University of Mohaghegh ArdabiliJournal of Operation and Automation in Power Engineering2322-457613Special Issue20251201Enhanced Stability in Microgrids Using an Optimized Virtual Synchronous Generator Control for Voltage Source Converters116436810.22098/joape.2025.18690.2454ENMohammed ObayesYousifDepartment of Electrical Engineering, Razi University, Kermanshah, Iran.HassanMoradiDepartment of Electrical Engineering, Razi University, Kermanshah, Iran.HamdiAbdiDepartment of Electrical Engineering, Razi University, Kermanshah, Iran.0000-0002-7625-0036Journal Article20251028This study introduces a Virtual Dynamic Emulation–Virtual Synchronous Generator (VDE–VSG) control strategy for converter-dominated microgrids, explicitly linking DC-side energy dynamics with AC-side inertial behavior. The proposed framework integrates photovoltaic (PV) generation, bidirectional battery storage, and a three-phase voltage-source converter, in which the DC-link voltage informs the virtual inertia and damping response. A systematic analysis of the synthetic inertia (J) and damping (D) parameters highlights their critical role in balancing transient speed and oscillation suppression. To achieve optimal performance, Particle Swarm Optimization (PSO) is applied offline to identify the J–D pair that minimizes frequency deviations under varying load and fault conditions. Simulation results demonstrate that the PSO-optimized VDE–VSG substantially outperforms both conventional dual-loop control and baseline VSG schemes. Under step load disturbances, the optimized controller reduces maximum frequency deviation by 62% and accelerates active power settling by 57%. During DC-side short-circuit faults, DC-link voltage depression decreases from 43% to 17%, while recovery time is shortened by 93%. These findings underscore the physical coherence of DC-aware virtual inertia and damping, confirming that coordinated tuning via PSO enhances stability, transient response, and robustness in microgrid operation. The study presents a reproducible methodology and validates the practical feasibility of implementing the VDE–VSG on contemporary real-time platforms.https://joape.uma.ac.ir/article_4368_6492cb6686a525ab2b4291c7bd892e26.pdfUniversity of Mohaghegh ArdabiliJournal of Operation and Automation in Power Engineering2322-457613Special Issue20251201Dynamic Stability Analysis and Control of AC/DC Microgrids with Energy Storage Systems for Transient State Mitigation1728434310.22098/joape.2025.18911.2464ENSadridinEshkaraevTermez University of Economics and Service, Termez, Uzbekistan.Kuzieva DinoraBakhodirovnaTashkent State University of Economics, Tashkent, Uzbekistan.IsayevFakhriddinScientific Research Center "Scientific Foundations and Problems of the Development of the Economy of Uzbekistan "under Tashkent State University of Economics, Tashkent, Uzbekistan.AlimovaNodira BatirdjanovnaTashkent State Technical University named after Islam Karimov, Tashkent, Uzbekistan.IxtiyorjonAskarovJizzakh Polytechnic Institute, Jizzakh, Uzbekistan.SukhrobUmarovTashkent Institute of Irrigation and Agricultural Mechanization Engineers National Research University, Uzbekistan.OtabekMirzaevUrgench State University named after Abu Rayhan Biruni, Urgench, Uzbekistan.Journal Article20251127Modern AC/DC microgrids have undergone significant changes due to the increase in high-power loads and the development of energy storage systems. This paper presents a nonlinear dynamic modeling and control framework for a combined AC/DC microgrid incorporating a synchronous generator, a six-pulse rectifier, and an energy storage system (ESS). An ESS-based PI-controlled DC/DC converter is designed to regulate the DC-link voltage and mitigate transient disturbances. System stability is analytically assessed using the second Lyapunov method, providing a rigorous nonlinear stability guarantee under multiple disturbance scenarios. Time-domain simulations demonstrate that the proposed control strategy significantly improves transient performance, reducing DC-link voltage sag to below 6%, limiting overshoot to under 8%, and shortening settling time by more than 55% compared to the uncontrolled case. The results confirm the effectiveness of the proposed ESS-based control approach in enhancing the dynamic stability and robustness of hybrid AC/DC microgrids.https://joape.uma.ac.ir/article_4343_9634a1d12bc40d719a7a558a64ae088f.pdfUniversity of Mohaghegh ArdabiliJournal of Operation and Automation in Power Engineering2322-457613Special Issue20251201Dynamic Modeling and State Feedback Control for Enhancing Small Signal Stability in Islanded Microgrids with Diesel Generator Frequency Damping2943434410.22098/joape.2025.18912.2465ENShakhbozMeylikulovTermez University of Economics and Service, Termez, Uzbekistan.IsayevFakhriddinScientific Research Center Scientific Foundations and Problems of the Development of the Economy of Uzbekistan under Tashkent State University of Economics, Tashkent, Uzbekistan.KochkarovaMadinaTurin Polytechnic University in Tashkent, Tashkent, Uzbekistan.0009-0007-9286-121XTurayevMuhammadiKattakurgan State Pedagogical Institute, Samarkand, Uzbekistan.0009-0006-3926-6211ZakirjonMusabekovTashkent State Technical University named after Islam Karimov, Tashkent, Uzbekistan.OchilovFarxodjonTashkent State University of Economics, Tashkent, Uzbekistan.UlugbekAnarboyevich NurmanovThe Banking and Finance Academy of the Republic of Uzbekistan.0000-0003-1937-0872Journal Article20251127Microgrids face significant challenges due to the stochastic behavior of distributed energy resources, which may excite low-damped oscillatory modes and compromise stability. This paper develops a unified small-signal model of an islanded microgrid and proposes a state-feedback controller (SFC) as a power system stabilizer (PSS) to enhance low-frequency damping in the diesel generator subsystem. The controller and inverter control parameters are tuned using a genetic algorithm (GA). Numerical results show that the proposed approach increases the damping ratio of the dominant electromechanical mode from 0.03 to 0.18 (a six-fold improvement) and reduces rotor-speed overshoot by nearly 60% under a 10% disturbance. Time-domain simulations further confirm a 70% reduction in settling time for rotor-angle deviations. These results demonstrate that the optimized SFC significantly improves electromechanical damping and strengthens the small-signal stability margin of the islanded microgrid.https://joape.uma.ac.ir/article_4344_a290b598c188fa2091c06c4229d6bdcb.pdfUniversity of Mohaghegh ArdabiliJournal of Operation and Automation in Power Engineering2322-457613Special Issue20251201Load–Frequency Control of a Restructured Multi-Area Power System with DFIG-Based Wind Integration Using Coordinated AVR–PSS–FACTS and Optimized PID Controllers4453434510.22098/joape.2025.18913.2466ENIsayevFakhriddinScientific Research Center "Scientific Foundations and Problems of the Development of the Economy of Uzbekistan", under Tashkent State University of Economics, Tashkent, Uzbekistan.OlimTursunovAlfraganus University, House 2a, Yuqori Qoraqamish Street, Tashkent, 100190, Uzbekistan.KhamidovaSuluv YangiboevnaTermez State University of Engineering and Agrotechnologies, Termez , Uzbekistan.MirzokhidErnazarovTermez University of Economics and Service, Termez, Uzbekistan.OtabekMirzaevUrgench State University named after Abu Rayhan Biruni, Urgench, Uzbekistan.FakhriddinKhurramovich KarimovTashkent State University of Economics, Tashkent, Uzbekistan.KhudoyarovAnvar NazirjonovichDoctor (DSc) of technical sciences, Professor Andijan institute of agriculture and agrotechnology, Andijan, Uzbekistan.Journal Article20251127This study investigates load–frequency control (LFC) in a restructured multi‑area power system with significant renewable energy integration, where increased stochasticity and reduced inertia intensify frequency deviations. A coordinated control strategy combining automatic voltage regulator (AVR), power system stabilizer (PSS), thyristor‑controlled phase shifter (TCPS), and optimized PID controllers is proposed to improve dynamic performance under competitive market conditions. Metaheuristic optimization techniques are employed to tune controller parameters for robustness across operating scenarios, including high renewable penetration and contractual power transactions. Simulation results demonstrate that the coordinated scheme substantially reduces overshoot, settling time, and frequency oscillations compared with conventional control approaches. The findings confirm that integrating auxiliary damping devices with optimized LFC significantly enhances frequency stability and resilience in modern deregulated power systems.https://joape.uma.ac.ir/article_4345_90aa68a8e8f0b90c5ccb42596b4d9070.pdfUniversity of Mohaghegh ArdabiliJournal of Operation and Automation in Power Engineering2322-457613Special Issue20251201Decentralized Energy Management in Electrical and Thermal Microgrids Utilizing Reinforcement Learning4561434610.22098/joape.2025.18916.2468ENUmarovShukhratDepartment of Engineering of Electrical Machines and Drives, Tashkent State Technical University, University Street No2, Tashkent, Uzbekistan.IsaqovaMatlubaTashkent Institute of Irrigation and Agricultural Mechanization Engineers Institute" National Research University, Kari Niyazov Street 39, 100000, Tashkent, Uzbekistan.OtabekMukhitdinovKimyo International University in Tashkent, Shota Rustaveli Street 156, 100121, Tashkent, Uzbekistan.BoboxujayevKudratPhD, Assistant Professor, Alfraganus University, Uzbekistan.AbdullayevDadaxonTashkent Institute of Irrigation and Agricultural Mechanization Engineers National Research University, Uzbekistan.SamievLuqmon NUrgench State University named after Abu Rayhan Biruni, Urgench, Uzbekistan.NosirovNozimbekResearch Institute of Environmental and Nature Protection Technologies, 100000, Tashkent, Uzbekistan.SapayevValisherDepartment of General Professional Subjects, Mamun University, Khiva, Uzbekistan.Journal Article20251127This paper proposes a fully decentralized reinforcement learning–based energy management framework for hybrid electrical–thermal microgrids with distributed energy resources. Uncertainties in renewable energy generation, variations in load demand, and the nonlinear nature of battery systems make it difficult to achieve optimal energy management in microgrids. Additionally, using centralized controller techniques in large-scale systems increases computational complexity and makes controller procedure implementation more challenging. This study proposes a fully decentralized multi-agent architecture in which the stochastic performance of agents in the microgrid is modeled using Markov decision processes. This model treats consumers, batteries, and distributed thermal and electrical resources as intelligent, self-governing agents that learn from their surroundings and converge to their best policies through decentralized exploitation. The proposed model-free learning-based approach is designed to not only maximize the profits of producers but also minimize the costs for consumers and reduce the microgrid's reliance on the main grid. Finally, using real-world data from renewable power plants and electricity market data, the performance of the proposed method is evaluated through simulation and accuracy assessment.https://joape.uma.ac.ir/article_4346_a9c0d4459d7a23de3d6e8ca3d90c9afa.pdfUniversity of Mohaghegh ArdabiliJournal of Operation and Automation in Power Engineering2322-457613Special Issue20251201A Multi-Objective Optimization Method for the Best Concurrent Involvement of Energy Networks and Energy Hubs6274434710.22098/joape.2025.18918.2469ENJavohirZokirovTermiz University of Economics and Service, Farovon Street 4-b, Termez, Surxondaryo, Uzbekistan.DilafruzKholmurodovaScientific and Practical Center of Immunology, Allergology and Human Genomics, Samarkand State Medical University, Samarkand, Uzbekistan.AkmalRustamovKimyo International University in Tashkent, Tashkent, Uzbekistan.RovshanKhakimovTashkent State Transport University, 100167 Tashkent, Uzbekistan.IlkhomRasulovDepartment of Russian Language and Literature, Kokand State University, Kokand, Uzbekistan.YusufTurdibekovSamarkand State University of Architecture and Civil Engineering, Samarkand, Uzbekistan.SardorbekYusufovDepartment of “Architecture”, Urgench State University, Urgench, Uzbekistan.FazliddinJumaniyazovMamun University, 220912 Khiva, Khorezm, Uzbekistan.0000-0003-4008-5072Journal Article20251127In this study a framework for the best possible simultaneous involvement of energy systems and energy hubs in day-ahead energy shops is presented. The suggested method is a multi-objective optimization problem and takes into account both wholesale and retail market structures. The primary objective function seeks to reduce the overall energy costs of thermal, gas, and electricity networks. By optimizing the difference between energy purchase and sales costs, the second goal function aims to reduce the energy costs of energy hubs in the retail market. The operational model of active resources and loads inside the energy hubs, as well as the optimal power flow calculations of the integrated energy systems, place limitations on the suggested model. To solve the optimization problem, a Pareto-based weighted sum method combined with fuzzy decision-making is employed to derive a compromise optimal solution. Finally, the proposed framework is implemented on a test system, and the numerical results confirm its effectiveness in successful economic performance of energy hubs and simultaneously enhancing the cost-effective and operational conditions of integrated energy networks which reduce energy cost up to a 40%.,https://joape.uma.ac.ir/article_4347_65d8eb5d0120ae9e807d5594a554a1b8.pdfUniversity of Mohaghegh ArdabiliJournal of Operation and Automation in Power Engineering2322-457613Special Issue20251201Designing a Centralized Charging and Discharging Management Strategy for Electric Vehicles to Enhance Transformer Lifespan in Distribution Networks7588434810.22098/joape.2025.18919.2470ENOtabekMukhitdinovKimyo International University in Tashkent, Shota Rustaveli Street 156, 100121, Тashkent, Uzbekistan.JavohirZokirovTermiz University of Economics and Service, Farovon Street 4-b, Termez, Surxondaryo, Uzbekistan.BoburbekMakhmudovDepartment of Traumatology and Orthopedics, Fergana Medical Institute of Public Health, 2A Yangi Turon Street, Fergana, Uzbekistan.KudratBoboxujayevPhD, Assistant Professor, Alfraganus University, Uzbekistan.AslbekYulchievAndijan State University, 170100 Andijan, Uzbekistan.IbrokhimKhudayev“Tashkent Institute of Irrigation and Agricultural Mechanization Engineers” National Research University, 39, Street Kari Niyaziy, 100000, Tashkent, Uzbekistan.YuldashTakhirovDepartment of Chemistry, Urgench State University named after Abu Rayhan Biruni, 220100 Urgench, Uzbekistan.ValisherSapayevDepartment of General Professional Subjects, Mamun University, Khiva, Uzbekistan.Journal Article20251127The widespread adoption of electric vehicles (EVs) is increasing the loading of distribution transformers and accelerating insulation aging. This paper proposes a centralized charging and discharging strategy that jointly co-optimizes EV operating cost and the monetized cost of transformer loss-of-life, explicitly linking technical asset degradation to economic decision-making. Unlike prior centralized EV-scheduling approaches that either constrain temperature or evaluate aging only in post-processing, the proposed framework embeds an IEEE C57.91-based aging model directly into the optimization objective and converts aging into an equivalent financial cost.The model further introduces a stakeholder compensation mechanism in which part of the deferred transformer replacement savings is redistributed to EV owners, allowing an independent aggregator (or the distribution utility) to coordinate EV charging while preserving consumer economic incentives. The framework considers grid-to-vehicle (G2V), vehicle-to-home (V2H), and vehicle-to-grid (V2G) modes and is formulated as a mixed-integer nonlinear optimization problem. Simulation results for a residential network with six EVs demonstrate that centralized coordination can reduce transformer loss-of-life by up to 80% compared with decentralized charging, while increasing daily EV operating costs by only 4-6%. The remuneration mechanism enables all stakeholders—transformer owner, aggregator, and consumers—to benefit economically. These findings show that integrating monetized transformer aging into EV scheduling, combined with explicit profit-sharing, provides a technically effective and financially viable pathway for extending transformer lifespan under growing EV penetration.https://joape.uma.ac.ir/article_4348_c3770d2da5fbf3d19526bb56b586692b.pdfUniversity of Mohaghegh ArdabiliJournal of Operation and Automation in Power Engineering2322-457613Special Issue20251201A Novel Approach to Optimized Frequency Load Shedding in Microgrids with Wind Power Integration using ANFIS Networks89100434910.22098/joape.2025.18921.2472ENJavohirZokirovTermiz University of Economics and Service, Farovon Street 4-b, Termez, Surxondaryo, Uzbekistan.DilafruzKholmurodovaScientific and Practical Center of Immunology, Allergology and Human Genomics, Samarkand State Medical University, Samarkand, Uzbekistan.SaodatAsilovaKimyo International University in Tashkent, Tashkent, Uzbekistan.MukhtasarkhonAbdullayevaFergana State Technical University, Fergana, Uzbekistan.GafarovaAzizaDepartment of Use of Hydromelioration Systems, Tashkent Institute of Irrigation and Agricultural Mechanization Engineers, National Research University, Tashkent, Uzbekistan, and Western Caspian University, Scientific Researcher, Baku, Azerbaijan.RovshanKhakimovTashkent State Transport University, 100167 Tashkent, Uzbekistan.SardorbekYusufovDepartment of “Architecture”, Urgench State University, Urgench, Uzbekistan.FazliddinJumaniyazovMamun University, 220912 Khiva, Khorezm, Uzbekistan.0000-0003-4008-5072Journal Article20251127The increasing significance of renewable energy sources has led to a growing penetration of distributed generation units in distribution systems. This not only offers numerous economic benefits but also enables energy supply in islanded microgrid operation. In islanded mode, an effective load shedding scheme is crucial to maintain frequency balance and voltage stability within acceptable limits. This paper presents novel load shedding criteria, considering the impact of wind power integration and its inherent uncertainty in microgrids. Given the short electrical distances in microgrids, reactive power balance is of particular importance. Accordingly, the proposed load shedding method employs a combination of frequency and voltage criteria. The required amount of load shedding is determined through transient stability examination, and the load shedding process is implemented using an Adaptive Neuro-Fuzzy Inference System (ANFIS) in the microgrid. Simulation results demonstrate the effectiveness of the proposed method in load shedding and maintaining the stability of the microgrid. Specifically, by jointly exploiting frequency, voltage, and wind-speed information within an ANFIS framework trained from detailed transient stability studies, the proposed scheme is capable of preventing severe frequency drops and voltage instability under uncertain wind power generation. Furthermore, by quantifying the impact of including voltage as an ANFIS input, the study shows that the proposed microgrid-oriented design can reduce unnecessary load shedding and improve the economic performance of the system.https://joape.uma.ac.ir/article_4349_3f2a0fbd1badec0fac26cd601ee12882.pdfUniversity of Mohaghegh ArdabiliJournal of Operation and Automation in Power Engineering2322-457613Special Issue20251201Impact of Electric Vehicle Integration on Power Distribution Networks Considering Energy Pricing and Load Management Techniques101113435010.22098/joape.2025.18934.2474ENBunyodjonErdanayevTermez University of Economics and Service, Termez, Uzbekistan.ZokirMamadiyarovMamun University, Khiva, Uzbekistan.Alfraganus University, Tashkent, Uzbekistan.KamolaMiraliyevna YuldashevaInternational School of Finance Technology and Science, Tashkent, Uzbekistan.0009-0003-9290-580XAkrom NormuxammadovichErkaevUzbekistan State World Languages University, 21A Building, 9A Block, Small Ring Road Street, Uchtepa District, Tashkent, Uzbekistan.NigoraTuropovaTermez State University, 190100, Termez, Uzbekistan.AbdusalimTukhtakuziyevScientific-Research Institute of Agricultural Mechanization, Samarkand Str. 41, Yangiyul Dis., Tashkent Reg., Uzbekistan.Laylo FrunzeyevnaSharipovaBukhara State Pedagogical Institute, Bukhara, Uzbekistan.Journal Article20251130This research initially examines the concepts related to distribution networks, electric vehicles (EVs), distributed generation sources, and EV load management programs over a 24-hour period from an energy pricing perspective. This analysis aims to optimize energy utilization and enhance system parameters. Additionally, the presence of renewable energy sources, such as solar energy, in the network is considered. For a distribution network incorporating distributed generation sources with variable energy prices, a load management program was implemented to optimize EV charging throughout the day. The proposed method was designed with the objectives of minimizing operational costs, power losses, and voltage drops while considering network loads in the presence of EVs. The total losses in a 33-bus network with the assumed hourly loads indicate that implementing demand response (DR) reduces network losses, whereas the presence of EVs increases these losses. Simulation results show that coordinated EV–DR scheduling effectively shifts charging away from peak hours, reduces daily operational cost by up to 7.4%, limits EV-induced loss increases from 19.4% to 6.1%, and improves voltage profiles while maintaining all network constraints. The results demonstrate that integrating EV flexibility with price-driven demand response provides a practical and effective solution for mitigating the adverse impacts of EV penetration and enhancing renewable energy utilization in distribution networks.https://joape.uma.ac.ir/article_4350_481d47a69c37c57a8e24eb15504106b1.pdfUniversity of Mohaghegh ArdabiliJournal of Operation and Automation in Power Engineering2322-457613Special Issue20251201Fuel-Cost Reduction and Energy-Efficient Control of Plug-in Hybrid Electric Vehicles Using Fuzzy Cognitive Maps by Optimization of Control Strategy in Real Traffic Conditions114127435110.22098/joape.2025.18936.2475ENAnber AbraheemShlash MohammadDigital Marketing Department, Faculty of Administrative and Financial Sciences, University of Petra, JordanSuleimanIbrahim MohammadDepartment of Business Administration, Business School, Al al-Bayt University, Mafraq 25113, JordanResearch follower, INTI International University, 71800 Negeri Sembilan, MalaysiaAsokanVasudevanFaculty of Business and Communications, INTI International University, 71800 Negeri Sembilan, Malaysia.Shinawatra University, 99 Moo 10, Bangtoey, Samkhok, Pathum Thani 12160 ThailandZokirovJavohirPhD researcher (Education), Termiz University of Economics and Service, Farovon street 4-b, Termez, Surxondaryo, UzbekistanKhаlmatjanovaGulchexraCandidate of Economic Sciences, Associate Professor, Fergana State University, Murabbiylar street, Home 19, Fergana, UzbekistanJurayevaNodirakhanPhD, Associate Professor, Head of the department "World and regional economy", Fergana State University, Murabbiylar street, Home 19, Fergana, Uzbekistan0000-0001-6753-5382YunusovAlisherCandidate of Economic Sciences, Associate Professor, Fergana State University, Murabbiylar street, Home 19, Fergana, UzbekistanTadjibaevZakirDSc, Acting Professor, Department of World and Regional Economics, Fergana State University, Murabbiylar street, Home 19, Fergana, UzbekistanMannopovaMuazzamkhonAssociate Professor, Fergana State University, Murabbiylar street, Home 19, Fergana, UzbekistanAbdullaevaShakhnozaTeacher, Fergana State University, Murabbiylar street, Home 19, Fergana, UzbekistanJournal Article20251130This study introduces a novel supervisory control framework based on Fuzzy Cognitive Maps (FCM) for optimal energy management in plug-in hybrid electric vehicles (PHEVs). The proposed supervisory controller is structured to simultaneously satisfy the driver’s demanded power, maintain the battery state of charge (SOC) within an acceptable operating range, and reduce fuel consumption. Owing to the fact that the presented method does not require an accurate system model, the computational burden associated with deriving the control policy is significantly reduced, and the overall implementation becomes less complex compared with classical control approaches. The target PHEV considered in this research features a series–parallel powertrain architecture. To evaluate the effectiveness of the proposed control strategy, simulations are conducted using three standard driving cycles along with an urban driving cycle representative of metropolitans. The results demonstrate that the proposed FCM-based supervisory controller not only fulfills the demanded traction power but also lowers fuel consumption relative to conventional fuzzy controllers, while maintaining the SOC within an appropriate operational window.https://joape.uma.ac.ir/article_4351_4bbd2a84782fa9fc1940358c62754aa1.pdf