Stochastic Battery Operations using Deep Neural Networks

Yize Chen, Md Umar Hashmi, Deepjyoti Deka, Michael Chertkov

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

2 Citations (Scopus)

Abstract

In this paper, we introduce a scenario-based optimal control framework to account for the forecast uncertainty in battery arbitrage problems. Due to the uncertainty of prices and variations of forecast errors, it is challenging for battery operators to design profitable strategies in electricity markets. Without any explicit assumption or model for electricity price forecasts' uncertainties, we generate future price scenarios via a data-driven, learning-based approach. By aiding the predictive control with such scenarios representing possible realizations of future markets, our proposed real-time controller seeks the optimal charge/discharge levels to maximize profits. Simulation results on a case-study of California-based batteries and prices show that our proposed method can bring higher profits for different battery parameters.

Original languageEnglish
Title of host publication2019 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538682326
DOIs
Publication statusPublished - Feb 2019
Externally publishedYes
Event2019 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2019 - Washington, United States
Duration: 18 Feb 201921 Feb 2019

Publication series

Name2019 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2019

Conference

Conference2019 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2019
Country/TerritoryUnited States
CityWashington
Period18/02/1921/02/19

Keywords

  • Battery energy storage
  • generative model
  • machine learning
  • power system economics
  • scenario forecasts

Fingerprint

Dive into the research topics of 'Stochastic Battery Operations using Deep Neural Networks'. Together they form a unique fingerprint.

Cite this