development. Comparing two major developing countries, Iran and India, would provide a deeper understanding of the future trends and policy scenarios related to renewable energy. This paper provides a review of the governance structure, current renewable energy policies, legislation, economic policies, and incentives that would analyze the renewable energy development in both countries. The final section of the paper discusses the current status of renewable energy generation in both countries followed by comparative policy analysis. The results concluded from the paper are that India shows a much stronger commitment towards renewable energy with a higher percentage of generation, well-structured governance, and independent ministry which presides over the whole sector. Iran although new to the industry shows a lot of promise with better feed-in tariffs and an open market for independent investors and producers. The paper suggests lesser investment interest rates, better technical expertise, and incentives for off-grid producers for India while a more developed legislative structure for Iran along with the formation of subordinate research organizations.

Load forecasting is an essential issue in future smart grids where inaccurate forecasting causes energy waste, power shortages, or cross-blackouts. Therefore, increasing forecasting accuracy is crucial due to the expansion of the type of loads and the amount of consumption and parameters that affect the load changes. Machine learning is a powerful tool for achieving artificial intelligence, and it is used for load forecasting as one of its applications. In this paper, short-term load forecasting is performed using an ensemble supervised learning based on random forest method named Deep Forest Regression. This method is also derived from deep learning and deep neural network theory. This forecast has been done using the data of residential consumption of an Iranian city for five months, including from half of May to half of September. The data is gathered every 30 minutes and stored in the system. By comparing the proposed method with some common methods, it can be seen that the proposed method has higher accuracy than those.

In this paper, a novel current controller for selective compensation with active power filter (APF) in a microgrid (MG) is proposed. Power generation with sinusoidal voltage and high quality is essential in the microgrid. Hence, a current control-based method for shunt active power filters aiming to compensate the selective harmonic is proposed. Also, voltage source converter is used in the APF to improve MG power quality. Using the suggested control method can reduce total harmonic distortion (THD). The obtained results from the MATLAB simulations proved the superior of our method than other methods to decrease the current harmonic in a microgrid to an admissible area. Additionally, the practical results obtained from the implementation of the proposed control approach on an actual microgrid confirm the efficacy of the proposed method.

Contingency conditions in distribution networks create financial losses for different parts of the system including electricity customers, electricity retailers, distributed generation (DG) units, etc. Therefore, protective device allocation methods have been introduced in recent years to enhance the reliability of the power system. In this study, a new formulation is proposed to find the optimal places of sectionalizing switches and fuses while taking the financial loss of both electricity customers and DG units into account. The current method has the flexibility to consider DG effect on any location of the network and its islanded operation in case of contingencies. Moreover, the uncertainty in load and renewable generation is taken into account using stochastic programming. The results demonstrate that the DG units and their financial loss can change the results of switch and fuse placement dramatically when there are no tie switches in the network. Furthermore, it is found that this method can decrease the total reliability costs by 3.86% when high penetration of DG units is introduced into a modified Roy Billinton test system (RBTS). The problem is modeled as a mixed-integer nonlinear (MINLP) formulation and is handled using BARON solver in GAMS environment.