Improving renewable self-consumption in residential DC nanogrids through droop-based load control

This paper investigates droop-based load control as a decentralized strategy for maximizing renewable self-consumption (RSC) in a residential direct current (DC) nanogrid without local energy storage. While droop control is widely applied to sources and storage units in DC microgrids, its systematic use for residential load management remains insufficiently explored. In this study, a residential DC nanogrid model was developed in the PLECS simulation environment using measured photovoltaic (PV) generation and household load profiles from two single-family homes in Estonia. The model follows a 350 V DC architecture and includes a PV converter, an active front-end (AFE) grid interface, and controllable thermal loads. Droop control was applied to a geothermal heat pump and the defrosting function of a heat recovery ventilation (HRV) system using an energy-based calibration approach to maintain equivalent weekly energy consumption. Simulation results over a one-week measurement period show that baseline operation achieved an RSC of 0.51. Applying droop control to the geothermal heat pump increased RSC to 0.69, corresponding to a 26% improvement, by shifting consumption towards periods of high PV generation. In contrast, droop control of the HRV provided only marginal improvement due to its smaller energy share. Combined droop control of both loads resulted in an RSC of 0.69. The results demonstrate that droop-based load control can significantly enhance renewable energy utilization in residential DC nanogrids without battery storage.

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Publication Details

Journal
Proceedings of the Estonian Academy of Sciences
Published
2026-10-29
DOI
https://doi.org/10.3176/proc.2026.4.07
Primary Topic
Smart Grid Energy Management
Type
article
Field-Weighted Citation Impact
0.00

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article

Improving renewable self-consumption in residential DC nanogrids through droop-based load control

Indrek Roasto, Dmitri Vinnikov, Andrei Blinov
2 links
Proceedings of the Estonian Academy of Sciences
Smart Grid Energy Management
article

Improving renewable self-consumption in residential DC nanogrids through droop-based load control

Indrek Roasto, Dmitri Vinnikov, Andrei Blinov
article en

Abstract

This paper investigates droop-based load control as a decentralized strategy for maximizing renewable self-consumption (RSC) in a residential direct current (DC) nanogrid without local energy storage. While droop control is widely applied to sources and storage units in DC microgrids, its systematic use for residential load management remains insufficiently explored. In this study, a residential DC nanogrid model was developed in the PLECS simulation environment using measured photovoltaic (PV) generation and household load profiles from two single-family homes in Estonia. The model follows a 350 V DC architecture and includes a PV converter, an active front-end (AFE) grid interface, and controllable thermal loads. Droop control was applied to a geothermal heat pump and the defrosting function of a heat recovery ventilation (HRV) system using an energy-based calibration approach to maintain equivalent weekly energy consumption. Simulation results over a one-week measurement period show that baseline operation achieved an RSC of 0.51. Applying droop control to the geothermal heat pump increased RSC to 0.69, corresponding to a 26% improvement, by shifting consumption towards periods of high PV generation. In contrast, droop control of the HRV provided only marginal improvement due to its smaller energy share. Combined droop control of both loads resulted in an RSC of 0.69. The results demonstrate that droop-based load control can significantly enhance renewable energy utilization in residential DC nanogrids without battery storage.

Proceedings of the Estonian Academy of SciencesVol. 75(4)
Tallinn University of Technology (EE)
Eesti Teaduste Akadeemia, Eesti Teadusagentuur
Affordable and clean energy, Industry, innovation and infrastructure, Climate action
Openalex Percentile: Top 24%
Smart Grid Energy Management
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