Impact of model predictive control-enabled home energy management on large-scale distribution systems with photovoltaics
•Stochastic home energy management system (HEMS) with more granular model details.•Model predictive control coupling HEMS to quasi-steady-state time-series simulations.•Impact of 1977 homes with photovoltaics and HEMS on an 8500-node distribution feeder. Residential customers use more than one-quart...
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Published in | Advances in applied energy Vol. 6; p. 100094 |
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Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
Elsevier Ltd
01.06.2022
Elsevier |
Subjects | |
Online Access | Get full text |
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Summary: | •Stochastic home energy management system (HEMS) with more granular model details.•Model predictive control coupling HEMS to quasi-steady-state time-series simulations.•Impact of 1977 homes with photovoltaics and HEMS on an 8500-node distribution feeder.
Residential customers use more than one-quarter of the electricity in the world. Optimally managing home energy consumption is an effective way of easing the operational challenges facing the electric grid with increasing solar photovoltaics (PV). This paper studies the impact of the future proliferation of home energy management systems (HEMS) in the presence of PV on large-scale distribution systems. First, we present a stochastic HEMS model that minimizes residential customers’ thermal discomfort and energy costs under uncertainty. The HEMS model schedules the optimal operations of residential appliances in the presence of PV within a mixed-integer linear programming-based model predictive control framework that links the proposed HEMS to a quasi-steady-state time-series simulation tool. Extensive simulations are conducted for a stand-alone residential home using two tariff structures and for 1977 homes on an 8,500-node distribution feeder. Simulation results quantify the impact of the future proliferation of HEMS on the large-scale distribution system with PV. |
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Bibliography: | USDOE Grid Modernization Laboratory Consortium National Natural Science Foundation of China (NSFC) NREL/JA-5D00-81969 AC36-08GO28308; 1929147; 1856084 USDOE Office of Energy Efficiency and Renewable Energy (EERE), Strategic Programs |
ISSN: | 2666-7924 2666-7924 |
DOI: | 10.1016/j.adapen.2022.100094 |