M. Karimi; M. Eslamian
Abstract
This paper presents a resilience-based approach for critical load restoration in distribution networks using microgrids during extreme events when the main supply is disrupted. Reconfiguration of the distribution network using graph theory is investigated, for which Dijkstra's algorithm is first used ...
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This paper presents a resilience-based approach for critical load restoration in distribution networks using microgrids during extreme events when the main supply is disrupted. Reconfiguration of the distribution network using graph theory is investigated, for which Dijkstra's algorithm is first used to determine the shortest paths between microgrids and critical loads, and then the feasible restoration trees are established by combining the restorable paths. A mixed-integer linear programming (MILP) model is then used to find the optimal selection of feasible restoration trees to make a restoration scheme. The service restoration is implemented with the objectives of maximizing the energy delivered to the critical loads and minimizing the number of switching operations. The limited fuel storage of the generation sources in microgrids, the operational constraints of the network and microgrids, as well as the radiality constraint of the restored sub-networks, are considered the constraints of the optimization problem. The presented method can be used for optimal restoration of critical loads including the number of switching operations which is essential for the ease of implementation of a restoration plan. The results of simulations on a 118-bus distribution network demonstrate the efficiency of the procedure.
A. Komijani; M. Kheradmandi; M. Sedighizadeh
Abstract
Voltage drop during the fault can be effected on the performance of generation units such as wind turbines. The ability to ride through the fault is important for these generation units. Superconducting fault current limiter and superconducting magnetic energy storage can improve the fault ride through ...
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Voltage drop during the fault can be effected on the performance of generation units such as wind turbines. The ability to ride through the fault is important for these generation units. Superconducting fault current limiter and superconducting magnetic energy storage can improve the fault ride through due to fault current limiting and voltage restoring ability during the fault, respectively. This paper presents a method for optimal allocation and control of superconducting magnetic energy storage and superconducting fault current limiters in meshed microgrids. For this purpose, the doubly-fed induction generator voltage deviation, the point of common coupling power deviation, the fault current of transmission lines, and superconducting fault current limiter and superconducting magnetic energy storage characteristics were considered as objective functions. In this paper, the optimization is performed in single-step and two-step by particle swarm optimization algorithm, and the system with the optimal superconducting magnetic energy storage and superconducting fault current limiters are analyzed and compared. The results of simulations show superconducting fault current limiter and superconducting magnetic energy storage reduce 85% of voltage drop, decreases 63% of doubly fed induction generator power deviation, and limits the maximum fault current of transmission lines by 9.8 pu. Finally, the status of the studied system variables has been investigated, in two scenarios related to the different fault locations with equipment that the optimal allocated.
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.
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.
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.
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.
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.
J. Salehi; F.S. Gazijahani; A. Safari
Abstract
Executing interruptible loads (ILs) can be significantly effective for optimal and secure operation of power systems. These ILs can aid the operators not only to increase the reliability of the power supply but also to reduce the procurement costs of the whole system. Therefore, determining the optimal ...
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Executing interruptible loads (ILs) can be significantly effective for optimal and secure operation of power systems. These ILs can aid the operators not only to increase the reliability of the power supply but also to reduce the procurement costs of the whole system. Therefore, determining the optimal location and capacity of ILs for a given incentive rate is of great interest to distribution companies. To do so, in this paper simultaneous allocation and sizing of ILs, wind turbines (WT), photovoltaic (PV) and capacitors have been done in the radial distribution network for different demand levels and subsequently the optimal value of compensation price for the ILs has been determined. Given the probabilistic nature of load, wind and solar generation as well as the price of energy at the pool, we have also proposed a stochastic model based on fuzzy decision making for modelling the technical constraints of the problem under uncertainty. The objective functions are technical constraint dissatisfaction, the total operating costs of the Distribution Company and CO2 emissions which are minimized by NSGA2. To model the uncertainties, a scenario-based method is used and then by using a scenario reduction method the number of scenarios is reduced to a certain number. The performance of the proposed method is assessed on the IEEE 33-node test feeder to verify the applicability and effectiveness of the method.
Renewable Energy
H. Shayeghi; Y. Hashemi
Abstract
In this paper, an optimal design of the renewable combustion plant has been investigated with the aim of ensuring the required load on the Gorgor station. The purpose of this study is to minimize the cost of the proposed hybrid unit during the period of operation of the designed system simultaneously. ...
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In this paper, an optimal design of the renewable combustion plant has been investigated with the aim of ensuring the required load on the Gorgor station. The purpose of this study is to minimize the cost of the proposed hybrid unit during the period of operation of the designed system simultaneously. Information on the intensity of solar radiation and the intensity of wind blowing in the area are taken and applied in the simulation of the system. The intended target function includes the cost of investment, replacement cost and maintenance cost. After the design phase, the main objective is to check the economic benefits of the project's utilization from the grid and compare it with the renewable electricity system, as well as to calculate the initial investment return in renewable electricity. First, the initial cost of consuming electricity from this project is calculated using a renewable electricity system, and then the cost of project is determined using the national grid. Further, by calculating the annual current cost of each of these combinations, the investment return in each mode is obtained. Various options for the use of renewable energies are surveyed separately and in combination. The technical-economic analysis is done on each of these options and ultimately the best one is presented.
Evolutionary Computing
M. Dehghani; M. Mardaneh; O. P. Malik
Abstract
These days randomized-based population optimization algorithms are in wide use in different branches of science such as bioinformatics, chemical physics andpower engineering. An important group of these algorithms is inspired by physical processes or entities’ behavior. A new approach of applying ...
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These days randomized-based population optimization algorithms are in wide use in different branches of science such as bioinformatics, chemical physics andpower engineering. An important group of these algorithms is inspired by physical processes or entities’ behavior. A new approach of applying optimization-based social relationships among the members of a community is investigated in this paper. In the proposed algorithm, search factors are indeed members of the community who try to improve the community by ‘following’ each other. FOA implemented on 23 well-known benchmark test functions. It is compared with eight optimization algorithms. The paper also considers for solving optimal placement of Distributed Generation (DG). The obtained results show that FOA is able to provide better results as compared to the other well-known optimization algorithms.
