Stochastic Model Predictive Control for Demand Response in a Home Energy Management System: Preprint

Dane Christensen; Kaitlyn Garifi; Kyri Baker; Behrouz Touri

Stochastic Model Predictive Control for Demand Response in a Home Energy Management System: Preprint

Dane Christensen, Kaitlyn Garifi, Kyri Baker, Behrouz Touri

Building Technologies and Science Center

Research output: Contribution to conference › Paper

Abstract

This paper presents a chance constrained, model predictive control (MPC) algorithm for demand response (DR) in a home energy management system (HEMS). The HEMS optimally schedules controllable appliances given user preferences such as thermal comfort and energy cost sensitivity, and available residentially-owned power sources such as photovoltaic (PV) generation and home battery systems. The proposed control architecture ensures both the DR event and indoor thermal comfort are satisfied with a high probability given the uncertainty in available PV generation and the outdoor temperature forecast. The uncertainties are incorporated into the MPC formulation using probabilistic constraints instead of computationally limiting sampling-based approaches. Simulation results for various user preferences and probabilistic model parameters show the effectiveness of the HEMS algorithm response to DR requests.

Original language	American English
Number of pages	8
State	Published - 2018
Event	IEEE Power and Energy Society General Meeting - Portland, Oregon Duration: 5 Aug 2018 → 10 Aug 2018

Conference

Conference	IEEE Power and Energy Society General Meeting
City	Portland, Oregon
Period	5/08/18 → 10/08/18

Bibliographical note

See NREL/CP-5500-73330 for paper as published in IEEE proceedings

NREL Publication Number

NREL/CP-5500-70492

Keywords

demand response
DR
HEMS
home energy management system

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

https://www.nrel.gov/docs/fy19osti/70492.pdf

Cite this

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title = "Stochastic Model Predictive Control for Demand Response in a Home Energy Management System: Preprint",

abstract = "This paper presents a chance constrained, model predictive control (MPC) algorithm for demand response (DR) in a home energy management system (HEMS). The HEMS optimally schedules controllable appliances given user preferences such as thermal comfort and energy cost sensitivity, and available residentially-owned power sources such as photovoltaic (PV) generation and home battery systems. The proposed control architecture ensures both the DR event and indoor thermal comfort are satisfied with a high probability given the uncertainty in available PV generation and the outdoor temperature forecast. The uncertainties are incorporated into the MPC formulation using probabilistic constraints instead of computationally limiting sampling-based approaches. Simulation results for various user preferences and probabilistic model parameters show the effectiveness of the HEMS algorithm response to DR requests.",

keywords = "demand response, DR, HEMS, home energy management system",

author = "Dane Christensen and Kaitlyn Garifi and Kyri Baker and Behrouz Touri",

note = "See NREL/CP-5500-73330 for paper as published in IEEE proceedings; IEEE Power and Energy Society General Meeting ; Conference date: 05-08-2018 Through 10-08-2018",

year = "2018",

language = "American English",

}

TY - CONF

T1 - Stochastic Model Predictive Control for Demand Response in a Home Energy Management System: Preprint

AU - Christensen, Dane

AU - Garifi, Kaitlyn

AU - Baker, Kyri

AU - Touri, Behrouz

N1 - See NREL/CP-5500-73330 for paper as published in IEEE proceedings

PY - 2018

Y1 - 2018

N2 - This paper presents a chance constrained, model predictive control (MPC) algorithm for demand response (DR) in a home energy management system (HEMS). The HEMS optimally schedules controllable appliances given user preferences such as thermal comfort and energy cost sensitivity, and available residentially-owned power sources such as photovoltaic (PV) generation and home battery systems. The proposed control architecture ensures both the DR event and indoor thermal comfort are satisfied with a high probability given the uncertainty in available PV generation and the outdoor temperature forecast. The uncertainties are incorporated into the MPC formulation using probabilistic constraints instead of computationally limiting sampling-based approaches. Simulation results for various user preferences and probabilistic model parameters show the effectiveness of the HEMS algorithm response to DR requests.

AB - This paper presents a chance constrained, model predictive control (MPC) algorithm for demand response (DR) in a home energy management system (HEMS). The HEMS optimally schedules controllable appliances given user preferences such as thermal comfort and energy cost sensitivity, and available residentially-owned power sources such as photovoltaic (PV) generation and home battery systems. The proposed control architecture ensures both the DR event and indoor thermal comfort are satisfied with a high probability given the uncertainty in available PV generation and the outdoor temperature forecast. The uncertainties are incorporated into the MPC formulation using probabilistic constraints instead of computationally limiting sampling-based approaches. Simulation results for various user preferences and probabilistic model parameters show the effectiveness of the HEMS algorithm response to DR requests.

KW - demand response

KW - DR

KW - HEMS

KW - home energy management system

M3 - Paper

T2 - IEEE Power and Energy Society General Meeting

Y2 - 5 August 2018 through 10 August 2018

ER -

Stochastic Model Predictive Control for Demand Response in a Home Energy Management System: Preprint

Abstract

Conference

Bibliographical note

NREL Publication Number

Keywords

UN SDGs

Access to Document

Fingerprint

Cite this