https://www.jemat.org/ Journal of Energy Management and Technology en daily 1 Tue, 01 Sep 2026 00:00:00 +0330 Tue, 01 Sep 2026 00:00:00 +0330 https://www.jemat.org/article_250760.html This study presents a three-dimensional numerical simulation of chilled water flow in copper pipes installed on an aluminum plate using ANSYS Fluent to optimize the thermal performance of radiant ceiling panels. Unlike previous studies that have examined design parameters in isolation, the present work simultaneously investigates the coupled effects of four key variables pipe spacing, pipe diameter, mass flow rate, and inlet fluid temperature on cooling capacity and surface temperature uniformity. The flow was modeled as turbulent, incompressible, and transient, and the numerical model was validated against ASHRAE experimental data. The results show that reducing tube spacing from 0.30 m to 0.05 m increases cooling capacity by up to 35%; a practical range of 10–15 cm (60–63 W) is recommended. Reducing pipe diameter from 20 mm to 10 mm yields an 8% performance gain, with 12 mm (102 W) identified as the practical optimum. Increasing mass flow rate raises cooling capacity by 25%, with a pronounced step increase at the laminar-to-turbulent transition (0.016–0.017 kg/s). Inlet temperature exerts the largest influence, with capacity ranging from 108 W at 12 °C to 46 W at 18 °C; 15 °C is recommended as the optimal operating point. Transient analysis confirms that the panel reaches thermal steady state within approximately 3 minutes significantly faster than concrete-embedded alternatives. The coupled parametric framework provides a systematic basis for the component-level design of energy-efficient radiant cooling systems, advancing beyond prior single-parameter studies by quantifying the interactions among geometric, hydraulic, and thermal design variables within a unified model. https://www.jemat.org/article_248163.html Finding suitable land for electric vehicle charging stations (EVCSs) is difficult in densely populated cities. Meanwhile, the growing adoption of electric vehicles (EVs) can substantially raise electricity consumption and put additional stress on the power grid. This paper suggests large shopping and commercial centers as suitable locations for solar-powered EVCSs to address these two challenges. The commercial complex will have photovoltaic (PV) power generation on the roof area and the parking capacity is also considered for fast EV charging. The case study is the commercial and recreation complex of Iran Mall in Tehran and the proposed PV system is simulated by PVsyst in three scenarios, fixed panels, single-axis tracking panels and dual-axis tracking panels. Simulation results show that the annual electricity generation of the fixed-panel scenario, single-axis tracking scenario, and dual-axis tracking scenario are 4,195,704 kWh, 5,490,709 kWh, and 5,688,468 kWh respectively for the available rooftop area of 11,000 m2. The PV system is taken as a grid-connected system, which generates electricity during the available solar hours and the assumed EV charging operation window is from 8 a.m. to 2 p.m. In this charging period, the proposed EVCS can charge up to 180 EVs per day with 15 fast charging points, each EV with 50 kWh battery capacity and charging time of 30 min. The findings demonstrate that commercial and recreational complexes can be effectively utilized for solar electricity generation and EV charging, thereby reducing grid dependence and contributing to the reduction of urban air pollution. https://www.jemat.org/article_248162.html This study explores the role of double-layer and electrochromic windows compared to simple single-layer windows in energy conservation within buildings. The study investigates the energy-saving potential of these advanced window technologies by analyzing their thermal performance, daylighting capabilities, and overall impact on building energy consumption. A comparative analysis evaluates each window type's benefits and limitations, considering factors such as insulation properties, solar heat gain control, and visual comfort. The findings suggest that double-layer and electrochromic windows offer significant advantages over traditional single-layer windows in reducing energy usage, enhancing occupant comfort, and promoting sustainable building practices. The building under study is modeled in DesignBuilder, and the impact of these three types of windows on the application is analyzed. On the hottest day of the year, the operative temperature is 1.3 degrees Celsius lower when utilizing double-layer windows compared to single-layer windows, and 0.2 degrees lower compared to electrochromic windows. On the coldest day of the year, using double-layers results in an operative temperature of 14.14 degrees Celsius, which is 1.3 degrees higher than single layers and 0.13 degrees lower than electrochromic windows. https://www.jemat.org/article_251626.html The share of renewable energy sources (RESs)-based generation within contemporary power systems has expanded substantially in recent years, driven by environmental concerns and policy incentives to promote clean and sustainable electricity generation. However, the inherently variable and difficult-to-predict behavior of renewable generation, with a particular emphasis on wind-based resources, introduces substantial operational challenges for system operators that require effective flexibility resources. This paper addresses these challenges by formulating a network-constrained unit commitment (NCUC) model that incorporates mobile-based battery energy storage systems (MBESSs) and price-based demand response program (DRP) as key flexibility resources. A time–space network is employed to optimize the charging, discharging, and transportation schedules of MBESSs. Moreover, a new version of the information gap decision theory (IGDT) method is developed to handle wind power uncertainty more accurately. Unlike the conventional IGDT approach, the proposed method introduces a time-varying uncertainty radius, effectively capturing the dynamic and variable behavior of wind power. The model is formularized as a mixed-integer linear programming (MILP) problem and tested on a six-bus power system integrated with a three-station railway network. Simulation outcomes demonstrate that the proposed framework completely eliminates load shedding and wind power curtailment. Furthermore, compared to the conventional IGDT, the proposed novel method can provide different levels of uncertainty for each interval of the scheduling horizon. https://www.jemat.org/article_250315.html This paper explores static voltage stability enhancement in radial distribution feeders by integrating PV farms, utilizing a backward-forward sweep (BFS) load flow. Increasing penetration of distributed generation, such as PV systems, introduces challenges in maintaining voltage stability within distribution networks. Traditional load flow techniques often fail to address these issues efficiently in radial feeders. Meanwhile, BFS offers a robust solution by iteratively solving power flow equations from end nodes backward to source one, then forward to update voltages. A case study is conducted on a standard IEEE 33-bus radial distribution grid to evaluate performance. PV farms are placed at selected nodes to assess their impact on voltage profiles and stability margins. Numerical results demonstrate that strategic PV integration significantly improves voltage stability, reducing active power losses and enhancing system loadability. Sensitivity analyses examine effects of varying load levels on static voltage stability of system. Key findings indicate that BFS effectively identifies weak nodes to mitigate voltage instability. Compared to conventional algorithms, this approach provides faster convergence and higher accuracy in radial grid https://www.jemat.org/article_250363.html This paper proposes a robust load frequency control (LFC) scheme for a multi-area hybrid power system (HPS) incorporating Interline Power Flow Controllers (IPFCs) and Energy Storage Systems (ESSs). The stochastic nature of renewable generation sources, combined with potential parametric deviations in generation units and network components, necessitates an LFC design that simultaneously addresses multiple conflicting performance objectives. Improving frequency regulation and tie-line power tracking typically demands more aggressive control actions, thereby increasing control effort, while enhanced robustness tends to deteriorate transient response and prolong settling time. To systematically capture these trade-offs, the LFC design is formulated as a multi-objective optimization problem with three competing objectives: minimization of the integral time-weighted absolute error (ITAE), minimization of the H∞-norm of the complementary sensitivity function, and minimization of the control effort. The Strength Pareto Evolutionary Algorithm 2 (SPEA2) is employed to generate a Pareto-optimal set of candidate controllers, and a knee-point selection criterion is applied to identify a representative solution that balances the competing objectives without undue sacrifice of any single criterion. The proposed scheme is validated under parametric uncertainty, variable load disturbances, and model nonlinearities, and its superiority is demonstrated through comparative analysis with established control approaches. https://www.jemat.org/article_251625.html This study examines the integration of renewable energy into Iran's electricity grid to meet growing demand while reducing the environmental burden of fossil fuel generation. A Unit Commitment (UC) model is used to simulate power generation schedules and to calculate the Levelized Cost of Energy (LCOE) for each generation technology. In the baseline case, combined cycle, gas, and steam power plants supplied about 91% of Iran's electricity demand, while wind and solar power together contributed less than 0.2%, highlighting the limited role of renewables in the current generation mix. Three scenarios were evaluated: redistribution of 10% of non-renewable capacity to wind and solar, raising the renewable share to 16.11%; meeting a 10% increase in demand through renewable expansion, yielding a combined wind and solar share of 14.9%; and increasing solar and wind capacity to 10 GW each, achieving a total renewable share of 23.52%. The results indicate that an optimal energy mix drawing on Iran's abundant solar and wind resources could reduce dependence on fossil fuels, strengthen grid resilience, and support a more economical and sustainable power system. https://www.jemat.org/article_251794.html Iran has experienced persistent electricity shortages due to rapid demand growth, aging generation infrastructure, electricity price subsidies, and institutional challenges that have constrained investment and system modernization. This study evaluates the underlying causes of these shortages and assesses the potential of demand-side management (DSM), renewable energy deployment, and policy reforms to improve electricity system reliability. A mixed-methods approach was employed, combining quantitative analysis of electricity demand, generation capacity, and supply–demand imbalance with qualitative assessment of governance structures, regulatory frameworks, and DSM implementation. The results indicate that existing DSM programs have achieved only modest peak-demand reductions because of limited smart-meter deployment, weak consumer participation, and insufficient financial incentives. The analysis further suggests that utility-scale solar and wind projects can complement DSM in reducing medium-term supply deficits when supported by appropriate regulatory reforms and grid flexibility measures. Moreover, institutional fragmentation and non-cost-reflective electricity tariffs remain major barriers to effective electricity sector reform. The study concludes that improving electricity security in Iran requires an integrated policy framework combining phased tariff reform, expanded DSM programs, accelerated renewable energy deployment, strengthened institutional coordination, and greater private-sector participation. The proposed framework provides practical policy recommendations for enhancing electricity system resilience while supporting the country's long-term energy transition. https://www.jemat.org/article_252106.html The application of phase change materials (PCMs) in smart window technologies offers a promising strategy for improving building energy efficiency and enhancing indoor environmental quality. However, climate-specific performance optimizations—particularly evaluating local commercial PCM formulations in semi-arid conditions with high diurnal temperature variations—remain insufficiently explored. This study investigates the thermo-energy performance of PCM-integrated double-glazed windows for residential buildings in Karaj, Iran, using validated EnergyPlus simulations with the Conduction Finite Difference (CondFD) algorithm. Unlike generic studies, this research evaluates locally available PCM formulations across various phase-change transition temperatures (21°C, 24°C, and 28°C). Results demonstrate that PCM-integrated glazing substantially reduces both heating and cooling energy demands. Specifically, annual cooling electricity loads decrease by 6.6% to 7.5% (with PCM24 offering the highest cooling performance), while annual heating natural gas consumption drops by 36.8% to 39.8% (with PCM21 yielding the greatest heating savings). The PCM21 configuration achieves the highest total annual energy saving of 4,063.1 kWh (representing a 17.5% reduction in overall building energy consumption). Comparative analysis indicates that integrating PCMs into glazing under semi-arid conditions yields up to a 10% higher heating load reduction than reported in humid or cold climate baselines, driven by substantial diurnal temperature fluctuations. These findings identify optimal phase-change threshold criteria for semi-arid Iranian climates, providing actionable insights for energy-conscious architectural design and building envelope retrofits. https://www.jemat.org/article_252428.html Universities, as major energy consumers, play a key role in reducing energy consumption through intelligent planning. Optimizing course scheduling is one of the less explored but highly impactful solutions for reducing energy use in educational buildings. This study aims to develop an energy-efficient class scheduling model that considers temporal, spatial, and infrastructural constraints of academic campuses. The proposed model formulates the scheduling process by incorporating constraints such as building capacity, simultaneous sessions, professor availability, and the need for symmetrical timetables. Unlike conventional scheduling, which is typically based on administrative convenience, this approach leverages real meteorological data (temperature, humidity, wind speed) from the fall semester in Tehran to align class times with periods of lower energy demand. The innovation of this study lies in the integration of energy-saving goals with scheduling algorithms, creating a dual-objective framework that balances academic and environmental priorities. While many studies have addressed either scheduling or energy management, few have proposed an integrated, constraint-based model tailored for postgraduate academic settings. The results show that applying the optimized scheduling model leads to a significant reduction in energy consumption, between 8% to 12% depending on the scheduling scenario. Additionally, the spatial-temporal optimization allows for better use of daylight and ventilation potential, contributing to improved energy performance of buildings without requiring any physical retrofitting. https://www.jemat.org/article_252803.html This study addresses Generation Expansion Planning (GEP) with the primary objectives of cost reduction, emissions minimization, and enhanced grid reliability through a lower Loss of Load Probability (LOLP). We proposed a hybrid system integrating nuclear and renewable sources to pave the way for a carbon-free grid, effectively mitigating the limitations inherent in each technology when operated independently. A key innovation of this research lies in the strategic integration of a Battery Energy Storage System (BESS) to directly address the intermittency of renewable sources and substantially improve grid reliability. To accurately model the real-world uncertainties affecting this hybrid system, including energy demand, fuel prices, wind speed, solar irradiation, and plant depreciation rates, we employ Probability Density Functions (PDFs) and Beta distributions. The stochastic behavior of these variables is then simulated using a Markov Chain Monte Carlo (MCMC) method for robust forecasting. The model is subsequently optimized using a Stochastic Dynamic Optimization algorithm. The efficacy of our introduced BESS is demonstrated through a significant improvement in a critical reliability metric. Our simulation results show that the cumulative LOLP index over a 13-year planning horizon was reduced from 2.85 to 2.38. This marked improvement was achieved with an initial BESS capacity of only 20 MW in the first year, expanded at a modest annual growth rate of 10%, highlighting the practical efficiency and scalability of our presented solution. https://www.jemat.org/article_252804.html This study develops a robust scheduling framework for an integrated electricity–gas–heat–hydrogen energy hub with Behind-the-Meter (BTM) photovoltaic–battery systems and multi-carrier demand response. The framework jointly represents renewable-generation and electrical, gas, and heat demand uncertainties through polyhedral uncertainty sets, while incorporating battery degradation-aware operating constraints and technology-specific emissions. Four configurations are evaluated under common system conditions to isolate the effects of BTM resources and demand response. The baseline operating cost of $14,528 decreases to $11,281 with BTM resources, $13,661 with demand response, and $9,650 when both are coordinated, corresponding to a maximum cost reduction of 33.6%. Emissions decrease from 14,805 kg CO₂e in the baseline to 14,115 kg CO₂e with BTM resources, whereas demand response alone increases emissions to 15,383 kg CO₂e. Their combined operation limits emissions to 14,239 kg CO₂e while achieving the lowest operating cost. The results show that the economic and environmental effects of demand-side flexibility depend strongly on its coordination with local generation and storage under uncertainty. https://www.jemat.org/article_253212.html The increase in the global population, along with the industrialization of human life, has led to a daily rise in energy consumption. This trend has, in turn, contributed to issues such as greenhouse gas emissions. A significant portion of energy consumption is attributable to the building sector. Nowadays, various technologies, such as atriums, are employed to reduce energy consumption in buildings. In this study, four atrium designs were proposed and evaluated for a school building in Tehran. The Design Builder software was used to assess the respective systems. The results indicate that Sketches 3 and 4 are not energy-efficient and lead to an increase in the building’s overall thermal load. In Sketch 2, the heating load decreased by 23.15%, while the cooling load increased by 9.59%, resulting in an overall reduction of the building’s total thermal load by 7.56%. With Sketch 1, the heating load dropped by 19.52%, while the cooling load rose by 17.47%, leading to an overall decrease in the building’s thermal load by 1.73%. Given that using an atrium increases the window-to-wall ratio, a concurrent reduction in heating load and increase in cooling load is expected. Based on the building’s total thermal load, Sketch 2 is ultimately recommended as the optimal atrium design for this building. Implementing an atrium according to Sketch 2 would lead to a 7.56% reduction in the building’s total energy consumption. https://www.jemat.org/article_254577.html Urban multi-energy communities face coupled carbon regulations, volatile markets, and uncertain renewable generation, creating a need for decision systems that adapt beyond static optimization. This study develops an adaptive computational knowledge-based expert system for coordinating electricity, heat, gas, hydrogen, and carbon flows under uncertainty. The architecture combines carbon accounting and tiered carbon trading with a joint conditional value-at-risk and chance-constrained decision structure. It comprises three intelligent cores: an adaptive knowledge-updating layer for dynamic parameter refinement, a digital-twin-based state inference engine for interconnected energy hubs, and a synchronization core that aligns operational decisions with observed system conditions. Distributed coordination is implemented through a decentralized alternating direction method of multipliers framework, supporting scalable reasoning while preserving modularity. Carbon trading is treated as a policy-sensitive decision component, enabling reward–penalty signals to influence operational inference rather than acting as a simple linear cost. The integrated risk-aware engine evaluates feasible schedules under renewable uncertainty and market volatility. Simulations for an energy-intensive industrial community show that the system reduces total operating cost by nearly 20% relative to a carbon-trading-only configuration, lowers carbon emissions by more than 51% through coordinated carbon reuse, and cuts carbon-trading expenditures by approximately 80%. Renewable energy is fully absorbed under adaptive coordination. Sensitivity analysis further indicates that higher carbon prices activate risk-aware responses that simultaneously reduce emissions and improve operating economics. https://www.jemat.org/article_254578.html Objective: This paper provides a comprehensive analysis of the solar energy revolution, examining technological evolution, socio-economic benefits, grid integration challenges, and the pivotal role of governance and public trust in the global energy transition. Methodology: A systematic literature review following PRISMA guidelines was conducted across Scopus, Web of Science, ScienceDirect,& IEEE Xplore for studies published between 2015 and 2025. From 240 initial records, 29 high-quality studies were retained after rigorous quality assessment and synthesized thematically. Findings: The global weighted-average Levelized Cost of Energy (LCOE) for utility-scale solar PV declined from approximately USD 0.371/kWh in 2010 to USD 0.043/kWh in 2024, while battery storage costs fell from about USD 1,000/kWh to USD 130/kWh. Crystalline silicon remains dominant, while third-generation perovskites promise higher efficiency, targeting 30% by 2030, contingent on resolving stability issues. Solar deployment drives job creation, energy security, and public health benefits. However, intermittency creates grid integration challenges, including the duck curve and nonlinear storage requirements exceeding 50% PV penetration. Gridforming inverters, battery storage, and smart grids are critical enablers. Governance and public trust conceptualized as institutional, technological, and procedural are decisive determinants of deployment success. Comparative analysis of Iran, India, and South Africa reveals that energy crises, not climate policy, drive adoption in developing countries, where inequality and institutional distrust shape outcomes. Conclusion: Solar power is a cornerstone of the energy transition, delivering cost reductions, employment, and climate mitigation. Its largescale adoption requires coordinated advances in storage, smart grids, and supportive, trust-building policies, with particular attention to energy justice.. https://www.jemat.org/article_254580.html This study investigates the effect of collector surface area on the outlet fluid temperature and collector surface temperature of a photovoltaic–thermal (PVT) collector integrated with a solar-powered reverse osmosis desalination system. The analysis focuses on the thermal behavior of the PVT collector under the climatic conditions of two southern coastal cities of Iran, Bandar Bushehr and Bandar-e Lengeh, representing regions with high solar potential. A thermodynamic model was developed and validated against published data before performing a parametric analysis of collector surface area. The results show that increasing the collector area significantly enhances both the outlet fluid temperature and the collector surface temperature due to greater solar energy absorption. Increasing the collector area from 0.5 to 2.0 m² increases the collector surface temperature by approximately 13% and the outlet fluid temperature by nearly 30% in both locations. In addition, Bandar-e Lengeh exhibits a more stable thermal response than Bandar Bushehr because of its smaller daily ambient temperature fluctuations. These findings demonstrate that collector surface area is a key design parameter for improving the thermal performance of PVT-driven desalination systems under the climatic conditions of southern Iran.