scholarly journals Bacterial Foraging-Based Algorithm for Optimizing the Power Generation of an Isolated Microgrid

2019 ◽  
Vol 9 (6) ◽  
pp. 1261 ◽  
Author(s):  
Betania Hernández-Ocaña ◽  
José Hernández-Torruco ◽  
Oscar Chávez-Bosquez ◽  
Maria Calva-Yáñez ◽  
Edgar Portilla-Flores
Keyword(s):  
Information Technologies ◽  
Numerical Solutions ◽  
Renewable Energy Sources ◽  
Parameter Tuning ◽  
Point Of View ◽  
Bacterial Foraging Optimization ◽  
Bacterial Foraging ◽  
Reducing Costs ◽  
Best Parameter ◽  
Electrical Distribution Network

An Isolated Microgrid (IMG) is an electrical distribution network combined with modern information technologies aiming at reducing costs and pollution to the environment. In this article, we implement the Bacterial Foraging Optimization Algorithm (BFOA) to optimize an IMG model, which includes renewable energy sources, such as wind and solar, as well as a conventional generation unit based on diesel fuel. Two novel versions of the BFOA were implemented and tested: Two-Swim Modified BFOA (TS-MBFOA), and Normalized TS-MBFOA (NTS-MBFOA). In a first experiment, the TS-MBFOA parameters were calibrated through a set of 87 independent runs. In a second experiment, 30 independent runs of both TS-MBFOA and NTS-MBFOA were conducted to compare their performance on minimizing the IMG using the best parameter tuning. Results showed that TS-MBFOA obtained better numerical solutions compared to NTS-MBFOA and LSHADE-CV, an Evolutionary Algorithm, found in the literature. However, the best solution found by NTS-MBFOA is better from a mechatronic point of view because it favors the lifetime of the IMG, resulting in economic savings in the long term.

scholarly journals Real-Time Implementation of the Predictive-Based Control with Bacterial Foraging Optimization Technique for Power Management in Standalone Microgrid Application

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1723
Author(s):  
Félix Dubuisson ◽  
Miloud Rezkallah ◽  
Hussein Ibrahim ◽  
Ambrish Chandra
Keyword(s):  
Real Time ◽  
Power Management ◽  
Renewable Energy Sources ◽  
Optimization Technique ◽  
Euler Method ◽  
Small Scale ◽  
Diesel Generator ◽  
Bacterial Foraging Optimization ◽  
Time Model ◽  
Bacterial Foraging

In this paper, the predictive-based control with bacterial foraging optimization technique for power management in a standalone microgrid is studied and implemented. The heuristic optimization method based on the social foraging behavior of Escherichia coli bacteria is employed to determine the power references from the non-renewable energy sources and loads of the proposed configuration, which consists of a fixed speed diesel generator and battery storage system (BES). The two-stage configuration is controlled to maintain the DC-link voltage constant, regulate the AC voltage and frequency, and improve the power quality, simultaneously. For these tasks, on the AC side, the obtained power references are used as input signals to the predictive-based control. With the help of the system parameters, the predictive-based control computes all possible states of the system on the next sampling time and compares them with the estimated power references obtained using the bacterial foraging optimization (BFO) technique to get the inverter current reference. For the DC side, the same concept based on the predictive approach is employed to control the DC-DC buck-boost converter by regulating the DC-link voltage using the forward Euler method to generate the discrete-time model to predict in real-time the BES current. The proposed control strategies are evaluated using simulation results obtained with Matlab/Simulink in presence of different types of loads, as well as experimental results obtained with a small-scale microgrid.

scholarly journals An Intelligent Parkinson’s Disease Diagnostic System Based on a Chaotic Bacterial Foraging Optimization Enhanced Fuzzy KNN Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-24 ◽  
Author(s):  
Zhennao Cai ◽  
Jianhua Gu ◽  
Caiyun Wen ◽  
Dong Zhao ◽  
Chunyu Huang ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Nearest Neighbor ◽  
Characteristic Curve ◽  
Parameter Tuning ◽  
Support Vector ◽  
Good Prospect ◽  
Bacterial Foraging Optimization ◽  
Fruit Fly Optimization ◽  
Bacterial Foraging

Parkinson's disease (PD) is a common neurodegenerative disease, which has attracted more and more attention. Many artificial intelligence methods have been used for the diagnosis of PD. In this study, an enhanced fuzzy k-nearest neighbor (FKNN) method for the early detection of PD based upon vocal measurements was developed. The proposed method, an evolutionary instance-based learning approach termed CBFO-FKNN, was developed by coupling the chaotic bacterial foraging optimization with Gauss mutation (CBFO) approach with FKNN. The integration of the CBFO technique efficiently resolved the parameter tuning issues of the FKNN. The effectiveness of the proposed CBFO-FKNN was rigorously compared to those of the PD datasets in terms of classification accuracy, sensitivity, specificity, and AUC (area under the receiver operating characteristic curve). The simulation results indicated the proposed approach outperformed the other five FKNN models based on BFO, particle swarm optimization, Genetic algorithms, fruit fly optimization, and firefly algorithm, as well as three advanced machine learning methods including support vector machine (SVM), SVM with local learning-based feature selection, and kernel extreme learning machine in a 10-fold cross-validation scheme. The method presented in this paper has a very good prospect, which will bring great convenience to the clinicians to make a better decision in the clinical diagnosis.

scholarly journals A Survey on Microgrid Energy Management Considering Flexible Energy Sources

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2156 ◽  
Author(s):  
Hossein Shayeghi ◽  
Elnaz Shahryari ◽  
Mohammad Moradzadeh ◽  
Pierluigi Siano
Keyword(s):  
Energy Management ◽  
Renewable Energy Sources ◽  
Uncertainty Modeling ◽  
Optimization Techniques ◽  
Point Of View ◽  
Energy Sources ◽  
Market Prices ◽  
Distributed Generations ◽  
Peak Loads ◽  
Microgrid Energy Management

Aggregation of distributed generations (DGs) along with energy storage systems (ESSs) and controllable loads near power consumers has led to the concept of microgrids. However, the uncertain nature of renewable energy sources such as wind and photovoltaic generations, market prices and loads has led to difficulties in ensuring power quality and in balancing generation and consumption. To tackle these problems, microgrids should be managed by an energy management system (EMS) that facilitates the minimization of operational costs, emissions and peak loads while satisfying the microgrid technical constraints. Over the past years, microgrids’ EMS have been studied from different perspectives and have recently attracted considerable attention of researchers. To this end, in this paper a classification and a survey of EMSs has been carried out from a new point of view. EMSs have been classified into four categories based on the kind of the reserve system being used, including non-renewable, ESS, demand-side management (DSM) and hybrid systems. Moreover, using recent literature, EMSs have been reviewed in terms of uncertainty modeling techniques, objective functions (OFs) and constraints, optimization techniques, and simulation and experimental results presented in the literature.

scholarly journals Comparison between Concentrated Solar Power and Gas-Based Generation in Terms of Economic and Flexibility-Related Aspects in Chile

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1063
Author(s):  
Catalina Hernández Moris ◽  
Maria Teresa Cerda Guevara ◽  
Alois Salmon ◽  
Alvaro Lorca
Keyword(s):  
Natural Gas ◽  
Solar Power ◽  
Renewable Energy Sources ◽  
Cost Effective ◽  
Hybrid Plant ◽  
Point Of View ◽  
Concentrated Solar Power ◽  
Economic Point ◽  
Hybrid Plants ◽  
Cost Of Energy

The energy sector in Chile demands a significant increase in renewable energy sources in the near future, and concentrated solar power (CSP) technologies are becoming increasingly competitive as compared to natural gas plants. Motivated by this, this paper presents a comparison between solar technologies such as hybrid plants and natural gas-based thermal technologies, as both technologies share several characteristics that are comparable and beneficial for the power grid. This comparison is made from an economic point of view using the Levelized Cost of Energy (LCOE) metric and in terms of the systemic benefits related to flexibility, which is very much required due to the current decarbonization scenario of Chile’s energy matrix. The results show that the LCOE of the four hybrid plant models studied is lower than the LCOE of the gas plant. A solar hybrid plant configuration composed of a photovoltaic and solar tower plant (STP) with 13 h of storage and without generation restrictions has an LCOE 53 USD/MWh, while the natural gas technology evaluated with an 85% plant factor and a variable fuel cost of 2.0 USD/MMBtu has an LCOE of 86 USD/MWh. Thus, solar hybrid plants under a particular set of conditions are shown to be more cost-effective than their closest competitor for the Chilean grid while still providing significant dispatchability and flexibility.

Economic Load Dispatch Using Bacterial Foraging Optimization Algorithm Based on Evolution Strategies

2013 ◽  
Vol 860-863 ◽  
pp. 2040-2045 ◽  
Author(s):  
Xiao Hua Feng ◽  
Yu Yao He ◽  
Juan Yu
Keyword(s):  
Search Space ◽  
Complex Problem ◽  
Evolution Strategies ◽  
Power Losses ◽  
Bacterial Foraging Optimization ◽  
Thermal Generation ◽  
Bacterial Foraging ◽  
Global And Local ◽  
Generation Test ◽  
Bacterial Foraging Optimization Algorithm

This paper presents a novel modified bacterial foraging optimization(BFO) to solve economic loaddispatch (ELD) problems. BFO isalready successfully employed to solve variousoptimization problems. However original BFOfor small problems with moderate dimensionand searching space is satisfactory. As searchspace and complexity growexponentially in scalable ELD problems, it shows poorconvergence properties. To tackle this complex problem considering itshigh-dimensioned search space, the Evolution Strategies is introduced to thebasic BFO. The chemotactic step is adjusted to have a dynamic non-linearbehavior in order to improve balancing the global and local search. Theproposed algorithm is validated using several thermal generation test systems.The results are compared with those obtained by other algorithms previouslyapplied to solve the problem considering valve-point effects and power losses.

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