Construction of Nonlinear Feedback Strategies for Energy Storage Systems: A Stochastic Dynamic Programming Approach

Nilanjan Roy Chowdhury, Dmitry Baimel, Juri Belikov, Yoash Levron

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

Abstract

It is currently observed that rapid changes in generation and consumption can significantly impact the stability of a power system. In this light, this paper proposes a nonlinear feedback strategy for energy storage systems which operates under uncertainty conditions, and does not require statistical representations of future signals. We employ stochastic dynamic programming (SDP) to derive the nonlinear feedback policy and then verify its stability properties using a Lyapunov-based analysis. A central challenge in such problems arises due to the calculation of two-dimensional value functions at each time instant, which requires considerable computational resources. To address this challenge, we consider a quadratic cost function and model the load power as a first-order auto-regressive stochastic process. We show that these considerations help solve the optimal control problem using SDP, and evaluate a feedback policy which is sub-optimal and stable. Numerical experiments reveal that this proposed feedback policy always keeps the stored energy within bounds, and allows it to follow the optimal path.

Original languageEnglish
Title of host publication2021 IEEE Madrid PowerTech, PowerTech 2021 - Conference Proceedings
ISBN (Electronic)9781665435970
DOIs
StatePublished - 28 Jun 2021
Event2021 IEEE Madrid PowerTech, PowerTech 2021 - Madrid, Spain
Duration: 28 Jun 20212 Jul 2021

Publication series

Name2021 IEEE Madrid PowerTech, PowerTech 2021 - Conference Proceedings

Conference

Conference2021 IEEE Madrid PowerTech, PowerTech 2021
Country/TerritorySpain
CityMadrid
Period28/06/212/07/21

Keywords

  • Lyapunov analysis
  • Stochastic dynamic programming
  • energy storage
  • sub-optimal feedback strategies

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Artificial Intelligence
  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment

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