Optimal Operation of Community Energy Storage using Stochastic Gradient Boosting Trees

Juan S. Giraldo, Mauricio Salazar, Pedro P. Vergara, Georgios Tsaousoglou, J.G. Slootweg, Nikolaos G. Paterakis

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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 languageEnglish
Title of host publication2021 IEEE Madrid PowerTech
PublisherIEEE
ISBN (Electronic)978-1-6654-3597-0
ISBN (Print)978-1-6654-1173-8
DOIs
Publication statusPublished - 29 Jul 2021
Externally publishedYes
Event14th IEEE PowerTech 2021 - Virtual Event
Duration: 28 Jun 20212 Jul 2021
Conference number: 14

Conference

Conference14th IEEE PowerTech 2021
CityVirtual Event
Period28/06/212/07/21

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