Research paper
M. Kavitha; S.J. Mahendra; S. Chupradit; A.S. Nurrohkayati; S.B. Kadhim; Y.F. Mustafa; A.T. Jalil; M.H. Ali; D. Sunarsi; L. Akhmetov
Abstract
Electric energy demand is increasing rapidly in developing countries, making the installation of additional generating units necessary. Private generating stations are encouraged to add new generations in deregulated energy networks. Planning for transmission expansion must ensure increased market competition ...
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Electric energy demand is increasing rapidly in developing countries, making the installation of additional generating units necessary. Private generating stations are encouraged to add new generations in deregulated energy networks. Planning for transmission expansion must ensure increased market competition while maintaining high levels of dependability and system operation safety. New objectives and demands have been made for the transmission expansion issue as a result of the deregulation of the energy network. This study has attempted to provide a new population-base algorithm; called Modified Honey Bee Mating Optimization (MHBMO) for expansion development in deregulated energy systems that are applied in multi-objective processes. In addition, to diminish the elaborateness of the issue the benders decomposition is used in this study which categorize the original issue into two subproblems. First maximizing the profits of each PBGEP (GENCO) and second, satisfying security network constraints (SCGEP). Therefore, using the suggested MHBMO algorithm, value of each GENCO's profit and overall profit could be obtained. To demonstrate the viability and capabilities of the suggested algorithm, the planning methodology has been evaluated using the IEEE 30-bus test system. The results of the current study served as an example of the effectiveness of the suggested methodology.
Research paper
D. Abdullah; W.K. Al-Azzawi; J.K. Abbas; S.H. Talib; B.M. Ali; S.H. Hlail; A.A. Ali; M.Q. Mohammed; C.I. Erliana
Abstract
Numerous factors, such as the expansion of the growing demand for energy, depletion of fossil resources, environmental disasters caused by fossil fuels, global warming of the atmosphere, the greenhouse effect, and the need to balance the emission of polluting gases, have prompted a new scientific approach ...
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Numerous factors, such as the expansion of the growing demand for energy, depletion of fossil resources, environmental disasters caused by fossil fuels, global warming of the atmosphere, the greenhouse effect, and the need to balance the emission of polluting gases, have prompted a new scientific approach to natural renewable energies. However, large-scale electricity production and transfer to consumers are accompanied by significant losses. The purpose of this study was to design and optimize the use of a hybrid photovoltaic system and a gasoline-powered engine to generate electricity and heat. In this study, the design and operation of a hybrid photovoltaic system and a gas engine as a combined heat and power source were explored using the following three thermal loads, following electric load methods, and the GAMS-optimized simultaneous optimization model. With a description of the revenues, costs, and limitations of the problem, these optimizations were performed to reduce the net pure cost and determine the rate of return on investment, and the following results were obtained. This investigation was conducted to find ways to reduce operational costs. The amount of electricity produced by the following thermal load and optimal methods is greater than the amount of electricity consumed during the majority of hours in a day. This indicates that the system has made the decision to sell electrical energy to the network to reduce the costs associated with operating the system. When compared to the following thermal load method, the simultaneous optimal method for operation results in an approximately 15% reduction in the costs associated with operation.
Research paper
T. Aigul Sauletzhanovna; A. Majed Althahabi; R. Khalid; A. Hussein Obead Al Mansor; A. Read Al-Tameemi; S. Hameed Hlail
Abstract
The incorporation of sustainable energy sources holds significant significance in the economic, social, and environmental domains of any country because of the exhaustion of non-renewable energy resources and escalation of ecological degradation. This holds true irrespective of the country's geographical ...
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The incorporation of sustainable energy sources holds significant significance in the economic, social, and environmental domains of any country because of the exhaustion of non-renewable energy resources and escalation of ecological degradation. This holds true irrespective of the country's geographical location. Owing to these two factors, the employment of this particular type of energy is emerging as a progressively noteworthy tactic. The present investigation involved the generation of power transmission network simulations using solar power plants. The network model was chosen as the preferred approach for a specific locality in Baghdad, Iraq, which is characterized by a noteworthy concentration of solar and wind energy generators. A numerical model was employed to construct a power transmission network model that integrated Renewable Energy Sources (RES). The Digsilent/Power Factory software has been utilized for the purpose of modeling the power transmission network that incorporates RES. The developed network model integrates several distinct case studies, each of which includes diverse links that exhibit varying levels of renewable energy sources (RES). As per the simulation findings, the 346 kV shared bus is linked to the 154 kV Iraqi transmission line via a 156/35.8 kV step-up transformer. The transmission system was represented by bus voltages of 156 and 290 kV. The permissible range of operational voltages for a transmission system should not deviate beyond 5\% of the voltage level at the corresponding substations.
Research paper
Application of Automatic Control in Power System
A.S. Altuma; R. Khalid; A.I. Alanssari; A. Hussien; Y.S. Mezaal; K. Al-Majdi; T. Alawsi
Abstract
Insufficient synchronization between the operational efficiency of capacitors and tap-changer transformers in regulating voltage presents a fundamental challenge in distribution networks, which in turn hinders the control performance. This challenge is caused by the inability of these two components ...
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Insufficient synchronization between the operational efficiency of capacitors and tap-changer transformers in regulating voltage presents a fundamental challenge in distribution networks, which in turn hinders the control performance. This challenge is caused by the inability of these two components to synchronize their respective operations properly. In this study, a novel control strategy is presented with the objective of achieving synchronization in the functioning of capacitors and tap transformers. Depending on the load change, various devices can be used to control the distribution network voltage. On Load Tap Changers (OLTCs) and Capacitor Banks (CBs) respond slowly to voltage changes. If the voltage changes rapidly, such devices are useless and should be avoided. Keying may shorten lifespan. This study investigated a new optimal control mechanism for coordinating tap transformers and capacitors. The optimization of tap trans- and capacitor-stage operation through the use of a Genetic Algorithm (GA) results in the reduction of superfluous switching. The limits for Point of Common Coupling (PCC) bus voltage and power factor are 0.94 and 1.02 per unit, respectively. The secondary control stage regulates the voltage of the feeder bus within the range of 0.95 to 1.05 per unit. Following the second-stage regulation of the terminal buses in the N network feeder, the third stage governs the PCC bus voltage. To prevent an infinite control loop, the voltage of the PCC bus is regulated within the range of 0.95 to 1.05 per unit (PU). These findings indicate that the optimization model is capable of achieving maximum efficiency in controlling the voltage of the distribution network. In the interim, this optimization technique produces outcomes of greater accuracy, as evidenced by a voltage value that remains consistently close to unity [Root Mean Square Error (RMSE) = 0.85] across a broad spectrum of network-loading scenarios.
Research paper
Application of Automatic Control in Power System
A. Sadratdin; A.A. Sabah; M. Zaidi; K. Raed; K.A. Jamal; H.O. Al-Mansor; F. Khattab
Abstract
Over the last few decades, the majority of industrialized and developed countries have placed a strong emphasis on reducing the amount of wasted energy. In this study, electrical energy consumption is optimized by monitoring power consumption caused by residents' activities at various times of the day ...
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Over the last few decades, the majority of industrialized and developed countries have placed a strong emphasis on reducing the amount of wasted energy. In this study, electrical energy consumption is optimized by monitoring power consumption caused by residents' activities at various times of the day and storing this data in a database. An optimization algorithm was used in this study to smarten up the management of energy consumption in the building based on inhabitants' activities. The Genetic Algorithm (GA) was used to optimize the energy consumption in a smart building compared to a traditional building. Furthermore, the algorithm will enable the creation of a smart building that requires no human intervention by presenting a model based on the energy efficiency management system for the automatic operation of household equipment based on the presence of the resident scenario. The main benefit of implementing smart grid technology in the studied building was the ability to manage and monitor the energy supply and demand process. The results showed that the proposed management system in the smart building consumes less energy and power than conventional buildings. The smart building reduces energy consumption for outlets, lighting, cooling, and heating by 38%, 28%, 34%, and 33%, respectively.
Research paper
Application of Automatic Control in Power System
А. Sadratdin; W.K. Al-Azzawi; B.M. Ali; A.N. Obeed; N.A. Hussien; A.M. Shareef; Kadhum Al-Majdi; A.S. Ibrahim
Abstract
This study investigates a hybrid electric system that utilizes novel energy sources and is subject to variable production and uncertainty. The study proposes a multi-objective optimization methodology using Genetic Algorithm (GA) to optimize energy source consumption and utilization, accounting for variations ...
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This study investigates a hybrid electric system that utilizes novel energy sources and is subject to variable production and uncertainty. The study proposes a multi-objective optimization methodology using Genetic Algorithm (GA) to optimize energy source consumption and utilization, accounting for variations in production/load levels across different time intervals. The proposed approach enables the end-user to achieve desired operational outcomes while adhering to specified constraints, taking into account both economic constraints and environmental considerations. The study explores the implementation of intelligent electric energy management in a model electric motor system that incorporates various electric energy generators, including solar cells, fuel cells, micro-turbines, and batteries. The optimization problem was formulated with multi-objectives of minimizing operating cost and environmental pollution. The presented approach demonstrated that the energy management system or electrical system operator is a proficient mechanism. Ultimately, the investigation has resulted in the development of an intelligent energy management system aimed at enhancing the efficiency of the energy production and storage sampling and planning system. The findings of the optimization clearly demonstrate an inverse link between the operating costs and pollution emissions in the system under study.
Research paper
Application of Automatic Control in Power System
S.A. Abdul-Ameer; A.K.J. Al-Nussairi; R. Khalid; J.K. Abbas; A.H.O. Al-Mansor
Abstract
In this study, the Particle Swarm Optimization (PSO) method was employed to optimize the anticipated energy yield of a wind farm. The architecture of a wind farm, including its location, height, and shadow reduction, is determined using the PSO algorithm based on the turbine height and rotor diameter. ...
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In this study, the Particle Swarm Optimization (PSO) method was employed to optimize the anticipated energy yield of a wind farm. The architecture of a wind farm, including its location, height, and shadow reduction, is determined using the PSO algorithm based on the turbine height and rotor diameter. The proposed model presents two potential scenarios for the wind velocity and dispersion direction originating from a level wind location. The findings indicate that the optimization of the wind farm layout, encompassing factors such as location, height based on hub and rotor diameter of turbines, and maximum energy output, leads to a reduction in the shadow effect. This is in contrast to prior methodologies that optimized only one or two elements at a time. The wind farm's output power was observed to have a significant increase (ranging between 40% and 98%), despite having the same total number of wind turbines. This increase was attributed to the utilization of different hub heights and rotor diameters in comparison to the wind farm with different hub heights and rotor diameters, but the same number of wind turbines.
Research paper
Modeling and Identification of Technological Processes in the Fields of Power Engineering
D.S. Talgatkyzy; N.H. Haroon; S.A. Hussein; S.Kh. Ibrahim; K.A. Jabbar; B.A. Mohammed; S.M. Hameed
Abstract
Given the significant uncertainty surrounding future electricity prices, which is widely regarded as the most critical factor in this context, market participants must engage in forecasting to facilitate their exploitation and planning activities. The success of electricity market actors is dependent ...
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Given the significant uncertainty surrounding future electricity prices, which is widely regarded as the most critical factor in this context, market participants must engage in forecasting to facilitate their exploitation and planning activities. The success of electricity market actors is dependent on the availability of more appropriate tools to address this issue. In contrast, there is a prediction of prices in the electricity market for varying periods due to the increasing use of renewable energy in global energy generation and the unsteady and disjointed configuration of renewable energy production. The fluctuating characteristics of wind energy production have increased the complexity of real-time demand management in power systems. This paper investigates the impact of renewable energy production on price forecasting using data from the Nord pool market's electricity market. The primary goal is to present a framework for forecasting market settlement prices using a hybrid wavelet-particle swarm optimization-artificial neural network (W-PSO-ANN). In two scenarios, the results showed that the proposed model accurately represents data and is more precise than the ANN and WANN models. Machine learning has demonstrated promise in predicting electricity prices, but it is not without limitations. The ANN, WANN, and W-PSO-ANN models have training phase RMSE indices of 0.09, 0.07, and 0.04 respectively. During testing, the values were 0.15, 0.11, and 0.08. This demonstrates that the proposed model outperforms previous models.
Research paper
Power Electronic
Y. Yerkin; A.H.O. Al Mansor; A.A. Ibrahim; A.R.T. Zaboun; J.K. Abbas; S.H. Hlail; D.A. Lafta; K. Al-Majdi
Abstract
DC-DC converters play a crucial role in fuel cell power generation systems, serving as an interface between the fuel cell and the load. Boost converters have gained popularity due to their ability to increase input voltage. However, the performance and efficiency of DC-DC converters in ...
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DC-DC converters play a crucial role in fuel cell power generation systems, serving as an interface between the fuel cell and the load. Boost converters have gained popularity due to their ability to increase input voltage. However, the performance and efficiency of DC-DC converters in fuel cell power systems have posed significant challenges. This study proposes the use of Model Predictive Control (MPC) and the Firefly Optimization Algorithm (FA) for designing and controlling boost DC-DC converters in the most efficient manner. Initially, stability analysis and precise modeling techniques were employed to optimize the characteristics of boost DC-DC converters in fuel cell power generation systems. Subsequently, the predictive control method, utilizing the Firefly optimization algorithm, was applied to enhance converter performance under diverse conditions. The outcomes of the designed control system were compared with conventional methods. Both predictive control and the Firefly optimization algorithm were integrated into the design and control processes of boost DC-DC converters in fuel cell. Based on the simulation results and stability evaluations, the application of the Firefly algorithm and predictive control led to a significant improvement, increasing the system efficiency by approximately 4.7%. These findings highlight the effectiveness of the proposed approach in enhancing the performance of DC-DC boost converters in fuel cell.
Research paper
Energy Management
S.M.H. Kamona; H.A. Abbas; A.A. Ibrahim; N.Q. Mohammed; A.A. Ali; B.A. Mohammed; M.S. Hamza
Abstract
The implementation of electric vehicles for this specific purpose could potentially cause an impact on the load on the network. From one standpoint, it is more advantageous to initiate the charging process of electric vehicle batteries as soon as they are connected to the grid, in order to guarantee ...
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The implementation of electric vehicles for this specific purpose could potentially cause an impact on the load on the network. From one standpoint, it is more advantageous to initiate the charging process of electric vehicle batteries as soon as they are connected to the grid, in order to guarantee sufficient charge levels in the event of unforeseen events. The current investigation showcases an innovative algorithm specifically engineered for the smart grid, wherein the principal aim is to approximate the time needed to fully charge electric vehicles. The algorithm being evaluated prioritizes the decrease in both the unfulfilled energy demand and the daily load profile standard deviation. The algorithm has been purposefully designed to regulate and supervise the charging process in an efficient manner. The algorithm incorporates various elements pertaining to the anticipated conduct of specific electric vehicles, such as their projected arrival and departure times, as well as their initial charge status upon arrival. In situations involving a substantial quantity of automobiles, statistical techniques are applied to decrease the number of variables, thereby diminishing the algorithm's computational time. The optimization technique implemented in this research is inspired by natural phenomena and is founded upon the cuckoo orphan search pattern. The proposed algorithm and the PSO algorithm were implemented in order to simulate the 34-bus IEEE standard radio distribution network. Upon comparing the outcomes derived from the analysis, it was discovered that the implementation of the CS algorithm led to a substantial decrease in peak load by 33% in comparison to the situation in which no optimization was executed. Furthermore, the CS algorithm accomplished a 27% reduction in peak load, which was superior to the PSO algorithm.
Research paper
Renewable Energy
M. Nurgul; A.A. Ibrahim; A. Al Mansor; A.A. Almulla; M.S. Hamza; A.A. Ali; N.Q. Mohammed; M.A. Hussein
Abstract
A micro-grid consists of loads, power generation, and energy storage. There are residential and commercial micro-grids. Active is the distributed micro-network. The production resources of micro-grids are either based on fossil fuels or renewable energy. Micro-grids can be independent or connected to ...
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A micro-grid consists of loads, power generation, and energy storage. There are residential and commercial micro-grids. Active is the distributed micro-network. The production resources of micro-grids are either based on fossil fuels or renewable energy. Micro-grids can be independent or connected to the grid. This study investigates the viability and optimal design of a micro-grid based on renewable energy sources, taking pollution control into account, for the iron and steel production project of Mass Group Holdings (MGH) in Sulaymaniyah, Bazian, Iraq. After modeling the considered micro-grid in two modes, grid-connected and grid-independent, and entering the required data, such as weather data, Net Pure Cost (NPC) and pollution are used to calculate the consumption load of the superior plans. Multi-objective optimization utilizing the proposed optimization model yields an objective function value of 0.5237, whereas the PSO algorithm yields 0.5279, demonstrating that the proposed grid-connected method is superior. For off-grid mode, however, the objective functions in the proposed model and PSO optimization are 0.7241 and 0.7282, respectively. In the event that a battery is connected to the network, the diesel generator works for 620 hours less, saving fuel and making the diesel generator more economical from an economic standpoint. In this regard, the network-connected mode produced superior results to the mode that was not connected to the network.
Research paper
Smart Grid
B.A. Usmanovich; T.M.H. Kinanah; A.H.O. Al-Mansor; K. Al-Majdi; S.H. Hlail; D.A. Lafta; A.R.T. Zaboun; J.K. Abbas
Abstract
The optimum location of electric vehicle (EV) parking lots is critical in distribution network design for lowering costs, boosting revenues, and enhancing dependability. However, conventional distribution network schedulers were not designed with these variables in mind. Furthermore, the increased use ...
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The optimum location of electric vehicle (EV) parking lots is critical in distribution network design for lowering costs, boosting revenues, and enhancing dependability. However, conventional distribution network schedulers were not designed with these variables in mind. Furthermore, the increased use of EVs for environmental reasons mandates the planning of EV parking spaces. As a result, distribution network designers must examine network technical difficulties, design approaches, and changing consumer needs. The placement of dispersed manufacturing resources and EV parking without sufficient planning and ideal location leads in economic challenges for investors and technical concerns for the network. As a result, future distribution networks should prioritize the ideal placement of EV parking lots and distributed production resources in order to maximize network capabilities and meet the needs of companies and power applications in the digital society. According to the findings, the rate of EV parking installations is very high. When power consumers remain connected to the grid during peak hours, distribution businesses benefit significantly, and the overall voltage profile improves. Variations in electric vehicle (EV) battery capacity, power cost, EV adoption, and the weighting coefficients required for optimization will all have different outcomes. It is critical to precisely determine the battery capacity of electric vehicles (EVs) as well as the efficiency of inverters in order to produce more accurate results. According to the findings, increasing the number of parking lot for EVs in a network enhances the benefit from minimizing losses, and providing peak load significantly. So that using 2 parking lot for EVs in a network can increase the overall profit to 129%.
Research paper
Energy Management
A.Y. Dewi; M.Y. Arabi; Z.F. Al-lami; M.M. Abdulhasan; A.S. Ibrahim; R. Sattar; D.A. Lafta; B.A. Usmanovich; D. Abdullah; Y. Yerkin
Abstract
Sustainable and efficient energy solutions are needed in the fast-growing energy sector. Meeting these objectives requires smart distribution networks that maximize energy utilization, eliminate losses, and improve system reliability. However, these networks' usefulness and durability depend on their ...
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Sustainable and efficient energy solutions are needed in the fast-growing energy sector. Meeting these objectives requires smart distribution networks that maximize energy utilization, eliminate losses, and improve system reliability. However, these networks' usefulness and durability depend on their ability to quickly recover from faults. Intelligent distribution networks can self-heal, which speeds up restoration and ensures energy delivery. This paper proposes a comprehensive strategy for intelligent distribution network self-healing after flaws. Restoration involves identifying and isolating the damaged area using offline and online methods. Online approaches, notably islanding, have helped restore services in the affected region. This paper presents a novel linear mathematical approach to optimize online islanding. The model estimates the boundaries of islanded microgrids and the appropriate number of microgrids for faults, enabling quick restoration. This analysis also seeks to determine the fault-affected area's system layout. A mathematical model defines the ideal arrangement in the first layer of the two-layered approach. The next layer analyzes unit participation in the intelligent distribution system, focusing on rescheduling, allocation, and organization. Additionally, the study identifies the best energy storage solutions to aid restoration. The recommended strategy uses adaptive load reduction and demand response to maximize system recovery. The mathematical model benefits from various strategies, including faster execution and better outcomes. This research advances intelligent distribution networks by combining advanced mathematical modeling, self-healing, and smart load control. These upgrades boost distribution networks' effectiveness.
Research paper
Energy Management
J. Napitupulu; A. Al-khalidi; Z.F. Al-Lami; A.S. Ibrahim; M.Y. Arabi; A.A. Ali; M.M. Abdulhasan; K.I. Nematovich; D. Sholeha; Y. Yerkin
Abstract
The concept of hybrid energy systems has emerged as a distinct alternative in the past few decades, with the aim of enhancing the resilience and adaptability of energy systems to fluctuations and diverse energy sources. One of the principal objectives of hybrid energy systems is to mitigate the environmental ...
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The concept of hybrid energy systems has emerged as a distinct alternative in the past few decades, with the aim of enhancing the resilience and adaptability of energy systems to fluctuations and diverse energy sources. One of the principal objectives of hybrid energy systems is to mitigate the environmental repercussions associated with the generation and utilization of energy. Using more than one energy source at the same time, like solar panels, wind turbines, and combined heat and power (CHP) systems, has many benefits, such as higher efficiency, less reliance on fossil fuels, and lower greenhouse gas emissions. This study presents an optimal approach for the design of hybrid energy systems utilizing the Firefly algorithm within the given paradigm. Incorporated into the structure are vital components like wind turbines, solar panels, combined heat and power (CHP) systems, battery storage, and converters. Furthermore, it considers the various uncertainties pertaining to production capacity, demand, and costs. The firefly optimization technique is being employed to effectively identify the most optimal solutions within a context characterized by several uncertainties. The optimization results of this framework are demonstrated to be superior in effectiveness and efficiency when compared to those obtained from other optimization algorithms. This finding provides confirmation of the algorithm's effectiveness and efficiency in enhancing the performance and stability of hybrid energy systems.
Research paper
Energy Management
M.K. Guerreros; Y.L. Huamán-Romaní; D.L. Pérez; E.N. Carbajal; M.F. Quispe-Aguilar; M.A.A. Bedrinana; L.K. Carrillo-De la Cruz
Abstract
This paper introduces a novel model for optimizing renewable energy systems, specifically focusing on the integration of wind turbines and photovoltaic panels to minimize net present value (NPV) costs. Addressing a significant gap in current literature, our model considers both economic and energy factors ...
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This paper introduces a novel model for optimizing renewable energy systems, specifically focusing on the integration of wind turbines and photovoltaic panels to minimize net present value (NPV) costs. Addressing a significant gap in current literature, our model considers both economic and energy factors to design an efficient hybrid system. The key contributions of this study lie in investigating the impact of incentives on cost reduction across various scenarios and proposing an optimization approach utilizing the harmonic search algorithm. In contrast to existing approaches, which often overlook economic considerations, our model accounts for the dynamic nature of electricity prices. Through simulation results, we demonstrate that the cost-effectiveness of renewable energy systems varies with electricity prices. Our findings reveal that in our study area, current electricity prices do not render renewable resources economically viable, highlighting the need for optimization strategies. By employing the proposed method, we determine the optimal configuration of solar panel and wind turbine surfaces to achieve cost-effective energy production. This research not only advances the understanding of renewable energy integration but also provides practical insights for policymakers and industry stakeholders. Overall, our study underscores the importance of considering economic factors alongside technical aspects in designing renewable energy systems.
Research paper
Energy Management
H. Hartono; T.M. Hanoon; S.A. Hussein; H.A. Abdulridui; Z.S.A. Ali; N.Q. Mohammed; M.S. Alhassan; K.M.M Qizi; D. Abdullah; Y. Yerkin
Abstract
Solar panel collectors are considered a highly promising technology for renewable energy in urban areas. In this study, the optimization of solar collector orientation to achieve maximum energy efficiency in Sohar, Oman, and Hillah, Iraq, is investigated. A novel approach is introduced, where optimal ...
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Solar panel collectors are considered a highly promising technology for renewable energy in urban areas. In this study, the optimization of solar collector orientation to achieve maximum energy efficiency in Sohar, Oman, and Hillah, Iraq, is investigated. A novel approach is introduced, where optimal deflection angles are determined using a mathematical optimization model, incorporating rigorous numerical calculations based on sun position, solar radiation models, and non-isotropic models. Dynamic variations in solar radiation patterns are revealed, emphasizing the significance of tailored approaches. Optimal tilt angles are identified in Sohar and Hillah, resulting in notable increases in annual energy intake. Additionally, nuanced insights into solar panel orientation optimization are provided through the inclusion of non-isotropic models. The numerical findings illustrate a dynamic interaction among monthly, seasonal, and yearly fluctuations in solar radiation patterns, underscoring the importance of tailored approaches. In Sohar and Hillah, optimal tilt angles are identified, demonstrating significant enhancements in annual energy intake when aligned with these variations. Moreover, the incorporation of non-isotropic models offers nuanced insights into the influence of azimuth angles on radiant energy, stressing the necessity to optimize solar panel orientation toward the equator for improved energy capture. The outcomes indicate a boost of 22%, 8%, and 4% in Sohar, achieved by aligning panels with optimal angles for optimal monthly, seasonal, and yearly performance, respectively. Similarly, in Hillah, a corresponding increase of 23%, 9%, and 4% is observed. Importantly, the study emphasizes that the zenith of energy reception aligns with a zero azimuth angle. As the azimuth angle deviates from zero, both positively and negatively, the quantity of received energy exhibits a proportional increase. The findings contribute to the advancement of solar energy optimization and offer valuable insights for the design of sustainable solar energy systems in urban environments.
Research paper
A.M. Alee; S. Golshannavaz; T. Ghanizadehbolandi; V. Talavat
Abstract
This paper presents a novel method to improve the efficiency of active distribution networks (ADNs) by optimal placement of distributed energy resources (DERs) and utilizing the unused capacity of inverter-interfaced photovoltaic (PV) units for reactive power compensation. After investigating the mathematical ...
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This paper presents a novel method to improve the efficiency of active distribution networks (ADNs) by optimal placement of distributed energy resources (DERs) and utilizing the unused capacity of inverter-interfaced photovoltaic (PV) units for reactive power compensation. After investigating the mathematical model of PV systems, wind turbines, other non-renewable distributed generations, energy storage systems, and responsive loads, a genetic algorithm (GA)-based approach is used to find the optimal placement and allocation of all units. The modeling also takes into account the uncertainty of PV units and wind turbines to represent real-world operational conditions more accurately. Additionally, although the IEEE 33-bus system is used to formulate the presented method, one can easily extend it to any other network with an arbitrary number of buses. The effectiveness of the proposed method is verified by designing three different scenarios. The simulation results obtained based on MATLAB clearly show the capability of the proposed method to improve the voltage profile and the cost of losses in ADN. This is done by properly utilizing the excess capacity of inverter-interfaced PV units as a static compensator (STATCOM), even in the absence of sunlight. The findings indicate that the inclusion of DERs and PV-STATCOM results to a notable enhancement of approximately 68.46% in power losses reduction and around 65% in the voltage deviation minimization.
Research paper
Energy Management
S.R. Kumisbekovna; G. Kakimzhan; N. Darimbayeva; A. Besterekova; Z. Toygozhinova; E. Darkenbaeva; M. Sakitzhanov
Abstract
Traditional energy management focuses on ensuring a reliable and sustainable energy supply through meticulous planning, coordination, and optimization of resources. However, integrating renewable energy sources like solar, wind, and hydropower introduces a new layer of complexity. These sources, while ...
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Traditional energy management focuses on ensuring a reliable and sustainable energy supply through meticulous planning, coordination, and optimization of resources. However, integrating renewable energy sources like solar, wind, and hydropower introduces a new layer of complexity. These sources, while environmentally friendly, are inherently intermittent and variable in their production, posing unique challenges for energy management. Effective energy management in the presence of renewable energy requires strategies to balance supply and demand, optimize energy use, and ensure grid stability. This study introduces a new model designed to significantly improve the accuracy of estimating both energy production and demand. This enhanced level of precision plays a decisive role in the decision-making process for energy management. This innovative model employs a fuzzy neural network trained on historical energy production data, integrating weather information through fuzzy functions to improve precision in estimating energy production for future intervals. The objective functions prioritize renewable energy use to minimize overall system costs. The simulations evaluated the total system cost under various conditions. The results showed that more accurate estimation and maximized utilization of renewable energy sources led to a significant reduction in the cost per kilowatt-hour. In essence, this study offers a promising approach to managing energy systems that heavily rely on renewable sources. By improving estimation accuracy and prioritizing renewable energy use, the model paves the way for a more reliable, sustainable, and cost-effective energy future.
