Shifting demand: Reduction in necessary storage capacity through tracking of renewable energy generation

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9 Scopus Citations

Abstract

Renewable energy (RE) generation systems are rapidly being deployed on the grid. In parallel, electrified devices are quickly being added to the grid, introducing additional electric loads and increased load flexibility. While increased deployment of RE generation contributes to decarbonization of the grid, it is inherently variable and unpredictable, introducing uncertainty and potential instability in the grid. One way to mitigate this problem is to deploy utility-scale storage. However, in many cases the deployment of utility-scale battery storage systems remain unfeasible due to their cost. Instead, utilizing the increased amounts of data and flexibility from electrified devices on the grid, advanced control can be applied to shift the demand to match RE generation, significantly reducing the capacity of required utility-scale battery storage. This work introduces the novel forecast-aided predictive control (FAPC) algorithm to optimize this load shifting in the presence of forecasts. Extending upon an existing coordinated control framework, the FAPC algorithm introduces a new electric vehicle charging control algorithm that has the capability to incorporate forecasted information in its control loop. This enables FAPC to better track a realistic RE generation signal in a fully correlated simulation environment. Results show that FAPC effectively shifts demand to track a RE generation signal under different weather and operating conditions. It is found that FAPC significantly reduces the required capacity of the battery storage system compared to a baseline control case.

Original languageAmerican English
Article number100131
Number of pages17
JournalAdvances in Applied Energy
Volume10
DOIs
StatePublished - 2023

Bibliographical note

Publisher Copyright:
© 2023

NREL Publication Number

  • NREL/JA-5000-85135

Keywords

  • Battery energy storage systems
  • Commercial building control
  • Electric vehicle charging control
  • Forecasting
  • Model predictive control
  • Shifting demand

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