Abstract
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.
Original language | English |
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Title of host publication | 2021 IEEE Madrid PowerTech |
Publisher | IEEE |
ISBN (Electronic) | 978-1-6654-3597-0 |
ISBN (Print) | 978-1-6654-1173-8 |
DOIs | |
Publication status | Published - 29 Jul 2021 |
Externally published | Yes |
Event | 14th IEEE PowerTech 2021 - Virtual Event, Spain Duration: 28 Jun 2021 → 2 Jul 2021 Conference number: 14 |
Conference
Conference | 14th IEEE PowerTech 2021 |
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Country/Territory | Spain |
City | Virtual Event |
Period | 28/06/21 → 2/07/21 |