Optimization Location Selection Analysis of Energy Storage Unit in Energy Internet System Based on Tabu Search

2019 ◽  
Vol 29 (07) ◽  
pp. 941-954
Author(s):  
Haitian Chen ◽  
Yan Zhao ◽  
Yanwei Ji ◽  
Shunjiang Wang ◽  
Weichun Ge ◽  
...  
Keyword(s):  
Energy Storage ◽  
Tabu Search ◽  
Industrial Revolution ◽  
Search Algorithm ◽  
Reference Value ◽  
Tabu Search Algorithm ◽  
Storage Unit ◽  
Energy Internet ◽  
Location Selection ◽  
Energy Storage Unit

Energy Internet has become the theme of the new round of industrial revolution. Energy storage, as a key technical support for the development of energy Internet, has always been of concern to numerous people, since the energy Internet consists of various energy networks that can provide energy support for different energy subnetworks. Therefore, the energy storage unit is in a crucial position in the entire energy network. This paper points out the importance of various energy storage technologies in the energy Internet. An energy storage unit location analysis method based on Tabu search algorithm is proposed to reduce the network energy loss, pressing mimizing network loss as constraint on the location of the energy storage unit as a search target. The Tabu search algorithm is programmed using Matlab and is used to search for the location of energy storage unit in the IEEE example. Besides, the optimal node solution is obtained, which verifies the feasibility of this algorithm to analyze the location selection of energy storage unit in the energy Internet. This paper has some reference value for the coordinated optimization of energy storage units in the energy Internet.

Capacity Optimization of Energy Storage Unit in Distributed Generation System

2012 ◽  
Vol 608-609 ◽  
pp. 1116-1119
Author(s):  
Cai Yun Guo ◽  
Hong Bin Wu
Keyword(s):  
Energy Storage ◽  
Distributed Generation ◽  
Search Algorithm ◽  
Generation Model ◽  
Storage Unit ◽  
Energy Storage Unit ◽  
Optimal Capacity ◽  
Battery Energy ◽  
Pv Generation ◽  
System Power

The photovoltaic(PV) generation model and the wind power generation model are introduced in this paper. Taking the best economy and reliability of system operation as the objective functions and the system power balance and battery storage performance indices as the constraints, the optimal capacity of battery energy storage can be determined with the Tabu search algorithm. With the example system, the simulation results show that the proposed models and the algorithm are correct.

scholarly journals Optimal Economic Modelling of Hybrid Combined Cooling, Heating, and Energy Storage System Based on Gravitational Search Algorithm-Random Forest Regression

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Muhammad Shahzad Nazir ◽  
Sami ud Din ◽  
Wahab Ali Shah ◽  
Majid Ali ◽  
Ali Yousaf Kharal ◽  
...  
Keyword(s):  
Energy Storage ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Energy Coupling ◽  
Energy Utilization ◽  
Energy Sources ◽  
Storage Unit ◽  
Random Forest Regression ◽  
Energy Storage Unit ◽  
Combined Cooling

The hybridization of two or more energy sources into a single power station is one of the widely discussed solutions to address the demand and supply havoc generated by renewable production (wind-solar/photovoltaic (PV), heating power, and cooling power) and its energy storage issues. Hybrid energy sources work based on the complementary existence of renewable sources. The combined cooling, heating, and power (CCHP) is one of the significant systems and shows a profit from its low environmental impact, high energy efficiency, low economic investment, and sustainability in the industry. This paper presents an economic model of a microgrid (MG) system containing the CCHP system and energy storage considering the energy coupling and conversion characteristics, the effective characteristics of each microsource, and energy storage unit is proposed. The random forest regression (RFR) model was optimized by the gravitational search algorithm (GSA). The test results show that the GSA-RFR model improves prediction accuracy and reduces the generalization error. The detail of the MG network and the energy storage architecture connected to the other renewable energy sources is discussed. The mathematical formulation of energy coupling and energy flow of the MG network including wind turbines, photovoltaic (PV), CCHP system, fuel cell, and energy storage devices (batteries, cold storage, hot water tanks, and so on) are presented. The testing system has been analysed under load peak cutting and valley filling of energy utilization index, energy utilization rate, the heat pump, the natural gas consumption of the microgas turbine, and the energy storage unit. The energy efficiency costs were observed as 88.2% and 86.9% with heat pump and energy storage operation comparing with GSA-RFR-based operation costs as 93.2% and 93% in summer and winter season, respectively. The simulation results extended the rationality and economy of the proposed model.

scholarly journals Performance Improvement of Decision Tree: A Robust Classifier Using Tabu Search Algorithm

2021 ◽  
Vol 11 (15) ◽  
pp. 6728
Author(s):  
Muhammad Asfand Hafeez ◽  
Muhammad Rashid ◽  
Hassan Tariq ◽  
Zain Ul Abideen ◽  
Saud S. Alotaibi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tabu Search ◽  
Decision Tree ◽  
Decision Trees ◽  
Search Algorithm ◽  
Learning Algorithms ◽  
Performance Comparison ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Tabu Search Algorithm

Classification and regression are the major applications of machine learning algorithms which are widely used to solve problems in numerous domains of engineering and computer science. Different classifiers based on the optimization of the decision tree have been proposed, however, it is still evolving over time. This paper presents a novel and robust classifier based on a decision tree and tabu search algorithms, respectively. In the aim of improving performance, our proposed algorithm constructs multiple decision trees while employing a tabu search algorithm to consistently monitor the leaf and decision nodes in the corresponding decision trees. Additionally, the used tabu search algorithm is responsible to balance the entropy of the corresponding decision trees. For training the model, we used the clinical data of COVID-19 patients to predict whether a patient is suffering. The experimental results were obtained using our proposed classifier based on the built-in sci-kit learn library in Python. The extensive analysis for the performance comparison was presented using Big O and statistical analysis for conventional supervised machine learning algorithms. Moreover, the performance comparison to optimized state-of-the-art classifiers is also presented. The achieved accuracy of 98%, the required execution time of 55.6 ms and the area under receiver operating characteristic (AUROC) for proposed method of 0.95 reveals that the proposed classifier algorithm is convenient for large datasets.

An extreme‐point tabu‐search algorithm for fixed‐charge network problems

Networks ◽  
2021 ◽  
Vol 77 (2) ◽  
pp. 322-340 ◽  
Author(s):  
Richard S. Barr ◽  
Fred Glover ◽  
Toby Huskinson ◽  
Gary Kochenberger
Keyword(s):  
Tabu Search ◽  
Extreme Point ◽  
Search Algorithm ◽  
Fixed Charge ◽  
Tabu Search Algorithm ◽  
Network Problems
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