Stochastic Home Energy Management Systems with Varying Controllable Resources

Dane Christensen, Kaitlyn Garifi, Kyri Baker, Behrouz Touri

Research output: Contribution to conferencePaperpeer-review

9 Scopus Citations

Abstract

This paper studies the performance of a model predictive control (MPC) algorithm in a home energy management system (HEMS) as the set of controllable resources varies and under both a constant and a time-of-use (TOU) electricity price structure. The set of controllable resources includes residentially-owned photovoltaic (PV) panels, a home battery system (HBS), an electric vehicle (EV), and a home heating, ventilation, and air conditioning (HVAC) system. The HEMS optimally schedules the set of controllable resources given user preferences such as indoor thermal comfort and electricity cost sensitivity. The home energy management system is built on a chance constrained, MPC-based algorithm, where the chance constraint ensures the indoor thermal comfort is satisfied with a high probability given uncertainty in the outdoor temperature and solar irradiance forecasts. Simulation results for varying sets of controllable resources under two different electricity price structures demonstrate the variation in the HEMS control with respect to HBS operation, electricity cost, and grid power usage.

Original languageAmerican English
Number of pages5
DOIs
StatePublished - Aug 2019
Event2019 IEEE Power and Energy Society General Meeting, PESGM 2019 - Atlanta, United States
Duration: 4 Aug 20198 Aug 2019

Conference

Conference2019 IEEE Power and Energy Society General Meeting, PESGM 2019
Country/TerritoryUnited States
CityAtlanta
Period4/08/198/08/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

NREL Publication Number

  • NREL/CP-5500-72783

Keywords

  • control
  • HEMS
  • home energy
  • optimization
  • uncertainty

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