Abstract
This paper presents a comprehensive scheduling framework for residential demand response (DR) programs considering both the day-ahead and real-time electricity markets. In the first stage, residential customers determine the operating status of their responsive devices such as heating, ventilation, and air conditioning (HVAC) systems and electric water heaters (EWHs), while the distribution system operator (DSO) computes the amount of electricity to be purchased in the day-ahead electricity market. In the second stage, the DSO purchases insufficient (or sells surplus) electricity in the real-time electricity market to maintain the supply-demand balance. Due to its computational complexity and data privacy issues, the proposed model cannot be directly solved in a centralized manner, especially with a large number of uncertain scenarios. Therefore, this paper proposes a combination of stochastic programming (SP) and the alternating direction method of multipliers (ADMM) algorithm, called SP-ADMM, to decompose the original model and then solve each sub-problem in a distributed manner while considering multiple uncertain scenarios. The simulation study is performed on the IEEE 33-bus system including 121 residential houses. The results demonstrate the effectiveness of the proposed approach for large-scale residential DR applications under weather and consumer uncertainties.
| Original language | English |
|---|---|
| Article number | 9302627 |
| Pages (from-to) | 3004-3016 |
| Number of pages | 13 |
| Journal | IEEE Transactions on Power Systems |
| Volume | 36 |
| Issue number | 4 |
| DOIs | |
| State | Published - Jul 2021 |
Funding
The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http:// energy.gov/downloads/doe-public-access-plan). This research is sponsored by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the U.S. Department of Energy. Manuscript received March 22, 2020; revised July 21, 2020 and September 26, 2020; accepted October 1, 2020. Date of publication December 22, 2020; date of current version June 18, 2021. This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the US Department of Energy. This article was based upon the work supported by the U.S. Department of Energy, Energy Efficiency and Renewable Energy, Building Technology Office under contract numberDEAC05-00OR22725, as well as the work supported by CURENT which is an Engineering Research Center of the U.S. National Science Foundation (NSF) and DOE funded under NSF award EEC-1041877. Paper no. TPWRS-00436-2020. (Corresponding author: Fangxing Li.) Xiao Kou and Fangxing Li are with the Dept. of EECS, University of Tennessee, Knoxville, TN 37996 USA (e-mail: [email protected]).
| Funders | Funder number |
|---|---|
| CURENT | |
| Energy Efficiency and Renewable Energy, Building Technology Office | |
| National Science Foundation | |
| U.S. Department of Energy | EEC-1041877, TPWRS-00436-2020 |
| Oak Ridge National Laboratory |
Keywords
- Demand response (DR)
- Distribution system operator (DSO)
- Electric water heater (EWH)
- HVAC
- Home energy management system (HEMS)
- Stochastic programming based alternating direction method of multipliers (SP-ADMM)
- Uncertainty