Sizing, Operation, and Evaluation of Battery Energy Storage with Dynamic Line Rating and Deep Learning

• Author / Creator

  Avkhimenia, Vadim

• Integration of renewables has the potential to reduce society’s reliance on carbon-based fuels. Sizing renewable-energy installations is key to making a successful business case for the construction of new assets. Dynamic thermal line rating is the amount of current in real-time that a transmission line can safely carry, and, if utilized together with utility-scale battery installations, has the potential to reduce the capacity and power rating of batteries.
  Today, battery energy storage systems are operated generally with rule-based approaches where utility-scale batteries charge when they can, and discharge then they have to, or change their energy amount with the time of the day. This works aims to improve on that by introducing smart control of batteries using deep learning and deep reinforcement learning-based methods. Transmission operators today generally assume the line ampacity of the transmission lines to be stable, referred to as static line rating. Dynamic line rating, however, can be multiple times higher than static line rating, enabling the ability to sent more power across the transmission lines.
  The aim of this work is to size battery energy storage systems taking into account dynamic thermal line rating, transmission line outages, and battery degradation, and explore decentralized control of batteries. A combination of non-linear programming for battery action prediction is used together with deep learning-based forecasting of ampacity and load. The approach is tested on IEEE 24-bus test grid. A deep reinforcement learning-based approach is utilized to predict battery actions, and test it on IEEE 6-bus test grid. A method for evaluating battery capacity and power rating sizes based on comparing the selection criteria with the allowed tolerance in unserved energy, unserved energy duration, and the number of unserved energy events is presented. A method to generate synthetic 100-step time series of Alberta electricity pool prices and dynamic thermal line rating is demonstrated, that can be used to supplement existing data sets, and be applied in reinforcement-learning simulations.

• Subjects / Keywords

  • Battery Sizing
  • Battery Operation
  • Dynamic Line Rating
  • Synthetic Time Series
  • Wasserstein GAN
  • Deep Learning Battery Control
  • Deep Reinforcement Learning Battery Control
  • Linear Programming Battery Control
  • Physics Informed Neural Network
  • Soft Actor Critic Battery
  • DDPG Battery
  • Battery Sizing Evaluation
  • Utility Battery Sizing
  • Dynamic Thermal Line Rating
  • Dynamic Line Rating Forecast
  • Ampacity Forecast
  • Load Forecast
  • SAC Battery Control
  • DDPG Battery Control
  • MASAC Battery
  • MASAC Battery Control
  • MADDPG Battery
  • MADDPG Battery Control
  • Battery Degradation
  • PINN
  • PINN Battery
  • Transmission Line
  • Transmission Battery Size
  • Transmission Battery Sizing
  • Deep Reinforcement Learning Battery
  • Deep Learning Battery
  • CNN Attention Forecast
  • Sliding Window
  • Sliding Window Battery Evaluation
  • Sliding Window Battery Sizing
  • Battery Feasible Region
  • IEEE 24-bus RTS
  • IEEE 6-bus RTS
  • IEEE 24 bus
  • IEEE 6 bus
  • Actor Critic Battery
  • Single Agent Battery
  • Multi Agent Battery
  • Single-agent Battery
  • Multi-agent Battery
  • Multi Agent Battery Control
  • Multi-agent Battery Control

• Graduation date

  Spring 2023

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/r3-12jg-3f55

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.