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.
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.
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.
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.
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%.
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.
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.
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.
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.
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.
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.
Distribution Systems
S. Panjeie; A. Fakharian; M. Sedighizadeh; A. Sheikhi fini
Abstract
Microgrid operators (MGOs) try to restore as much demand as possible when they are faced with electrical power outages corre-sponding to extreme events. This work suggests an outage management strategy (OMS) to improve microgrid resilience by using two optimal actions that are distribution feeder reconfiguration ...
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Microgrid operators (MGOs) try to restore as much demand as possible when they are faced with electrical power outages corre-sponding to extreme events. This work suggests an outage management strategy (OMS) to improve microgrid resilience by using two optimal actions that are distribution feeder reconfiguration (DFR) and scheduling of the distributed energy resources (DERs). Later happening a line fault, the radial network topology is determined by the proposed model using an evaluation of the inci-dence matrix. The presented work handles the uncertain behavior of non-dispatchable DERs and the electrical loads which model by the robust optimization approach. To expand the flexibility of the proposed model, the demand response program (DRP) is treated as the curtailed demand. The aim of optimization is the minimization of the total cost for dispatchable DER operation and electrical load decrease. The recommended robust linear problem (RLP) model is simulated by the CPLEX solver in GAMS software. Applying the suggested model in the 69-bus unbalanced test system demonstrate that the proposed model averagely decreases total operation cost and execution time by 10.62% and 22.23% on all scenarios in comparison with the de-terministic model.
Power Electronic
H. Shayeghi; R. Mohajery; N. Bizon
Abstract
This research introduces a modified design for non-isolated DC-DC converters with a high voltage gain using the design concepts of a coupled inductor (CI) and a hybrid voltage multiplier cell. It is attainable to further increase the output gain without requiring a higher duty cycle or a large turn ratio ...
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This research introduces a modified design for non-isolated DC-DC converters with a high voltage gain using the design concepts of a coupled inductor (CI) and a hybrid voltage multiplier cell. It is attainable to further increase the output gain without requiring a higher duty cycle or a large turn ratio of CI. This means that the power switch won't be under too much voltage stress. The suggested converter's important features are low maximum voltage across all semiconductor components, considerable efficiency, and a substantial voltage conversion ratio. In addition, the suggested topology includes diodes with soft switching conditions, which allows for a reduction in reverse recovery losses and an improvement in system efficiency. The proposed topology includes input current continuity, a single power switch, and a common ground between the source and the load. Operating analysis, theoretical definitions, efficiency investigation, and a literature review of comparable structures have been considered to demonstrate the proposed structure's functionality. An experimental prototype has also been established, featuring 115V output voltage, 20V input voltage, and 40kHz switching frequency, to facilitate the assessment of the proposed converter's efficacy.
Distribution Systems
S. Behzadi; N. Osali; A. Younesi; A. Bagheri
Abstract
Nowadays, with the detrimental impacts of air pollution on human health and its significant societal expenses, it has been imperative to utilize renewable energy sources (RESs) and energy storage systems (ESSs). This study introduces a new objective function aimed at achieving a long-term optimal plan ...
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Nowadays, with the detrimental impacts of air pollution on human health and its significant societal expenses, it has been imperative to utilize renewable energy sources (RESs) and energy storage systems (ESSs). This study introduces a new objective function aimed at achieving a long-term optimal plan where it contrasts the outcomes of meeting network load demand with and without the integration of renewable/non-renewable distributed energy resources (DERs). The analysis considers installation and operational costs, addressing uncertainties through Monte-Carlo and scenario-based methodologies. The proposed problem is structured as a convex optimization model. Simulations are conducted on the IEEE 33-bus system, showcasing the model’s efficacy through cost efficiency and reduced emission expenses. The study confirms that the investment in renewable energy resources and ESS units can be recouped in less than five years. It was observed that in the long-term, there is a cost reduction of 29.4\% when DER units are incorporated. Also, the emission cost for the horizon year is diminished by 43.2\% compared to the case where the DERs are absent.
