scholarly journals Economic emission dispatch on unit commitment-based microgrid system considering wind and load uncertainty using hybrid MGWOSCACSA

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Bishwajit Dey ◽  
Biplab Bhattacharyya ◽  
Saurav Raj ◽  
Rohit Babu
Keyword(s):  
Wind Power ◽  
Wind Farm ◽  
Unit Commitment ◽  
Search Algorithm ◽  
Test System ◽  
Grey Wolf Optimizer ◽  
Generation Cost ◽  
Sine Cosine Algorithm ◽  
Emission Dispatch ◽  
Economic Emission Dispatch

Abstract Economic emission dispatch (EED) of a three-unit stand-alone microgrid system supported by a wind farm is percolated in this paper. The adverse effects of stochastic and uncertainty nature of wind energy in raising the generation cost of the microgrid system are studied in this article. Unit commitment (UC) of the generating units is taken into account which helps in reducing the generation cost and provides relaxation time to the generation units. Three cases are contemplated for the study. For the first two cases, the generation cost of the test system was minimized without and with the involvement of wind power, respectively. The third case considered the involvement of wind power along with the UC of the conventional generation units. A novel hybrid of recently developed superior optimization algorithms, viz. grey wolf optimizer (GWO), sine–cosine algorithm (SCA) and crow search algorithm (CSA), is implemented to perform EED, and the results are compared with basic GWO and other hybrid algorithms. Results are then analysed to compare and contrast among these cases and justify the reliable and profitable one. Statistical analysis claims the superiority of the proposed hybrid MGWOSCACSA over other hybrids and GWO.

scholarly journals Smart Energy Management of Residential Microgrid System by a Novel Hybrid MGWOSCACSA Algorithm

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3500 ◽  
Author(s):  
Bishwajit Dey ◽  
Fausto Pedro García Márquez ◽  
Sourav Kr. Basak
Keyword(s):  
Low Voltage ◽  
Search Algorithm ◽  
Hybrid Approach ◽  
Energy Resources ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Renewable Energy Resources ◽  
Generation Cost ◽  
Sine Cosine Algorithm ◽  
The Impact

Optimal scheduling of distributed energy resources (DERs) of a low-voltage utility-connected microgrid system is studied in this paper. DERs include both dispatchable fossil-fueled generators and non-dispatchable renewable energy resources. Various real constraints associated with adjustable loads, charging/discharging limitations of battery, and the start-up/shut-down time of the dispatchable DERs are considered during the scheduling process. Adjustable loads are assumed to the residential loads which either operates throughout the day or for a particular period during the day. The impact of these loads on the generation cost of the microgrid system is studied. A novel hybrid approach considers the grey wolf optimizer (GWO), sine cosine algorithm (SCA), and crow search algorithm (CSA) to minimize the overall generation cost of the microgrid system. It has been found that the generation costs rise 50% when the residential loads were included along with the fixed loads. Active participation of the utility incurred 9–17% savings in the system generation cost compared to the cases when the microgrid was operating in islanded mode. Finally, statistical analysis has been employed to validate the proposed hybrid Modified Grey Wolf Optimization-Sine Cosine Algorithm-Crow Search Algorithm (MGWOSCACSA) over other algorithms used.

scholarly journals Hybrid PSOGSA technique for solving dynamic economic emission dispatch problem

2020 ◽  
Vol 40 (3) ◽  
pp. 96-104
Author(s):  
Hardiansyah Hardiansyah
Keyword(s):  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Main Idea ◽  
Population Based ◽  
Test System ◽  
Hybrid Population ◽  
Swarm Optimization ◽  
Emission Dispatch ◽  
Population Based Algorithm ◽  
Economic Emission Dispatch

In this paper, a new hybrid population-based algorithm is proposed with the combining of particle swarm optimization (PSO) and gravitational search algorithm (GSA) techniques. The main idea is to integrate the ability of exploration in PSO with the ability of exploration in the GSA to synthesize both algorithms’ strength. The new algorithm is implemented to the dynamic economic emission dispatch (DEED) problem to minimize both fuel cost and emission simultaneously under a set of constraints. To demonstrate the efficiency of the proposed algorithm, a 5-unit test system is used. The results show the effectiveness and superiority of the proposed method when compared to the results of other optimization algorithms reported in the literature.

scholarly journals LSA-GA: A Hybrid Algorithm for Solving Economic Emission Dispatch Problem

2020 ◽  
Vol 8 (5) ◽  
pp. 4661-4669
Keyword(s):  
Genetic Algorithm ◽  
Wind Power ◽  
Hybrid Algorithm ◽  
Search Algorithm ◽  
Hybrid Strategy ◽  
Proposal A ◽  
Artificial Neural Network Ann ◽  
Emission Dispatch ◽  
Reproduction Stage ◽  
Economic Emission Dispatch

In this proposal, a hybrid algorithm is conveyed for unraveling Economic Emission Dispatch (EED) issue of the hybrid warm wind power age framework. The hybrid philosophy is a mix of Lightning Search Algorithm (LSA) with Genetic Algorithm (GA). In this, the consolidated endeavor of LSA-GA is utilized for upgrading the warm generators blends dependent on the vulnerability states of wind power. For catching the vulnerability states of wind power, Particle Swarm Optimization (PSO) with Artificial Neural Network (ANN) is utilized, so framework guaranteed the breeze power usage at higher. In this manner, the proposed philosophy is utilized for streamlining of the hybrid warm wind power age framework and limited the all out expense of activity. For assessing the adequacy of the proposed hybrid strategy, the six and the ten units of warm age is examined initially without wind power and besides with wind power. The two clashing goals for example fuel cost and outflow are streamlined at a similar interim of time. The proposed procedure is actualized in MATLAB/reproduction stage and results are analyzed by contrasting the got outcome and the consequence of Genetic Algorithm (GA). The examination uncovers that proposed approach has ability to deal with multi-target issues of advancement of electrical force frameworks, more efficiently.

scholarly journals A Compound Wind Power Forecasting Strategy Based on Clustering, Two-Stage Decomposition, Parameter Optimization, and Optimal Combination of Multiple Machine Learning Approaches

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3586 ◽  
Author(s):  
Sizhou Sun ◽  
Jingqi Fu ◽  
Ang Li
Keyword(s):  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Search Algorithm ◽  
Interpolation Method ◽  
Least Square ◽  
Support Vector ◽  
Two Stage ◽  
Wind Power Forecasting ◽  
Power Forecasting

Given the large-scale exploitation and utilization of wind power, the problems caused by the high stochastic and random characteristics of wind speed make researchers develop more reliable and precise wind power forecasting (WPF) models. To obtain better predicting accuracy, this study proposes a novel compound WPF strategy by optimal integration of four base forecasting engines. In the forecasting process, density-based spatial clustering of applications with noise (DBSCAN) is firstly employed to identify meaningful information and discard the abnormal wind power data. To eliminate the adverse influence of the missing data on the forecasting accuracy, Lagrange interpolation method is developed to get the corrected values of the missing points. Then, the two-stage decomposition (TSD) method including ensemble empirical mode decomposition (EEMD) and wavelet transform (WT) is utilized to preprocess the wind power data. In the decomposition process, the empirical wind power data are disassembled into different intrinsic mode functions (IMFs) and one residual (Res) by EEMD, and the highest frequent time series IMF1 is further broken into different components by WT. After determination of the input matrix by a partial autocorrelation function (PACF) and normalization into [0, 1], these decomposed components are used as the input variables of all the base forecasting engines, including least square support vector machine (LSSVM), wavelet neural networks (WNN), extreme learning machine (ELM) and autoregressive integrated moving average (ARIMA), to make the multistep WPF. To avoid local optima and improve the forecasting performance, the parameters in LSSVM, ELM, and WNN are tuned by backtracking search algorithm (BSA). On this basis, BSA algorithm is also employed to optimize the weighted coefficients of the individual forecasting results that produced by the four base forecasting engines to generate an ensemble of the forecasts. In the end, case studies for a certain wind farm in China are carried out to assess the proposed forecasting strategy.

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