Examinando por Autor "Kamel, Salah"

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A Common Framework for Developing Robust Power-Flow Methods with High Convergence Rate
(MDPI, 2021-07) Tostado-Véliz, Marcos; Kamel, Salah; Escámez-Álvarez, Antonio; Vera-Candeas, David; Jurado-Melguizo, Francisco
This paper presents a novel Power-Flow solution paradigm based on the structure of the members of the Runge–Kutta family. Solution approaches based on the introduced solution paradigm are intrinsically robust and can achieve high-order convergences rates. It is demonstrated that some well-known Power-Flow solution methods are in fact special cases of the developed framework. Explicit and embedded formulations are discussed, and two novel solution methodologies based on the Explicit Heun and Embedded Heun–Euler’s methods are developed. The introduced solution techniques are validated in the EU PEGASE systems, considering different starting points and loading levels. Results show that the developed methods are quite reliable and efficient, outperforming other robust and standard methodologies. On the basis of the results obtained, we can affirm that the introduced solution paradigm constitutes a promising framework for developing novel Power-Flow solution techniques.
A mixed-integer-linear-logical programming interval-based model for optimal scheduling of isolated microgrids with green hydrogen-based storage considering demand response
(Elsevier, 2022-04) Tostado-Véliz, Marcos; Kamel, Salah; Hasanien, Hany; Turky, Rania; Jurado-Melguizo, Francisco
Hydrogen produced from renewable sources (green hydrogen) will be recognized as one of the main trends in future decarbonized energy systems. Green hydrogen can be effectively stored from surplus renewable energy to thus reducing dependency of fossil fuels. As it is entirely produced from renewable sources, green hydrogen generation is strongly affected by intermittent behaviour of renewable generators. In this context, proper uncertain modelling becomes essential for adequately management of this energy carrier. This paper deals with this issue, more precisely, a novel optimal scheduling model for robust optimal scheduling of isolated microgrids is developed. The proposal encompasses a green hydrogen-based storage system and various demand-response programs. Logical rules are incorporated into the conventional optimal scheduling tool for modelling green hydrogen production, while uncertain character of weather and demand parameters is added via interval-based formulation and iterative solution procedure. The developed tool allows to perform the scheduling plan under pessimistic or optimistic point of views, depending on the influence assumed by uncertainties in the objective function. A case study serves to validate the model and highlight the paper of green hydrogen-based storage facilities in reducing fossil fuel consumptions and further exploit renewable sources.
A Novel Family of Efficient Power-Flow Methods With High Convergence Rate Suitable for Large Realistic Power Systems
(IEEE, 2021-03) Tostado-Véliz, Marcos; Kamel, Salah; Jurado-Melguizo, Francisco
High-order power-flow (PF) solution methods are techniques with higher convergence rate than the standard Newton-Raphson (NR). This feature normally provokes that less iterations are necessary for achieving the required convergence tolerance. This advantage enables important computational savings in realistic large-scale power systems, since some expensive computations may be avoided. This article develops and analyzes a novel family of multistep PF solution techniques. The introduced solution paradigm just involves an LU decomposition along with other cheap computations each iteration. In addition, it is proved that its convergence order is equal to the number of steps considered. A comparison with other high-order PF techniques available in the literature reveals that the introduced paradigm is more efficient when more than three steps are taken. Hence, two PF methods with fourth and fifth convergence rate are developed and validated in 12 systems under different demand conditions. Results prove that the developed PF solution methods are able to notably outperform NR and the other high-order PF solvers proposed in the literature.
A novel hybrid lexicographic-IGDT methodology for robust multi-objective solution of home energy management systems
(Elsevier, 2022-08-15) Tostado-Véliz, Marcos; Kamel, Salah; Aymen, Flah; Jurado-Melguizo, Francisco
With the emergence of smart appliances and communication infrastructures, Home Energy Management Systems have gained importance to help home users to reduce their electricity bills. A Home Energy Management System is a tool able to optimally coordinate the different home assets such as controllable appliances, onsite generators and storage facilities, among others. Such kind of tools has become more complex with the appearance of dynamic pricing tariffs and novel appliances such as electric vehicles. In this context, scheduling tools must attain a high level of robustness against uncertainties as well as being able to consider the particular behaviour of unpredictable energy pricing and vehicle routines, while some complementary objectives like thermal comfort are not ignored. This paper addresses this issue by developing a novel hybrid robust-multi objective home energy management approach. The novel proposal is based on information gap decision theory and lexicographic optimization, which are combined in an original way to attain a scheduling plan immune against the negative effect of uncertainties, while the economy and comfort of the building are jointly considered. The developed mathematical formulation is Mixed Integer Linear Programming, employing advanced linearization techniques to overcome the problems arisen from nonlinear models. Its particular integer-linear structure makes the developed optimization problem easily tractable by conventional solvers and average machines. Also, further capabilities are explored such as the possibility of selling energy to the grid and the vehicle-to-home ability of electric vehicles. Extensive simulations are performed on a benchmark prosumer environment considering real time pricing and time-of-use tariffs. The result serves to prove that the developed methodology is able to deal with uncertainties whereas different objective functions are jointly accounted.
A Novel Power Flow Solution Paradigm for Well and Ill-Conditioned Cases
(IEEE, 2021-08) Tostado-Véliz, Marcos; Alharbi, Talal; Alrumayh, Omar; Kamel, Salah; Jurado-Melguizo, Francisco
This paper develops a novel four-stage power flow solver for ill-conditioned systems. Although the developed solver could be considered efficient, it is not competitive with the Newton-Raphson method in well-conditioned cases. With the aim of being fully competitive in a wide range of cases and scenarios, the developed algorithm is integrated within a novel efficient solution paradigm. As a result, a robust and efficient solution framework, competitive in both well and ill-conditioned cases, is obtained. The new proposals are tested in various well and ill-conditioned cases from 30-, to 13,659-buses. Results obtained with the developed solvers are promising.
A powerful power-flow method based on Composite Newton-Cotes formula for ill-conditioned power systems
(Elsevier, 2020-03) Tostado-Véliz, Marcos; Kamel, Salah; Jurado-Melguizo, Francisco
This paper proposes a novel Power-Flow method based on Composite Newton-Cotes formula. The proposed method aims to be robust and efficient enough to properly manage ill-conditioned power systems. Self-adapted mechanisms for tuning the parameters of proposed PF method are provided. The proposed method is tested on several ill-conditioned systems ranging from 3012 to 27,318 buses at loadbase and heavy loading conditions. Moreover, its ability to handle the generators’ reactive limits is also tested. The obtained results prove that the proposed method is remarkably robust and more efficient than other well-known Power-Flow techniques.
A Proposed Uncertainty Reduction Criterion of Renewable Energy Sources for Optimal Operation of Distribution Systems
(MDPI, 2022-01) Ali, Eman S.; El-Sehiemy, Ragab A.; Abou El-Ela, Adel A.; Tostado-Véliz, Marcos; Kamel, Salah
Power system operation and planning studies face many challenges with increasing of renewable energy sources (RESs) penetration. These challenges revolve around the RESs uncertainty and its applications on probabilistic forecasting, power system operation optimization and power system planning. This paper proposes a novel and effective criterion for uncertainties modeling of the RESs as well as system loads. Four sorting stages are applied for the proposed uncertainty cases reduction. Added to that, it proposes three different uncertainty reduction strategies for obtaining different accuracy and speed options. The proposed reduction strategies are tested on medium and large scale distribution systems; IEEE 69-bus and 118-bus systems. The obtained results verify the effectiveness of the proposed criterion in uncertainties modeling in distribution systems with acceptable level of accuracy.
A Robust Power Flow Algorithm Based on Bulirsch–Stoer Method
(IEEE, 2019-07) Tostado-Véliz, Marcos; Kamel, Salah; Jurado-Melguizo, Francisco
In this paper, we address the load-flow (LF) problem of very large scale systems. These types of systems show a very narrow region of attraction, and most of LF solvers tend to fail when a flat initial guess point is used. On the other hand, the solution of these systems frequently involves very large matrices and vectors. Consequently, a robust LF method must be used to find the correct solution of these systems. This paper proposes a robust and efficient LF solver based on the Bulirsch-Stoer algorithm. Moreover, a simple modification is proposed in order to improve its computational performance. The proposed methods are tested using various very large scale systems (i.e., more than 3000 buses) and compared with several standard and robust LF techniques. The obtained results show that the proposed methods are more suitable for solving the LF problem of very large scale systems.
A Stochastic-IGDT model for energy management in isolated microgrids considering failures and demand response
(Elsevier, 2022-07-01) Tostado-Véliz, Marcos; Kamel, Salah; Aymen, Flah; Jordehi, Ahmad Rezaee; Jurado-Melguizo, Francisco
In power systems, contingencies and outages are more frequent nowadays due to climate changing effects. This circumstance along equipment aging may lead to unexpected failures and outages in power and energy systems. This issue is especially critical in isolated microgrids, which must be supplied by means of own resources and onsite assets. In these systems, unexpected failures may notably provoke a detriment of the economy and users’ satisfaction. In order to minimize the impact of these incidents, this paper proposes a novel energy management tool for isolated microgrids that are robust against failures. To this end, a novel stochastic-IGDT formulation is developed, by which typical uncertainties are comfortably modelled via scenarios while components’ failures are treated in a robust fashion using IGDT. A solution procedure is proposed in which the operation cost is also considered in order to reproduce useful results and limit the cost of reliability. A variety of simulations are conducted in order to validate the developed model and discuss the particularities derived from considering failures in the energy management task. Moreover, the role of demand response programs is also foregrounded. In particular, the demand response programs allow reducing the operation costs by 3 % while the scheduling result admits up to 13 h more of accumulated failures, thus confirming a positive effect of such initiatives in both economy and robustness against failures.
A stochastic-interval model for optimal scheduling of PV-assisted multi-mode charging stations
(Elsevier, 2022-08-15) Tostado-Véliz, Marcos; Kamel, Salah; Hasanien, Hany M.; Arévalo, Paul; Turky, Rania A.; Jurado-Melguizo, Francisco
Nowadays, photovoltaic-assisted charging stations are becoming popular worldwide because its capacity to accommodate more clean energy, reduce carbon emissions, alleviate peak charging loads and provide wider charging infrastructures worldwide. When these infrastructures are operated locally, energy management becomes a challenge due to the large number and heterogeneity of uncertainties involved. This aspect is especially noticeable in the case of charging demand, which is difficult to predict. To address this issue, this paper develops a novel stochastic-interval model for optimal scheduling of multi-mode photovoltaic-assisted charging stations. The developed model uses interval formulation to model uncertainties from photovoltaic generation and energy price, while a comprehensive stochastic model is proposed for charging demand. The developed optimal scheduling model is solved using a developed iterative model, which avoids using interval arithmetic explicitly. This methodology encompasses two Mixed-integer linear programming problems and one Quadratic-programming problem, that can be efficiently addressed by conventional solvers, and allows adopting optimistic or pessimistic strategies. A case study is presented on a benchmark mid-size charging station to validate the developed model. As a sake of example, the system profit grows by 9% and decreases by 3% adopting optimistic and pessimistic point of view, respectively. Likewise, total PV generation increases by 150 kWh/day and reduces by 50 kWh/day. Similar conclusions are extracted for other parameters like monetary balances, PV peak power or satisfied EV demand.
A Three-Stage Algorithm Based on a Semi-Implicit Approach for Solving the Power-Flow in Realistic Large-Scale ill-Conditioned Systems
(IEEE, 2020) Tostado-Véliz, Marcos; Kamel, Salah; Alquthami, Thamer; Jurado-Melguizo, Francisco
Solving the Power-Flow in realistic large-scale ill-conditioned systems supposes a challenging task for most of available solution methodologies. This paper tackles this issue by developing a novel efficient and robust Power-Flow method. It is mainly based on a Semi-Implicit approach but incorporates other numerical arrangements for enhancing its features. The resulting three-stage algorithm is validated using several realistic ill-conditioned systems ranging from 3012 to 70000-buses. Results show that the developed methodology constitutes an efficient and robust Power-Flow solution technique, outperforming the results obtained with other available approaches.
A three-stage Stochastic-IGDT model for photovoltaic-battery domestic systems considering outages and real-time pricing
(Elsevier, 2022-10-10) Tostado-Véliz, Marcos; Jha, Bablesh Kumar; Kamel, Salah; Pindoriya, Naran M.; Jurado, Francisco
Energy consumption from residential sector is a growing concern nowadays. It has been widely demonstrated that domestic consumption worldwide could be notably reduced implementing home energy management tools. This kind of programs allows to schedule the different controllable home assets to optimize the electricity bill and increment the penetration of onsite renewable technologies, which contributes to a further reduction in residential consumption. On the other hand, grid outages are still frequent nowadays because natural disasters or weak infrastructures. Therefore, home energy management tools do not ignore these events. To tackle this issue, this paper develops a novel three-stage solution procedure for home energy management problems considering grid outages. To this end, a novel stochastic-IGDT formulation of the home energy management problem is proposed, by which the uncertain parameters are incorporated via scenarios and grid outages are modelled using IGDT. The presented formulation is Mixed-Integer Linear programming and allows to obtain a schedule result for the home appliances immune against outages without ignoring the economy of system. A benchmark prosumer environment with Real-Time pricing is considered as a case study. The results prove the effectiveness of the developed methodology. In particular, the effectiveness of the developed formulation in modelling grid outages as well as the scheduling performance at different conflictive objectives are highlighted. In addition, the effect of primarily considering robustness or economy is discussed together the importance of onsite photovoltaic generation in incrementing the reliability of the home system. Finally, the capability of the developed procedure to jointly consider cost and outages on a whole is foregrounded. In fact, the developed methodology is able to assume up to 6.5 outage hours without increasing the electricity bill, thus evidencing its capability to obtain a result immune against outages, whereas economic concerns are also considered on a whole.
An Adaptive Protection Scheme for Coordination of Distance and Directional Overcurrent Relays in Distribution Systems Based on a Modified School-Based Optimizer
(MDPI, 2021-10) Abdelhamid, Mohamed; Kamel, Salah; Korashy, Ahmed; Tostado-Véliz, Marcos; Banakhr, Fahd A.; Mosaad, Mohamed I.
This paper presents an adaptive protection scheme (APS) for solving the coordination problem that deals with coordination directional overcurrent relays (DOCRs) and distance relays second zone time, in relation to coordination with DOCRs. The coordination problem becomes more complex with the impact of renewable energy sources (RES) when added to the distribution grid. This leads to a change in the grid topology, caused by the on/off states of the distribution generators (DG). The frequency of topological changes in distribution grids poses a challenge to the power system’s protection components. The change in the state of DGs leads to malfunction in reliability and miscoordination between protection relays, since that causes a direct effect to the short circuit currents. This paper used the school-based optimization (SBO) algorithm, which simulates the educational process, in order to deal with coordination problems. That algorithm is modified (MSBO) by modified both learning and teaching processes. The IEEE 8-bus test system and IEEE 14-bus distribution network are used to validate the proposed coordination system’s effectiveness when dealing with the coordination process between distance and DOCRs, at both the near- and far-end in the typical topological grid and with DGs in working order.
An Effective Load-Flow Approach Based on Gauss-Newton Formulation
(Elsevier, 2019-12) Tostado-Véliz, Marcos; Kamel, Salah; Jurado-Melguizo, Francisco
Despite that most of power systems can be categorized as well-conditioned, ill-conditioned cases are becoming more frequent. Consequently, developing new robust LF techniques is necessary to efficiently solve these cases. In this paper, an effective load-flow (LF) approach based on Gauss-Newton’s formulation is proposed. Moreover, efficient strategies for exploring the unsolvable region and calculating the solution space boundary are developed. The proposed LF approach is comprehensively validated using a wide variety of ill-conditioned systems in loadbase conditions, near of the maximum loadability points and considering generator’ reactive limits. The studied systems range from 1888-bus to 70000-bus. The results prove the efficiency and superiority of proposed approach over other well-known LF methods.
An efficient and reliable power flow solution method for large scale Ill-conditioned cases based on the Romberg’s integration scheme
(Elsevier, 2020-12) Tostado-Véliz, Marcos; Kamel, Salah; Jurado-Melguizo, Francisco
Recently, the Power Flow solution in large scale ill-conditioned systems has attracted huge attention. Despite the considerable efforts conducted in this line, this issue may be considered an open topic nowadays. Consequently, the paper aims at filling this gap by proposing a novel robust and efficient Power Flow solution technique inspired by the Romberg’s Integration Scheme. The resulting algorithm is numerically stable and has a computational burden comparable to the Newton’s technique. Several numerical results in different ill-conditioned systems from 3012-, to 13659-bus systems serve to show the performance of the developed method. Other available Power Flow solvers are considered for comparison. In all studied cases, results obtained with the developed method were promising, outperforming the other considered methodologies.
An Efficient Power-Flow Approach Based on Heun and King-Werner’s Methods for Solving Both Well and Ill-conditioned Cases
(Elsevier, 2020-07) Tostado-Véliz, Marcos; Kamel, Salah; Jurado-Melguizo, Francisco
Solving the Power-Flow in ill-conditioned cases is still challenging as most of the available robust methodologies are not efficient enough to be widespread used in industry applications. This paper addresses this issue by developing a novel approach suitable for both ill and well-conditioned power cases. Since the developed approach arises from the combination of the King-Werner and Heun’s methods, it is called Heun-King-Werner method. The developed approach naturally performs as a robust method in ill-conditioned cases and as a high order Newton-like method in well-conditioned systems, which makes it very suitable for solving both cases. The developed approach is tested using various realistic well and ill-conditioned cases under different demanding scenarios. Its performance is compared with other well-known Power-Flow methods. Results show that the developed approach is robust, reliable and computationally much more efficient than other well-known methods. In well-conditioned systems, it performs similar to the standard NR method but improving its convergence features in some cases. Based on the results, the developed approach may be widespread used in industry tools.
An Improved Arithmetic Optimization Algorithm for design of a microgrid with energy storage system: Case study of El Kharga Oasis, Egypt
(Elsevier, 2022-07) Kharrich, Mohammed; Abualigah, Laith; Kamel, Salah; El-Sattar, Hoda Abd; Tostado-Véliz, Marcos
The microgrid design problem needs efficacy tools to reach good results with optimal convergence characteristics. Stochastic metaheuristic algorithms are the best choice to address complex problems. This paper proposes new hybrid renewable energy systems (HRES) design, composed of PV, wind turbine, diesel generator, and battery system. The objective function is minimizing the total net present cost, which includes all expenses during the project lifetime; the optimization respects other aspects, technical and ecologic. The improved algorithm is called IAOA that developed by modifying the original Arithmetic Optimization Algorithm (AOA) using the leading operators of the Aquila Optimizer (AO). The modified version is conducted to improve the search ability of the original AOA and avoid its weaknesses like being trapped in a local search. Moreover, the proposed IAOA makes a learning stage from the search process of the AO to enhance the research history of the AOA. Two HRES scenarios are suggested, the first is based on using PV/wind/diesel/battery, while the second scenario consists of PV/diesel/battery. This proposed HRES is located in El Kharga Oasis in Egypt with latitude of 25.42 and longitude of 30.581. The obtained results prove that the proposed IAOA gives better results compared with the other well-known algorithms, namely the original algorithm of AOA, Equilibrium Optimizer (EO), Gray Wolf Optimization (GWO), Artificial Electric Field Algorithm (AEFA), and Harris Hawks Optimization (HHO). It is recognized that the proposed IAOA is a promising alternative to solve hybrid renewable energy systems.
An improved wild horse optimization algorithm for reliability based optimal DG planning of radial distribution networks
(Elsevier, 2022-11) Ali, Mohammed Hamouda; Kamel, Salah; Hassan, Mohamed H.; Tostado-Véliz, Marcos; Zawbaa, Hossam M.
This paper introduces a novel technique for optimal distribution system (DS) planning with distributed generation (DG) systems. It is being done to see how active and reactive power injections affect the system’s voltage profile and energy losses. DG penetration in the power systems is one approach that has several advantages such as peak savings, loss lessening, voltage profile amelioration. It also intends to increase system reliability, stability, and security. The main goal of optimal distributed generation (ODG) is a guarantee to achieve the benefits mentioned previously to increase the overall system efficiency. For extremely vast and complicated systems, analytical approaches are not suitable and insufficient. Therefore, several meta-heuristic techniques are favored to obtain better performance from were convergence and accuracy for large systems. In this paper, an Improved Wild Horse Optimization algorithm (IWHO) is proposed as a novel metaheuristic method for solving optimization issues in electrical power systems. IWHO is devised with inspirations from the social life behavior of wild horses. The suggested method is based on the horse’s decency. To assess the efficacy of the IWHO, it is implemented on the 23 benchmark functions Reliability amelioration is the most things superb as a result of DGs incorporation. Thus, in this research, a customer-side reliability appraisal in the DS that having a DG unit was carried out by a Monte Carlo Simulation (MCS) approach to construct an artificial history for each ingredient across simulation duration. For load flow calculations, the backward Forward Sweep (BFS) technique has been employed as a simulation tool to assess the network performance considering the power handling restrictions. The proposed IWHO method has been measured on IEEE 33 69 and 119 buses to ascertain the network performing in the presence of the optimal DG and the potential benefits of the suggested technique for enhancing the tools used by operators and planners to maintain the system reliability and efficiency. The results proved that IWHO is an optimization method with lofty performance regarding the exploration–exploitation balance and convergence speed, as it successfully handles complicated problems.
Comparison of various robust and efficient load-flow techniques based on Runge–Kutta formulas
(Elsevier, 2019-09) Tostado-Véliz, Marcos; Kamel, Salah; Jurado-Melguizo, Francisco
Based on Continuous Newton’s method, any well-assessed numerical scheme can be adapted for solving the Load-Flow (LF) problem. So far, LF techniques based on 4th order Runge–Kutta formula (RK4) and Adams–Bashfort’s methods (AB) have been proposed. However, there is a huge variety of numerical methods whose adequacy for solving the LF problem has not been explored yet. This paper tries to fill this gap by proposing several LF solvers based on Runge–Kutta (RK) formulas. Thus, several LF techniques based on Midpoint (MP), 3rd order Heun (H3), Simpson 3/8 (S3/8) and an accelerated 3rd order Runge–Kutta (ARK3) formulas are proposed. The performance of the proposed LF techniques is assessed using several medium and large-scale ill-conditioned power systems. The proposed techniques are compared with RK4, AB and other robust LF methods. In addition, their scalability and influence of the loading level are analyzed. The obtained results prove that the proposed LF techniques are faster than RK4 and robust enough to successfully tackle medium and large-scale ill-conditioned power systems. As main conclusion, it is proved that lower order methods might be as robust as higher order ones but more efficient. Therefore, its usage with respect higher order methods (e.g. 4th order ones), should be frequently preferable.
Developed Newton-Raphson based Predictor-Corrector load flow approach with high convergence rate
(Elsevier, 2019-02) Tostado-Véliz, Marcos; Kamel, Salah; Jurado-Melguizo, Francisco
In this paper, a new methodology called Newton-Raphson-Predictor-Corrector (NR-PC) is applied to solve the load-flow (LF) problem of well and ill-conditioned power systems. In the proposed LF method, the Predictor-Corrector mechanism is developed to achieve convergence rate of order 1 + sqrt(2) = 2.4 instead of 2 for the standard Newton Raphson (NR). The proposed NR-PC LF method is validated on different test systems; IEEE 30-bus, 57-bus, 118-bus and 300-bus systems as well-conditioned test cases, 13-bus and 20-bus systems as naturally ill-conditioned test systems, 1354-bus, 2869-bus and 9241-bus systems as realistic very large-scale test systems. The sensitivity of the proposed method with different R/X transmission line ratios and loading conditions is validated and compared with well-known methods. The simulation results show that the proposed LF method has better convergence characteristics and low computation time compared with benchmark methods.

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Examinando por Autor "Kamel, Salah"