University of Mohaghegh ArdabiliJournal of Operation and Automation in Power Engineering2322-457614320260801Novel Electricity Pricing Method Based on the Customers’ Risk Aversion Function154164356710.22098/joape.2024.14882.2139ENEmad JadeenAbdualsada AlshebaneyFaculty of Electrical and Computer Engineering, Urmia University, Urmia, Iran.0000-0002-4953-9571MohammadFarhadi-KangarluFaculty of Electrical and Computer Engineering, Urmia University, Urmia, Iran.VahidTalavatFaculty of Electrical and Computer Engineering, Urmia University, Urmia, Iran.0000-0003-0598-2472Journal Article20240408Electricity pricing approaches are generally categorized into flat-rate and dynamic pricing models. Flat-rate pricing charges a fixed rate regardless of market conditions, whereas dynamic pricing adjusts rates based on system and market factors. Traditional pricing methods often lack flexibility, preventing consumers from choosing their preferred pricing plans. This study introduces a Selective Electricity Pricing (SEP) model that allows customers to select a Maximum Tolerable Price (MTP) tailored to their needs and benefit from Real-Time Pricing. The SEP model also includes a retailer-funded mechanism to shield customers from high market prices, acting as a risk hedge. Using a risk aversion function to gauge consumer preferences, the SEP method was implemented on the IEEE-24 test system. Results indicate that low-risk customers are more likely to engage in dynamic pricing. The SEP model significantly outperforms flat-rate pricing, yielding 17.27% higher retailer profits, 11.32% lower demand, and a 2.73% increase in average customer payments, compared to a 2,500MW drop under flat-rate pricing.https://joape.uma.ac.ir/article_3567_fcc440c1e9313a241d8a54e2a5e3526c.pdfUniversity of Mohaghegh ArdabiliJournal of Operation and Automation in Power Engineering2322-457614320260801Optimal Load Distribution Based on Decision Theory with Information Gap in the Presence of Wind Farms Connected to the Power System165180356810.22098/joape.2024.14914.2140ENKouroshAlijanzadehDepartment of Electrical and Biomedical Engineering, Mazandaran University of Science and Technology, Babol, Iran.AliGhasemi-MarzbaliDepartment of Electrical and Biomedical Engineering, Mazandaran University of Science and Technology, Babol, Iran.Journal Article20240417Optimal load distribution in power systems is crucial for minimizing overall costs while adhering to technical constraints. This process becomes increasingly complex with the integration of wind energy due to the inherent uncertainty in wind turbine production caused by variable wind conditions. This paper presents a novel approach to address these uncertainties within the context of the optimal power flow (OPF) problem by employing Information Gap Decision Theory (IGDT). Unlike traditional scenario-based methods, IGDT provides a computationally efficient and reliable framework for decision-making under uncertainty without extensive probabilistic data. The methodology uses the Weibull probability density function to model wind speed, allowing for realistic estimation of wind farm output power. The Evolutionary Particle Swarm Optimization (EPSO) algorithm, an advanced version of PSO, is utilized to solve the optimization problem, reducing the risk of convergence to local optima. Results are computed under two strategies: risk-averse and risk-taking, represented by immunity functions. These strategies highlight the impact of user demand on adjusting calculation parameters. Comparative analysis with scenario-based probabilistic optimization shows that the IGDT approach enhances system load cost evaluation by 0.12%. This study provides a robust framework for optimal power allocation under uncertainty, ensuring resilient and secure power generation.https://joape.uma.ac.ir/article_3568_81d755e9b1b9138014bbcf74d56f860c.pdfUniversity of Mohaghegh ArdabiliJournal of Operation and Automation in Power Engineering2322-457614320260801An Inductively Coupled Bidirectional DC-DC Converter With a Non-Pulsating Input Current for Renewable Energy Systems Energy Storage181189356910.22098/joape.2024.14945.2142ENMohammadrezaBanaeiDepartment of Electrical Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran.MohamadGolmohamadiDepartment of Electrical Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran.HadiAfshariradDepartment of Electrical Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran.Journal Article20240423The present study proposes a bidirectional DC-DC converter (BDC) that uses a non-isolated coupled-inductor (NI-CI). This converter can transfer power bidirectionally between the DC bus of a microgrid, supplied by PV or other renewable sources and energy storage system. The device exhibits a high voltage conversion ratio while using a few components. The converter applies the input inductance as a current ripple filter and uses a CI configuration to enhance the gain in boost mode. Also, the turns ratio of a coupled inductor is implemented to enhance the voltage conversion ratio to lower voltage stress. In addition, the converter’s operation is more efficient considering its soft-switching advantages. The duty cycle control is applied to generate the desired voltage on both sides of the converter by controlling the corresponding power switch. It is worth noting that the low-voltage side current ripple is not significant. Besides, the results show an increase in voltage gain throughout boost mode and a decrease in voltage gain in the buck mode. Furthermore, the converter is mathematically studied in the following, and a PID converter is designed to illustrate the converter’s stability. Finally, the practicality of the proposed NI-CI-BDC structure was validated by incorporating experimental results from a 200-watt prototype.https://joape.uma.ac.ir/article_3569_7ce8edfc786231ca09137a52787999b5.pdfUniversity of Mohaghegh ArdabiliJournal of Operation and Automation in Power Engineering2322-457614320260801Optimized Design and Performance Enhancement of Concentrated Winding BLDC Motors for Aircraft Actuators190197368210.22098/joape.2025.15488.2190ENRezaGhanizadehDepartment of Electrical Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran.TaherMohammadiDepartment of Electrical Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran.FarhadRazaviDepartment of Electrical Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran.Journal Article20240721Permanent magnet brushless DC (BLDC) motors are increasingly preferred in industrial applications, particularly for low- and medium-power scenarios, due to their commutator-free operation, higher efficiency, reduced maintenance, compact size, and versatile speed control. This work presents the development of an enhanced BLDC motor prototype with concentrated windings, specifically tailored for aircraft actuator applications. The primary objective is to maximize electromagnetic torque and torque per kilogram through a novel dimensional optimization approach. A systematic design procedure, incorporating sensitivity analysis and finite element method (FEM) modeling, was established to identify and optimize key parameters affecting overall performance. Our results demonstrate significant improvements in power density, torque-to-weight ratio, and efficiency compared to conventional designs, offering a robust solution for the demanding requirements of aerospace applications.https://joape.uma.ac.ir/article_3682_f1333afdcc7ce6b7c7c291b749863c07.pdfUniversity of Mohaghegh ArdabiliJournal of Operation and Automation in Power Engineering2322-457614320260801A New Fast and Accurate Method Based on Fourier Transform for Fault Detection in DC Microgrids198208378610.22098/joape.2025.15858.2218ENMohamadKohzadipourDepartment of Electrical Engineering, Ilam University, Ilam, Iran.MajidValizadehDepartment of Electrical Engineering, Ilam University, Ilam, Iran.SabahDaniarDepartment of Electrical Engineering, Ilam University, Ilam, Iran.AmirhoseinKhosravi SarvenoeeDepartment of Electrical Engineering, Ilam University, Ilam, Iran.Journal Article20240916This paper utilizes the Fast Fourier Transform (FFT) technique to extract the apparent power of DC microgrids for fault detection. The proposed method separates the real and imaginary components of power and compares the imaginary part with a predetermined threshold. To determine the relay threshold, PP and PG faults are simulated at various distances along each line connected to each bus. The Inverse Fast Fourier Transform (IFFT) is then calculated for each fault at each line and location. The relay threshold is selected based on the lowest significant value among the highest IFFT values calculated for all microgrid lines. This study proposes a novel relay threshold calculation approach, enabling precise fault detection and localization in DC microgrids. The relay threshold value is calculated at the control center and then sent to the microgrid relays. Fault detection is achieved by comparing the IFFT values obtained within the microgrid with the relay threshold value. Once the relay threshold is surpassed, the microgrid detects the fault and promptly sends a trip signal to the circuit breaker. This fault detection strategy accurately identifies the fault location by measuring the current and voltage between the terminals of the faulty section. The proposed method swiftly detects all PP and PG faults (including HIF up to 50 ohms) in grid-connected and islanded modes within 2-3 milliseconds. It accurately locates faults with minimal deviation across various positions. Rigorous simulations using MATLAB and EMTP-RV programs confirm the effectiveness of the protection scheme, emphasizing its reliable performance.https://joape.uma.ac.ir/article_3786_0e0cda94c7fde47e78eaf67a8dd95629.pdfUniversity of Mohaghegh ArdabiliJournal of Operation and Automation in Power Engineering2322-457614320260801Designing a Multi-Objective Optimized Parallel Process Controller for Frequency Stabilization in an Islanded Microgrid209221369910.22098/joape.2025.16246.2255ENHosseinShayeghiEnergy Management Research Center, University of Mohaghegh Ardabili, Ardabil, Iran.AlirezaRahnamaEnergy Management Research Center, University of Mohaghegh Ardabili, Ardabil, Iran.HamedMojaradEnergy Management Research Center, University of Mohaghegh Ardabili, Ardabil, Iran.Journal Article20241123Load-frequency control plays a critical role in maintaining the stability and reliability of islanded microgrids, where the absence of a large interconnected grid makes frequency regulation more challenging. With the increasing integration of renewable energy sources and energy storage systems, the stochastic and uncertain nature of µGs component’s behavior has amplified the need for advanced LFC mechanisms, making it a focal area of research for decades. This paper introduces a novel parallel process FOPI–FPOD controller optimized for robust LFC and stability in µGs. Employing time and frequency domain objective costs, a multi-objective particle swarm optimization algorithm with nonlinear time-varying coefficients generates a Pareto front, with fuzzy decision-making selecting optimal designs. The proposed controller demonstrates strong robustness by effectively handling uncertainties such as sudden load changes, RES fluctuations, and parametric variations, while maintaining stable frequency regulation. The controller's performance is evaluated under four scenarios: sudden load changes with time delays, uncertainties in RESs, parametric system uncertainties, and energy storage systems' impact. Comparative analysis with PID, FOPID, and PD(1+PI) controllers demonstrates the proposed design's superior stability and resilience, providing a robust solution for frequency stabilization in µGs. Numerical results demonstrate that the proposed FOPI–FOPD controller significantly outperforms traditional methods, achieving lower error indices, reduced frequency deviations, and more efficient utilization of energy storage systems under various scenarios and energy storage systems participation levels. These findings highlight its robust and adaptive performance in ensuring stable and efficient LFC task for an islanded µGs control.https://joape.uma.ac.ir/article_3699_8ab85e7c75eef3531b1e779eefc28c00.pdfUniversity of Mohaghegh ArdabiliJournal of Operation and Automation in Power Engineering2322-457614320260801A Two-Stage Multi-Objective Optimal Day-Ahead Peer to Peer Energy Trade and Pricing Considering Electric Vehicles in Microgrid222232378710.22098/joape.2024.14762.2130ENMostafaKafaeiKhorasan Razavi Power Distribution Company, Mashhad, Iran.Journal Article20240302Due to recent developments in communications and the increasing penetration rate of distributed generation (DGs), new players in the energy market, known as prosumers, have emerged. Prosumers can both produce and consume power, offering benefits such as on-site power consumption, peak shaving, and postponing the power transmission network investment costs. This paper presents a two-stage day-ahead peer-to-peer pricing and power exchange model among local market participants, including the upstream grid, consumers, prosumers (equipped with rooftop solar panels), and electric vehicles. In the first stage, initial pricing is determined using the mid-market rate pricing method, taking into account each participant's declared demand and the forecasted solar production of prosumers. In the second stage, the random behavior of electric vehicles is modeled through scenario generation, and their dynamic behavior is incorporated into the pricing scheme. The proposed model aims to minimize two objectives: trading costs and electrical power losses due to the exchange of power among participants. This two-objective problem is reformulated as a single objective using the epsilon-constraint method. The resulting MINLP model is solved in GAMS using the DICOPT solver, and the best-compromised solution is identified through the Min-Max method. Simulation results indicate a 6.7% reduction in costs, with all participants benefiting economically. Additionally, on-site interactions led to a decrease in congestion on two lines connecting to the upstream grid by 5.02% and 6.66%, respectively.https://joape.uma.ac.ir/article_3787_cacfe85702e9b46894689d8cd2cfabd4.pdfUniversity of Mohaghegh ArdabiliJournal of Operation and Automation in Power Engineering2322-457614320260801Predictive Current Control of PMSM Drive Supplied with 4-Level Diode-Clamped Inverter233240425810.22098/joape.2025.15727.2209ENPegahHamedaniDepartment of Railway Engineering and Transportation Planning, University of Isfahan, Isfahan, Iran.SajadSadrFaculty of Electrical Engineering, Tafresh University, Tafresh, Iran.Journal Article20240828A mandatory issue in the control of a permanent magnet synchronous motor (PMSM) drive is to overcome its nonlinear dynamic characteristics. This challenge becomes more severe when a multilevel diode-clamped inverter (DCI) is used for supplying the PMSM drive. In this case, it is indispensable to provide an accurate speed tracking performance as well as the capacitor voltage balance. The model predictive control (MPC) approach can properly solve this issue by considering different objectives in the cost function of the controller. This paper concentrates on the model predictive current control (MPCC) of the PMSM drive fed through a 4-level DCI. A comparative assessment of the 4-level DCI and 2-level VSI is performed in the PMSM drive with the MPCC approach. Different objectives are considered in the MPCC process including the d-q current control, limitation of the stator currents, voltage balance of capacitors, mitigating of the CM voltage, and reduction of the switching frequency. Simulation results reveal the superior dynamic performance and capacitor voltage balance in the suggested MPCC of PMSM drive with the 4-level DCI. Results manifest that the current total harmonic distortion (THD) and the torque ripple are lower in the PMSM drive with a 4-level DCI than with the 2-level VSI. The current THD is reduced from 8.61% in the 2-level VSI to 4.59% in the 4-level DCI. Moreover, the torque ripple is reduced from 1.2 Nm in the 2-level VSI to 0.32 Nm in the 4-level DCI.https://joape.uma.ac.ir/article_4258_eebdd049ae8d79546e96b756e4948c1c.pdf