Energy Market Prediction: Papers from the 2014 AAAI Fall Symposium
Modeling Solar PV Adoption:
A Social-Behavioral Agent-Based Framework
Charles M. Macal, Diane J. Graziano, and Jonathan Ozik
Decision & Information Sciences Division
Argonne National Laboratory
Lemont, IL USA
{macal, graziano, jozik}@anl.org
nomic thinking in their decision making (Kahneman 2011).
In addition to financial factors, consumer decisions are often shaped by attitudes, likes and dislikes, experiences,
past practices, reference points, social interactions, perceived risks, and competing value propositions. Simon
proposed the bounded rationality model, in which the decision-making rationality of individuals is limited by the information they have available to them, their cognitive limitations, and the time constraints for making a decision (Simon 1991). Roger (2005) highlights the importance of social interactions and behavioral factors in technology diffusion, which he contends is a “process by which an innovation is communicated through certain channels over time
among the members of a social system.” Studies suggest
that people fail to adopt existing technologies that would
save them money by using less energy, in part due to behavioral barriers (Allcott and Mullainathan 2010). Stern
(2000) contends that a useful model for environmentally
significant individual behavior has to account for motivations, attitudes, and values; contextual or situational factors; social influences; personal capabilities; and habits.
Applying these ideas, Dietz et al. (2009) use a behavioral
approach to examine the potential for near-term emission
reductions by altered adoption and use of available technologies in U.S. homes and non-business travel.
A substantial gap exists between the theories of behavioral scientists and their application in computational models, especially models of consumer adoption of new technologies. New research is revealing the behavioral and social determinants of PV adoption (Rai and McAndrews
2012, Rai and Sigrin 2013). Agent-based modeling (ABM)
offers a framework for including this information into new
models for assessing PV adoption (Robinson et al. 2013,
Rai 2014). ABM allows for the disaggregated representation of consumer decisions, learning behaviors, and interactions among market participants for a population of
agents (Macal and North 2010). There is a growing body
of literature that can inform the factors affecting solar PV
adoption behaviors for such models (Drury et al. 2012).
Abstract
Behavioral scientists contend that individuals, and organizations rarely make decisions solely on the basis of economic
factors. Decisions are also shaped by perceived risk, social
interactions, currency and salience of information, and other
value propositions. Social diffusion of information on consumer experiences, entrance of new business models better
aligned with customers’ concerns when evaluating investments, and perceived improving economic conditions are all
factors in consumers’ decisions to adopt a new technology,
such as solar photovoltaics (PV). We describe a new conceptual agent-based model, BE-Solar, that incorporates a
social and behavioral decision framework for technology
adoption decisions. We demonstrate the feasibility of including heterogeneity and behavioral factors into an agentbased model of the solar PV market, which is being applied
to the Southern California market.
Introduction
The transformation to clean energy solutions will evolve
from the cumulative effects of decisions made by a great
many individuals and organizations. A potentially promising area of research is to investigate whether these agent
decision processes and their outcomes can be realistically
represented in energy models. Energy models are used to
inform budget decisions, R&D planning, investment, and
policy analysis. Most of the models in use today model decisions based on standard economic assumptions of rational choice, which assumes that individuals optimize their
expected utility; rational choice is the foundation of consumer preference theory (Jackson 2005). For example, the
Solar Deployment Systems (SolarDS) is used for assessing
the impacts of PV adoption, based primarily on a range of
financial variables and financing options (Denholm, Drury,
and Margolis 2009; Drury, Denholm, and Margolis 2010).
Behavioral economists have shown that individuals and
organizations often do not conform to purely rational ecoCopyright © 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
24
ence has greater persuasive impact than positive personal
influence; personal influence has more impact than mass
media when the two are in conflict; personal influence is
more impactful to decision makers when they are seeking
information versus conformity; and being influenced by
similar individuals is more common than being influenced
by dissimilar individuals, but being influenced by dissimilar individuals is common among innovators (Gatignon
and Robertson 1985).
Within their networks, individuals can be influenced by
interpersonal communications and also through social
learning – observing and modeling the behaviors of others.
An individual’s threshold for adoption is related to their attitudes towards risk and uncertainty and depends on their
preference for the number of others who must adopt before
the individual will adopt. An individual’s perceived risk
and uncertainty associated with an innovation are eased
through the observation that others have successfully
adopted.
Information search includes both internal search (using
information previously obtained and recalled from
memory) and external search (seeking information from
outside sources). A primary reason consumers engage in
information search is to reduce the uncertainty associated
with their decision to an acceptable level. Theories on information search span economic, psychological, and sociological perspectives. Schmidt and Spreng (1996) assimilate
a large body of research and identify many factors influencing external information search into a model based on
four variables: perceived ability to search, perceived benefits of search, perceived costs of search, and motivation to
search. A consumer’s motivation to search is positively
correlated with the perceived benefits and negatively correlated with the perceived costs. Schmidt and Spreng also
identify antecedents to search: educational level, objective
knowledge, subjective knowledge, perceived financial sacrifice, perceived risk, situational involvement, need to justify decision, product complexity, information accessibility, time pressure, enduring involvement, and need for
cognition. In their model, subjective knowledge has a defining role in describing the consumer’s information
search.
In the BE-Solar model, a residential owner’s decision to
seek more information on solar PV is initiated by several
factors including a general increase in the awareness of solar PV options and benefits through information dissemination. We assume a consumer agent who has not adopted
reevaluates whether to seek information when conditions
change. An owner seeks information on solar PV when
the owner's propensity to seek information exceeds a
threshold. The owner population is segmented based on
decision factors used to define the agent's probability of
seeking more information on PV. The segmentations have
associated weights that define their relative influence on
In this paper, we describe a social and behavioral decision framework and a new conceptual agent-based model,
BE-Solar, that incorporates social and behavioral factors
into individuals’ decision models for technology adoption.
To assess the modeling approache’s viability, BE-Solar is
being applied to study the adoption of residential rooftop
solar photovolatics (PV) in Southern California.
Social-Behavioral Framework
Our design premise for modeling decision behaviors of residential consumers is that the decision to purchase a PV
system is the outcome of a deliberative process undertaken
by motivated consumers. We incorporate principles from
behavioral economics and psychology into the representation of residential consumer decision behaviors. We adapt
concepts from innovation diffusion theory and marketing
to model a five-step consumer decision process: (1) recognition: consumer becomes aware of PV systems or the opportunity to purchase or lease PV; (2) information search:
consumer is interested enough in PV to seek more information, which results in the consumer forming a favorable
or unfavorable opinion about adopting the technology; (3)
evaluation of alternatives: consumer applies their value
proposition to comparing PV purchase options and the null
option to not adopt; (4) purchase decision: consumer
chooses to purchase or not purchase a PV system; and (5)
post-decision behavior: consumer seeks reinforcement of
their adoption decision (or reverses it) and communicates
positive (or negative) PV perceptions within their social
network.
Recognition and Information Search
Embedded in the concepts of the decision process and the
diffusion network is the postulate that prior to adopting an
innovation, individuals must first recognize the need to
consider making an adoption decision. They must hear and
learn about the technology through their communication or
information network. Central questions of technology
adoption are when an agent decides to evaluate the technology for possible adoption and what factors initiate this
adoption evaluation. We identify several network activities
that can trigger recognition and entry into the PV purchase
decision process: urge to consider PV purchase from a trust
relation; observation of PV systems in the neighborhood;
marketing contacts by PV retailers; information shared
within the consumer’s social network; media messaging
about electricity price forecasts, incentive programs, and
PV adoption successes and failures; and utilities offering
PV incentive programs or increasing their electricity rates.
Diffusion networks (Rogers 2005) involve communication channels and flows, opinion leadership, and social
learning. Research suggests that negative personal influ-
25
their personal norms (e.g., sense of obligation to take proenvironmental action), and then influence their behaviors.
They note that the VBN theory can account for some, but
not all, variance in observed behavior. Situational context
is also an important factor.
We consider the effect of three PV perceptions: (1) relative advantage, to address quantitative and qualitative benefits, (2) perceived risk, to address technology barriers, and
(3) complexity, to address behavioral barriers. The consumer's perceptions of the relative advantage of PV is
based on consideration of PV upfront costs, electricity bill
reduction, savings from incentives or tax credits, provisions of warranty, maintenance package, customer service,
environmental action, and social acceptance. For residential consumers with no prior knowledge or memory related
to PV, their perceived PV relative advantage is measured
against the do-nothing case. For others, the relative advantage is evaluated against information in their memory.
The agent's PV perceptions can change over the course of
the simulation from the information, social networking,
and marketing messages they receive and assimilate. The
agent's perceived risks and complexity are values between
1 (favoring adoption) and 0 (disfavoring adoption), attributed to each of the elements of the consumer’s PV perceptions (risk associated with performance, reliability,
O&M costs and hassles, negative impact on environment,
and unpopular decision; and complexity associated with financing, decision making, paperwork, and PV technology).
The relative advantage of PV, perceived risk, and complexity elements are classified according to the consumer’s
decision making focus – either cost, certainty, ease, environment, or social. Given their PV perceptions of relative
advantage, perceived risk, and complexity, a residential
consumer agent evaluates alternatives in a series of decision steps:
the decision to seek information. In the simulation, the
agent's propensity (probability) to seek information at any
point in time is calculated as the weighted sum of five factors: energy attitude, PV affect, pressure to act, knowledge
level, and time available for searching for information. The
influence of these factors on the probability to seek information can be adjusted by the normalized weighting factors associated with the population segments (wiEa, wiPV,
wiPA, wiKL, and wiTA):
PropensityToSeekInfo = wiEa x EnergyAttitude +
wiPV x PVAffect + wiPA x PressureToAct + wiKL x
KnowledgeLevel + wiTA x TimeAvailable, PropensityToSeekInfo ∈ [0,1]
Owners are initially populated with values for the five attributes. An owner's PropensityToSeekInfo can vary over
time if, for example, energy attitudes change due to public
concern about energy (EnergyAttitudes increases), consumers are made more aware of solar PV ease of installation or benefits (PVAffect increases), or information campaigns boost perceived knowledge (KnowledgeLevel increases). If PropensityToSeekInfo > 0.5, an agent acts to
seek information on solar PV such as calling an installer or
initiating the solar PV decision process (evaluation) on the
part of the consumer. Other functional forms are possible
for modeling the propensity to seek information, and techniques such as logistics regression may be useful at deriving agent decisions to seek information (Haifeng et al.
2014).
Evaluating Alternatives and Adoption Decision
If no conflicting or negative information is obtained during
the information search, the consumer pursues the purchase
decision by evaluating alternatives. For this evaluation, we
invoke concepts from Rogers (2005) diffusion of innovation theory. Rogers postulates, and supports with empirical
evidence, five perceived attributes of innovations that affect their rate of adoption: (1) relative advantage (degree
of superiority over its replacement or alternative); (2) compatibility (degree of consistency with existing values, past
experiences and needs; (3) complexity (degree of difficulty
to understand and/or use); (4) trialability (ability for limited testing or experience); and (5) observability (visibility
to others). Gatignon and Robertson (1985) also include
perceived risk (expectations for economic or social loss) as
a separate innovation attribute. We adopt the value-beliefnorm (VBN) theoretical framework (Stern et al. 1999,
2000) proposed to explain environmentally significant behaviors. In Stern et al.’s causal chain, an individual’s values (e.g., altruism, egotism) shape their beliefs (e.g., ecological worldview, adverse consequences for valued objects, perceived ability to reduce the threat), which define
1.
2.
3.
Elimination heuristic 1 (Ability to pay): Is PV affordable?
Elimination heuristic 2 (Perceptions of PV risks
and complexity): Are the PV risks acceptable?
Valuation of PV options (purchasing, leasing, no
action)
The first elimination heuristic compares the PV up-front
cost with the residential consumer’s ability to pay, based
on income and other financial factors. The second elimination heuristic considers the residential consumer’s perceptions of PV risks and complexity. For the evaluation of PV
options, two frames are considered. For purchase/lease decisions these are: (1) relative advantage of purchasing PV
compared to leasing or the status quo (no purchase), or (2)
relative advantage of purchasing/leasing PV at the most recent offer compared to the best past offer. The rigor of the
evaluation depends on the consumer’s motivation. A con-
26
fect the sustainability of the innovation by their communications inside their network and continued or discontinued
use. We implement PV adoption post-decision behavior by
incorporating a function that stochastically changes PV
perceptions on the part of PV adopters. The model assigns
a positive PV perception to consumers when they adopt.
As time goes on in the simulation, the PV perceptions of
randomly selected consumers change from positive to negative. The negative signal will decrease the probability of
consumers in their network to adopt. A fraction of consumers whose perceptions change from positive to negative also discontinue use of their PV systems, affecting the
cumulative PV results. In this way, we can test the impact
of negative factors feeding back into adoption decisions of
the population.
sumer with high motivation is more likely to seek an evaluation approach (or value proposition) that favors purchase. A consumer with low motivation is more likely to
seek a reason not to purchase PV. We assume that the consumer’s adoption propensities are correlated with their motivation level for considering PV adoption.
The consumer’s probability to adopt is derived from a
weighted average of the segmentations for energy attitudes,
PV affect, adopter threshold, expected years to own building, and pressure to act, with the associated weights waEA,
waPV, waAT, waTO, and waPA. An agent's decision to
purchase PV will occur when their value proposition is met
and with a probability equal to their adoption propensity:
PropensityToAdopt = waEA x EnergyAttitude +
waPV x PVAffect + waAT x AdopterThreshold +
waTO x TimeToOwn + waPA x PressureToAct, PropensityToAdopt ∈ [0,1]
Modeling Solar PV Adoption
This section describes the development of an experimental
model currently in progress to test the viability and value
of incorporating the conceptual model described above in
an agent-based model.
Figure 1 summarizes the factors in calculating the residential consumer adoption propensity.
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We develop two synthetic populations (Wheaton et al.
2009): one of people (agents), consisting of residential decision makers, and the other of housing for Los Angeles
County, California. A baseline set of agent attributes were
drawn from publically available data sources including the
U.S. Census. This was supplemented with data from specialized sources, such as solar technology adoption data
from the California Solar Initiative (CSI 2014), and residential energy consumption data from the residential energy consumption survey (RECS 2014). Housing and parcel
data were obtained from the County of Los Angeles, California.
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Residential Home Owner Agents
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An owner agent is associated with a single housing unit.
An owner attribute (ownoccStat) signifies whether the
owner occupies or rents their housing unit. If an owner occupies their housing unit, the owner may decide whether or
not to install a solar PV unit for the house. If an owner
does not occupy their housing unit, the unit is either vacant
or rented. In either of these cases, we assume that no decision is made on whether to install PV. The owner has no
incentive to install solar if the renters pay the utility bills,
which is the most common arrangement. The renter may
have the incentive to install solar but is not able to do so
under the usual rental agreements. Only owners that occupy their housing unit are included in the BE-Solar model.
An owner is characterized by socio-demographics attributes that are relevant to their decision-making process:
Figure 1 Residential Consumer Adoption Factors
Post-Decision Behavior
The PV decision process shapes the PV technical perceptions and beliefs that an agent communicates post-decision.
An agent adjusts their PV technical perceptions and beliefs
to post-justify his decision. The agent’s PV technical perceptions are adjusted to align with the PV offer that was either accepted or rejected. If an agent adopts PV, their perceptions and beliefs may be nudged towards the positive.
If an agent chooses not to adopt, they may be nudged towards the negative. Consumers’ post-decision actions af-
27
Energy Market Prediction: Papers from the 2014 AAAI Fall Symposium
owner decides to adopt solar. Owner attributes are summarized in Table 1.
age, education, income, and race. Drury et al. (2012) identify a set of demographic variables relevant to the leasevs.-buy decision on solar PV for Southern California. We
classify each owner as an adopter type according to five
adopter categories: innovator, early adopter, early majority,
late majority, and laggard (Rogers 2005).
An owner considers several financial variables and calculations in their decision-making process in addition to a
number of socio-behavioral factors. These include the minimum payback time an owner is willing to accept for making an investment, the remaining length of time an owner
intends to stay in their house, and their discount rate. The
owner applies their discount rate to the costs and revenues
value stream through a standard net present value calculation.
An owner is influenced by social factors in their decision-making process. The social variables that owners consider in their decisions include the owner's ambient affect
for solar PV, the owner's attitude toward energy conservation or using renewable energy in general, and the owner's
immediate neighbors at the street level. The ambient affect
for solar PV (ownpvAffectwPA) is meant to capture the
owner's general awareness of and feeling toward adopting
PV based on ambient information available through media,
advertising, blog sites, etc. The ambient affect is allowed to
vary by owner and over time, reflecting the variation in
owner experiences and access to information. Ambient affect is constructed on a continuous scale of -1 to +1, with 1 indicating the most negative view, +1 indicating the most
favorable affect, and 0 indicating a neutral position. The
owner' s attitude toward energy conservation (ownenergyAttwEA) reflects the owner's attitude toward going
green or using renewable energy in general.
Attitude is constructed on a continuous scale of 0 to +1,
with +1 indicating a willingness to pay for expanding the
renewable energy pool in spite of the investment not being
economic, and 0 indicating an unwillingness to pay any
premium for solar PV. The network effect (ownstreetNet)
is meant to capture the owner's awareness of and feeling
toward adopting solar PV gained from interacting with
neighbors at the street level.
The network effect is a composite of two factors: the
proportion of street neighbors who have adopted solar PV
(a continuous variable between 0 and 1) and the experiences of the adopters, constructed on a continuous scale of -1
to +1, with -1 indicating the most unfavorable view, +1 indicating the most favorable view, and 0 indicating a neutral
position.
The owner agent also has dynamic attributes that change
over the course of the simulation that reflect the agent’s
state at any time. These include the date on which the owner decides to adopt solar (owndecideDate) and the date on
which the solar PV unit is installed (owninstallDate) if the
Table 1 Residential home owner agent characteristics
Characteristic
ownerId
ownerZip
ownoccStat
ownAge
ownIncome
ownEd
ownRace
ownEnergyAttwEA
ownstreetNet
ownpvAffectwPA
ownadopterType
ownminStayTime
ownreqdPaybackTimewMP
owndiscRate
Description
owner ID
owner zip code (same as housing unit
zip code)
whether the building is owner- or
renter-occupied
owner age (years)
owner income (years)
owner education (years)
race of owner
propensity to support green / clean
energy issues
network effect
affect for solar PV
adopter category (Rogers 2007) of
{innovator, early adopter, early majority, late majority, laggard}
minimum time owner expects to stay
in home (years)
required payback time for solar PV
investment by owner (years) (ownreqdPaybackTimewMP
<
ownminStayTime)
owner discount rate (%)
Table 2 Economic characteristics of solar PV
Decision Attribute
capitalEquity
capitalDebt
electricityGridCostSolar
loanPayment
costOM
stateTaxCredit
federalTaxCredit
28
Description
capital equity downpayment($). Include for owners but not leasers.
capital debt loan ($). Include for
owners but not leasers.
electricity grid cost solar, annual
($/yr). Consumer electricity cost,
with solar PV (assume same for
owner and leaser).
loan payment principal and interest
($/yr). Include for owners but not
leasers.
annual O&M cost ($/yr). Include for
owners but not leasers.
state tax credit, lump sum ($). Applies to owners and leasers, but at
different rates.
federal tax credit, lump sum ($). Ap-
stateTaxSavings
federalTaxSavings
electricityGridCostBase
electricityGridCostSolar
revenueGridElecSales
leasingCost
Agent Social-Behavioral Model
plies to owners and leasers, but at
different rates.
state tax savings, annual, ($/yr). Assumed to be zero for all.
federal tax savings, annual ($/yr).
Assumed to be zero for all.
electricity grid cost base, annual
($/yr). Consumer electricity cost,
without solar PV.
electricity grid cost solar, annual
($/yr). Consumer electricity cost,
with solar PV (assume same for
owner and leaser).
revenue from electricity sales to grid
($/yr). Assumed to be zero.
leasing cost ($/yr). Cost of leasing
solar PV.
The factors described below are either screening criteria
or quantitative measures. The screening criteria signify a
go or no-go decision based on hard constraints that the
consumer cannot affect. An example of a hard constraint is
a consumer who cannot pay for a solar PV installation
from either cash on hand or through a loan. Each of the
screening factors results in a single outcome: screening criteria result in a yes or no outcome; quantitative criteria result in a numeric quantity. A no for any screening criteria
means the decision is to not adopt solar PV. If all screening
criteria have a yes result, the other factors are computed
and the results are combined in a value function. The decision to adopt is based on a value threshold. If the value
function is greater than the threshold, the decision is to
adopt solar PV. If the value function is less than the
threshold, the decision is to not adopt solar PV.
Screening Criteria
Ability to Pay (for ownership only). Ability to pay is estimated as a scaled factor relative to household income
level, as in higher income implies greater ability to pay.
The present value streams faced by owners for leasing and
buy options are computed. Ability to pay is compared with
the actual system cost from the ownership present value
stream. Ability to pay is an exclusion criteria for ownership
and is used as follows:
Housing Units
A housing unit is represented as an object rather than an
agent, as it has no decision-making capabilities. A housing
unit on a parcel has several characteristics (Table 3). Building size is used to determine the appropriate size for the solar PV unit and is used in calculating electricity costs.
Table 3 Housing unit characteristics
Characteristic
huId
huZip
huType
buildingSize
financeType
rateBin
rateType
huSubscript[ElecRate, h]
huSubscript[ElecUse, h]
huSubscript[SolarGen, h]
If[AbilityToPay > upfrontPVCost for ownership,
Continue, Else: Do not purchase], AbilityToPay ∈
{yes, no}
Description
housing unit ID
zip code
residential building type of:
{detached home, attached
home, mobile home, apartment with 2-4 tenants, apartments with more than 4 tenants}
building size (sq. ft.)
finance type of {lease, buy}
electricity rate bin
electricity rate type
electricity rate daily profile
over 24 hours ($/kWh): {elecRate1,..., elecRate24}
total housing unit electricity
usage daily profile over 24
hours (kW/hr): {elecUse1,...,
elecUse24}
potential electricity generation by solar unit daily profile
over 24 hours (kW/hr): {solarGen1, ... , solarGen24}
Energy Attitude. It is hypothesized that higher income
and more education means consumers are more likely to be
favorable to environmentally-supportive (green) propostions, and consumers consider this in their purchasing decisions. We model EnergyAttitude as a category parameter.
EnergyAttitude ∈ {negative, neutral, positive}
Social Affect. Decisions are based on likes and dislikes.
The PV affect can be defined in one of two ways, by a social contact network, or by an ambient factor. For the network, pvAffect is positive if the consumer knows at least
one other consumer in its network who has adopted solar
PV and likes it and does not know any consumers who
have adopted PV that dislike it. For the ambient factor, social effects are modeled as a characteristic of the environment rather than as explicit network information. As more
people in the zip code area adopt, there is a greater likelihood that the owner knows some of them. In addition, the
adopter either like or dislikes their solar PV installation.
29
Pr(owner knows someone who adopted) = (current
adopters in zip code) / (number of owners in zip code)
x factor106
financialMetricBuy method reasons over these distributions to come up with the financial parameter outcome.
financialMetricBuy = If[expectedTimeStayInHome
>= paybackPeriod, If[paybackPeriod <= consumerReqdMinPaybackPeriod, 1, 0], 0], financialMetricBuy ∈ {0, 1}
where factor106 is a scaling factor. Buildings are observable by people considering adopting solar PV. Further,
Pr(adopter experience is positive) could be estimated by interviewing early adopters and discerning the portion that
are satisfied versus dissatisfied with their installation.
Then, the PV affect is calculated as:
where expectedTimeStayInHome is the length of time the
consumer expects to stay in their home (years) and is updated based on general economic conditions; expectedTimeStayInHome is an attribute of the home owner created
from the general distribution of consumer expectations,
paybackPeriod is the payback period for the solar PV installation given the specific residence, cost of the PV unit
installation, and the assumed savings stream for the residence based on the grid electricity savings; consumerReqdMinPaybackPeriod is the the minimum payback period required by the consumer (years), which is an attribute
of the home owner created from acquired data on consumer required payback periods.
For a leasing decision, the length of time consumers expect to stay in their homes is not a factor. The only factor is
the distribution of the preference for the minimum monthly
bill reduction, in dollars. The financialMetricLease method
reasons over the distribution to come up with the financial
parameter outcome:
pvAffect = Pr(owner knows someone who adopted) x
If[Pr(adopter experience is positive) < factor108, -1,
+1], pvAffect ∈ {-1,1}
where factor108 is an adopter satisfaction threshold.
Adopter Threshold: We consider the adopter threshold to
be a measure of risk aversion. We capture this using Roger's characterization and distribution of consumer types and
their "adoption thresholds." The adoption threshold (adopterThresholdwAT) is the percentage of other consumers
who a consumer must observe to have adopted a new technology before the consumer adopts the new technology
themselves.
Perceived Reliability. Perceived reliability is estimated
from empiricial data (e.g., 39% of the respondents said solar PV is reliable according to a recent SolarTech survey)
and is attributed as either yes or no by an individual
adopter.
Financial Metric. The financial calculations in BE-Solar
build on previous solar adoption assessment models
(Denholm, Drury, and Margolis 2009). For PV sales, this is
the minimum payback period in years. For PV leases this is
the minimum monthly bill reduction, in dollars. For both
PV sales and leases, the financial metrics are also intrinsically connected to the length of time the consumers expect
to stay in their homes, reflecting their expectations to recover value from the PV system before they move. The
relevant empirical data are (1) the length of time consumers expect to stay in their homes and (2) the preference for
minimum payback period for PV sales. The financial metric is not considered a screening criteria because it is a soft
constraint, under the control of the consumer; the consumer could change their requirement for the minimum payback period, for example
financialMetricLease =
If[expectedMonthlyBillReduction >= consumerReqdMinMonthlyBillReduction, 1, 0], financialMetricLease ∈ {0, 1}
where expectedMonthlyBillReduction is the expected reduction in the monthly bill due to the SolarPV installation,
given the specific residence and the expected cost savings
in grid electricity and other factors (dollars); expectedMonthlyBillReduction is computed from the installation
cost for the PV unit for the residence and the calculated
savings stream based on grid electricity savings. In lieu of
an empirical distribution it is assumed to be a fixed parameter that represents the average savings for a region. The
expected monthly bill reduction is compared to the required reduction in the monthly bill by the consumer from
the Solar PV installation ($/month) and the consumer places a value on the difference, which is another factor that
enters into the consumers’ decisions to adopt.
Buy vs. Lease Decisions
For a buying decision, the length of time the consumers
expect to stay in their home reflects their expectations to
recover value from the solar PV system. This is characterized by two distributions, which can be measured independently: the distribution of the length of time consumers
expect to stay in their home, and the distribution of the
preferences for minimum payback period for PV sales. The
Conclusions
In this paper, we have demonstrated a way to incorporate
behavioral principles into a model of consumer decisionmaking and applied it to solar PV adoption. The bottom-
30
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diversity of consumers and the buildings they own and the
decision-making behaviors affecting solar PV adoption.
The model helps to integrate all of the data and theories or
hypothesizes on the solar PV adoption process. Our future
goals are to calibrate the model to reproduce the market
penetration curve observed in the Southern California solar
PV market. If successful we would like to apply the sociobehavioral adoption model to other areas of the country in
which solar PV adoption is yet to occur and understand
factors that could accelerate that acceptance. We would also like to consider other technologies (such as the advancement of new storage devices) that would augment the
adoption of solar PV technologies.
Acknowledgments
This work is supported by the U.S. Department of En-ergy
under contract number DE-AC02-06CH11357. We
acknowledge Easan Drury and Mackay Miller of the National Renewable Energy Laboratory for helpful discussions on sources of data and solar technology markets.
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