Optimizing Storage Operation for a Probabilistic Locational Marginal Pricing Forecast: Preprint

Aadil Latif, Dheepak Krishnamurthy, Bryan Palmintier

Research output: Contribution to conferencePaper

Abstract

This paper explores the applicability of using dynamic programing (DP) and approximate dynamic programming (ADP) based methods for optimal dispatch of utility scale energy storage systems (ESS). In this study, the effectiveness of these approaches have been tested using the IEEE 13 node test feeder with distributed photovoltaics (PVs) and a utility scale storage system. In this work, a co-simulation based approach has been used to setup the experiment to be able to implement detailed ESS and network models. The results obtained from DP/ADP runs have been compared with three other control strategies both myopic and intelligent. Simulations results show that DP/ADP algorithms are a good candidate for optimal EES dispatch in terms of both solution quality and execution time.
Original languageAmerican English
Number of pages8
StatePublished - 2018
Event2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) - Boise, Idaho
Duration: 24 Jun 201828 Jun 2018

Conference

Conference2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)
CityBoise, Idaho
Period24/06/1828/06/18

NREL Publication Number

  • NREL/CP-5D00-70548

Keywords

  • approximate dynamic programming
  • co-simulation
  • dynamic programming
  • energy storage

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