Abstract

Traditional wisdom has dictated that in the Northern Hemisphere, residential rooftop photovoltaic (PV) systems should be facing true south when possible to maximize energy production. However, an abundance of south-facing PV systems in communities generating power simultaneously at peak solar times has caused excessive reverse power flows which introduce multiple challenges within the broader power grid. At the aggregate level, this has caused sharp ramps in generation at sunrise and sunset, created challenges for grid reliability, and introduced local grid issues like voltage spikes. As a result, utilities have begun to reduce payments to customers for feeding solar generation back to the grid, moving away from so-called net-metering schemes. This has incentivized west-facing solar panels to better overlap with typical residential demand, reducing reverse flows, but it remains unclear as to how best to choose orientation of panels to achieve community-level goals like self-reliance (e.g., in the case of a microgrid) while minimizing the amount of battery capacity required. In this paper, we consider a community-level design problem for the optimal placement of residential rooftop solar panels considering different home designs and demand patterns. We develop a flexible simulation testbed that solves a mixed-integer linear optimization problem to determine, given a particular community-level objective, the orientation of panel placement within the restrictions of roof geometry and space. The results indicate that optimal placement can help a community maximize self-consumption, facilitating a transition to subhourly net-zero energy goals and reducing the need for energy storage.

Practical Applications

The following paper includes a novel optimization objective for designing solar panel installations. Whereas historically solar installers have emphasized maximization of solar power generation, utilities have begun to push back on excess power production from so-called prosumers (customers who also generate power) during peak solar production hours. Our proposed objective is to maximize the amount of usable solar power rather than the potential solar power. Solar power that is temporally offset from the conventional south-facing peak production can lessen the impacts of ramp down and ramp up periods. The models presented in this paper result in an optimal ratio of solar panel orientation for a given demand profile and location. We suggest that developers use this information to create a temporally robust solar generation profile in the community. The existing (or designed) roof profiles of a community can be input to the optimization constraints with a limited number of solar panels. The resulting profile assists in reducing the need for rapid ramp up and ramp down capacity as well as reducing battery storage capacity in the event that the community operates in an islanded microgrid mode.

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Data Availability Statement

Some data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. Some data, models, or code used during the study were provided by a third party. Direct requests for these materials may be made to the provider as indicated in the Acknowledgments.

Acknowledgments

The authors would like to thank Dar-Lon Chang and the Geos neighborhood for allowing us to use their measured 15-minute demand data.

Notation

The following symbols are used in this paper:
Eb(h,t)
The state of charge (kWh) of home h’s battery at time t;
Emax
The maximum state of charge of the communal battery over time (kWh);
Emax(h)
The maximum state of charge of home h’s battery over time (kWh);
Fh
The set of roof facets belonging to house h;
H
The set of houses in the community;
Ibeam
Beam irradiance profile (W);
Idif
Diffuse irradiance profile (W);
Iglo
Global irradiance profile, incident on plane (W);
Ihorz
Horizontal irradiance profile (W);
np(h,i)
The number of panels on a given facet i of the roof of house h;
n¯(h,i)
The maximum number of panels that fit on facet i of house h;
Pbuy(h,t)
The power imported to home h at time t (kW);
Pcons(h,t)
The total consumption of house h at time t (kW);
Pgen(h,t)
The total generation of house h at time t (kW);
P¯gen(h,t)
The maximum total generation of house h at time t (kW);
Pnet(h,t)
The net import/export of power to home h at time t (kW);
Psell(h,t)
The power exported to the grid h at time t (kW);
p^(h,i,t)
The power production of a single solar panel of house h, facet i at time t (kW);
t
Time elapsed since midnight of the previous day (hr);
α
Multiobjective optimization tuning parameter;
δ
Solar declination;
ηch
The efficiency of the battery during charge (including inverter efficiency);
ηdis
The efficiency of the battery during discharge (including inverter efficiency);
ηPV
The efficiency of the solar panel installation (including inverter efficiency);
θh,i
The angle of roof facet i on house h;
θn
Angle of incidence on plane;
θp
Vertical orientation angle of the plane;
θs
Solar zenith angle;
λ
Latitude angle of the plane’s location;
ρg
Ground reflectance;
ϕh
The orientation of house h (angle from true north to project north);
ϕh,i
The orientation of facet (angle from true north) i on house h;
ϕp
Horizontal orientation angle of the plane;
ϕs
Solar azimuth angle; and
ω
Hour angle of the day.

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Information & Authors

Information

Published In

Go to Journal of Architectural Engineering
Journal of Architectural Engineering
Volume 29Issue 4December 2023

History

Received: Apr 10, 2023
Accepted: Aug 11, 2023
Published online: Sep 29, 2023
Published in print: Dec 1, 2023
Discussion open until: Feb 29, 2024

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Authors

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Dept. of Civil, Environmental, and Architectural Engineering, Univ. of Colorado Boulder, Boulder, CO 80309 (corresponding author). ORCID: https://orcid.org/0000-0002-8661-3777. Email: [email protected]
Kendall Baertlein, S.M.ASCE [email protected]
Dept. of Civil, Environmental, and Architectural Engineering, Univ. of Colorado Boulder, Boulder, CO 80309. Email: [email protected]
Dept. of Civil, Environmental, and Architectural Engineering, Univ. of Colorado Boulder, Boulder, CO 80309. ORCID: https://orcid.org/0000-0002-6854-9134. Email: [email protected]

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