Journal of Green Energy Research and Innovation

Abstract Renewable energy hubs have the potential to significantly improve the technical performance of microgrids while reducing environmental pollutants. This is achieved through efficient energy management within the hubs and determining their optimal capacities and placements in the energy network. This article focuses on the planning and operation of renewable energy hubs integrated with storage systems in microgrids. The objective is to minimize the overall costs related to building resources and storage facilities within these hubs. Key constraints addressed include power flow equations, operational limitations, and the planning-operational model of the hubs. The innovation of this approach lies in combining a comprehensive planning-operation model for renewable energy hubs with the implementation of a bio-waste unit model. The numerical results underscore the effectiveness of this strategy, demonstrating improvements in microgrid performance through efficient hub planning and operation. Specifically, the optimal planning process achieved the lowest construction costs for the hubs, while the optimal operation led to substantial reductions in energy losses and voltage drops within the microgrid by approximately 33.8% and 51.3%, respectively, compared to traditional power flow analysis. In this condition for the used case study, the planning cost of EHs is 46.43 million $.

Abstract supporting industrial and societal growth. Power generation is carried out through various sources such as solar energy, wind, and thermal systems, each with unique infrastructural demands and environmental impacts. Assessing the environmental consequences of constructing power plants is essential for minimizing harm to ecosystems and communities. This study aims to identify optimal locations for establishing electricity generation facilities by evaluating both infrastructure requirements and environmental considerations. A comprehensive site selection process is conducted, taking into account the characteristics of different types of power plants. Cost estimation for each potential site includes factors such as land acquisition and is tailored to the type and capacity of the plant. Environmental variables are also incorporated into the analysis to provide a more holistic view of each option. Using GAMS optimization software, the study identifies the most cost-effective and environmentally responsible sites for power plant installation and recommends the most suitable capacities for each selected location. The findings contribute to more sustainable energy planning and informed decision-making in the energy sector.

Abstract The Z-source inverter (ZSI) is considered a suitable configuration for renewable energy systems due to its ability to provide both voltage step-up and step-down in a single stage. ZSI in renewable energy systems typically requires an appropriate closed-loop control strategy to ensure voltage regulation, grid connection, and maximum power point tracking (MPPT). Linear control strategies are generally formulated based on local linearization around a predefined operating point. Nevertheless, this approach does not guarantee the stability of a nonlinear system over a wide range of operating conditions. In this research, a novel multi-input multi-output (MIMO) Interconnection and Damping Assignment Passivity-Based Control (IDA-PBC) scheme is proposed for controlling the operation of a grid-connected single-phase ZSI. The dynamic behavior of the ZSI is modeled using the state-space averaging technique. Subsequently, the obtained averaged model is transformed into a port-controlled Hamiltonian (PCH) framework. The proposed IDA-PBC approach is capable of regulating all system state variables, including the grid-side AC current and the voltage across the DC-link capacitors. These states are regulated through the amplitude modulation index and the shoot-through duty cycle. Utilizing Lyapunov-based analysis, the asymptotic stability of the proposed control strategy is analytically verified. Furthermore, the controller achieves zero steady-state error in tracking both the DC-link reference voltage and the grid-injected AC reference current. The control design also demonstrates strong stability and robustness with respect to variations in system parameters. A series of simulations in MATLAB/Simulink have been conducted to assess the performance and robustness of the proposed control methodology.

Abstract This study explores the optimization of light shelf systems to enhance daylight performance and reduce energy consumption in office buildings located in hot and dry climates, with a focus on Isfahan. Given the region’s high solar radiation and the critical role of buildings in energy usage, the research employs a hybrid methodology that integrates parametric simulation with multi-objective optimization. The modeling was performed using Rhinoceros and Grasshopper, while daylight analysis utilized the Honeybee plugin with Radiance, and energy simulation relied on EnergyPlus. Findings indicate that the optimal light shelf configuration—an 85 cm external depth, a 28° tilt angle, and a 0.9 reflectance—can reduce annual Energy Use Intensity (EUI) by up to 5.8% and improve Spatial Useful Daylight Illuminance (sUDI) by 53%. Additionally, glare was reduced by 70% during midday hours. These results highlight the effectiveness of integrating passive daylighting strategies in sustainable architectural design for hot and arid climates.

Abstract The rapid pace of urbanization and rising energy demands have intensified environmental and climatic vulnerabilities in cities, necessitating resilient and sustainable urban planning. This study, Key Factors of Green Energy Synergy in Urban Planning with Emphasis on Environmental Resilience, aims to identify critical factors enhancing the integration of renewable energy in urban systems to bolster environmental resilience. Employing a mixed-methods approach, the study utilized the Delphi technique with 14 experts to identify 32 key indicators, followed by structural analysis using MICMAC software to map their interactions. The results revealed a high interaction matrix fill rate of 81.5%, confirming strong interdependencies among the factors. The total direct influence and dependency scores across all indicators were 1571 each, with indirect effects exceeding 120,000, highlighting the systemic complexity of the urban energy–resilience nexus. Among the indicators, thermal energy recovery (C20, direct influence = 55), urban system flexibility (C14, dependency = 54), and infrastructure recovery speed (C12, influence = 54) demonstrated the highest leverage in shaping system dynamics. Nine strategic indicators emerged as pivotal: smart energy grid efficiency, urban system flexibility, thermal energy recycling, green transportation share, urban innovation, resilience to climate change, digital infrastructure resilience, greenhouse gas emission reduction, and policy stability. These factors collectively enhance energy efficiency, reduce environmental impacts, and strengthen urban adaptability, particularly in developing countries. The findings offer a robust framework for policymakers to prioritize green energy integration, fostering sustainable and resilient urban development.

Abstract This study presents a hybrid CNN-LSTM model for short-term energy consumption prediction in Iranian residential buildings, focusing on Tehran. By integrating multimodal data—meteorological, temporal, occupancy proxies, and building metadata—and employing deep feature engineering via a stacked denoising autoencoder, the model achieves high accuracy (R² = 0.89) and robustness against data imperfections. The framework demonstrates the critical role of cultural and contextual features, such as Iranian holidays, in enhancing prediction validity. SHAP analysis provides interpretability, aligning model logic with local realities. The results offer a scalable, context-aware solution for intelligent energy management in Iran’s urban environment.

Abstract With the growing need for sustainable and optimized energy supply in industrial areas, microgrids have emerged as an effective solution. Accordingly, selecting capable companies to provide energy through industrial microgrids requires a structured evaluation and ranking process. This paper presents a hybrid decision-making framework that integrates Step-wise Weight Assessment Ratio Analysis (SWARA) and Complex Proportional Assessment (COPRAS) to assess and rank energy supply applicants. Key criteria and sub-criteria—including technical-operational, economic, environmental, managerial, service-oriented, and future development aspects—were identified and weighted based on expert judgment. A customized utility formulation was applied within the COPRAS method to accommodate domain-specific priorities. A case study involving three candidate companies demonstrated the practical application of the framework: Supplier A achieved the highest utility score (Q=0.3445Q = 0.3445) and consistently ranked first across multiple weighting scenarios. Comparative analysis with AHP + TOPSIS confirmed the reliability of the results. The proposed framework offers transparency, adaptability, and robustness, making it a valuable decision-support tool for industrial energy procurement.

Abstract This paper addresses the optimal, coordinated, and flexible management and operation of resources within an energy hub, focusing on its role in energy market participation. The study emphasizes the integration of distributed generation sources, electrical storage systems, thermal storage units, heat exchangers, and combined heat and power (CHP) systems. Various technologies, including transformers, compressors, energy storage devices, and electric vehicles, are employed for the conversion, storage, and transmission of energy. The proposed network is analyzed in conjunction with an integrated energy grid, ensuring flexible and secure operations with high reliability across diverse energy networks. A mathematical model is introduced as an optimization problem, where the network operator aims to achieve optimal operation of the integrated system by considering load flow equations across electricity, gas, and thermal networks. This approach facilitates a flexible management strategy for energy hubs that aligns with the integrated system to enhance market participation. Additionally, the proposed model incorporates various parameters alongside uncertainties, thus presenting a robust optimization method to address these uncertainties effectively. By leveraging this comprehensive framework, the study aims to contribute to the advancement of energy hub management practices and their integration into dynamic energy markets.

Abstract The use of ethanol can be a solution for reducing of gasoline consumption and concentration of emissions in the agricultural SI engines. The engine performance changes when using a blended fuel. In this study, first, the effect of various bioethanol-gasoline blends at different loads and speeds was experimentally investigated. The brake thermal efficiency at all conditions decreases with increasing ethanol percentage, and that advanced spark timing provides a good opportunity to increase it. as well as concentrations of HC and CO increased while amount of CO2 and NOX are decreased compared to basic state. In the second stage, the effect of different advanced spark timings and the determination of the appropriate spark timing were carried out using GT-POWER. Brake thermal efficiency increases with appropriate spark advance and decreases with increasing engine speed. The maximum brake thermal efficiency was obtained with E10, E20, and E30 in 2500rpm as 27.02%, 27.03%, and 26.75%, respectively. Also, the positive and negative effects of adding ethanol to gasoline are nearly neutralized; therefore, the maximum brake thermal efficiency is almost identical. In the third part, the brake thermal efficiency and concentration of emissions with E30 and appropriate advanced spark timing (24BTDC) at a speed of 2500 rpm was experimentally investigated. The brake thermal efficiency increased from 25.01% to 27.37%. Also, the concentrations of HC and CO decreased, while the concentrations of CO2 and NOx increased and close to basic state (E30 & 20 BTDC), but concentrations of CO2 and NOx are less than pure gasoline state.

Abstract This plan outlines an energy scheduling strategy for a distribution grid with multi-microgrid systems, based on the assessment of economic and operational indicators within the microgrids. The microgrid operates on a multi-bus structure, incorporating renewable sources like solar and wind alongside non-renewable resources and hydrogen storage systems. The primary objective is to minimize the operational costs of microgrids and associated resources. The problem is subject to various constraints, including the optimal power flow formulation for the microgrids, performance models of renewable and non-renewable units, and the dynamics of storage devices. Due to the non-convex and nonlinear nature of the formulation, a honey-bee mating optimization method is employed to ensure a reliable solution with minimal deviation toward the final result. The findings demonstrate the plan’s potential to enhance both the technical and economic performance of microgrids. Specifically, energy scheduling for the included units and storage systems can improve operational efficiency by approximately 59.2% and economic conditions by about 44.2%. Furthermore, the proposed solution approach achieves sustainable computing conditions by delivering near-optimal solutions with low computational time and a standard deviation of just 0.97% in the final results.

Abstract Enhanced Gas Recovery (EGR) and Enhanced Oil Recovery (EOR) offer dual benefits of hydrocarbon extraction and Carbon Capture and Storage (CCS), supporting global net-zero ambitions. This paper presents a comprehensive review that synthesizes technical, economic, environmental, and social dimensions of CO2-EGR, CO2-EOR, non-CO2 methods (e.g., N₂, steam, chemicals), and hybrid EGR–EOR systems in mixed hydrocarbon reservoirs. While prior studies have explored these approaches separately, few works provide an integrated review that combines hybrid recovery strategies with Artificial Intelligence (AI) applications for reservoir modeling and leakage monitoring representing the novelty of this study. Key findings from the reviewed literature show that CO2-EGR achieves 20–40% incremental methane recovery with 0.5–2 tons CO2 stored per ton methane, whereas CO2-EOR yields 5–15% additional oil recovery with 0.2–0.5 tons CO2 per barrel stored. Hybrid systems deliver 15–30% overall recovery and 0.4–1.2 tons CO2 stored per unit hydrocarbon, though phase separation can reduce efficiency by 10–15%. Reported mitigation strategies such as alternating CO2–water injection (WAG), optimized well placement, and cyclic injection patterns can reduce efficiency loss to ~5–7%. Economic viability (NPVs: $60–120M) is strongly influenced by oil/gas price fluctuations (±15% NPV) and carbon credit incentives ($5–45/ton CO2). Environmental risks include leakage probabilities of 1–5% (EGR) and 5–10% (EOR), as well as seismicity of 0.1–1%. AI-driven monitoring validated on Sleipner, Weyburn, and simulation datasets achieves up to 95% leakage detection accuracy and can be integrated into dynamic reservoir models for real-time risk mitigation. By synthesizing insights across disciplines, this review underscores the potential of hybrid EGR–EOR projects to contribute to the projected 280 Mt/year CO2 storage target by 2035 and highlights the role of AI in reducing uncertainty, guiding pilot validation, and supporting scalable deployment toward net-zero by 2050.

Abstract Power systems integrated with renewable energy sources represent inherently complex and nonlinear structures, often subject to significant frequency deviations and power oscillations, particularly during periods of production shortfall under dynamic and high-load conditions. Furthermore, continuous fluctuations in load demand contribute to variations in grid frequency, transmission line power flow, and the output of generation units. To address these challenges, modern power grids employ Automatic Generation Control (AGC) systems. AGC functions to restore system frequency to its nominal value by utilizing a control metric known as the Area Control Error (ACE), while also ensuring that scheduled inter-area power transfers are maintained at their predefined levels. Achieving production balance in such situations is highly challenging. To address this issue, advanced control techniques and rapid energy storage systems (ESS ) are required. ESS units, such as Capacitive Energy Storage (CES ) systems, exhibit remarkable capabilities in balancing production demand and grid frequency demand. These systems effectively reduce power frequency oscillations caused by sudden and variable load disturbances, thereby regulating the frequency of the power system.
Accordingly, this study investigates the influence of Compressed Energy Storage (CES) units on the performance of AGC within a robust, interconnected power system. Given that fuzzy control methodologies are known to outperform traditional techniques under highly dynamic and uncertain operational environments, a novel multi-stage intelligent fuzzy Proportional-Integral-Derivative controller with an integrated filter (1+PI), referred to as FPIDF (1+PI), is proposed. This advanced control strategy aims to improve the overall effectiveness and stability of AGC operations. Simulation outcomes substantiate the enhanced performance of the proposed controller relative to conventional approaches.

Abstract This paper presents a comprehensive analysis of the joint distribution of electrical and thermal energy in a smart home connected to a microgrid integrating renewable resources, combined heat and power (CHP) units, and storage systems. While most existing studies have primarily focused on generation planning and storage management, fewer works have examined the simultaneous optimization of electricity and heat flows in residential environments. To address this gap, a mixed-integer linear programming (MILP) model is developed to schedule household appliances and manage load shifting according to time-varying electricity prices, heating demand, and demand profiles, and the optimization is solved using MATLAB tools. The proposed framework is applied to a residential complex of 10 and 20 households under different CHP capacities and demand scenarios. Simulation results reveal that increasing CHP capacity from 5 kW to 20 kW significantly improves the coordination of electrical and thermal distribution, reduces reliance on the main grid, and lowers boiler operation, thereby enhancing overall efficiency. Additional analyses with different numbers of households confirm the scalability of the model, ensuring stable performance under varying load levels. A comparative scenario without microgrid integration further highlights the substantial benefits of the proposed system in reducing operational costs and improving resilience. To address this gap, a unified MILP jointly schedules electrical–thermal resources (CHP, renewables, and dual electrical/thermal storage) and employs a price–energy iterative scheme with explicit fairness constraints, ensuring no household is worse off than in non-cooperative operation. These results demonstrate that the coordinated consideration of both power and heat flows provides a more holistic strategy for smart home energy management than electricity-only approaches. In 24-h studies, increasing installed CHP capacity from 5 to 100 kW reduced total operating cost from $379.68 to $191.52 (≈49.6%). By integrating demand response (DR) programs with CHP and renewable resources, the proposed method reduces energy costs, strengthens supply security, and contributes to the sustainability of residential microgrids.

Abstract The escalating concentration of atmospheric CO2 primarily driven by hydrocarbon combustion necessitates innovative strategies that not only mitigate emissions but actively restore carbon balance. This paper presents a critical and analytical review of two synergistic pathways for carbon-negative electricity generation: Bioenergy with Carbon Capture and Storage (BECCS) and the supercritical CO2 (sCO2) Brayton cycle. BECCS leverages biomass as a renewable carbon sink to achieve net-negative emissions through geological CO2 sequestration, while the sCO2 Brayton cycle exploits the exceptional thermodynamic properties of CO2 near its critical point to significantly enhance thermal efficiency (45–55%), reduce plant footprint, and enable water-free operation. The integration of these technologies amplifies their individual benefits, yielding higher net efficiency (40–50%), reduced solvent regeneration energy, and enhanced CO2 removal capacity (up to –1.5 tCO2/MWh). Despite these advantages, challenges persist including high capture and compression costs, material durability under extreme sCO2 conditions, sustainable biomass supply constraints, and limited CO2 transport infrastructure. A comprehensive techno-economic and environmental assessment reveals that with supportive policies, technological maturation, and strategic system integration, the BECCS–sCO2 hybrid can play a decisive role in the global transition to a net-zero and eventually net-negative energy future.

Abstract This study proposes a control-integrated architecture for a Fully Parallel Embedded Z-Source Inverter (FPEZSI) with a Quad-Leg topology, designed for Flexible Photovoltaic (FPV) systems in low-voltage four-wire distribution networks. The inverter addresses critical power quality challenges under distorted grid conditions and non-linear, unbalanced loading. Its topology incorporates two isolated DC sources—typically PV arrays—within an X-shaped impedance network, ensuring continuous DC input current and eliminating the need for bulky LC filters, thereby improving system compactness and cost-efficiency. The Quad-Leg configuration enables full neutral current compensation, essential for unbalanced four-wire grids. A corrected p-q control strategy is developed to support multifunctional operation, including Maximum Power Point Tracking (MPPT), Active Power Filtering (APF), and DC bus voltage regulation. This unified control approach allows simultaneous energy extraction, harmonic mitigation, and voltage stabilization. Performance evaluation under distorted voltage and asymmetrical load conditions confirms the inverter’s dynamic stability and harmonic suppression capabilities. The system achieves a boost factor of B=1.8 at a modulation index of D=0.3, maintains a stable DC link voltage at VC=550 V, and reduces source current Total Harmonic Distortion (THD) to 0.81%, significantly outperforming the IEEE 519 standard threshold of 5%. These results validate the FPEZSI’s reliability, scalability, and suitability for smart grid integration and advanced energy management, particularly in distributed renewable environments requiring robust power quality and multifunctional inverter control.

Abstract Considering the growing use of electric vehicles (EVs) and renewable energy sources (RESs) in recent years, determining the appropriate location for EV parking lots (EVPLs) and RESs can help the distribution system operator (DSO) to reduce network losses, increase reliability, and improve the voltage profile. This article models the simultaneous siting of EVPLs and RESs to minimize the total costs of the distribution network. The objective function includes the expenses of EVPL and RESs investment, power losses, purchasing energy, charging EVs, the power generated by RESs, and the cost of increasing maximum power loss. In addition, we regarded the revenue from parking tariffs in parking lots. The problem is modeled as a mixed-integer linear programming (MILP) and implemented on the 33-bus test system. The results show that the cost of energy purchase and energy losses due to the presence of EVPLs and RESs are 31% and 62% of the initial value in the network, respectively.

Abstract The Hall–Héroult process is the only industrially and economically viable method for primary aluminum production, and its stable operation critically depends on a continuous supply of direct current (DC) electricity. Short-term power instabilities can lead to significant technical issues, including voltage fluctuations, anode burn-off, anode effects, and localized freezing in cell corners, which disrupt current distribution and reduce cell efficiency. A case study from the Iran Aluminum Company (IRALCO) shows that even brief power interruptions compromise cell stability, increase energy consumption, and reduce daily production. To mitigate these effects, the installation of a 146 MW solar photovoltaic (PV) system is proposed to cover daytime temporary outages during the four-month peak period. The estimated investment cost for this project is approximately 209 million USD, with a payback period of around 6.5 years. Implementing this solution can reduce losses from production decline and anode effects while improving operational stability and safety.Index Terms- Hall-Héroult process, Aluminum electrolysis cell, Instability in electrolysis cell, Power outage in potlines, Anode effect.

Abstract This research investigates the potential of establishing solar panels within the Water and Wastewater Plants of MARKAZI Province (Iran) and analyzes its contribution to mitigate the water crisis. To this end, data on electricity consumption, area, geographical coordinates, and site specifications were collected for all available locations managed by Water and Wastewater Company. After applying specific environmental constraints, a total of 62 sites were identified, with a combined nominal capacity of 80 MW. Subsequently, solar power plants were designed using the PVsyst software. The analysis then proceeded under three scenarios up to the year 2031 including (1) Current Situation scenario, (2) 50% construction scenario, and (3) 100% construction scenario. Since the primary electricity producer in this province is the SHAZAND Thermal Power Plant, the results were examined concerning the water consumption within this plant under each scenario. The findings indicated that the construction of solar power plants could lead to a reduction in water consumption at the SHAZAND Power Plant by 17% and 36% in the second and third scenarios, respectively, compared to the current situation. This reduction consequently exerts a positive influence on the Water Crisis Index. To ensure the quantitative effects of solar power plant development, the results were further analyzed using SimaPro software, assuming domestic production of the solar panels and comparing this against the overall water footprint of the electricity generation. The SimaPro analysis suggested that domestic panel production is not justifiable from the perspective of the social and economic cost of water in Iran.

Abstract Dropwise condensation (DWC) increases phase change heat transfer efficiency, enabling more energy efficient thermal processes that directly support greener energy technologies and help lower overall carbon emissions. Correspondingly, this research work presents a numerical assessment of isolated R134a droplet during DWC on solid surfaces, focusing on the coupled impacts of Marangoni convection, contact angle across horizontal (β=0°) and vertical (β=90°) surfaces to investigate their impact on heat transfer during DWC. In this regard, droplet geometry was modeled utilizing Surface Evolver, while computational fluid dynamics (CFD) simulations were performed in ANSYS FLUENT with a pressure-based solver and Semi-Implicit Method for Pressure-Linked Equations (SIMPLE) algorithm. The simulation outcomes were validated through comparison with established theoretical models and previously published experimental measurements. Regarding the results, greater Marangoni numbers (Ma) enhance internal circulation, leading to more uniform temperature distributions and increased heat transfer coefficients. Also, contact angle was found to positively influence average heat flux (AHF) by reducing liquid–solid contact area, while vertical surfaces consistently exhibited higher AHF because of smaller droplet footprints. The results indicate that at Ma = 11020 and θ = 110°, the vertical surface yields an AHF that is nearly 4.5% superior than that of the horizontal surface. Furthermore, unlike previous studies, which primarily examined these phenomena in general condensation contexts, this work specifically addresses their implications for refrigeration systems. By investigating R134a droplets, the findings provide novel insights into droplet scale condensation mechanisms that can contribute to reducing energy consumption in refrigerators.

Abstract Almost 95% of over 15 million tons of world carbon black production is manufactured by furnace black process method which provides an efficiency of about 40%. The highest usage of carbon black, following the rubber industry which accounts for 68% of global consumption, is in the automotive rubber parts manufacturing, mastics, inks, and paint industries. In order to save energy, reduce environmental pollutions, and improve overall efficiency, tail gas from the production process is usually utilize to generate electricity and steam; by which, approximately, a 7% increase in efficiency would be achievable. In this study, the potential for generating electricity from the exhaust fumes of the carbon black production process at Sadaf Company in Iran, one of the country's largest carbon black production plants, was analyzed both technically and economically. The heat value of exhaust fumes from the production process was measured to assess its electricity generation potential. By injecting a precise volume of the outlet gas into a chromatograph device, the molar percentage of the gas components was determined to calculate the heating value, Subsequently, considering a steam cycle as a method of waste heat recovery, 17.4 MW of power generation capacity can be available.

Abstract The multi-area dynamic economic dispatch (MADED) problem stands as a central challenge in power system engineering, primarily focused on minimizing the operating costs of committed units. In this formulation, each area comprises its own set of generating units, local demand, and tie-line constraints with neighboring areas. A strategic method for improving power system efficiency and reducing expenses involves the optimal utilization of available energy resources. In this context, demand response programs are particularly relevant. This study introduces a unified framework that integrates emergency demand response programs and direct load control into the MADED formulation. Within the integrated model, both generation costs and incentive values are optimized concurrently. The proposed framework is tested on a 40-unit system over a 24-hour horizon using the crow search algorithm. Results demonstrate a 1.33% reduction in total cost and improvements in daily load profile characteristics, including peak shaving, load factor enhancement, and a lower peak-to-valley ratio. Load factor increased from 82.84% to 87.98% and peak to valley ratio decreased from 32.11% to 22.59%.

Abstract Iran’s electricity system faces concurrent challenges of power deficits, severe water scarcity, and environmental degradation, necessitating planning approaches that integrate resource constraints with long-term sustainability objectives. This study develops a national-scale decision framework that combines satellite-based environmental data, geospatial analytics, and mixed-integer optimization to identify optimal power plant siting across 31 provinces of Iran. Renewable resource indicators (solar radiation and wind speed) and air quality constraints (NO2) were derived from remote sensing platforms and integrated with electricity deficits, population pressure, and land acquisition costs within a unified optimization model implemented in GAMS. The results demonstrate that a renewable-dominant configuration minimizes total system cost while substantially reducing environmental stress. The optimized portfolio supplies 7,430 MWh, of which 5,650 MWh originates from solar, 180 MWh from wind, and only 1,600 MWh from thermal generation. Compared with fossil-based expansion scenarios, the proposed allocation reduces annual water consumption by over 102 billion liters and limits new emission-intensive capacity in highly polluted demand centers. Spatial results reveal a strategic decoupling between electricity demand hubs and generation sites, relying on interregional transmission to enhance supply reliability without intensifying local environmental pressures. By explicitly incorporating water scarcity as a binding planning constraint, this study advances national-scale power system modeling beyond demand-driven expansion and provides a transferable framework for climate-informed energy planning in water-stressed regions.

Abstract This paper introduces a new design of a nine-level inverter utilizing switched capacitors, combined with a novel Switched Capacitor Multilevel Inverter and Novel Cuckoo Search Optimization Algorithm (SCMLi-NOCSA), eliminating the need for transformers or inductors. This configuration can produce an AC voltage waveform that closely mimics a sine wave and has a greater amplitude than the input voltage. The inverter employs Phase Disposition Pulse Width Modulation (PDPWM) to reduce harmonic distortion, while the Cuckoo Search Algorithm is utilized to obtain swift and accurate Maximum Power Point Tracking (MPPT), particularly in sustainable sources systems such as photovoltaic (PV) arrays. The system's performance is additionally evaluated by simulating the waveforms of the boost converter and switched capacitor inverter using MATLAB/SIMULINK.

Abstract This study develops and analyzes dynamic models of an industrial Hall-Héroult aluminum electrolysis cell with a focus on Lyapunov-based stability assessment and control-oriented representation. Two modeling approaches are presented: a simplified formulation capturing the essential dynamics of alumina concentration and bath temperature, and an extended formulation that additionally accounts for the accumulation of produced aluminum mass and the evolution of bath height over time. For both models, Lyapunov candidate functions are systematically proposed and their time derivatives are derived to rigorously establish local asymptotic stability. Furthermore, the nonlinear models are linearized around practical steady-state operating points, and the resulting state-space matrices are computed numerically based on representative process parameters, including a line current of 200 kA and a nominal cell voltage of 4.3 V. A comparative analysis of the two formulations highlights the trade-offs between model simplicity and predictive fidelity, providing valuable insights to support advanced control design and state estimation in modern aluminum smelting operations. The validated nonlinear models and stability guarantees provide a critical foundation for designing advanced controllers that can enhance operational stability and pave the way for significant energy efficiency improvements in industrial smelting operations.

Abstract The growth of industrial centers and loads in distribution systems results in increased system losses and unacceptable voltage deviations, imposing significant costs on electric power distribution companies. Distribution System Reconfiguration (DSR) to achieve real power loss reduction at minimal cost is a cost-effective approach to overcome these issues. Furthermore, with the increasing use of Plug-in Electric Vehicles (PEVs), the demand in the distribution system will further rise which have negative impacts on the distribution system in terms of losses and voltage drops. In this paper, the DSR considering electric vehicles and Distributed Generation (DG) is performed. The reconfiguration is accomplished using DIgSILENT PowerFactory software to achieve the minimum real power losses at minimal cost based on finding optimized open ring points. The results show that reconfiguration with and without the presence of electric vehicles can improve the technical performance indices and utilizing the Vehicle-to-Grid (V2G) system and DG units improve system operational performance. Simulation results on the IEEE 69-bus test system show that network reconfiguration reduces power losses more than 50% in the base case and in V2G operation. Moreover, in the simultaneous presence of electric vehicles and distributed generation, power losses are reduced from 66.76 kW to 48.00 kW, confirming the effectiveness of the proposed method even in highly distributed and unbalanced operating conditions.

Abstract Power flow analysis and power loss allocation are essential tasks in the operation and planning of radial distribution systems, especially in the presence of distributed generation. Conventional transmission-oriented power flow methods often face convergence and accuracy issues when applied to distribution networks due to radial topology, high R/X ratios, and bidirectional power flows. In addition, loss allocation is commonly treated as a separate post-processing step rather than being inherently integrated into the power flow formulation. This paper proposes a direct power flow algorithm with a self-adjusted loss allocation mechanism for radial distribution systems. By introducing virtual power variables, the nonlinear lossy power flow problem is reformulated into a lossless-equivalent model, enabling efficient and numerically stable matrix-based computations. The proposed approach inherently captures and allocates power losses to system participants without additional calculations. Simulation results on several standard radial distribution test systems demonstrate close agreement with Newton–Raphson power flow solutions. Furthermore, loss allocation results are validated against the branch current decomposition and superposition-based methods, confirming the accuracy and physical consistency of the proposed framework.

Abstract This study proposes optimal sizing approach for an Islanded MicroGrid (IMG) to determines the optimal component sizes for the IMG, such that the life-cycle cost is minimized. The proposed algorithm takes advantages of the typical meteorological year-based simulation and an economic evaluation model is presented. The proposed sizing approach identifies the global minimum, and simultaneously provides the optimal component sizes as well as the power management strategies. This proposed optimization algorithm is examined in a typical islanded load in Baghdad; Iraq and the results is compared. Our results show that integrating PV and battery storage significantly enhances system efficiency and sustainability, providing a more economical and environmentally sound alternative to traditional diesel-only setups.

Abstract Abstract:
The increasing demand for electricity consumption, along with the integration of renewable energy sources such as wind and solar power into the distribution network, can lead to exceeding power flow limits. Consequently, this may result in lines currents exceeding permissible limits, as well as increased losses and voltage fluctuations at certain buses. In this paper, a demand response-based method is proposed for managing congestion in the distribution network. In this approach, residential, commercial, and industrial loads submit offers to the network operator for adjusting their consumption based on their respective costs. Five scenarios are considered for congestion mitigation, with the following objectives respectively: minimizing voltage deviations at network buses, minimizing cost, minimizing lines power losses, minimizing cost, losses, and voltage deviations simultaneously, and a fifth scenario that includes a wind-based distributed generation unit connected to one of the buses. The network operator then executes the program using the proposed prices for the hours of the day when certain lines are congested, aiming to resolve line congestion with the minimum value of the objective function. To find the best possible solution across different scenarios, the Particle Swarm Optimization (PSO) algorithm is utilized. The proposed method has been simulated in MATLAB/Simulink software and applied to IEEE 33-bus network, and the results have been evaluated.

Abstract This study reviews approaches and models for WT and tower grounding systems and their protection against lightning, based on a core set of approximately sixty (∼60) grounding-focused scientific articles. This article also deals with various aspects such as the dependence of electrical properties of soil on frequency, the effect of ionization, heterogeneity and classification of soil and the optimal position of electrodes as well as the connection arrangements of WTs in a field. This review also deals with the effects of direct and indirect lightning on turbines, voltage distribution between turbines and the behavior of the earth system at different frequencies. At the end, the points in the reviewed articles are in the form of diagrams for a better understanding of the various topics discussed and a comprehensive view. This work shows the limitations and study gaps of previous works with recommendations for the evolution of lightning protection systems for WTs and tall electrical structures.

Abstract In this study, a decision-support index is proposed for the proper operation of a grid-connected smart microgrid, aiming to establish a trade-off between microgrid resilience and operational cost under Storm scenarios accompanied by the simultaneous outage of multiple distribution lines. The studied microgrid, benefiting from smart infrastructure, is capable of implementing demand response programs, including bidirectional power management in electric vehicle (EV) parking facilities and automatic network reconfiguration. To quantitatively assess system resilience, a novel quantitative–technical index is developed that considers not only the amount of unserved load but also voltage deviation as an indicator of power quality in microgrid operation. Moreover, to enhance the realism of the proposed model, uncertainties associated with renewable energy generation are incorporated within a probabilistic framework. Simulation results demonstrate that the coordinated application of network reconfiguration and demand response programs leads to a 76.6% reduction in the proposed resilience index and an 86.9% decrease in voltage deviation. These findings confirm the effectiveness of the proposed approach in enhancing technical resilience as well as achieving stable and cost-effective operation of smart microgrids under critical conditions.

Abstract This paper introduces a new approach for short-term forecasting of solar and wind energy potential in Ahvaz City. The method is based on the StackedBoost-XG model, a hybrid ensemble that combines Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) with XGBoost as the final estimator. The study focuses on accurately predicting energy generation using real-time meteorological data. Key inputs include temperature, humidity, wind speed, and solar irradiance factors that are crucial for reliable energy forecasting. These variables are integrated into energy production formulas to estimate outputs for both solar and wind sources. This improves prediction accuracy. The model’s performance is assessed using standard evaluation metrics: RMSE, MAE, and R². Results indicate that StackedBoost-XG significantly outperforms the individual SVM and KNN models. It shows higher accuracy in forecasting both solar and wind energy. The research also explores the effect of wind turbine height. It finds that optimal energy output occurs at heights between 15 and 25 meters. In addition, the study highlights the importance of managing thermal losses in solar panels, especially during warmer months, to maintain system efficiency. Finally, it emphasizes the complementary nature of solar and wind energy. Solar power offers relatively stable output throughout the year, while wind energy provides higher peaks in specific seasons. By integrating both energy sources, the study proposes a promising solution to address energy demand imbalances in Ahvaz. This study introduces a hybrid forecasting method that uses advanced machine learning and weather data. Its goal is to optimize renewable energy systems and enhance the management of the energy grid. 

Abstract In recent decades, because of some main and principle world problems such as increasing the population, global warming, climate changes, and fossil fuel sources reduction, the using of renewable energies has impressively increased that can solve and reduce the caused problems by traditional power plants, and also can control power system the important indexes such as losses, voltage drop, transferring capacity. Reactive power has an important role in controlling and minimizing of losses, the optimal distribution of reactive power in presence of Distributed generation (DG) units in distribution networks is an important and key problem. In this paper, for uncertainties modelling of DG units and optimizing the reactive power, the statistical-quality based Taguchi method and Genetic algorithm are used, respectively.  The simulation of this paper is checked and done in MATLAB and MINITAB using IEEE 57-bus standard network, and simulation results show 5.5 MW reduction of the distribution network losses.

Abstract This study proposes an integrated framework that combines photovoltaic (PV) generation with hydrogen-based storage to enhance energy management in smart distribution systems. The framework is designed to address the multi-objective concerns of distribution system operators (DSOs), focusing on minimizing operating costs, reducing energy losses, and mitigating greenhouse gas emissions. To ensure technical rigor and practical applicability, the model incorporates alternating current (AC) power flow equations along with operational constraints and system-specific performance characteristics. Recognizing the inherent uncertainties associated with load demand, renewable PV output, and fluctuating market prices, the research employs a scenario-based stochastic optimization method. This approach integrates the Kantorovich method, which efficiently manages complex multi-dimensional problems, with the Roulette Wheel Mechanism (RWM), a probabilistic selection tool that enhances solution robustness under uncertainty. Numerical simulations validate the effectiveness of the proposed method, demonstrating significant improvements compared with conventional load flow analyses. The results show reductions of approximately 14.5% in operational costs, 28.9% in energy losses, and 21% in emissions, indicating the capacity of the approach to promote sustainable and cost-efficient system operation. Beyond its quantitative achievements, the study provides meaningful insights for DSOs, offering a structured roadmap to navigate the technical, economic, and environmental challenges posed by evolving energy systems. Ultimately, the research underscores the transformative potential of PV-hydrogen integration for building resilient, efficient, and environmentally responsible distribution networks, contributing both theoretical advancements and practical guidance to the broader discourse on sustainable energy management.‎

Abstract Today, due to environmental and political reasons, countries around the world are required to use green energies, such as wind and solar energy. Also, most countries have switched to using electric vehicles (EVs) to reduce environmental pollution. Since smart distribution systems’ distributed generation (DG) power output is limited, this paper addresses this issue by planningcharging parking lots of EVs. The problem was formulated as a nonlinear optimization model. The objective function was to increase the power output, reduce the loss cost, and reduce the bus voltage deviations. Also, technical and economic limitations were considered in solving the planning problem. The uncertainty of consumption load, the behavior of EVs, and the output power of wind DGs were modeled using a combination of Monte Carlo and means methods. The improved gray wolf optimization (IGWO) algorithm was adopted to optimize the objective function. A standard IEEE 33-bus smart distribution system was studied to show the efficacy of the suggested solution. The results demonstrated the proposed solutions' high performance in improving the wind DG power output of the distribution system (PODS).

Abstract This study aims to enhance regional planning for green energy synergy in Yazd Province by ‎addressing energy imbalances and promoting sustainable development. Significant disparities ‎in production and consumption persist, with Yazd County exhibiting an 8.4-million-kWh ‎surplus in 2022, while counties such as Ashkezar and Bafgh show deficits of approximately 1 ‎million kWh. The analysis indicates that, without intervention, surpluses will decrease and ‎deficits will increase by 2035. Using quantitative trend analysis in MATLAB and participatory ‎Delphi-based scenario development, the study constructs four scenarios: synergistic ‎development, which maximizes renewable energy use; foreign dependency, which sustains ‎disparities; local self-sufficiency, which supports localized solutions; and energy isolation, ‎which exacerbates imbalances. Proposed strategies include decentralized solar power plants, ‎smart grid systems, and a green investment fund. The concept of “green energy synergy” offers ‎a novel framework for transforming regional disparities into opportunities for renewable ‎energy development. This research provides a scalable model for sustainable planning in Yazd ‎and comparable regions, contributing to more effective energy policy formulation‎‎‎.‎

Abstract The demand for non-renewable energy sources in power generation is crucial for residential and commercial uses, significantly impacting national development. However, with the depletion of fossil fuels, there is a shift towards renewable energy sources such as solar, water, and wind, which have seen a surge in use over recent decades. In Iran, despite abundant fossil fuel resources, solar energy is vital due to the country's favorable geographic conditions for solar exploitation. This study applies the analytic network process (ANP) and Genetic algorithm (GA) to identify optimal locations for Solar Power Plant Stations in Markazi province, Iran. Key morphological factors considered include slope, elevation, and solar radiation. The research identified the northwest and northern parts of Markazi province as the most suitable for solar photovoltaic systems, primarily due to their simpler topography. Using a genetic algorithm, which outperformed the ANP, it was found that about 24,000 km² in these areas are apt for solar power facilities, categorized into highly suitable (2,429.312 km²), moderately suitable (16,818.49 km²), and suitable (5,029.007 km²). Saveh showed the highest potential, while Ashtian, Khondab, and Shazand had the least. These findings provide crucial insights for stakeholders looking to develop solar energy projects in Markazi province.

Abstract Environmental concerns, advancements in the new energy industry, and rising power generation and transmission costs are driving the electricity industry towards innovation. This has led to the installation of distributed energy resources (DERs) in many regions. Virtual power plants (VPPs), which manage decentralized energy systems by aggregating the capacity of various distributed generations (DGs), storage devices, and distributable loads, are used for trading energy and services. The research focuses on the planning of price-based unit commitment (PBUC) managed by VPPs over a 24-hour period. Simulation results demonstrate that the proposed framework effectively develops strategies for VPP market production and consumer interaction, especially with load-shedding capabilities. A key aspect of the study is examining the impact of uncertainties on VPP strategies. Probability density functions and the Monte Carlo method are used to model and assess these uncertainties. Simulations without energy price and demand uncertainties indicate that the model is suitable for strategizing VPP market production and consumer interaction. However, simulations that include market price uncertainties and predicted VPP loads show that VPP profits are influenced by market volatility. Increased price and demand fluctuations significantly affect VPP strategies, sometimes resulting in losses during certain periods.