Optimization of Operation and Control Strategies for Battery Energy Storage Systems by Evolutionary Algorithms

Müller, Jan; März, Matthias; Mauser, Ingo; Schmeck, Hartmut

doi:10.1007/978-3-319-31204-0_33

Cited by 8 publications

(9 citation statements)

References 28 publications

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“…The ESHL is managed and optimized by the OSH, which has already been used in various smart building scenarios, such as a residential building comprising DG and electric vehicles [13] as well as a commercial building comprising a trigeneration system [14]. The OSH is used in both, real-world energy management in productive systems, such as the ESHL at KIT, and simulations of smart buildings, such as scenarios comprising buildings having hybrid home appliances [11] or stationary battery storage systems [15]. Figure 3 presents an overview of the general system architecture of the OSH, which is described in more detail in [10], [14], and the Hybrid Energy Storage Control System (see Sect.…”

Section: A Energy Management and Simulation Systemmentioning

confidence: 99%

“…The optimization in the OSH uses a Genetic Algorithm, i. e., a meta-heuristic, and an abstract representations of the devices in so-called Interdependent Problem Parts [14]. These abstract representations enable the optimization not only of operation times of appliances, ESS, and DG systems but also of other parameters, such as the parameters of control systems [15].…”

Section: A Energy Management and Simulation Systemmentioning

confidence: 99%

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Establishing a hardware-in-the-loop research environment with a hybrid energy storage system

Kochanneck

Mauser

Bohnet³

et al. 2016

2016 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia)

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The KIT Energy Smart Home Lab is a smart residential building comprising building automation, metering systems, sensors, intelligent appliances, heating, ventilation, and airconditioning equipment, distributed generation, and energy storage systems. Currently, the lab is extended by a hybrid energy storage system and a linear voltage amplifier for real-time simulations, to facilitate fully functional power hardware-in-the-loop simulations and evaluations. This paper presents the setup of the lab, the hardwarein-the loop research environment, and first measurements when using a simulated artificial mains network.

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Section: A Energy Management and Simulation Systemmentioning

confidence: 99%

Section: A Energy Management and Simulation Systemmentioning

confidence: 99%

Establishing a hardware-in-the-loop research environment with a hybrid energy storage system

Kochanneck

Mauser

Bohnet³

et al. 2016

2016 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia)

Self Cite

View full text Add to dashboard Cite

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“…These techniques are different, but the goal is always improving. As is the case with the authors of References [2,3], when it comes to a structure change, they deal with optimization problems using evolutionary strategies through self-adaptation as opposed to other authors who preferred, without any informatized interventions but with chemical enhancements [4,5], an adaptation between the photovoltaic source and the battery representing the load. Another particle swarm optimization process was addressed in References [6,7] by using multi-collinearity and using the stepwise method to control several numbers of parameters according to the battery terminals.…”

Section: Introductionmentioning

confidence: 99%

A New Approach for Optimizing Management of a Real Time Solar Charger Using the Firebase Platform Under Android

Rezoug

Chenni

Taibi

2019

JLPEA

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With the continuous growth of energy consumption, the rationalization of energy has become a priority. The photovoltaic energy sector remains a major occupation for researchers in the field of production optimization or storage methods. The concept developed in this work is a mixed optimization approach for energy management during battery charging with a duty cycle. A selective collaborative algorithm intervenes to choose and use the appropriate results of the few techniques to optimize the charging time of a battery and estimate its state of charge by using the minimum possible tools. This is done using a collective database that is accessible in real time. It also effectively allows the synchronization of information between several customers. This approach is performed on a mobile application on android, through a Google Firebase platform that allows us to secure collaborative access between multiple customers and use the results of the calculations of some algorithms. It gives us the values obtained by the various sensors in real time to accelerate the charging speed of the battery. The validation of this approach led us to practice a few scenarios using an Arduino board to show that this approach has a better performance.

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“…Muller et al . detailed a closed loop controller designed using an Evolutionary Algorithm that controls charging and discharging of the battery in a smart building. The use of fast acting dynamic support, storage and PV resource forecasting are considered by John et al .…”

Section: Introductionmentioning

confidence: 99%

“…Overshoot, undershoot and transients occurring in DC link were highlighted. Muller et al [33] detailed a closed loop controller designed using an Evolutionary Algorithm that controls charging and discharging of the battery in a smart building. The use of fast acting dynamic support, storage and PV resource forecasting are considered by John et al [34] that would help to alleviate some of the major issues of grid integration of solar.…”

Section: Introductionmentioning

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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Optimization of Operation and Control Strategies for Battery Energy Storage Systems by Evolutionary Algorithms

Cited by 8 publications

References 28 publications

Establishing a hardware-in-the-loop research environment with a hybrid energy storage system

Establishing a hardware-in-the-loop research environment with a hybrid energy storage system

A New Approach for Optimizing Management of a Real Time Solar Charger Using the Firebase Platform Under Android

Development of battery intervention power supply for solar roof top installations

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