Examinando por Autor "Hasanien, Hany M."

Mostrando 1 - 20 de 21

A data-driven methodology to design user-friendly tariffs in energy communities
(Elsevier, 2024-03) Tlenshiyeva, Akmaral; Tostado-Véliz, Marcos; Hasanien, Hany M.; Khosravi, Nima; Jurado-Melguizo, Francisco
In recent years, energy communities have emerged as a feasible solution to empower domestic end-users to engage in local power trading with their neighbours, in an attempt to improve the efficiency and economy of residential consumers. From a mercantilist point of view, launching local markets with eventual local electricity prices might be beneficial for community users as they are inhibited from external volatile prices and possible market imperfections. However, local pricing strategies should take into account users’ preferences and avoid undesirable effects of response fatigue (i.e. excessive number of response signals within a short-time period). This way, local electricity tariffs should be stable and send coherent response signals easily interpretable by users. In this sense, the necessity of developing proper designing tools for local electricity tariffs is clear. This paper focuses on this issue. In particular, the main novelties of this paper are twofold: on the one hand, the developed tool designs community tariffs over a year basis instead of daily spot prices, as made in existing approaches. Thereby, the resulting tariff keeps stable yearly similar to conventional tariffs offered by retailers worldwide. Secondly, the designed tariff takes into account the negative effects of response fatigue, so that the considered pricing mechanism limits the number of pricing signals sent to consumers, taking this feature as an external parameter. This way, the designer is able to tune up the total number of pricing signals that users received within a time period, thus ensuring that they are not discouraged to partake in the community. The proposed design approach is raised as a data-driven framework, taking advantage of real databases collecting demand, renewable generation and retailer prices. Such profiles serve as inputs for a designed bi-level Stackelberg-based problem, in which the reaction of prosumers is implicitly assumed. A case study is conducted on a benchmark energy community. Different tariff mechanisms are analysed such as flat, time-of-use and happy hours tariffs. The results obtained serve to validate the new proposal as well as analyse the effect of local market mechanisms in energy communities.
A Robust-Based Home Energy Management Model for Optimal Participation of Prosumers in Competitive P2P Platforms
(MDPI, 2024-11-16) Al Zetawi, Alaa; Tostado-Véliz, Marcos; Hasanien, Hany M.; Jurado-Melguizo, Francisco
Nowadays, advanced metering and communication infrastructures make it possible to enable decentralized control and market schemes. In this context, prosumers can interact with their neighbors in an active manner, thus sharing resources. This practice, known as peer-to-peer (P2P), can be put into practice under cooperative or competitive premises. This paper focuses on the second case, where the peers partaking in the P2P platform compete among themselves to improve their monetary balances. In such contexts, the domestic assets, such as on-site generators and storage systems, should be optimally scheduled to maximize participation in the P2P platform and thus enable the possibility of obtaining monetary incomes or exploiting surplus renewable energy from adjacent prosumers. This paper addresses this issue by developing a home energy management model for optimal participation of prosumers in competitive P2P platforms. The new proposal is cast in a three-stage procedure, in which the first and last stages are focused on domestic asset scheduling, while the second step decides the optimal offering/bidding strategy for the concerned prosumer. Moreover, uncertainties are introduced using interval notation and equivalent scenarios, resulting in an amicable computational framework that can be efficiently solved by average machines and off-the-shelf solvers. The new methodology is tested on a benchmark four-prosumer community. Results prove that the proposed procedure effectively maximizes the participation of prosumers in the P2P platform, thus increasing their monetary benefits. The role of storage systems is also discussed, in particular their capability of increasing exportable energy. Finally, the influence of uncertainties on the final results is illustrated.
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.
Adaptive PI Control Strategy for Optimal Microgrid Autonomous Operation
(MDPI, 2022-11-11) Hussien, Ahmed M.; Kim, Jonghoom; Alkuhayli, Abdulaziz; Alharbi, Mohammed; Hasanien, Hany M.; Tostado-Véliz, Marcos; Turky, Rania A.; Jurado-Melguizo, Francisco
The present research produces a new technique for the optimum operation of an isolated microgrid (MGD) based on an enhanced block-sparse adaptive Bayesian algorithm (EBSABA). To update the proportional-integral (PI) controller gains online, the suggested approach considers the impact of the actuating error signal as well as its magnitude. To reach a compromise result between the various purposes, the Response Surface Methodology (RSMT) is combined with the sunflower optimization (SFO) and particle swarm optimization (PSO) algorithms. To demonstrate the success of the novel approach, a benchmark MGD is evaluated in three different Incidents: (1) removing the MGD from the utility (islanding mode); (2) load variations under islanding mode; and (3) a three-phase fault under islanding mode. Extensive simulations are run to test the new technique using the PSCAD/EMTDC program. The validity of the proposed optimizer is demonstrated by comparing its results with those obtained using the least mean and square root of exponential method (LMSRE) based adaptive control, SFO, and PSO methodologies. The study demonstrates the superiority of the proposed EBSABA over the LMSRE, SFO, and PSO approaches in the system’s transient reactions.
Circle Search Algorithm: A Geometry-Based Metaheuristic Optimization Algorithm
(MDPI, 2022-05-10) Qais, Mohammed H.; Hasanien, Hany M.; Turky, Rania A.; Alghuwainem, Saad; Tostado-Véliz, Marcos; Jurado-Melguizo, Francisco
This paper presents a novel metaheuristic optimization algorithm inspired by the geometrical features of circles, called the circle search algorithm (CSA). The circle is the most well-known geometric object, with various features including diameter, center, perimeter, and tangent lines. The ratio between the radius and the tangent line segment is the orthogonal function of the angle opposite to the orthogonal radius. This angle plays an important role in the exploration and exploitation behavior of the CSA. To evaluate the robustness of the CSA in comparison to other algorithms, many independent experiments employing 23 famous functions and 3 real engineering problems were carried out. The statistical results revealed that the CSA succeeded in achieving the minimum fitness values for 21 out of the tested 23 functions, and the p-value was less than 0.05. The results evidence that the CSA converged to the minimum results faster than the comparative algorithms. Furthermore, high-dimensional functions were used to assess the CSA’s robustness, with statistical results revealing that the CSA is robust to high-dimensional problems. As a result, the proposed CSA is a promising algorithm that can be used to easily handle a wide range of optimization problems.
Coot Bird Algorithms-Based Tuning PI Controller for Optimal Microgrid Autonomous Operation
(IEEE, 2022) Hussien, Ahmed Moreab; Turky, Rania A.; Alkuhayli, Abdulaziz; Hasanien, Hany M.; Tostado-Véliz, Marcos; Jurado-Melguizo, Francisco; Bansal, Ramesh C.
This paper develops a novel methodology for optimal control of islanded microgrids (MGs) based on the coot bird metaheuristic optimizer (CBMO). To this end, the optimum gains for the PI controller are found using the CBMO under a multi-objective optimization framework. The Response Surface Methodology (RSM) is incorporated into the developed procedure to achieve a compromise solution among the different objectives. To prove the effectiveness of the new proposal, a benchmark MG is tested under various scenarios, 1) isolate the system from the grid (autonomous mode), 2) islanded system exposure to load changes, and 3) islanded system exposure to a 3 phase fault. Extensive simulations are performed to validate the new method taking conventional data from PSCAD/EMTDC software. The validity of the suggested optimizer is proved by comparing its results with that achieved using the LMSRE-based adaptive control, sunflower optimization algorithm (SFO), Ziegler-Nichols method and the particle swarm optimization (PSO) techniques. The article shows the superiority of the suggested CBMO over the LMSRE-based adaptive control, SFO, Ziegler-Nichols and the PSO techniques in the transient responses of the system.
Coot Bird Algorithms-Based Tuning PI Controller for Optimal Microgrid Autonomous Operation
(IEEE, 2022) Hussien, Ahmed Moreab; Turky, Rania A.; Alkuhayli, Abdulaziz; Hasanien, Hany M.; Tostado-Véliz, Marcos; Jurado, Francisco
This paper develops a novel methodology for optimal control of islanded microgrids (MGs) based on the coot bird metaheuristic optimizer (CBMO). To this end, the optimum gains for the PI controller are found using the CBMO under a multi-objective optimization framework. The Response Surface Methodology (RSM) is incorporated into the developed procedure to achieve a compromise solution among the different objectives. To prove the effectiveness of the new proposal, a benchmark MG is tested under various scenarios, 1) isolate the system from the grid (autonomous mode), 2) islanded system exposure to load changes, and 3) islanded system exposure to a 3 phase fault. Extensive simulations are performed to validate the new method taking conventional data from PSCAD/EMTDC software. The validity of the suggested optimizer is proved by comparing its results with that achieved using the LMSRE-based adaptive control, sunflower optimization algorithm (SFO), Ziegler-Nichols method and the particle swarm optimization (PSO) techniques. The article shows the superiority of the suggested CBMO over the LMSRE-based adaptive control, SFO, Ziegler-Nichols and the PSO techniques in the transient responses of the system.
DC Nanogrids for Integration of Demand Response and Electric Vehicle Charging Infrastructures: Appraisal, Optimal Scheduling and Analysis
(MDPI, 2021-10-12) Habeeb, Salwan Ali; Tostado Véliz, Marcos; Hasanien, Hany M.; Turky, Rania A.; Meteab, Wisam Kareem; Jurado Melguizo, Francisco
With the development of electronic infrastructures and communication technologies and protocols, electric grids have evolved towards the concept of Smart Grids, which enable the communication of the different agents involved in their operation, thus notably increasing their efficiency. In this context, microgrids and nanogrids have emerged as invaluable frameworks for optimal integration of renewable sources, electric mobility, energy storage facilities and demand response programs. This paper discusses a DC isolated nanogrid layout for the integration of renewable generators, battery energy storage, demand response activities and electric vehicle charging infrastructures. Moreover, a stochastic optimal scheduling tool is developed for the studied nanogrid, suitable for operators integrated into local service entities along with the energy retailer. A stochastic model is developed for fast charging stations in particular. A case study serves to validate the developed tool and analyze the economical and operational implications of demand response programs and charging infrastructures. Results evidence the importance of demand response initiatives in the economic profit of the retailer.
Hybrid Particle Swarm and Gravitational Search Algorithm-Based Optimal Fractional Order PID Control Scheme for Performance Enhancement of Offshore Wind Farms
(MDPI, 2023-08) Mohamed, Nour A.; Hasanien, Hany M.; Alkuhayli, Abdulaziz; Akmaral, Tlenshiyeva; Jurado-Melguizo, Francisco; Badr, Ahmed O.
This article aimed to introduce a novel application of a hybrid particle swarm optimizer and gravitational search algorithm (HPSOGSA) that can be used for optimal control of offshore wind farms’ voltage source converter connected to HVDC transmission lines. Specifically, the algorithm was used to design fractional-order proportional-integral-derivative (FOPID) controller parameters designed to minimize the system’s objective function based on an integral squared error. The proposed FOPID controller was applied to improve offshore wind farm performance under different transient conditions, and its results were compared with a PI controller that was designed using a genetic algorithm and grey wolf optimization algorithm. The fault ride-through capabilities of the proposed control strategy were also evaluated. The findings suggest that the HPSOGSA-based FOPID controller outperformed the other two methods, significantly enhancing offshore wind farm operations. The control strategy was thoroughly tested using MATLAB/Simulink under various operating scenarios.
Hybrid State of Charge Estimation of Lithium-Ion Battery Using the Coulomb Counting Method and an Adaptive Unscented Kalman Filter
(MDPI, 2023-07) Fahmy, Hend M.; Swief, Rania A.; Hasanien, Hany M.; Alharbi, Mohammed; Maldonado-Ortega, José Luis; Jurado-Melguizo, Francisco
This paper establishes an accurate and reliable study for estimating the lithium-ion battery’s State of Charge (SoC). An accurate state space model is used to determine the parameters of the battery’s nonlinear model. African Vultures Optimizers (AVOA) are used to solve the issue of identifying the battery parameters to accurately estimate SoC. A hybrid approach consists of the Coulomb Counting Method (CCM) with an Adaptive Unscented Kalman Filter (AUKF) to estimate the SoC of the battery. At different temperatures, four approaches are applied to the battery, varying between including load and battery fading or not. Numerical simulations are applied to a 2.6 Ahr Panasonic Li-ion battery to demonstrate the hybrid method’s effectiveness for the State of Charge estimate. In comparison to existing hybrid approaches, the suggested method is very accurate. Compared to other strategies, the proposed hybrid method achieves the least error of different methods.
Mann-Iteration Process for Power Flow Calculation of Large-Scale Ill-Conditioned Systems: Theoretical Analysis and Numerical Results
(IEEE, 2021) Tostado-Véliz, Marcos; Hasanien, Hany M.; Turky, Rania A.; Alkuhayli, Abdulaziz; Kamel, Salah; Jurado-Melguizo, Francisco
Power Flow solution of realistic ill-conditioned systems has recently attracted huge attention. Nevertheless, there are still some gaps in this field. For example, most of available references do not provide exhaustive theoretical analysis about convergence properties of proposed approaches. In addition, efficient solution of large-scale ill-conditioned systems is still an open topic. This paper tackles these issues by comprehensively studying the suitability of the Mann Iteration Process for the solution of ill-conditioned systems. A comprehensive theoretical analysis is provided, from which is demonstrated that the Mann Iteration Process has with asymptotic stability, may achieve a high convergence rate and constitutes a robust methodology, improving the contractive properties of the Newton-Raphson method. Moreover, some interesting links with other Power-Flow approaches are obtained as by-product. Several numerical experiments serve to confirm the theoretical findings and to compare the performance of the Mann Iteration Process with other well-known PF solvers. In all cases, the results obtained with the Mann Iteration Process are superior to that obtained using other methodologies, being able to efficiently solve various large-scale ill-conditioned systems.
Manta Ray Foraging Optimization for the Virtual Inertia Control of Islanded Microgrids Including Renewable Energy Sources
(MDPI, 2022-04) Saleh, Amr; Omran, Walid A.; Hasanien, Hany M.; Tostado-Véliz, Marcos; Alkuhayli, Abdulaziz; Jurado-Melguizo, Francisco
Nowadays, the penetration level of renewable energy sources (RESs) has increased dramatically in electrical networks, especially in microgrids. Due to the replacement of conventional synchronous generators by RESs, the inertia of the microgrid is significantly reduced. This has a negative impact on the dynamics and performance of the microgrid in the face of uncertainties, resulting in a weakening of microgrid stability, especially in an islanded operation. Hence, this paper focuses on enhancing the dynamic security of an islanded microgrid using a frequency control concept based on virtual inertia control. The control in the virtual inertia control loop was based on a proportional-integral (PI) controller optimally designed by the Manta Ray Foraging Optimization (MRFO) algorithm. The performance of the MRFO-based PI controller was investigated considering various operating conditions and compared with that of other evolutionary optimization algorithm-based PI controllers. To achieve realistic simulations conditions, actual wind data and solar power data were used, and random load fluctuations were implemented. The results show that the MRFO-based PI controller has a superior performance in frequency disturbance alleviation and reference frequency tracking compared with the other considered optimization techniques.
Multiobjective home energy management systems in nearly-zero energy buildings under uncertainties considering vehicle-to-home: A novel lexicographic-based stochastic-information gap decision theory approach
(Elsevier, 2023-01-15) Tostado-Véliz, Marcos; Hasanien, Hany M.; Kamel, Salah; Turky, Rania A.; Jurado, Francisco; Elkadeem, M.R.
Residential sector is being promoted to evolve towards nearly-Zero Energy Buildings (nZEBs), which draw a yearly net energy consumption near zero. This target can be attained through on-site renewable generation, achieving high efficiency in consumption. In this context, Home Energy Management (HEM) systems become an indispensable tool for obtaining optimally coordinating smart appliances, renewable generation, and on-site storage facilities. Due to the high unpredictability of renewable generation and some emerging appliances like electric vehicles, these tools must be able to deal with different uncertainties properly. At the same time, a variety of objectives are jointly considered. The existing approaches commonly fail to deal jointly with these two premises. This paper aims to fill this gap by developing a novel solution for HEM systems in nZEBs. The proposed procedure uses lexicographic optimization to find a compromise solution among objectives. At the same time, the variety of uncertainties caused by unpredictable weather, demand, energy pricing, and electric vehicle behavior is adequately modeled using a hybrid stochastic-Information Gap Decision Theory (IGDT) approach. The mathematical modeling is sufficiently comprehensive (comprising various energy sources and vehicle-to-home capability) and tractable due to its integer-linear structure. A case study on a benchmark nearly zero energy home is considered to validate the developed approach, which its results reveal its effectiveness in terms of minimizing various objective functions while the degree of robustness is preserved and the whole procedure is efficient yet.
Optimal home energy management including batteries and heterogenous uncertainties
(Elsevier, 2023-04) Tostado-Véliz, Marcos; Hasanien, Hany M.; Turky, Rania A.; Assolami, Yasser O.; Vera, David; Jurado-Melguizo, Francisco
Energy storage will play a vital role in the decarbonization of the electricity sector, especially in domestic installations. In such kind of systems, home energy management applications are becoming essential. These kinds of tools enable active control of domestic appliances and storage systems to pursue a more efficient use of energy in household installations. However, the emergence of renewable generators and electric vehicles makes the operation of residential assets more difficult, as multiple uncertainties should be managed on a whole. These unknowns have a different character, in fact, some of them can be easily predicted while others are subject to a high level of randomness. This paper addresses this issue by developing a novel home energy management tool that accounts for the different levels of the randomness of the uncertainties involved in home operation. To this end, a novel Lexicographic-Interval formulation of the home energy management problem is presented, by which the uncertainties can be easily modelled using interval notation. Unlike conventional tools, the new proposal sorts the uncertainties according to their level of randomness. Then, the energy management mechanism performs the scheduling plan according to the predefined classification so that the more random parameters rule the impact of others, thus giving more importance to those uncertainties that are hardly predictable. A benchmark case study is performed to validate the new proposal and illustrate its capabilities. Different tariffs are compared, showing that the Time-of-Use mechanism is normally more expensive than Real-Time-Pricing tariffs, increasing the electricity bill by 12 % in some cases. However, the level of robustness achieved with Time-of-Use tariffs seems higher, allowing to assume an abruptly unexpected reduction of photovoltaic generation.
Optimal Model Predictive Control for Virtual Inertia Control of Autonomous Microgrids
(MDPI, 2023-03) Saleh, Amr; Hasanien, Hany M.; Turky, Rania A.; Turdybek, Balgynbek; Alharbi, Mohammed; Jurado-Melguizo, Francisco; Omran, Walid A.
For the time being, renewable energy source (RES) penetration has significantly increased in power networks, particularly in microgrids. The overall system inertia is dramatically decreased by replacing traditional synchronous machines with RES. This negatively affects the microgrid dynamics under uncertainties, lowering the microgrid frequency stability, specifically in the islanded mode of operation. Therefore, this work aims to enhance the islanded microgrid frequency resilience using the virtual inertia frequency control concept. Additionally, optimal model predictive control (MPC) is employed in the virtual inertial control model. The optimum design of the MPC is attained using an optimization algorithm, the African Vultures Optimization Algorithm (AVOA). To certify the efficacy of the proposed controller, the AVOA-based MPC is compared with a conventional proportional–integral (PI) controller that is optimally designed using various optimization techniques. The actual data of RES is utilized, and a random load power pattern is applied to achieve practical simulation outcomes. Additionally, the microgrid paradigm contains battery energy storage (BES) units for enhancing the islanded microgrid transient stability. The simulation findings show the effectiveness of AVOA-based MPC in improving the microgrid frequency resilience. Furthermore, the results secure the role of BES in improving transient responses in the time domain simulations. The simulation outcomes are obtained using MATLAB software.
Parameter Identification of Lithium-Ion Battery Model Based on African Vultures Optimization Algorithm
(MDPI, 2023-05-08) Fahmy, Hend M.; Sweif, Rania A.; Hasanien, Hany M.; Tostado-Véliz, Marcos; Alharbi, Mohammed; Jurado-Melguizo, Francisco
This paper establishes a study for an accurate parameter modeling method for lithium-ion batteries. A precise state space model generated from an equivalent electric circuit is used to carry out the proposed identification process, where parameter identification is a nonlinear optimization process problem. The African vultures optimization algorithm (AVOA) is utilized to solve this problem by simulating African vultures’ foraging and navigating habits. The AVOA is used to implement this strategy and improve the quality of the solutions. Four scenarios are considered to take the effect of loading, fading, and dynamic analyses. The fitness function is selected as the integral square error between the estimated and measured voltage in these scenarios. Numerical simulations were executed on a 2600 mAhr Panasonic Li-ion battery to demonstrate the effectiveness of the suggested parameter identification technique. The proposed AVOA was fulfilled with high accuracy, the least error, and high closeness with the experimental data compared with different optimization algorithms, such as the Nelder–Mead simplex algorithm, the quasi-Newton algorithm, the Runge Kutta optimizer, the genetic algorithm, the grey wolf optimizer, and the gorilla troops optimizer. The proposed AVOA achieves the lowest fitness function level of the scenarios studied compared with relative optimization algorithms.
Parameter Identification of Proton Exchange Membrane Fuel Cell Based on Hunger Games Search Algorithm
(MDPI, 2021-08) Fahim, Samuel Raafat; Hasanien, Hany M.; Turky, Rania A.; Alkuhayli, Abdulaziz; Al-Shamma'a, Abdulrrahman; Noman, Abdullah M.; Tostado-Véliz, Marcos; Jurado-Melguizo, Francisco
This paper presents a novel minimum seeking algorithm referred to as the Hunger Games Search (HGS) algorithm. The HGS is used to obtain optimal values in the model describing proton exchange membrane fuel cells (PEMFCs). The PEMFC model has many parameters that are linked in a nonlinear manner, as well as a set of constraints. The HGS was used with the aforementioned model to test its performance against nonlinear models. The main aim of the optimization problem was to obtain accurate values of PEMFC parameters. The proposed heuristic algorithm was used with two commercial PEMFCs: the Ballard Mark V and the BCS 500 W. The simulation results obtained using the HGS-based model were compared to the experimental results. The effectiveness of the proposed model was verified under various temperature and partial pressure conditions. The numerical output results of the HGS-based fuel cell model were compared with other optimization algorithm-based models with respect to their efficiency. Moreover, the parametric t-test and other statistical analysis methods were employed to check the robustness of the proposed algorithm under various independent runs. Using the proposed HGS-based PEMFC model, a model with very high precision could be obtained, affecting the operation and control of the fuel cells in the simulation analyses.
Probabilistic Optimal Power Flow Solution Using a Novel Hybrid Metaheuristic and Machine Learning Algorithm
(MDPI, 2022-08-23) Shaheen, Mohamed A.M.; Hasanien, Hany M.; Mekhamer, Said F.; Qais, Mohammed H.; Alghuwainem, Saad; Ullah, Zia; Tostado-Véliz, Marcos; Turky, Rania A.; Jurado-Melguizo, Francisco; Elkadeem, Mohamed R.
This paper proposes a novel hybrid optimization technique based on a machine learning (ML) approach and transient search optimization (TSO) to solve the optimal power flow problem. First, the study aims at developing and evaluating the proposed hybrid ML-TSO algorithm. To do so, the optimization technique is implemented to solve the classical optimal power flow problem (OPF), with an objective function formulated to minimize the total generation costs. Second, the hybrid ML-TSO is adapted to solve the probabilistic OPF problem by studying the impact of the unavoidable uncertainty of renewable energy sources (solar photovoltaic and wind turbines) and time-varying load profiles on the generation costs. The evaluation of the proposed solution method is examined and validated on IEEE 57-bus and 118-bus standard systems. The simulation results and comparisons confirmed the robustness and applicability of the proposed hybrid ML-TSO algorithm in solving the classical and probabilistic OPF problems. Meanwhile, a significant reduction in the generation costs is attained upon the integration of the solar and wind sources into the investigated power systems.
Solution of Probabilistic Optimal Power Flow Incorporating Renewable Energy Uncertainty Using a Novel Circle Search Algorithm
(MDPI, 2022-11-07) Shaheen, Mohamed A.M.; Ullah, Zia; Qais, Mohamed H.; Hasanien, Hany M.; Chua, Kian J.; Tostado-Véliz, Marcos; Turky, Rania A.; Jurado, Francisco; Elkadeem, Mohamed R.
Integrating renewable energy sources (RESs) into modern electric power systems offers various techno-economic benefits. However, the inconsistent power profile of RES influences the power flow of the entire distribution network, so it is crucial to optimize the power flow in order to achieve stable and reliable operation. Therefore, this paper proposes a newly developed circle search algorithm (CSA) for the optimal solution of the probabilistic optimal power flow (OPF). Our research began with the development and evaluation of the proposed CSA. Firstly, we solved the OPF problem to achieve minimum generation fuel costs; this used the classical OPF. Then, the newly developed CSA method was used to deal with the probabilistic power flow problem effectively. The impact of the intermittency of solar and wind energy sources on the total generation costs was investigated. Variations in the system’s demands are also considered in the probabilistic OPF problem scenarios. The proposed method was verified by applying it to the IEEE 57-bus and the 118-bus test systems. This study’s main contributions are to test the newly developed CSA on the OPF problem to consider stochastic models of the RESs, providing probabilistic modes to represent the RESs. The robustness and efficiency of the proposed CSA in solving the probabilistic OPF problem are evaluated by comparing it with other methods, such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and the hybrid machine learning and transient search algorithm (ML-TSO) under the same parameters. The comparative results showed that the proposed CSA is robust and applicable; as evidence, an observable decrease was obtained in the costs of the conventional generators’ operation, due to the penetration of renewable energy sources into the studied networks.
Solving realistic large-scale ill-conditioned power flow cases based on combination of numerical solvers
(Wiley, 2021-11) Tostado-Véliz, Marcos; Hasanien, Hany M.; Turky, Rania A.; Kamel, Salah; Jurado-Melguizo, Francisco
With the increasing electricity consumption and difficulty in upgrading exis-ting infrastructures, ill-conditioned power flow (PF) cases are becomingmore frequent nowadays. In this context, classical robust solvers may beunsuitable for realistic networks, which typically encompass thousands ofbuses, because of their high computational burden or low convergence rate.This article tackles this issue by proposing a novel PF solver, which presentsacceptable robustness and efficiency in solving large-scale ill-conditioned sys-tems. The proposed algorithm collects the advantage of various numericalsolvers, which by separate present different weaknesses, but actuating in coor-dination their strengths can be jointly exploited. More precisely, the robustForward-Euler and Trapezoidal rules are combined with the efficient Darvishicubic technique. Thereby, an original predictor-corrector algorithm is devel-oped to effectively coordinate the different numerical algorithms involved,obtaining a robust but efficient yet solution procedure. Various large-scale ill-conditioned benchmark systems are studied under different stressing condi-tions. The results obtained with the developed technique are promising, out-performing other robust and standard PF solvers.

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Examinando por Autor "Hasanien, Hany M."