The Combinatorial Optimization by Genetic Algorithm and Neural Network for Energy Storage System in Solar Energy Electric Vehicle

Zhou, Shiqiong; Guo, Guifang; Zhang, Yanning; Cao, Binggang; Kang, Longyun

doi:10.1109/wcica.2008.4593375

Cited by 9 publications

(2 citation statements)

References 3 publications

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“…This solution requires complex mathematical logic and structures with computational complexity that is not easily implementable for real‐time applications. Linear Programming , Dynamic Programming and Genetic Programming are some of the methods. Also these strategies require more computational time that further increases with number of charge/discharge cycles considered.…”

Section: Resultsmentioning

confidence: 99%

Development of battery intervention power supply for solar roof top installations

Valsala¹,

Premkumar²,

Beevi³

2019

Env Prog and Sustain Energy

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In India, battery storage and time‐based tariff are yet to be introduced in solar microgrid tied inverters. Here, the authors propose an improved multistage converter topology intended for single phase solar roof top applications with battery storage. It works in six modes depending on solar‐load‐battery‐grid profiles. The whole strategy is implemented in a battery intervention power supply (BIPS) that uses energy storage integrated with the solar generator that helps in smoothing of power, load shifting, load dispatch, and load angle control. Notably, load dispatch decision is arrived based on several independent parameters that take different values at a time. A Boolean equation is derived relating the different parameters so as to arrive at a single decision. This is achieved by a variable entity k‐map. A voltage control is also implemented which is done by load angle control of the inverter current. © 2019 American Institute of Chemical Engineers Environ Prog, 38: 570–583, 2019

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Section: Resultsmentioning

confidence: 99%

Development of battery intervention power supply for solar roof top installations

Valsala¹,

Premkumar²,

Beevi³

2019

Env Prog and Sustain Energy

View full text Add to dashboard Cite

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“…As an important part of energy storage battery system, BMS can monitoring and keep checking on the key operation parameters such as voltages, currents and the battery internal and ambient temperature during charging and discharging [5], [6]. Thus it can protect battery from over charge and discharge and then extend the battery service life; it can also provide status and parameter information to operators.…”

Section: B Battery Management System (Bms)mentioning

confidence: 99%

Design of Large-Capacity Battery Energy Storage System and Its Application in Coal Mine

Niu¹,

Zhou²,

Wu³

2016

IJOEE

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Battery energy storage technology is developing fast and has achieved widely application recently with the features of high reliability, less pollution, high flexible configuration and two-way flow of energy. This paper presents the characteristics, composition and architecture of the lead acid battery energy storage system. The design principle is also described in detail. The proposed Battery Energy Storage System (BESS) is composed of battery pack, Battery Management System (BMS), Power Conversion System (PCS) and monitoring system. According to the special safety requirements of electricity supply in coal mine, a battery energy storage technology based emergency power supply was proposed. The system will provide power support to coal mine ventilator to avoid accidents occur that caused by sudden power failure. The BESS also can be applied to hospital, communication base station and related areas where need stable and continuous power supply.

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Artificial intelligence applications for microgrids integration and management of hybrid renewable energy sources

et al. 2023

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The integration of renewable energy sources (RESs) has become more attractive to provide electricity to rural and remote areas, which increases the reliability and sustainability of the electrical system, particularly for areas where electricity extension is difficult. Despite this, the integration of hybrid RESs is accompanied by many problems as a result of the intermittent and unstable nature of RESs. The extant literature has discussed the integration of RESs, but it is not comprehensive enough to clarify all the factors that affect the integration of RESs. In this paper, a comprehensive review is made of the integration of RESs. This review includes various combinations of integrated systems, integration schemes, integration requirements, microgrid communication challenges, as well as artificial intelligence used in the integration. In addition, the review comprehensively presents the potential challenges arising from integrating renewable resources with the grid and the control strategies used. The classifications developed in this review facilitate the integration improvement process. This paper also discusses the various optimization techniques used to reduce the total cost of integrated energy sources. In addition, it examines the use of up-to-date methods to improve the performance of the electrical grid. A case study is conducted to analyze the impact of using artificial intelligence when integrating RESs. The results of the case study prove that the use of artificial intelligence helps to improve the accuracy of operation to provide effective and accurate prediction control of the integrated system. Various optimization techniques are combined with ANN to select the best hybrid model. PSO has the fast convergence rate for reaching to the minimum errors as the Normalized Mean Square Error (NMSE) percentage reaches 1.10% in 3367.50 s.

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The Combinatorial Optimization by Genetic Algorithm and Neural Network for Energy Storage System in Solar Energy Electric Vehicle

Cited by 9 publications

References 3 publications

Development of battery intervention power supply for solar roof top installations

Development of battery intervention power supply for solar roof top installations

Design of Large-Capacity Battery Energy Storage System and Its Application in Coal Mine

Artificial intelligence applications for microgrids integration and management of hybrid renewable energy sources

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