Control of a battery energy storage system connected to a low voltage grid

Dun, J.J.C.M. van; Groot, R.J.W. de; Morren, Johan; Slootweg, J.G.

doi:10.1109/ptc.2015.7232405

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…The implementation of a CES is discussed in [14], where a total of 2 MWh was connected to a 13.2 kV distribution feeder providing load leveling, reactive power compensation, and reliability enhancement. In [15], authors describe the development of a control algorithm for a CES of 400 kVA/232 kWh in an LV distribution feeder using forecasting techniques. The optimal operation of a CES to perform time-shifting on PV generation and demand load simultaneously is presented in [16], while authors in [17] propose a control framework for a community battery for grid congestion reduction, presenting experimental results with a battery of 55 kW and a capacity of 126 kWh.…”

Section: Introductionmentioning

confidence: 99%

Optimal Operation of Community Energy Storage using Stochastic Gradient Boosting Trees

Giraldo

Salazar

Vergara³

et al. 2021

2021 IEEE Madrid PowerTech

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This paper proposes an algorithm for the optimal operation of community energy storage systems (ESSs) using a machine learning (ML) model by solving a nonlinear programming (NLP) problem iteratively to obtain synthetic data. The NLP model minimizes the network's total energy losses by setting the community ESS's operation points. The optimization model is solved recursively by Monte Carlo simulations in a distribution system with high PV penetration, considering uncertainty in exogenous parameters. Obtained optimal solutions provide the training dataset for a stochastic gradient boosting trees (SGBT) ML algorithm following an imitation learning approach. The predictions obtained from the ML model have been compared to the optimal ESS operation to assess the model's accuracy. Furthermore, the ML model's sensitivity has been tested considering the sampling size and the number of predictors. Results showed a 98% of accuracy for the SGBT model compared to optimal solutions. This accuracy was obtained even after a reduction of 83% in the number of predictors.

show abstract

Section: Introductionmentioning

confidence: 99%

Optimal Operation of Community Energy Storage using Stochastic Gradient Boosting Trees

Giraldo

Salazar

Vergara³

et al. 2021

2021 IEEE Madrid PowerTech

Self Cite

View full text Add to dashboard Cite

show abstract

“…The implementation of a community ESS is discussed in [12], where a total of 2 MWh was connected to a 13.2 kV distribution feeder providing load leveling, reactive power compensation, and reliability enhancement. In [13], authors describe the development of a control algorithm for a community ESS of 400 kVA/232 kWh in an LV distribution feeder using forecasting techniques. The optimal operation of a community ESS to perform time-shifting on PV generation and demand load simultaneously is presented in [14], while authors in [15] propose a control framework for a community battery for grid congestion reduction, presenting experimental results with a battery of 55 kW and a capacity of 126 kWh.…”

Section: Introductionmentioning

confidence: 99%

Optimal Operation of Community Energy Storage using Stochastic Gradient Boosting Trees

Giraldo¹,

Salazar²,

Vergara³

et al. 2021

Preprint

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This paper proposes an algorithm for the optimal operation of community energy storage systems (ESSs) using a machine learning (ML) model, by solving a nonlinear programming (NLP) problem iteratively to obtain synthetic data. The NLP model minimizes the network's total energy losses by setting the operation points of a community ESS. The optimization model is solved recursively by Monte Carlo simulations in a distribution system with high PV penetration, considering uncertainty in exogenous parameters. Obtained optimal solutions provide the training dataset for a stochastic gradient boosting trees (SGBT) ML algorithm. The predictions obtained from the ML model have been compared to the optimal ESS operation to assess the model's accuracy. Furthermore, the sensitivity of the ML model has been tested considering the sampling size and the number of predictors. Results showed an accuracy of 98% for the SGBT model compared to optimal solutions, even after a reduction of 83% in the number of predictors.

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“…To handle high photovoltaic (PV) penetrations, this idea was already proposed often in literature. In [11], a battery storage system is used to prevent overvoltages due to high PV penetration or in [12] a battery is used to flatten the energy demand of a neighbourhood. Also for EV, this was already adapted and presented.…”

Section: Introductionmentioning

confidence: 99%

Operation of Battery Storage as a Temporary Equipment During Grid Reinforcement Caused by Electric Vehicles

et al. 2020

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Electric vehicle charging stresses distribution grids significantly with high penetrations of electric vehicles. This will lead to grid reinforcement works in several distribution grids. Battery storage is a possible solution to bypass times of grid reinforcement due to electric vehicle charging. In this paper, different operation strategies for such a battery storage are tested at first in simulations. The main difference between the strategies is the necessary input data. Following the simulations, selected strategies are tested in reality in the project ”Netzlabor E-Mobility-Allee”. It is proved that battery storage is a functioning possibility to bypass times of grid reinforcement.

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Control of a battery energy storage system connected to a low voltage grid

Cited by 4 publications

References 7 publications

Optimal Operation of Community Energy Storage using Stochastic Gradient Boosting Trees

Optimal Operation of Community Energy Storage using Stochastic Gradient Boosting Trees

Optimal Operation of Community Energy Storage using Stochastic Gradient Boosting Trees

Operation of Battery Storage as a Temporary Equipment During Grid Reinforcement Caused by Electric Vehicles

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