Optimal Battery Energy Storage System Based on VAR Control Strategies Using Particle Swarm Optimization for Power Distribution System

Prabpal, Prakasit; Kongjeen, Yuttana; Bhumkittipich, Krischonme

doi:10.3390/sym13091692

Cited by 14 publications

(7 citation statements)

References 34 publications

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“…One drawback of the research is that it does not consider the temperature profile of the batteries for the calculation of panel efficiencies. Optimal Battery Sizing was done with the help of GA, PSO, and IEEE 30 Test System was used for the implementations for optimal BESS [16]. OpenDNS with COM interfaced by the integration of IEEE 30 Test System were used for optimization.…”

Section: Related Workmentioning

confidence: 99%

Analyzing Optimal Battery Sizing in Microgrids Based on the Feature Selection and Machine Learning Approaches

et al. 2022

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Microgrids are becoming popular nowadays because they provide clean, efficient, and lowcost energy. Microgrids require bulk storage capacity to use the stored energy in times of emergency or peak loads. Since microgrids are the future of renewable energy, the energy storage technology employed should be optimized to provide power balancing. Batteries play a variety of essential roles in daily life. They are used at peak hours and during a time of emergency. There are different types of batteries i.e., lithium-ion batteries, lead-acid batteries, etc. Optimal battery sizing of microgrids is a challenging problem that limits modern technologies such as electric vehicles, etc. Therefore, it is imperative to assess the optimal size of a battery for a particular system or microgrid according to its requirements. The optimal size of a battery can be assessed based on the different battery features such as battery life, battery throughput, battery autonomy, etc. In this work, the mixed-integer linear programming (MILP) based newly generated dataset is studied for computing the optimal size of the battery for microgrids in terms of the battery autonomy. In the considered dataset, each instance is composed of 40 attributes of the battery. Furthermore, the Support Vector Regression (SVR) model is used to predict the battery autonomy. The capability of input features to predict the battery autonomy is of importance for the SVR model. Therefore, in this work, the relevant features are selected utilizing the feature selection algorithms. The performance of six best-performing feature selection algorithms is analyzed and compared. The experimental results show that the feature selection algorithms improve the performance of the proposed methodology. The Ranker Search algorithm with SVR attains the highest performance with a Spearman’s rank-ordered correlation constant of 0.9756, linear correlation constant of 0.9452, Kendall correlation constant of 0.8488, and root mean squared error of 0.0525.

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Section: Related Workmentioning

confidence: 99%

Analyzing Optimal Battery Sizing in Microgrids Based on the Feature Selection and Machine Learning Approaches

et al. 2022

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“…Optimal Battery Sizing was done with the help of GA, PSO and IEEE 30 Test System was used for the implementations for optimal BESS [14]. OpenDNS with COM interfaced by the integration of IEEE 30 Test System were used for optimization.…”

Section: Related Workmentioning

confidence: 99%

Analyzing Optimal Battery Sizing in Microgrids Based on the Feature Selection and Machine Learning Approaches

Khan¹,

Nizami²,

Qaisar³

et al. 2022

Preprint

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Microgrids are becoming popular nowadays because they provide clean, efficient, and low-cost energy. To use the stored energy in times of emergency or peak loads, microgrids require bulk storage capacity. Since microgrids are the future of renewable energy, the energy storage technology employed should be optimized to generate electricity. Batteries play a variety of essential roles in daily life and are used at peak hours and during a time of emergency. There are different types of batteries i.e., lion batteries, lead-acid batteries, etc. Optimal battery sizing of microgrids is a challenging problem, that limits modern technologies such as electric vehicles, etc. It is important to know different battery features such as battery life, battery throughput, and battery autonomy to get optimal battery sizing for microgrids. Mixed-integer linear programming (MILP) is an established technique for the integration and optimization of different energy sources and parameters for optimal battery sizing. A new MILP based dataset is introduced in this work. Support vector machine (SVM) is the machine learning application used to estimate the optimum battery size. The impact of feature selection algorithms on the proposed machine learning-based model is evaluated. The performance of the six best-performing feature selection algorithms is analyzed. The experimental results show that the feature selection algorithms improve the performance of the proposed methodology. Ranker search shows the best performance with a Spearman’s rank-ordered correlation constant of 0.9756, linear correlation constant of 0.9452, Kendall correlation constant of 0.8488 and root mean squared error of 0.0525.

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“…In the past few years, the concept of using energy storage system (ESS) to prevent overvoltage is a cost-effective method for the replacement of the power curtailment Hashemi and Østergaard (2018); Prabpal et al (2021). Besides, the ESS also performs a good performance in shaving peak power as well as backup power.…”

Section: Introductionmentioning

confidence: 99%

Coordinated active and reactive power operation of multiple dispersed resources for flexibility improvement

Cai

Luo

2023

Front. Energy Res.

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The purpose of this paper is to reach the optimal active and reactive power operation of multiple dispersed resources consisting of mobile energy storage system (MESS), demand response (DR) and photovoltaic (PV), for flexibility improvement of distribution network with uncertain PV and DR, minimization of power loss and operation cost whilst satisfaction of both power factor and voltage variation requirement. Especially, the flexibility aspect of distribution network is focused due to its significance for supporting economic operation without voltage rise issue during high PVs integration. Firstly, the active and reactive power operation spaces of MESS and PV inverter are discussed under power factor constraint. Then, the stochastic characteristics of PV generation and DR of microgrids are investigated using probability distribution. After that, the optimization framework coordination with dispersed MESS, PV inverter and DR to ensure operational flexibility of distribution network is proposed. Finally, the total cost minimization based flexibility improvement approach is presented by optimizing power loss, uncertain risk, operation cost of distribution network and MESS, satisfying operation constraints of both distribution network and dispersed resources. Simulation results conducted on the IEEE 69-bus system demonstrate the effectiveness of the proposed approach for PV accommodation, voltage quality improvement as well as peak load shaving.

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Optimal Battery Energy Storage System Based on VAR Control Strategies Using Particle Swarm Optimization for Power Distribution System

Cited by 14 publications

References 34 publications

Analyzing Optimal Battery Sizing in Microgrids Based on the Feature Selection and Machine Learning Approaches

Analyzing Optimal Battery Sizing in Microgrids Based on the Feature Selection and Machine Learning Approaches

Analyzing Optimal Battery Sizing in Microgrids Based on the Feature Selection and Machine Learning Approaches

Coordinated active and reactive power operation of multiple dispersed resources for flexibility improvement

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