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Solving the energy management (EM) problem in microgrids with the incorporation of demand response programs helps in achieving technical and economic advantages and enhancing the load curve characteristics. The EM problem, with its large number of constraints, is considered as a nonlinear optimization problem. Artificial rabbits optimization has an...
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Citations
... The optimal operation of MG EMS has attracted considerable research interest. Many optimization algorithms have been proposed for the energy management in MG, such as an improved weighted mean of vectors algorithm (LINFO) in Ref. [23], An improved bald eagle search (IBES) algorithm for home EMS in Ref. [24], and quantum artificial rabbits optimizer (QARO) in Ref. [25]. [26] developed a new optimizer inspired by the water flow for the cost reduction in a day-ahead EM system in the MG with the integration of DR. ...
Hydrogen energy storage systems (HESS) play an essential role in Microgrid (MG) systems to address the inherent generation characteristics of renewable energy sources. Also, the integration of Demand Response (DR) into the Energy Management System (EMS) of a renewable-based Multi-Microgrid (MMG) can lead to substantial technical and economic benefits. This paper proposes a modified optimization algorithm for optimizing MMG Energy Management (EM). The proposed algorithm is a modified version of the Student Psychology-Based Optimization (SPBO) technique called Modified Student Psychology-Based Optimization (MSPBO). This modification aims to improve issues such as slow convergence, low solution accuracy, lack of diversity, and getting stuck in local optima. The proposed MSPBO method incorporates a local escape operator and a collaborative student class to achieve a better balance between exploiting known solutions and exploring new possibilities. The MSPBO algorithm is applied to address the EM challenge within a MMG context. Considering the integration of renewable sources as Wind turbines and solar photovoltaic and the HESS, the EM problem is formulated as a two-stage multi-objective optimization: minimizing the operating cost of conventional generators and power transactions cost in addition to the cost of HESS, while maximizing operator benefits and peak load reduction. This multi-objective problem is tackled using a hybrid ε-lexicography–weighted-sum approach that avoids the need for normalization. The performance of the proposed MSPBO is evaluated using CEC 2017 benchmark test functions, utilizing various statistical measures such as best, average, worst, rank, and standard deviation (SD) of fitness values, along with Wilcoxon's rank-sum test. The MSPBO technique is compared with other optimization algorithms for these test functions, highlighting its efficiency and adeptness in achieving a harmonious trade-off between exploiting known solutions and exploring new ones. Furthermore, the MSPBO method is applied to solve two case studies. In Case 1, which involves a single stage with conventional demand response optimization, the results achieved using MSPBO are benchmarked against other optimization techniques, revealing its superior efficacy in addressing the EM challenge. In Case 2, a more complex two-stage multi-objective problem is tackled using MSPBO, and the assessment of the integration of HESS in the MMG is evaluated. In the second stage, there is a notable enhancement in peak load reduction percentage (PRP), from 13.9% to 16.13% without the HESS and from 12.68% to 16.46% with the integration of HESS.
... The Tied rank (TR) technique is utilized to rank these functions, where each technique is assigned, a rank based on its average value, with the algorithm having the smallest average value receiving rank 1, and so on. The algorithm with the lowest TR value is considered the most effective when compared to the other techniques 28 . ...
This study presents an advanced metaheuristic approach termed the Enhanced Gorilla Troops Optimizer (EGTO), which builds upon the Marine Predators Algorithm (MPA) to enhance the search capabilities of the Gorilla Troops Optimizer (GTO). Like numerous other metaheuristic algorithms, the GTO encounters difficulties in preserving convergence accuracy and stability, notably when tackling intricate and adaptable optimization problems, especially when compared to more advanced optimization techniques. Addressing these challenges and aiming for improved performance, this paper proposes the EGTO, integrating high and low-velocity ratios inspired by the MPA. The EGTO technique effectively balances exploration and exploitation phases, achieving impressive results by utilizing fewer parameters and operations. Evaluation on a diverse array of benchmark functions, comprising 23 established functions and ten complex ones from the CEC2019 benchmark, highlights its performance. Comparative analysis against established optimization techniques reveals EGTO's superiority, consistently outperforming its counterparts such as tuna swarm optimization, grey wolf optimizer, gradient based optimizer, artificial rabbits optimization algorithm, pelican optimization algorithm, Runge Kutta optimization algorithm (RUN), and original GTO algorithms across various test functions. Furthermore, EGTO's efficacy extends to addressing seven challenging engineering design problems, encompassing three-bar truss design, compression spring design, pressure vessel design, cantilever beam design, welded beam design, speed reducer design, and gear train design. The results showcase EGTO's robust convergence rate, its adeptness in locating local/global optima, and its supremacy over alternative methodologies explored.
... Ertenlice and Kalayci 2018). Since the mid-1990s, different algorithms of this type have been used to solve mathematical and engineering problems, including the well-known particle swarm optimisation (PSO), inspired by the behaviour of flocks of birds (Eberhart and Kennedy 1995) or others based on the behaviour of fireflies (Yang 2010), whales (Mirjalili and Lewis 2016), bees (Karaboga and Akay 2009), manta rays (Zhao et al. 2020), horses (Naruei and Keynia 2022) and rabbits (Wang et al. 2022;Alsaiari et al. 2023, Alamir et al. 2023, Nguyen et al. 2024), among others. This paper presents a methodology for the identification of cracks in rotating, at low rotation velocities, Euler Bernoulli beams based on the novel optimisation algorithm called the Artificial Rabbit Algorithm. ...
... Once we have formulated the direct problem that allows us to obtain the first two natural frequencies, taking into account the characteristics of the cracked rotating beam (Tables 1 and 2), we proceed to tackle the inverse problem that consists of determining the characteristics of the crack contained in the beam (if it exists) from the knowledge of the natural frequencies of the beam by applying a novel optimisation algorithm of the type known as swarm optimisation. In this case, the algorithm based on the behaviour of rabbits, Artificial Rabbit Optimization (ARO) (Wang et al. 2022), has been chosen because it is a simple algorithm to implement and its good results have been demonstrated in very recent works such as those referred to by the developers of the algorithm (Wang et al. 2022), with some engineering examples, and by other authors such as Alamir et al. (2023) who use it for the optimization of energy management. ...
... For instance, (Alsaiari et al., 2023) merged ARO with a multi-layer perceptrons (MLP) model to predict the water efficiency of different configurations of solar stills (SSs). Another study (Alamir et al., 2023) introduced a modified ARO approach, infused with principles from quantum mechanics, showcasing its efficacy in solving energy management (EM) problems by optimizing benefits and reducing time consumption. Furthermore, (Samal et al., 2023) effectively employed ARO to address economic load dispatch challenges. ...
... Recently, several optimization strategies have been applied to solve EM problems, such as improved weighted mean of vectors algorithm [17], Simulated Annealing (SA) algorithm in [18], the Artificial Hummingbird Algorithm (AHA) in [19]. QARO algorithm was proposed in [20], and its effectiveness in solving the energy management problem was proved. ...
... In this subsection, we assess the efficiency and accuracy of the LAEO algorithm on 23 benchmark functions. These benchmark functions comprise three types of test functions: unimodal, multimodal, and low-dimensional multimodal test functions [59]. We conduct a comparative analysis between the results obtained from the LAEO algorithm and those derived from the original AEO algorithm, as well as four recent optimization algorithms: Social Network Search (SNS) [60], Gray Wolf Optimizer (GWO) [61], Tunicate Swarm Algorithm (TSA) [62], and Supply-demand-based Optimization (SDO) [63]. ...
The utilization of accurate models is crucial in the various stages of development for photovoltaic (PV) systems. Modelling these systems effectively allows developers to assess new modifications prior to the manufacturing phase, resulting in cost and time savings. This research paper presents a viable approach to accurately estimate both static and dynamic PV models. The proposed estimation method relies on a novel and enhanced optimization algorithm called leader artificial ecosystem‐based optimization (LAEO), which improves upon the original artificial ecosystem‐based optimization (AEO). The proposed LAEO algorithm integrates the adaptive probability (AP) and leader‐based mutation‐selection strategies to enhance the search capability, improve the balance between exploration and exploitation, and overcome local optima. To evaluate the effectiveness of LAEO, it was tested on 23 different benchmark functions. Additionally, LAEO was applied to estimate the parameters of static three‐diode PV models, as well as integral‐order and fractional‐order dynamic models. This paper showcases practical implementations of photovoltaic (PV) parameter estimation in various scenarios, including the static three‐diode model, dynamic integral order model (IOM), and fractional order model (FOM). The results were assessed from various angles to examine the precision, performance, and stability of the LAEO algorithm.
... The pertinent factors for our specific case scenario are outlined in Table 2 [31]. 23 benchmark functions [39]. This paper employs 23 widely recognized benchmark test functions to evaluate the performance of the LBWO algorithm. ...
Demand side management (DSM) involves technologies and strategies that allow customers to actively participate in the optimization of their energy usage patterns, ultimately contributing to a more sustainable and efficient energy system. In this paper, leader beluga whale optimization improvement (LBWO) and original beluga whale optimization (BWO) are used to implement a DSM scheme that enables lower peak-to-average ratio (PAR) and decreasing the expenses associated with electricity consumption. In the context of this research, electricity consumers decide to store, buy, or sell the electricity to maximize profits while minimizing its costs and PAR. Electricity consumers make their decisions based on the amount of electricity generated from their mini-grid, electricity prices and demand from the public network. The mini-grid is a combination of a photovoltaic panel and a wind turbine connected to an energy storage system (ESS). An ESS is used for maintaining power system stability because the power generated from renewable energy source (RES) has intermittent characteristics depending on environmental conditions. The proposed scheme is tested on three different cases from a study, the first case is the traditional house, the second case is the smart house with DSM, and the last case is the smart house with its mini-grid and DSM. The simulation outcomes of the suggested approach validate the efficiency and strength of the suggested scheme with regard to reducing the cost of electricity and the PAR while increasing the profit for electricity consumers.
... In this section, the outcomes of the trials performed on 23 standard test functions utilizing the proposed optimizer and contemporary algorithms are exhibited. These benchmark functions are comprising three types of test functions: unimodal, multimodal, and low-dimensional multimodal test functions [40]. The experiments offer a thorough assessment of the methods from diverse angles, such as exploration and exploitation capabilities, convergence, scalability, and execution time. ...
Here, a new approach is proposed for solving the optimal power flow (OPF) problem in transmission networks using a Gradient Bald Eagle Search Algorithm (GBES) with a Local Escaping Operator (LEO). The method takes into account uncertainty of the renewable energy sources (wind energy and photovoltaic systems) and Vehicle‐to‐Grid (V2G) in the stochastic OPF problem. To improve the efficiency of the proposed technique and enhance its local exploitation capability, the LEO method's selection features are utilized. Monte Carlo methods are employed to estimate the generation costs of the renewable sources and PEVs and study their feasibility. The uncertainty of the renewable sources and PEVs is represented by Weibull, lognormal, and normal probability distribution functions (PDFs). The GBES approach is experimentally compared with well‐known meta‐heuristics using twenty‐three different test functions, and the results indicate its superiority over BES and other recently developed algorithms. Furthermore, the proposed method's effectiveness is evaluated using IEEE 30‐bus test system under various scenarios, and the simulation results demonstrate that it can effectively address OPF issues considering renewable energy sources and V2G, providing superior optimal solutions compared to other algorithms.
Energy storage is required to address the intermittent nature of renewable energy resources, thereby improving system stability and dependability. This paper proposes an assessment of the integration of the Demand Response Program (DRP) and hydrogen energy storage system (HESS) in enhancing the independence index (IPI) for residential Microgrids (MGs). In addition, this paper proposes a new application of the recently developed Young’s double-slit experiment (YDSE) optimizer to solve the MG Energy Management System (EMS). The proposed EMS is a day-ahead optimal operation for the MG resources for minimizing the operating cost, transaction cost, and hydrogen integration cost while maximizing the MG benefit. The Proposed YDSE simulation results are compared with other techniques, such as PSO and INFO. The results demonstrate the effectiveness of the proposed YDSE in solving the EM problem. Three different cases are simulated to assess the effect of DRP and HESS. And the results are compared. The integration of DRP reduced the operating cost by 29%, and the integration of HESS enhanced the IPI by 4%.
