Iran Energy Association (IEA)Journal of Energy Management and Technology2588-33729420251201Dynamic Incentive Pricing for Demand Response Programs Based on in-use Appliances Utilizing Non-Intrusive Load Monitoring21522319783710.22109/jemt.2024.449080.1499ENParsaEslamiElectrical Engineering Department, Shahid Bahonar University of Kerman, Kerman, Iran0009-0001-3306-0056AmirAbdollahiElectrical Engineering Department, Shahid Bahonar University of Kerman, Kerman, Iran.MasoudRashidinejadElectrical Engineering Department, Shahid Bahonar University of Kerman, Kerman, IranJournal Article20240319Demand response programs (DRPs) have gained significant importance in optimizing power systems by reducing peak demand, enhancing grid stability, promoting energy efficiency, and facilitating the integration of renewable energy sources. This paper introduces a novel approach for DRPs by utilizing dynamic incentive pricing strategies with in-use appliances. The proposed approach aims to incentivize consumers to curtail their energy consumption during peak periods, thereby alleviating strain on the grid. In order to address the challenges faced by existing DRPs in accurately monitoring appliance-level energy usage, this paper adopts non-intrusive load monitoring (NILM) as a powerful tool for monitoring and analyzing energy consumption patterns at the appliance level. The implemented DRP in this study is direct load control (DLC), complemented by the sequence to point (seq2point) algorithm for NILM. The proposed approach exhibits several advantages over traditional DRPs. Firstly, it enhances the accuracy of monitoring by utilizing NILM, allowing for appliance-level energy consumption analysis. Secondly, the dynamic incentive pricing strategy creates a financial incentive for consumers to reduce their energy consumption during peak periods, resulting in reduced strain on the grid and overall energy costs. The effectiveness of the proposed approach is evaluated through comprehensive economical and technical analyses. The results demonstrate its superiority compared to traditional DRPs. Notably, the proposed approach achieves a 15.7% reduction in peak demand and a 4% decrease in overall energy consumption. Furthermore, it significantly improves the load factor and peak-to-valley ratio, indicating enhanced grid stability and better utilization of energy resources.https://www.jemat.org/article_197837_81ea4af822345418edc725ec7dfe340d.pdfIran Energy Association (IEA)Journal of Energy Management and Technology2588-33729420251201Developing a Non-linear Fractional Order Model for Lithium-ion Batteries Considering the Affecting Factors22423521387910.22109/jemt.2025.482452.1530ENAliMoshar MovahhedEngineering faculty, Ferdowsi university of Mashhad, Mashhad, IranSeyed KamalHosseini SaniElectrical engineering department, Engineering faculty, Ferdowsi university of Mashhad, Mashhad, IranJournal Article20241011The growing applications of lithium-ion batteries worldwide demonstrate the importance of accurate estimation of the state of charge (SOC) of batteries, as this directly impact charging quality and battery performance, and serves as a key indicator for users. Despite the importance of SOC, there is no physical device to measure it from the battery terminal. This paper proposes a comprehensive nonlinear fractional order model incorporating key factors affecting SOC estimation such as battery hysteresis effect, temperature, and age of battery into the model structure. In order to acquire thorough and broad range of experimental data, a battery tester and a data logger were set up through gathering the data under different conditions over a substantial period of time. The electrical elements of the equivalent circuit model were obtained using a PSO algorithm, and then input into the state-space equations. Based on the model outputs, a look-up table was generated for various conditions. Being reasonably accurate, the table could reduce intensive calculations considerably. Proposed nonlinear fractional order model, significantly reduces the computational overhead compared to adaptive models. Therefor by considering the electrical parameters as constant values and transferring all non-linearity and changes such as hysteresis effect, temperature and life cycle of studied battery in VOC function, the experimental results confirm the accuracy of proposed model.https://www.jemat.org/article_213879_03ddd0a373c2e9f85c0b3ca3cd6b358b.pdfIran Energy Association (IEA)Journal of Energy Management and Technology2588-33729420251201A Novel Stochastic Approach for Modifying Non-Residential Buildings Demand Curve Using an Electric Vehicle Parking Lot23624520861210.22109/jemt.2024.450987.1502ENPaymanRezaeiDepartment of Power Engineering, Faculty of Engineering, Shahed University, Tehran, IranMahdiAkhbariDepartment of Power Engineering, Faculty of Engineering, Shahed University, Tehran, IranJournal Article20240404This paper addresses the critical challenge of managing peak load growth, which places significant financial burdens on governments due to the need for new power plants or upgrades to existing infrastructure. Flattening the demand curve through effective peak shaving and valley filling presents an opportunity to reduce these costs, yet traditional nonlinear optimization models struggle with the complexities introduced by the increasing prevalence of electric vehicles. To overcome these challenges, we propose a linearized approach based on a piecewise linear approximation technique, enhancing computational efficiency while maintaining accuracy. Additionally, we address the inherent uncertainty associated with electric vehicle arrival and departure times using Hong’s two-point estimation method. A microgrid case study utilizing real-world data collected at ten-minute intervals demonstrates the effectiveness of our approach, achieving a notable reduction in peak demand of 25.3% and decreasing computation time by 80% compared to conventional nonlinear models. Furthermore, sensitivity analysis conducted on parking availability, initial energy levels of the vehicles, and energy requirements for subsequent trips indicates the robustness and efficiency of the proposed framework. The findings suggest that this method not only optimizes electric vehicle charging management but also supports the integration of electric vehicles into the power grid, paving the way for sustainable energy management practices.https://www.jemat.org/article_208612_e46eb9c923af50ed242e8819da71a3c9.pdfIran Energy Association (IEA)Journal of Energy Management and Technology2588-33729420251201Technical and Economic Feasibility Assessment of Utilizing Renewable Energy Resources for Enhancing Energy Security and Freshwater Supply along the Makran Coast24626020595910.22109/jemt.2024.464018.1512ENMohammad JavadGhalandariFaculty of Electrical Engineering, University of Noshahr, Noshahr, Iran.MohsenZaliFaculty of the Mechanic, Malek Ashtar University of Technology, Tehran, Iran.KavehYazdiFaculty of Electrical Engineering, University of Noshahr, Noshahr, Iran.Journal Article20240621The increase in population, natural disasters, the growth of energy-intensive industries and power plant aging are among the most significant threats to energy security in Iran. A geographic feasibility assessment, along with technical and economic optimization, was conducted in this study to supply electricity to a population of 50,000 residents on the Makran coasts. Simultaneously, the excess electricity has been used to meet the energy requirements of a seawater desalination plant in this high-water scarcity area. To determine suitable locations based on available infrastructure, the Geographic Information System (GIS) was employed. Ultimately, the methods for primary power supply were identified using a HOMER-MATLAB based multi-criteria decision-making approach. The results indicate that the certain parts of Jask, Konarak, Chabahar, and Beris regions, have the capability to host hybrid renewable plants. Additionally, with less than 19 MW of renewable power connected to the national grid, the reliability of the power supply has increased by over 99%. The final excess electricity has been reduced to less than 6%, annual CO2 emissions have decreased by more than 30%, dependence on power transmission lines has decreased by over 40%, and energy costs are expected to decrease to less than $0.05/kWh.https://www.jemat.org/article_205959_66fbf55e25404846b3fa5b72498e9a61.pdfIran Energy Association (IEA)Journal of Energy Management and Technology2588-33729420251201Robust Peak Reduction in Distribution Networks Using Traditional and IoT-Based Demand Response Resources26127522137210.22109/jemt.2025.505953.1542ENTohidBabriFaculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran0000-0002-1163-1225MehdiAbapourFaculty of Electrical and Computer Engineering, Head of Energy Systems Research Institute (ESRI), Reliability & Energy Systems Management Research Lab, University of Tabriz, Tabriz, Iran0000-0003-0241-7021MortezaNazari-HerisDepartment of Engineering, East Carolina University, Greenville, NC 27858, USA0000-0001-9275-2856Journal Article20250214Abstract: Peak demand management is a significant challenge for power grids, primarily due to constraints in generation capacity and rapidly increasing energy consumption. The emergence of new, energy-intensive loads, such as Bitcoin mining farms, has exacerbated the pressure on power utilities during peak demand periods. To address these challenges, demand response programs have emerged as practical solutions to mitigate peak load problems. This study investigates peak demand reduction through two demand response schemes: Time of Use (TOU), a traditional approach, and Automatic Demand Response (ADR), which has attracted increasing attention recently. Participants in these programs act as demand response resources for distribution companies (DisCos) in managing sustained peak loads. The TOU program is designed for elastic load customers, while ADR is applied to residential users and mining operations. The main contribution of this work is the development of a risk-based integrated scheduling model for Demand Response Resources (DRRs), designed to reduce peak demand cost-effectively across various operational tariff structures . These tariffs include price-based Time-of-Use (TOU) for price-sensitive aggregators and incentive-based ADR structures that provide compensation for residential and mining farm customers. Notably, the ADR strategy utilizes Internet of Things (IoT) technology to control household appliances and temporarily shut down cryptocurrency mining equipment. The proposed components are assessed using a detailed optimization model that accounts for the operator's robusteness toward renewable energy generation in the day-ahead scheduling process.<br /><br />Keywords: Demand response, ADR, Internet of Things, TOU, Cryptocurrency, Distribution Networks.https://www.jemat.org/article_221372_d1f1c91f41c51238d19a505303ce14eb.pdfIran Energy Association (IEA)Journal of Energy Management and Technology2588-33729420251201A novel arrangement of feedback linearization, sliding mode control, multi-objective optimization and fuzzy logic for nonlinear under-actuated systems27628320892310.22109/jemt.2024.449117.1498ENMohammad JavadMahmoodabadiDepartment of Mechanical Engineering, Sirjan University of Technology0000-0002-4249-8623SalehVerdipour LomerDepartment of Mechanical Engineering, Sirjan University of Technology, Sirjan, Iran.MasoudSotoodeh BahrainiDepartment of Mechanical Engineering, School of Engineering, University of Birmingham, Birmingham, UK.Journal Article20240318In this research, a novel fuzzy optimal robust control approach based on the feedback linearization scheme is introduced for a nonlinear under-actuated ball-wheel system. At first, the feedback linearization idea is employed to transform the nonlinear formulations of the ball-wheel system to the linear terms via changing variables instead of approximating them. Then, a robust sliding mode control technique is directed to stabilize the respected under-actuated system. The optimum values of the parameters associated to the designed controller are obtained via a multi-objective optimization process established on the Non-dominated Sorting Genetic Algorithm II (NSGA II). Eventually, a fuzzy logic-based system is designed to enhance the performance of the control system. Comparison of the simulations obviously indicates that the recommended schemes including the sliding mode control, multi-objective optimization and fuzzy system are valid approaches to improve the stabilization task of the feedback linearization control idea for nonlinear systems such as the ball-wheel system.https://www.jemat.org/article_208923_5cc05239f893655c87a7994ab1948eef.pdfIran Energy Association (IEA)Journal of Energy Management and Technology2588-33729420251201Comparative Applications of Supervised and Unsupervised Machine Learning Models in Energy Systems28429023372410.22109/jemt.2025.547118.1573ENAmiraliSaifoddinSchool of Energy Engineering and Sustainable Resources, Head of Soft Technologies Institute, College of Interdisciplinary Science and Technology, University of Tehran, Tehran, Iran0000-0002-9996-5886NeginMirzaeiSchool of Energy Engineering and Sustainable Resources, College of Interdisciplinary Science and Technology, University of Tehran, Tehran, IranMohammadaliAllahrabbi ShiraziSchool of Energy Engineering and Sustainable Resources, College of Interdisciplinary Science and Technology, University of Tehran, Tehran, Iran0000-0001-6929-8835HosseinYousefiSchool of Energy Engineering and Sustainable Resources, College of Interdisciplinary Science and Technology, University of Tehran, Tehran, Iran0000-0002-6372-5127Journal Article20250914Modern energy systems are facing growing complexities, including the integration of renewable resources, the expansion of decentralized networks, and dynamic changes in consumption patterns. While traditional physics-based models may perform well under steady conditions, they exhibit clear limitations when confronted with uncertainties and real-time variations. In this context, data-driven and machine learning approaches have emerged as innovative tools that, by leveraging historical and real-time data, enable the analysis of nonlinear relationships and the identification of hidden patterns. The purpose of this study is to examine and compare two categories of artificial intelligence models in energy systems: supervised learning and unsupervised learning. The findings indicate that each category has its own strengths and limitations: supervised learning models are effective in load forecasting and energy generation prediction, whereas unsupervised learning models are valuable for pattern discovery and anomaly detection. The novelty of this paper lies in presenting an integrated analytical framework for comparing the applications of these models in energy systems, addressing both practical applications and theoretical challenges. Despite significant progress, a key research gap remains: the need for scalable and transparent models that can ensure both predictive accuracy and interpretability. The results show that while each approach individually addresses part of the requirements of energy systems, combining them in semi-supervised methods or hybrid frameworks can be an effective step toward improving efficiency, resilience, and sustainability. This advancement not only contributes scientifically but also leads, in practice, to optimized resource management, cost reduction, and enhanced grid security.https://www.jemat.org/article_233724_dd7fdd824c332ae6c0da8ce1063a038d.pdf