Artificial Bee Swarm Optimization Research Articles

Optimizing solar panel maximum power point tracking and parasitic parameter extraction in partial shading with Enhanced Slime Mold optimization

Solar energy offers several environmental, economic, and energy security advantages. Parasitic parameters and shading on solar panels can reduce efficiency. This paper presents a bio-inspired Enhanced Slime Mold (ESM) algorithm search strategy to find near-optimal power points by simulating the behaviour of slime molds in a virtual environment. In a solar panel, proposed ESM provides not only for parameter extraction but also serves as Maximum Power Point Tracking (MPPT) during Partial Shading Conditions (PSC). The proposed ESM dynamic behaviour is examined under solar irradiation and various temperature conditions. The effectiveness of proposed technique has been validated by extracting parameters from conventional polycrystalline and monocrystalline modules in the form of a 5 S–5P arrangement. In the instance of MPPT operation, the proposed ESM algorithm is compared with Ant Bee Colony and Perturb& Observe (ABC-PO) to determine its efficacy. Moreover, during extraction of unknown parameters of solar cell ESM is compared with existing optimization algorithms such as Artificial Bee Swarm Optimization (ABSO), Genetic Algorithm (GA), Covariant Matrix (CM), Ant Bee Colony (ABC), and Advanced Particle Swarm Optimization (APSO). In this connection, proposed ESM algorithm is superior to the above-mentioned algorithms due to high accuracy, a smaller number of computations, and minimum computational time.

Towards a sustainable environment and carbon neutrality: Optimal sizing of standalone, green, reliable, and affordable water-power cogeneration systems

Today, the limited sources of freshwater supply are a significant concern. Exploiting alternative sources, especially seawater, has been the focus, but purifying it is energy-intensive. Integrating desalination with renewable energy is a proposed solution, but it comes with high costs and environmental risks during construction. Hence, this study presents a framework to enhance the modeling, optimization, and evaluation of green water-power cogeneration systems to achieve the sustainability goals of cities and societies. An improved division algorithm (DA) determines the optimal component sizes based on criteria like minimal energy demand, reduced environmental and resource damage, low total life cycle cost (TLCC), and high reliability. Optimization considers varying loss of power supply probability (LPSP) levels (0 %, 2 %, 5 %, and 10 %). The environmental assessment utilizes a life cycle assessment (LCA) approach with IMPACT 2002+ and cumulative energy demand (CED) calculations. The study models the green cogeneration systems based on weather conditions, water demand, and power requirements of Al Lulu Island, Abu Dhabi, UAE. The system comprises photovoltaic panels, wind turbines, tidal generators, and backup systems (fuel cells). Results reveal that TLCC ranges from $186,263 to $486,876 for the highest LPSP. The solar-tidal-based configuration offers the lowest TLCC ($186,263) while substituting solar with wind energy increases TLCC by 160 %. The wind-tidal-based configuration has the lowest specific environmental impact (1020 mPt/yr) and cumulative energy demand (39.06 GJ/yr) for the highest LPSP. In contrast, the solar-tidal-wind-based configuration inflicts the most damage, with 62.63 GJ/yr and 1794 mPt/yr for the highest LPSP. The finding indicates that the DA is faster (100 iterations) than the genetic algorithm (1000 iterations), particle swarm optimization (400 iterations), and artificial bee swarm optimization (300 iterations). The study underscores the solar-tidal-based configuration as the optimal choice across multiple criteria, offering a promising solution for freshwater supply and environmental sustainability on Al Lulu Island.

A robust crow search algorithm based power system state estimation

The State Estimation (SE) computational procedure plays a crucial role in modern electric power system security control by monitoring and analyzing operational conditions and predicting any emergency. In order to estimate state variables, Power System State Estimation (PSSE) takes into account the magnitudes and phases of voltage on each bus. To address the state estimation challenges in power systems, in this paper, we propose a novel application of the Crow Search Algorithm (CSA) specifically tailored for the state estimation problem. We have assessed the introduced algorithm using the frameworks of both the IEEE 14-bus and IEEE 30-bus test systems. The first formulation is the Weighted Least Square (WLS) method, and the second is the Weighted Least Absolute Value (WLAV) method, both of which are objective function formulations. By comparing the results, it is clear that CSA-based SE is superior to the other metaheuristic algorithms considered, namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Artificial Bee Swarm Optimization (ABSO). As a point of comparison, we use the Newton–Raphson method for calculating load flow. It has been shown that the proposed CSA-based SE technique has better accuracy than the other two algorithms in all different test systems. With this study, the power system is operated more accurately and reliably by the operators operating the system.

Use of Artificial Bee Swarm Optimization (ABSO) for Feature Selection in System Diagnosis for Coronary Heart Disease

Use of Artificial Bee Swarm Optimization (ABSO) for Feature Selection in System Diagnosis for Coronary Heart Disease

Optimization of wind/solar energy microgrid by division algorithm considering human health and environmental impacts for power-water cogeneration

Although freshwater is necessary for the well-being of humankind, increasing population growth and limited resources lead to a serious crisis to supply freshwater. Since the Earth is surrounded by seawater, desalination based on electrical power is introduced as a promising technology to provide freshwater. It is well documented that the connection of remote areas that usually do not have access to freshwater into the electricity grid is not affordable and eco-friendly. Hence, the efforts to design and construct high reliability, cost-effective, and eco-friendly stand-alone hybrid renewable energy system in remote areas. In line with this, this paper describes a novel energy management system for the optimized operation of a stand-alone hybrid energy system based on photovoltaic panels, wind turbines, batteries, and diesel generator. For this purpose, a multi-objective optimization problem is formulated by combining three objective functions, i.e., minimum the total life cycle cost as well as environmental impacts on human health and ecosystems and the maximum system reliability that can conflict with each. To solve the multi-objective optimization problem, a division algorithm is proposed that is more flexible and faster compared with conventional algorithms such as genetic algorithm. In order to show the proposed framework, a real case study in Larak Island, Iran, with appropriate solar and wind is considered. The effectiveness of the applied approach compared with optimization results of genetic algorithm and the artificial bee swarm optimization algorithm that was previously used successfully to solve optimization problems related to desalination integrated with the renewable energy system. The optimization is performed based on different diesel fuel price amounts (0.2, 0.5, and 1 $/liter). It is seen that at fuel price set to 0.2 and 0.5 $/liter, the seawater reverses osmosis desalination/photovoltaic/diesel generator/battery is the most cost-effective energy system, and when fuel price is 1 $/liter, the seawater reverses osmosis desalination/photovoltaic/wind turbine/diesel generator/battery is the most cost-effective hybrid system. While at fuel price set to 0.2, 0.5, and 1 $/liter, the seawater reverse osmosis desalination /photovoltaic/wind turbine/diesel generator/battery is the most eco-friendly. Finally, the results of this study show proposed algorithm is faster and more accurate (100 iterations, 98.36% accuracy) than the genetic algorithm (1000 iterations, 83.03% accuracy) and the artificial bee swarm optimization (300 iterations, 95.49% accuracy).

Parameterization of Solar Cell Model Using Multiculture & Hybrid Mutation Based Evolutionary Programming

In this paper, parameterization of the single diode model for solar cell has presented. The problem of obtaining the optimal parameter has transformed as an optimization problem where individual absolute error has minimized by hybrid mutation strategy in the Evolutionary programming. Hybridization has given between Gaussian mutation strategy and Cauchy mutation strategy to obtain the better offspring. To increase the reliability of the solution, two stages based a multiculture architecture has proposed. On the first stage, a multi-population strategy has applied to form a multiculture environment, where each population evolved independently to explore the solution domain.This stage will prevent the solution to trap in the local minima. In the second stage, evolved population from first stage combine and members having high fitness are selected to form a new population of the same size as the individual population in the first stage. This second stage population evolved further to meet the final objective. The performance of the proposed method has evaluated over a 57mm diameter commercial solar cell. The obtained performance has compared with results available in current literature where various other approaches like, Levenberg–Marquardt with Simulated annealing, Global Grouping-based Harmony Search, Artificial Bee Swarm Optimization, Chaotic Particle Swarm Optimization, Differential Evolution, etc. have considered. The proposed solution has delivered the minimum error in comparison to other methods and very closer to the experimental data.

A novel framework for optimal photovoltaic size and location in remote areas using a hybrid method: A case study of eastern Iran

Photovoltaic (PV) energy generation is one of the more promising renewable energy technologies. However, determining optimal specifications (e.g., size, location) for PV systems is a significant challenge for planners and investigators of distribution systems. In this paper, a framework is provided for integrating geographical information systems, artificial bee swarm optimization, and simulation to determine the optimal size and location for PV panels on an annual basis. A geographical information system module is developed first to determine the suitable location and capacity for a stand-alone PV system in rural areas, taking into account the technical, environmental and socio-economic criteria affecting the site selection process of stand-alone PV systems. Then an efficient heuristic approach, namely artificial bee swarm optimization, is proposed for optimum sizing of a stand-alone PV system to continuously satisfy the load demand with minimal cost and enhanced energy system reliability. For this aim, the maximum allowable loss of power supply probability is also considered. The proposed framework is applied to a real case study in eastern Iran and the results show that it can effectively be used for off-grid photovoltaic projects in rural areas considering both power quality and cost.

An improved optimization technique for estimation of solar photovoltaic parameters

Abstract The nonlinear current vs voltage (I-V) characteristics of solar PV make its modelling difficult. Optimization techniques are the best tool for identifying the parameters of nonlinear models. Even though, there are different optimization techniques used for parameter estimation of solar PV, still the best optimized results are not achieved to date. In this paper, Wind Driven Optimization (WDO) technique is proposed as the new method for identifying the parameters of solar PV. The accuracy and convergence time of the proposed method is compared with results of Pattern Search (PS), Genetic Algorithm (GA), and Simulated Annealing (SA) for single diode and double diode models of solar PV. Furthermore, for performance validation, the parameters obtained through WDO are compared with hybrid Bee Pollinator Flower Pollination Algorithm (BPFPA), Flower Pollination Algorithm (FPA), Generalized Oppositional Teaching Learning Based Optimization (GOTLBO), Artificial Bee Swarm Optimization (ABSO), and Harmony Search (HS). The obtained results clearly reveal that WDO algorithm can provide accurate optimized values with less number of iterations at different environmental conditions. Therefore, the WDO can be recommended as the best optimization algorithm for parameter estimation of solar PV.

A new hybrid bee pollinator flower pollination algorithm for solar PV parameter estimation

The inaccurate I-V curve generation in solar PV modeling introduces less efficiency and on the other hand, accurate simulation of PV characteristics becomes a mandatory obligation before experimental validation. Although many optimization methods in literature have attempted to extract accurate PV parameters, all of these methods do not guarantee their convergence to the global optimum. Hence, the authors of this paper have proposed a new hybrid Bee pollinator Flower Pollination Algorithm (BPFPA) for the PV parameter extraction problem. The PV parameters for both single diode and double diode are extracted and tested under different environmental conditions. For brevity, the I01, I02, Ipv for double diode and I0,Ipv for single diode models are calculated analytically where the remaining parameters ‘Rs, Rp, a1, a2’ are optimized using BPFPA method. It is found that, the proposed Bee Pollinator method has all the scope to create exploration and exploitation in the control variable to yield a less RMSE value even under lower irradiated conditions. Further for performance validation, the parameters arrived via BPFPA method is compared with Genetic Algorithm (GA), Pattern Search (PS), Harmony Search (HS), Flower Pollination Algorithm (FPA) and Artificial Bee Swarm Optimization (ABSO). In addition, various outcomes of PV modeling and different parameters influencing the accurate PV modeling are critically analyzed.

Design of a cost-effective wind/photovoltaic/hydrogen energy system for supplying a desalination unit by a heuristic approach

The techno-economic assessment, optimization, and sizing of grid independent renewable energy systems affects not only the likelihood of deployment but also their reliability to supply potable water and electricity where needed. Despite many investigations on hybrid renewable energy systems (photovoltaic/wind), the reverse-osmosis desalination unit powered by solar and wind electricity production systems with hydrogen energy storage, and effects of integrating water desalination alongside meeting load demand, is rarely found. In this paper, a hybrid photovoltaic/wind/hydrogen/reverse osmosis desalination system is modeled and designed for increasing the fresh water availability and to meet the load demand to a stand-alone region in Iran. The configuration of the proposed hybrid system is optimally determined with respect to two optimization criteria, the life cycle cost of the economic evaluation and the loss of power supply probability concept for the reliability. For this aim, an efficient metaheuristic technique based on artificial bee swarm optimization is used. From the results it is seen that at a maximum loss of power supply probability set to 0–10% the photovoltaic/hydrogen/reverse osmosis desalination is the most cost-effective energy system and photovoltaic/wind/hydrogen/reverse osmosis desalination and wind/hydrogen/reverse osmosis desalination systems are in the other ranks.

Parameter extraction of solar cell models based on adaptive differential evolution algorithm

In this paper, a new approach based on adaptive Differential Evolution Technique (DET) is used to extract the parameters of solar cell models accurately. The adaption is achieved through crossover and mutation factor. It is indicated that the optimization with an objective function can minimize the difference between the estimated and measured values. In order to verify the performance of the proposed system, three different solar cell models: single diode model, double diode model, and photovoltaic module are used to extract the parameters. The analysis is performed by using the voltage and current data sets. The result shows that the proposed DET outperforms these other methods: chaos particle swarm optimization (CPSO), genetic algorithm (GA), harmony search algorithm (HSA), and artificial bee swarm optimization (ABSO). Furthermore, the DET technique is practically validated by two different solar cell types such as monocrystalline and multi-crystalline and modules. The performance of solar cell models has been verified and the outcome shows that it is an optimal method which suits the parameter extraction of solar cells and modules.

Optimal sizing of autonomous hybrid photovoltaic/wind/battery power system with LPSP technology by using evolutionary algorithms

Hybrid photovoltaic (PV)–wind turbine (WT) systems with battery storage have been introduced as a green and reliable power system for remote areas. There is a steady increase in usage of hybrid energy system (HES) and consequently optimum sizing is the main issue for having a cost-effective system. This paper evaluates the performance of different evolutionary algorithms for optimum sizing of a PV/WT/battery hybrid system to continuously satisfy the load demand with the minimal total annual cost (TAC). For this aim, all the components are modeled and an objective function is defined based on the TAC. In the optimization problem, the maximum allowable loss of power supply probability (LPSPmax) is also considered to have a reliable system, and three well-known heuristic algorithms, namely, particle swarm optimization (PSO), tabu search (TS) and simulated annealing (SA), and four recently invented metaheuristic algorithms, namely, improved particle swarm optimization (IPSO), improved harmony search (IHS), improved harmony search-based simulated annealing (IHSBSA), and artificial bee swarm optimization (ABSO), are applied to the system and the results are compared in terms of the TAC. The proposed methods are applied to a real case study and the results are discussed. It can be seen that not only average results produced by ABSO are more promising than those of the other algorithms but also ABSO has the most robustness. Also considering LPSPmax set to 5%, the PV/battery is the most cost-effective hybrid system, and in other LPSPmax values, the PV/WT/battery is the most cost-effective systems.

COMPARISON OF LEVENBERG-MARQUARDT BASED LEAST SQUARES METHOD AND A HEURISTIC TECHNIQUE FOR ELECTRICITY DEMAND ESTIMATION

This paper focuses on demand estimation of electricity in Iran using artificial bee swarm optimization (ABSO) algorithm which is a recently invented metaheuristic technique. For this aim, two types of exponential and quadratic models are investigated to estimate the Iran’s electricity demand. These models are defined based on socio-economic indicators of population, gross domestic product (GDP), import and export figures. Owing to the fluctuations of the economic indicators and nonlinearity of the electricity demand, an efficient technique must be employed to find optimal or near optimal values of the models’ weighting factors. This paper proposes ABSO as an efficient approach for solving this problem. The available data of electricity demand in Iran from 1981 to 1999 is used for finding the optimal weighing factors and the data from 2000 to 2005 is used for testing the models. In order to evaluate the performance of the proposed methodology, the results are compared with the result obtained by the traditional nonlinear least-squares optimization method of and Levenberg–Marquardt (LM)

Artificial bee swarm optimization for optimum sizing of a stand-alone PV/WT/FC hybrid system considering LPSP concept

This paper recommends a framework to optimally size a hybrid energy system based on photovoltaic (PV) panels, wind turbines (WTs) and fuel cell (FC). For this aim, all the components are modeled and an objective function is defined based on the total annual cost. In the optimization problem, the maximum allowable loss of power supply probability (LPSPmax) is also considered to have a reliable system.In order to minimize the objective function, an efficient artificial bee swarm optimization (ABSO) algorithm is proposed. ABSO is a recently invented metaheuristic algorithm which is inspired by the intelligent behaviors of honey bees such as collection and processing of nectar. Instead of gradient information, ABSO uses stochastic rules to escape local optima and find the global solution. The proposed methodology is applied to a real case study and the results are discussed. It is seen that at LPSPmax set to 0%, 0.3% and 1% the PV/WT/FC is the most cost-effective energy system and at LPSPmax=2% the WT/FC is the most cost-effective hybrid system.

Comparison of particle swarm optimization and other metaheuristics on electricity demand estimation: A case study of Iran

The importance of energy demand estimation stems from energy planning, formulating strategies and recommending energy policies. Most often, energy demand is mathematically formulated by socio-economic indicators. The challenging problem is to determine the optimal or near optimal weighting factors. Inspired by social behavior of bird flocking or fish schooling, PSO (particle swarm optimization) is a population-based search technique which has attracted significant attention to tackle the complexity of difficult optimization problems. This paper studies the performance of different PSO variants for estimating Iran's electricity demand. Seven PSO variants namely, original PSO, PSO-w (PSO with weighting factor), PSO-cf (PSO with constriction factor), PSO-rf (PSO with repulsion factor), PSO-vc (PSO with velocity control), CLPSO (comprehensive learning PSO) and a MPSO (modified PSO), are used to find the unknown weighting factors based on the data from 1982 to 2003. The validation process is then conducted by testing the optimized models by using the data from 2004 to 2009. It is seen that PSO-vc produces more promising results than the other variants, HS (harmony search) and ABSO (artificial bee swarm optimization) algorithms in terms of MAPE (mean absolute percentage error). This value is obtained 2.47 and 2.50 for the exponential and quadratic models, respectively.

Artificial bee swarm optimization algorithm for parameters identification of solar cell models

An accurate mathematical model is an extremely helpful tool for simulation, evaluation, control, and optimization of solar cell systems. Due to the non-linearity of the solar cell models and the inability of traditional optimization methods to accurately identify the unknown parameters, recently, metaheuristic algorithms have attracted significant attention. Artificial bee swarm optimization (ABSO) is a recently invented algorithm inspired by the intelligent behaviors of honey bees such as collection and processing of nectar. In this paper, we propose an ABSO-based parameter identification technique based on the single and double diode models for a 57mm diameter commercial (R.T.C. France) silicon solar cell. The results obtained by ABSO algorithm are quite promising and outperform those found by the other studied methods.

A new artificial bee swarm algorithm for optimization of proton exchange membrane fuel cell model parameters

An appropriate mathematical model can help researchers to simulate, evaluate, and control a proton exchange membrane fuel cell (PEMFC) stack system. Because a PEMFC is a nonlinear and strongly coupled system, many assumptions and approximations are considered during modeling. Therefore, some differences are found between model results and the real performance of PEMFCs. To increase the precision of the models so that they can describe better the actual performance, optimization of PEMFC model parameters is essential. In this paper, an artificial bee swarm optimization algorithm, called ABSO, is proposed for optimizing the parameters of a steady-state PEMFC stack model suitable for electrical engineering applications. For studying the usefulness of the proposed algorithm, ABSO-based results are compared with the results from a genetic algorithm (GA) and particle swarm optimization (PSO). The results show that the ABSO algorithm outperforms the other algorithms.