Effects of rooftop solar on household energy burden in the United States

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Effects of rooftop solar on household energy burden in the United States

https://doi.org/10.21203/rs.3.rs-3410920/v1

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Policymakers at the federal and state level have begun to incorporate energy burden into equity goals and program evaluations, aiming to reduce energy burden below a “high” level of 6% for lower income households. Pairing an empirical household-level dataset spanning U.S. geographies together with modeled hourly energy demand curves, we show that rooftop solar reduces energy burden across a majority of adopters from a median of 3.3–2.7%. For low- and moderate-income adopters, solar reduced median energy burden from 7.7–6.4%, and from 4.1–3.4%, respectively. Importantly, solar reduced the rate of high or severe energy burden from 67% of all low-income households before adoption to 54% of households following adoption, and correspondingly from 21–14% for moderate-income households. Overall, rooftop solar can support state and federal goals to reduce energy burden along with other strategies such as weatherization and bill assistance.

Scientific community and society/Energy and society/Energy justice

Scientific community and society/Energy and society/Energy policy

Spatial and structural inequities in our energy system have led some communities to benefit while others bear the burden of its byproducts such as local air pollution or cost shifts. In particular, energy affordability and access to supporting technologies, such as energy efficiency retrofits or rooftop solar photovoltaics, are distributed unevenly across U.S. households.^1–7 To target assistance to those households most vulnerable to increasing energy prices, policymakers are introducing indicators to quantify and track energy equity. One common energy affordability metric is been “energy burden” (EB), or the percentage of gross income that a household spends on energy costs.^8–10 Typically, an EB above 6% is considered “high” while an EB above 10% is considered “severe”.¹

Existing EB literature has focused on quantification across populations. For example, Drehobl et al. found that low-income households experienced an EB triple the magnitude of non-LMI households.¹ Studies have also quantified the uneven distribution of EB across heating fuel usage and health impacts,⁶ and correlated higher EB with: low-income households using fuel oil and in the mixed-humid climate zone,⁴ African American households experiencing simultaneous high rent burdens,⁵ low-income and minority households living in multifamily housing,^1,2 and island or rural populations.³ A high EB has been linked to health impacts as well, such as a higher level of winter deaths in cold climate regions¹¹ and other conditions associated with poor housing stock such as respiratory illness, thermal discomfort, and mental health stressors associated with ability to pay.¹⁰

Despite EB’s value as a proxy for affordability, this indicator has its shortcomings. Namely, while it can help identify homes committing a high percentage of income to pay for energy services, it cannot capture households who may be energy rationing, such as reducing their bill by keeping their homes at unsafe or uncomfortable temperatures.^12,13 Moreover, it cannot speak to the underlying structural causes of energy insecurity or measure its non-economic effects, including physical and mental health as well as housing conditions and comfort.^{10,11,14–20} Notwithstanding these limitations, the focus of this study on EB is primarily driven by its increasing use as an indicator and evaluation metric in U.S. goals and policy, at the state and federal levels. For example, the federal Department of Energy identified eight policy priorities with the first stating “decrease energy burden in disadvantaged communities” as a result of the Justice40 initiative.^21,22 Additionally, several states are shifting energy affordability efforts and energy efficiency policies to focus on EB reduction for lower income residents.^9,23

Several tools exist to increase energy affordability. Historically, most have focused on either providing short-term bill assistance and discounts or longer-term energy efficiency home retrofits. Energy insecurity has long been linked to low levels of home energy efficiency in the body of literature.^{3,10,17,24,25} In addition, weatherization can create sustained bill savings as well as generate non-energy benefits.^11,17,26 As a result, there is a strong body of weatherization research and policy to reduce EB.^2,4,27 More recently, work focusing on the potential for rooftop solar adoption to reduce EB has emerged as another option for sustained bill reduction as solar adoption by LMI households has increased, in part due to decreasing costs and LMI solar programs.^28–31 Solar, weatherization, and other methods of sustained net energy reduction are important since they reduce household exposure to potential increases in energy prices.³²

At present, few studies quantify the impact of rooftop solar on EB. Those that do, focus on a specific location and/or use aggregated data to make assumptions about income and energy costs.^7,33 Tidemann et al. found that the use of aggregated data to explore the distributional outcomes of rooftop solar led to results that overlooked vulnerable areas.³⁴ Indeed, the lack of micro-level data can be a large challenge.^35,36 This paper fills several important existing gaps in the literature. To our knowledge, it is the first to explore how residential rooftop solar impacts EB (electric and non-electric) at the household level across the U.S. Furthermore, existing literature on EB reduction from solar focuses solely on the bill impacts without accounting for ongoing off-bill financial impacts of solar adoption, such as ongoing solar loan or lease payments or ongoing production-based incentive payments based on solar production. Focusing exclusively on bill-impacts of rooftop PV overly-simplifies the economic impacts of solar adoption for homes and can lead to over-estimates of EB reductions. In this study, we consider a broader scope of financial impacts when estimating EB, including both the direct on-bill impacts as well as off-bill impacts. We henceforth use EB to characterize the percentage of household income spent on energy costs, inclusive of costs and incentives (unless otherwise specified).

2.1 Energy burden impacts of rooftop solar for the overall study population

After rooftop solar installation, energy bills for the entire sample of adopters shifted from a median of 3.3–1.4% of gross income (Fig. 1). However, taking off-bill loan and lease repayment into account muted the downward impact of rooftop solar on a household’s EB, while ongoing benefits such as renewable energy certificates for host-owned systems amplified savings. Altogether, adopters in our sample settled to a median EB of 2.7% (Fig. 1). From the perspective of customers, the median customer saw a 1.7 EB point reduction when looking at bill savings alone ($1,893 annually) versus a 0.5 point reduction ($600 annually) considering off-bill financial impacts. When off-bill impacts are excluded, the effect of solar installation on burden appears to be over three times as large as it actually is, illustrating the overestimation of rooftop solar EB reduction potential that may occur if off-bill impacts are disregarded.

Each customer was assumed to finance its system through either a loan or lease. The 56% of customers with host-owned (loan financed) systems saw a median burden of 3.0%, absent solar, decrease to 2.5% with individuals seeing a median 0.43 EB point drop ($558 annually). Those with third-party owned (leased) systems saw median burdens fall from 3.7–3.0% with individuals on average seeing a 0.56 point reduction ($635 annually) (Fig. 1). Overall, loan-financed and leased systems exhibit many financial similarities, and we merge the two separate groups throughout the analysis to best compare impacts and understand overall EB reduction of rooftop solar across all customers in the sample.

There were a number of households whose off-bill costs exceeded their bill savings in the analysis period of 2021, and thus their EB increased as a result of solar (Fig. 2). There are several reasons why this may be the case. First, since this was only a snapshot of 2021 impacts, it does not inform whether a system is economical over the course of its lifespan and an adopter may initially pay a premium to hedge against future rate increases. Indeed, Fig. SI.2 shows a positive net present value for 2021 adopters, regardless of cash, loan, or lease payment. Second, a number of our financial assumptions related to the loan and lease terms are based on state averages at the year of adoption. Third, an adopter may also choose solar for other reasons apart from economics as well.

We explore this topic further in SI 7 “Linear regression to explore instances where solar increased energy burden” and find that a low EB absent solar, low electricity prices, larger solar system sizes, more expensive per-Watt solar costs, smaller home square footage, and higher income all were linked to lower EB reduction (in that order of magnitude). We also find that homes using fuel oil or propane were more likely to see low levels of savings, as were homes located in the South.

2.2 Impacts by income group

Across the 500k households in the study sample, 23% were low-income (≤ 80% of area median income (AMI)), 21% were moderate-income (80–120% AMI), and 57% were non-LMI (> 120% AMI). Without solar, 34% of these low-income households would have experienced a severe EB over 10%, while an additional 32% would have experienced a high EB of 6–10%. By comparison, the corresponding numbers for moderate-income households experienced were 2.6% and 19%. and those for non-LMI households were 0.25% and 2.7% (Fig. 3). With solar, the number of low-income households experiencing severe and high EB experienced a percentage point drop of 7.8 and 4.7, respectively while moderate-income households saw corresponding point drops of 0.3 and 7.8, respectively. Overall, solar was able to reduce EB to manageable levels below 6% for 33% of the subset of LMI adopters experiencing severe or high burden.

Rooftop solar reduced median low-income EB from 7.7–6.4% and from 4.1–3.4% for moderate-income adopters (Fig. 4). Post-adoption EB above 6% may be linked with ongoing difficulty to pay for energy, indicating that solar alone may not be able to fully alleviate high EB for the majority of low-income adopters and a smaller group of moderate-income adopters. Consequently, households with post-adoption burdens exceeding 6% may require additional measures (e.g., energy efficiency) or incentives (e.g., low-income solar programs) to reduce all LMI households’ EB below 6%. Figure 4 further illustrates the importance of incorporating off-bill impacts into EB, particularly for lower income groups. For example, rooftop solar reduced the share of LMI households experiencing high or severe EB from 45–35%, however, solely using bill impacts would erroneously imply a reduction to just 16% of low-income households.

2.3 Energy burden impacts in 2021 based on year of adoption

This analysis looks at EB impacts for the year 2021, but incorporates empirical cost and incentive information specific to the year of adoption, as well as loan and lease terms from each respective year/state combination. Over time, EB absent solar has increased slightly across the entire sample from 2.9% in 2013 to 3.2% in 2021, consistent with the fact that incomes of adopters have been slowly migrating downwards.²⁸ While the 2021 income estimates for low-income 2021 adopters are 6% less than those of 2013 adopters, EB absent solar is 8% higher. Thus, while solar has been increasingly reaching lower income households, it has been comparatively less successful at increasing adoption among low-income households with higher EB (Fig. 5). This may be due to the broadening of the solar market into lower income regions, among other factors.²⁸

2.4 Regional and state differences

Across our sample, the impacts of solar adoption on household EB vary by region. Location-specific impacts include differences in income; solar incentives, cost, and resource; and energy costs impacted by prevalent heating fuel types and energy prices (Table 2). SI 7 “Linear regression to explore instances where solar increased energy burden” finds that, controlling for many of these variables still show regional differences, which could be due to additional factors such as heating degree days, cooling degree days, housing stock, or energy usage behavior.

Table 2

Descriptive statistics of solar adopters by region
	Med. AMI [%]	Med. Inc. [$1,000]	Med. PV Size [kW]	Med. PV Cost [$/W]	TPO [%]	Fuel oil or propane heat [%]	Med. Electricity Price [$/kWh]	Med. 2021 Energy Costs without PV [$]	Med. EB without PV [%]	Med. EB with PV [%]
Midwest	136%	120	6.6	5.05	0%	1%	$0.13	3250	2.8%	2.5%
Northeast	123%	117	7.2	4.29	64%	30%	$0.20	4508	4.1%	3.5%
South	146%	98	6.9	4.41	0%	0%	$0.08	2138	2.1%	2.6%
West	137%	121	5.8	4.64	38%	1%	$0.28	3598	3.1%	2.5%

Figure 6 shows that rooftop solar on average decreases EB across income groups in the Midwest, Northeast, and West. Notably, for low-income adopters, solar was able to push the majority of adopters in the West from a high energy burden (7.3% median) to just below the 6% level. However, across all income groups, median EB increases in the South. This is consistent with a study based in Florida³⁷ and is explored further in SI 7 “Linear regression to explore instances where solar increased energy burden”. We hypothesize that this is largely due to the relatively low cost of electricity which attenuates the bill reduction potential of rooftop solar. In the Northeast, EB demonstrated higher variation, likely due to the prevalence of propane and fuel oil heating (Table 2), which tend to be costlier than electricity and natural gas and cannot be offset by solar.

2.5 Limitations of rooftop solar for energy burden reduction

Solar alone can only impact the volumetric cost of electricity. As such, in utility territories with high fixed costs or minimum bills, solar will not fully offset electricity bills. Additionally, rooftop solar alone cannot offset energy costs associated with non-electric loads such as natural gas, propane, or fuel oil heating. Figure 7 illustrates that the highest EB across income groups are for households with either propane or fuel oil heating. Moreover, almost all households in the small group of non-LMI solar households whose EB remains above 6% have propane or fuel oil heating. Moderate-income solar households with propane heating also maintain a median EB just above 6% and of all low-income adopters with propane and fuel oil heating, 88% and 80%, respectively, maintain an EB greater than 6%.

Since rooftop solar adoption can only reduce EB a limited amount for those with non-electric heating, strategies such as weatherization may be necessary in order to reduce heating energy requirements regardless of fuel type. Alternatively, the electrification of large non-electric loads such as heating would allow for solar to offset those costs, albeit with a larger system. This is an area for further exploration.

Rooftop solar effectively reduced energy costs for the great majority of U.S. adopters. Across our sample, solar reduced EB from 3.3–2.7%. Taking costs into account as well as revenue from bill savings and incentives when calculating EB is critical as it avoids overestimating EB reduction from solar. In contrast, only taking bill savings into account erroneously shows median EB falling to 1.4%.

EB reduction from solar generally persists regardless of year of adoption, income, region, and fuel type. The one exception is in the South where electricity costs are low, reducing the bill reduction potential of solar and leading to higher costs than revenue. In some other cases of households with low incomes or heating from propane or fuel oil, solar reduces EB but levels remain high.

From our sample, 67% of low-income households and 21% moderate-income households experienced either high or severe EB. Solar adoption was able to successfully reduce EB to manageable levels below 6% for a third of households in this group (down to 54% and 13% of households, respectively). Overall, solar decreased median EB for low-income and moderate-income adopters from 7.7–6.4%, and from 4.1–3.4%, respectively.

Interpretation of our results should take into account certain methodological limitations in our research design. First, given data limitations on spatially and temporally granular energy demand profiles, we assign modeled hourly demand to our empirical household and solar system data. Using synthetic load data can propagate any biases in the underlying physical modeling and forces us to assign loads independently of household income and retail energy prices, effectively supposing income and price elasticities of energy demand equal to one. This could result in overestimation of energy costs for those that ration usage as a coping strategy to hedge against high expected energy costs. Second, Experian income estimates were provided for 2021 annual household income (as opposed to at the time of adoption). Their proprietary model may include biases unknown to the researchers. Third, while we use the most likely hourly residential retail electricity volumetric and fixed tariff by zip code and presence of solar, we do not include specific low-income discount rates. This may result in overestimating EB for those that are enrolled in these discounted tariffs.

Rooftop solar can support state and federal goals to reduce EB, including for LMI households. Nevertheless, there was a large fraction of low-income households whose post-adoption EB remained high (6%-10%) or severe (over 10%), indicating persistent energy affordability issues. Notably, for low-income adopters with propane or fuel oil heating, 81% maintained an EB above 6% after solar adoption. To alleviate this, policymakers may consider focusing low-income incentives to this group to reduce upfront costs, cost of capital, and/or penalties for non-payment. Policymakers may also wish to pair low-income solar incentives with additional interventions such as weatherization and/or fuel switching in order to reduce EB for those with a high percentage of non-electric energy costs.

This study uses a four-step framework to analyze the financial impacts of rooftop solar adoption that occurred between 2013 and 2021 on EB at the household level in the year 2021, summarized in Fig. 1. First, we merge household income estimates, solar system attributes, and housing stock data for roughly 500,000 residential solar adopters across the U.S (hereafter, “adopter households”). Second, we estimate hourly solar generation for each household based on its county and system size. To estimate hourly loads, we match each building to the simulated load profile of a building with similar characteristics. Third, we identify the applicable electricity rates and fuel prices for each adopter to calculate household energy bills. Fourth and finally, we calculate EB for the adopter households, considering incentives, upfront costs, and financing. The data sources for each of the steps in this modeling framework can be consulted in Supplementary Information “Data Sources”.

a. Step 1: Filtering and merging household data

The analysis relies on Berkeley Lab’s national dataset of residential solar adopters. The dataset version utilized for this study consists of roughly 2.5 million residential rooftop solar systems installed through 2021, representing 80% of all U.S. systems, and includes data on system size, cost, financing, and street address, among other items.²⁸ We append to this dataset estimated household incomes developed by Experian and housing stock data from CoreLogic.

We then filter this dataset to occupied single family homes with rooftop solar installed on or after 2000 and installations less than or equal to 20 kW with complete data on solar system size, cost, and estimated income.

b. Step 2: Estimating hourly solar generation and household load

To calculate adopter household’s net load, we separately estimate hourly solar generation and household energy consumption before rooftop solar installation.

We set out with a target of modelling 500k solar adopting homes, but aim to keep state-level geographical representativeness of the original set of solar adopter homes in the Solar Demographics dataset.²⁸ We hence specify a number of households to sample, per state, such that the geographic distribution of the 500k homes is equal to the original distribution of homes.

In order to estimate hourly load, we match each adopter household’s property characteristics to a modeled building with similar housing characteristics (Table 1). The modeled building characteristics and calculated consumption profiles come from NREL’s End-Use Load Profiles.³⁸ We associate the modeled building’s hourly load profiles to its matched adopter household. We then sample without replacement from this matched dataset until we reach the pre-determined state-level home counts, such that our final sample (n = 500,010), is geographically representative of the original dataset. For more details on this process, see SI “Matching adopter housing characteristics to modeled building characteristics”.

Table 1

Variables matched between empirical and modeled property data with hourly end use load profiles
Variable	Variable Type
State	Categorical
Climate zone	Categorical
Heating fuel type	Categorical
Air conditioning type	Categorical
Pool indicator	Binary
Number of persons	Numeric
Number of stories	Numeric
Square feet (living space)	Numeric
Year built	Numeric

In order to estimate hourly solar production for adopter households, we use each household’s county centroid to create an hourly profile with NREL’s System Advisor Model and then scale this based on respective installation size.³⁹ We assume a South-facing array, tilt at latitude, fixed array with a 1.2 inverter loading ratio and 14% system losses. For system degradation, we assume 0.5% degradation per year from year of installation to 2021, from the system’s install date.

c. Step 3: Calculating energy bills

To calculate energy bills for each adopter households, we downloaded the electricity tariffs that residential customers were most likely to take service under in 2021 based on their zip code, with and without presence of solar, using Genability software.⁴⁰ We then generated fixed [$] and volumetric [$/kWh] hourly electricity costs for each customer as they would be with and without rooftop solar. We assumed net metering was allowed for all adopter households (i.e., solar was compensated as negative load at the prevailing volumetric rate).

For non-electric energy costs, we used 2021 costs for natural gas, heating oil, and diesel, by state. With electricity rates and fuel costs together with the hourly net load calculated in Step 2, annual energy bills were calculated for adopter households. For more information on energy costs, see SI “Electricity rates and fuel costs”.

Step 4: Solar adoption costs and incentives

We use data on each adopter’s solar system costs, upfront incentives, and ownership structure to model different financing mechanisms available for adopter homes to pay for the upfront costs of rooftop solar installation. For third-party-owned systems, we assume a lease structure in which any tax credits or ownership-based benefits go to the third-party which may, in turn, pass along these financial benefits to the customer in the form of a lower lease rate.^41,42 Since this analysis focuses on impacts of off-bill repayment of solar systems, we assume that host-owned systems are loan financed, as opposed to an upfront cash purchase. In this case, any upfront incentives, performance-based incentives, investment tax credit(s), and solar renewable energy credits— flow directly to the owner (see SI “Incentives for solar adoption”). While we do not focus on systems paid for upfront with cash, we do include a comparison between loans and upfront cash payments via net present value calculations and find similar values, which indicates that the financial model for the loan would look similar to the financial model for an upfront payment (see SI “Financing mechanisms for rooftop solar”). Since the available loan and lease data only goes back to 2013, we henceforth concentrate our analysis on the adopter homes that installed solar after 2013, representing 422k adopters.

Finally, to calculate EB, adopter household’s income estimates, energy bills and off-bill costs and revenue are combined to quantify the comprehensive financial impact of rooftop solar adoption (Eq. 1).

Equation 1: Energy burden calculation

$$EB \left[\%\right]=\frac{Energy Bill{s}_{postPV}\left[\$\right]-PV incentiv{e}_{\$/kWh}\left[\$\right]+PV Repaymen{t}_{loan, lease}\left[\$\right]}{Annual Gross Income \left[\$\right]}$$

Acknowledgments:

This work was supported by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy under Solar Energy Technologies Office Agreement Number 388444 and Contract No. DE-AC02-05CH11231.

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