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Distributed generation with solar photovoltaic (PV) technology is economically competitive if net metered in the U.S. Yet there is evidence that net metering is misrepresenting the true value of distributed solar generation so that the value of solar (VOS) is becoming the preferred method for evaluating economics of grid-tied PV. VOS calculations are challenging and there is widespread disagreement in the literature on the methods and data needed. To overcome these limitations, this study reviews past VOS studies to develop a generalized model that considers realistic future avoided costs and liabilities. The approach used here is bottom-up modeling where the final VOS for a utility system is calculated. The avoided costs considered are: plant O&M fixed and variable; fuel; generation capacity, reserve capacity, transmission capacity, distribution capacity, and environmental and health liability. The VOS represents the sum of these avoided costs. Each sub-component of the VOS has a sensitivity analysis run on the core variables and these sensitivities are applied for the total VOS. The results show that grid-tied utility customers are being grossly under-compensated in most of the U.S. as the value of solar eclipses the net metering rate as well as two-tiered rates. It can be concluded that substantial future work is needed for regulatory reform to ensure that grid-tied solar PV owners are not unjustly subsidizing U.S. electric utilities.
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Preprint: Koami Soulemane Hayibo and Joshua M.Pearce. A review of the value of solar methodology with a case study of
the U.S. VOS. Renewable and Sustainable Energy Reviews 137, 2021, 110599. https://doi.org/10.1016/j.rser.2020.110599
A Review of the Value of Solar Methodology with a Case Study of the U.S. VOS
Koami Soulemane Hayibo1, Joshua M. Pearce2,3,*
1. Department of Electrical & Computer Engineering, Michigan Technological University, Houghton,
MI, USA
2. Department of Materials Science & Engineering, Michigan Technological University, Houghton, MI,
USA
3. Visiting Professor of Photovoltaics and Nanoengineering, School of Electrical Engineering, Aalto
University, Finland
* pearce@mtu.edu
khayibo@mtu.edu
Abstract
Distributed generation with solar photovoltaic (PV) technology is economically competitive if net
metered in the U.S. Yet there is evidence that net metering is misrepresenting the true value of
distributed solar generation so that the value of solar (VOS) is becoming the preferred method for
evaluating economics of grid-tied PV. VOS calculations are challenging and there is widespread
disagreement in the literature on the methods and data needed. To overcome these limitations, this
study reviews past VOS studies to develop a generalized model that considers realistic future avoided
costs and liabilities. The approach used here is bottom-up modeling where the final VOS for a utility
system is calculated. The avoided costs considered are: plant O&M fixed and variable; fuel; generation
capacity, reserve capacity, transmission capacity, distribution capacity, and environmental and health
liability. The VOS represents the sum of these avoided costs. Each sub-component of the VOS has a
sensitivity analysis run on the core variables and these sensitivities are applied for the total VOS. The
results show that grid-tied utility customers are being grossly under-compensated in most of the U.S. as
the value of solar eclipses the net metering rate as well as two-tiered rates. It can be concluded that
substantial future work is needed for regulatory reform to ensure that grid-tied solar PV owners are not
unjustly subsidizing U.S. electric utilities.
Highlights
Distributed generation solar photovoltaic (PV) economically competitive if net metered in U.S.
Value of solar (VOS) is becoming preferred method for evaluating economics of grid-tied PV.
Here review VOS calculations, inputs and sensitivity analysis on all core variables
Results: VOS eclipses the net metering rate as well as two-tiered rates in US
Regulatory reform needed: solar PV owners are unjustly subsidizing electric utilities
Keywords: utility policy; photovoltaic; distributed generation; value of solar; net metering; economics
Nomenclature:
BBurner tip fuel price [$/MMBtu]
CDDistribution capacity [MW]
CGUtility generation capacity [p.u.]
CHHealth cost of natural gas [$/kWh]
CPV PV capacity for year ‘n’ [kW]
1
CTTransmission capacity [p.u.]
DUtility Discount rate
DEEnvironmental discount rate
DHHeat rate degradation rate
DPV Degradation rate of PV
EEnvironmental cost [$/MMBtu]
FUtility discount factor
FEEnvironmental discount factor
hNumber of hours in the analysis period
HCHeat rate of combined cycle gas turbine [Btu/kWh]
HCT Heat rate of peaker combustion turbine [Btu/kWh]
HnHeat rate for year n [Btu/kWh]
HPHeat rate of the plant [Btu/kWh]
HSSolar heat rate [Btu/kWh]
iNumber of years in analysis period
ICInstallation cost of combined cycle gas turbine [$/kW]
IDInvestment on distribution capacity per year without PV [$]
IDP Investment on distribution capacity per year with PV [$]
IPInstallation cost of peaker combustion turbine [$/kW]
KGrowth rate
MReserve capacity margin
nnth year of analysis period
OOutput of the PV [kWh]
PL1 1st year load capacity [kW]
PL10 10th year load capacity [kW]
QDistribution cost [$/kW]
SPV fleet shape [kW]
SCSolar capacity cost [$/kW]
UCUtility cost [$]
UFUtility fixed operation and maintenance cost [$/kW]
UPUtility price [$/kWh]
UTUtility transmission capacity cost [$/kW]
UVUtility variables operation and maintenance cost [$/kWh]
VOS Value of solar [$/kWh]
Vx
V1: Avoided operation and maintenance fixed cost [$]
V2: Avoided operation and maintenance variable cost [$]
V3: Avoided fuel cost [$]
V4: Avoided generation capacity cost [$]
V5: Avoided reserve Capacity cost [$]
V6: Avoided transmission capacity cost [$]
V7: Avoided distribution cost [$]
V8: Avoided environmental cost [$]
V9: Avoided health liability [$]
1. Introduction
Solar photovoltaic (PV) technologies have had a rapid industrial learning curve [1-4], which has
resulted in continuous cost reductions and improved economics [5,6]. This constant cost reduction
2
pressure has resulted in a spot price of polysilicon Chinese-manufactured PV modules of only
US$0.18/W as of April 2020 [7]. There are several technical improvements, which are both already
available and slated to drive the costs further down such as black silicon [8-10]. The International
Renewable Energy Agency (IRENA) can thus confidently predict that PV prices will fall by another
60% in the next decade [11]. However, even at current prices, any scale of PV provides a levelized cost
of electricity (LCOE) [12] lower than the net metered cost of grid electricity [13] and this will only
improve with storage costs declining [14-18]. Specifically, PV already provides a lower levelized cost
of electricity [12,19,20] than coal-fired electricity [13,21,22]. In addition, PV technology can be
inherently distributed (e.g. each electricity consumer produces some or all of their electricity on site
thus becoming ‘prosumers’). Distributed generation with PV has several technical advantages,
including improved reliability, reduced transmission losses [23,24], enhanced voltage profile, reduced
transmission and distribution losses [25], transmission and distribution infrastructures deferment, and
enhanced power quality [26]. As PV prices decline, prices of conventional fossil fuel-based electricity
production are increasing due to aging infrastructure [27-29], increased regulations (in some
jurisdictions) [30-33], fossil fuel scarcity [34-36], and pollution costs [37-41]. Thus, PV represents a
threat to conventional utility business models [42] and there is evidence that some utilities are
manipulating rates to discourage distributed generation with solar [43], while others are embracing it
such as Austin Texas or the state of Minnesota [44]. Rates structures vary widely throughout the U.S.
[45-48] and there has been significant effort to determine the actual value of solar (VOS) electricity.
This shift towards VOS is fueled by criticisms of its predecessor [49], net metering, that is
misrepresenting the true value of distributed solar generation [50-52]. VOS is more representative of
the electricity cost because under a Value of Solar Tariff (VOST) scheme, the utility purchases part of,
or the whole net solar photovoltaic electricity generation from its customers, therefore dissociating the
VOST from the electricity retail price [51,53]. Performing a complete VOS calculation, however, is
challenging. One of the main challenges is data availability and accuracy [54,55]. Three data challenges
have been identified by [55] that are: 1) the time granularity of the solar irradiation data, 2) the origin
of the data, modeled versus measured, and 3) the data measurement accuracy. Other challenges faced
by utilities while assessing the VOS are which components to include in the calculations, and what
calculations method to assess the value of each components [56]. The possible components across the
literature that are suggested to be included in a VOS as avoided costs and solar benefits are: energy
production costs (operation and maintenance) [45-47,57-63], electricity generation capacity costs
[45-47,50,57-63], transmission capacity costs [45-47,50,57-61,63], distribution capacity costs [45-
47,50,57-63], fuel costs [45-47,50,57,60-63], environmental costs [45,47,57,58,60-63], ancillary
including voltage control benefits [47,57-59,63], solar integration costs [47], market price
reduction benefits [47,60], economic development value or job creation [46,47,57,60,61], health
liability costs [57,60,64], and value of increased security [47,57]. A guidebook has been developed
by the United States’ Interstate Renewable Energy Council (IREC) for the calculation of several of the
VOS components [57]. These methods have been further developed by the U.S. National Renewable
Energy Laboratory (NREL) [58]. NREL has provided more detailed calculation methods than the
guidebook from the IREC with a different level of accuracy. The methods with a higher level of
accuracy are more complicated to implement and require a higher level of data granularity. A
qualitative study on VOS performed in 2014 suggested the inclusion of all relevant components in a
VOS studies [64]. The calculation of the VOS can be done annually, as in the case of Austin Energy
[50,53], or can be fixed for a selected period, as per the case of Minnesota state’s VOS (25 years)
[45,53]. There are recently an increasing number of studies looking into externality-based components
of VOS especially environmental costs and health liability costs [65-67]. This is because a country
with high solar PV penetration rate provides a healthy population according to a German study [68]. An
estimated average of 1,424 lives could be saved each summer in the Eastern United States, and $13.1
billion in terms of health savings if the total electricity generation capacity in the Eastern United States
3
included 17% of solar PV [69]. For the entire U.S. if coal-fired electricity were replaced with solar
generation, roughly 52,000 premature American deaths would be prevented from reduced air pollution
alone [70]. Not surprisingly, the latest report from North Carolina Clean Energy Technology Center
found out that there are policy changes on VOS across the United States with 46 states, in addition of
DC considering making significant changes in their solar policies and might be transitioning to a VOS
model in coming years [63].
This indicates VOS is the way of the future for grid integrated PV, but how exactly should solar be
valued on the modern grid? In this study the VOS literature is reviewed, and a generalized model is
developed taking realistic future avoided costs and liabilities into account from the literature. The
approach used here is a bottom-up modeling where the final value of solar to a utility system is
calculated. This model factors in the existing parameters, that have been identified in VOS studies in
different U.S. jurisdictions. The approach starts from the existing formula to calculate the levelized cost
of electricity from solar PV technology [12] and updates the formula by adding the avoided and
opportunity costs and the effect of different externalities. The costs considered in the study are: avoided
plant operation and maintenance (O&M) fixed cost; avoided O&M variable cost; avoided fuel cost;
avoided generation capacity cost, avoided reserve capacity cost, avoided transmission capacity cost,
avoided distribution capacity cost, avoided environmental cost, and the avoided health liability cost.
The value of solar represents the sum of these costs. Each sub-component of the VOS has a sensitivity
analysis run on the core variables and these sensitivities are applied for the total VOS. These
sensitivities are limited by the best available data on the variables in the literature and future work is
needed to quantify the secondary costs that would lead to an even higher VOS. The conservative results
developed here are presented and discussed in the context of aligning policy and regulations with
appropriate compensation for PV-asset owners and electric utility customers.
2. Methods/Theory
2.1. Avoided Plant O&M – Fixed Cost (V1):
The use of solar energy results in a displacement of energy production from conventional energy
sources. The avoided cost of plant operation and maintenance (V1) [$] depends on the energy saved by
using solar PV for electricity generation instead of conventional energy generation processes. Equation
(1) describes the calculation of the capacity of solar PV (CPV) [kW] throughout the lifetime of the solar
PV system. During the first year of operation, the installed solar PV system is considered to not have
suffered any degradation. Therefore, the capacity has a value of one. The degradation of the installed
solar PV system is expressed by the degradation rate of PV (DPV) and for a marginal year (n), the
marginal capacity of the installed PV system for that year would be:
C
PV
=(1D
PV
)
n
( 1 )
The fixed O&M cost is directly linked to the need for new conventional electricity generation plants. If
the construction of new conventional generators in the location of interest can be avoided, there is no
need to include the fixed O&M in the valuation of solar for this location. To calculate the value of the
fixed O&M (V1), the value of the utility cost (UC) [$] needs to be known first. The utility cost depends
on four parameters, the capacity of solar PV (CPV) mentioned above, the utility capacity (CG) [p.u.], the
utility fixed O&M cost (UF) [$/kW], and the utility discount factor (F). To calculate this utility cost,
first the ratio of the capacity of solar to the utility capacity is calculated. This ratio is then multiplied by
the utility fixed O&M cost. A discount is applied to the result by multiplying it by the utility discount
factor [71]. The discount factor (F) depends on the year and can be calculated by using the discount
rate (D). The discount factor for year (n) is [45]:
4
F=1
(1+D)
n
( 2 )
The discount rate used in the formula describes the uncertainty and the fluctuation of the value of
money in time. The value of the discount rate differs when considered from a utility point of view or a
societal point of view and can highly impact the utility cost. While considering the economics of solar
PV systems, [57] has suggested the use of a discount rate lower than the value used by the utility.
UC=UF*CPV
CG
*F
( 2 )
The avoided plant O&M fixed cost (V1) is then calculated by summing the utility cost for all the years
included in the analysis period.
V
1
=∑ U
C0
i
( 3 )
2.2. Avoided Plant O&M – Variable Cost (V2):
The utility cost for the avoided variable O&M cost (V2) [$] is calculated by multiplying the utility
variable O&M cost (UV) [$/kWh] by the energy saved by using solar PV systems or the output of the
solar PV system (O) [kWh], and the result is discounted by the discount factor (F).
U
C
=U
V
*O*F
( 4 )
The avoided variable O&M (V2) cost is the sum of the utility cost over the analysis period:
V2= U C0
i
( 5 )
2.3. Avoided Fuel Cost (V3)
Additionally, the calculation of the utility price (UP) [$/kWh] require the knowledge of the equivalent
heat rate of a marginal solar. According to [72], the heat rate [Btu/kWh] describes how much fuel-
energy, on average, a generator uses in order to produce 1kWh of electricity. It is typically used in the
energy calculation of thermal-based plants and is therefore misleading for the calculation of solar
energy production. Since the method evaluates the avoided cost from thermal-based plants, however, it
is applied to solar PV generation. The heat rate (HS) [Btu/kWh] of solar PV or displaced fuel heat rate
during the first marginal year is calculated as:
HS=
0
h
(Hp*S)
0
h
S
( 6 )
In the equation above, the heat rate (Hp) [Btu/kWh] represent the real value of the utility plant’s heat
rate during the operation hours of the solar PV systems over the analysis period and the parameter (S)
[kW] describes the PV fleet shape that is the hourly PV fleet shape production over the hours (h) in the
analysis period.
After the heat rate for the first year has been calculated, the heat rate for the succeeding years in the
analysis period can be calculated by the following equation [45]:
5
( 7 )
The primary use of heat rates is the assessment of the thermal conversion efficiency of fuel into
electricity by conventional power plants. As a result, it is natural to deduce that the rate at which the
heat rate (DH) decreases corresponds to the efficiency lost rate of the power plant [73].
The utility price (UP) depends on the heat rates and can be calculated once the heat rate is known as:
U
P
=B*H
n
10
6
( 8 )
Another parameter to account for is the burner tip price (B)[$/MMBtu]. The burner tip price describes
the cost of burning fuel to create heat in any fuel-burning equipment [74].
The avoided fuel cost (V3) [$] is calculated in a similar way as the value of the fixed O&M. First, the
utility cost is calculated by multiplying the value of the per unit PV output (O) by the utility price (UP).
The result is then discounted by the discount factor. The discount factor used in the case of the avoided
fuel cost depends on the treasury yield [45]. The avoided fuel cost is obtained by summing up the
utility cost over the analysis period.
UC=UP*O*F
( 9 )
V
3
=∑ U
C0
i
( 10 )
2.4. Avoided Generation Capacity Cost (V4):
The installation of solar systems reduces the generation of electricity from new plants. This is
represented by the avoided capacity cost. To calculate the avoided generation capacity cost, the solar
capacity cost (SC) [$/kW] needs to be known. Two variables are essential to evaluate the solar capacity
cost, the cost of peaker combustion turbine (IP) [$/kW] and the installed capital cost (IC) [$/kW]. The
cost of peaker combustion turbine (IP) is the cost associated with the operation of a turbine that function
only when the electricity demand is at its highest. The installed capital cost (IC) describes the cost of
combined cycle gas turbine updated by the cost based on the heat rate. The solar capacity can be
calculated as follows [75]:
SC=IC+(HSHC)*IPIC
HCT HC
( 11)
HCT [Btu/kWh] and HC [Btu/kWh] are respectively the heat rate of the peaker combustion turbine, and
the combined cycle gas turbine. After the calculation of the solar capacity cost (SC), the utility cost can
be obtained by first, multiplying the ratio of solar PV capacity (CPV) and utility generation capacity (CG)
by the value of solar capacity cost (SC). Then, the result is discounted by the discount factor (F) to
obtain the final value of the utility cost. And as in the previous cases the value of avoided generation
capacity is the sum of the utility cost overs the analysis period.
U
C
=S
C
*C
PV
C
G
*F
( 12 )
V4=∑ UC0
i
( 13 )
2.5. Avoided Reserve Capacity Cost (V5):
6
The calculation of the avoided reserve capacity cost (V4) [$] follows the same pattern as the avoided
cost of generation capacity. But in this case, the effective solar capacity, that is the ratio of the solar PV
capacity (CPV) and utility generation capacity (CG) is multiply by the solar capacity cost, then the result
is multiplied by the reserve capacity margin (M) to obtain the utility costs. After that, the utility cost is
discounted as previously described by the discount factor (F). Then, the avoided reserve capacity is
calculated by adding up the utility cost over the analysis period [58].
U
C
=S
C
*C
PV
C
G
*M*F
( 14 )
V5= U C0
i
( 15 )
2.6. Avoided Transmission Capacity Cost (V6):
The avoided transmission capacity cost (V6) [$] calculation is also performed similarly to the avoided
generation capacity cost. This cost describes the losses that are avoided when electricity does not have
to be transported on long distance because of installed solar systems. It is calculated by first multiply-
ing the utility transmission capacity cost (UT) [$/kW] by the solar PV capacity (CPV). The result is then
divided by the transmission capacity (CT) [p.u.] and the discount factor (F) is applied to obtain the util-
ity cost for a marginal year. The avoided transmission cost is calculated by the sum, over the years in
the analysis period, of the corresponding utility costs [76].
UC=UT*CPV
CT
*F
( 16 )
V6=∑ UC0
i
( 17 )
2.7. Avoided Distribution Capacity Cost (V7):
The two major variables that influence the avoided distribution capacity cost (V7) [$] are the peak
growth rate (K) and the system wide costs. The system wide costs account for several financial aspects
of a distribution plant, among which, overhead lines and devices, underground cables, line
transformers, leased property, streetlights, poles, towers etc. [77].
All the deferrable system wide costs throughout a year have been summed up and the result divided by
the yearly peak load increase in kW over a total period of a decade to obtain the distribution cost per
growth of demand.
The ratio of the 10th year peak load (PL10) [kW] and the 1st year peak load (PL1) [kW] are used in the
calculation of the growth rate (K) of demand. The expression of the growth rate (K) is as follows
[45,78]:
K=¿
( 18 )
The distribution capital cost (Q) [$/kW] is utility owned data and depends on the utility, and the growth
rate (K) that can be obtained by using the previous formula. An escalation factor is necessary to
evaluate the distribution cost for deferral consecutive years [79].
After obtaining the distribution cost (Q) from the utility and growth rate (K) calculated, the distribution
capacity (CD) [kW] can be calculated from the growth rate. The result is then multiplied by the
distribution cost and discounted by the discount factor (F) to get the discounted cost for a particular
year. The discounted cost for the analysis period can in turn be used to calculate the investment during
each year (ID) [$] of the analysis period [45].
7
ID=CD*Q*F
( 19 )
When there is no other generation system than solar PV that comprised the installed capacity, the
investment per year (IDP) [$] in terms of deferred distribution can be calculated from the investment
deferred [45].
IDP =CD*Q*DF
(in terms of deferred distribution) ( 20 )
After obtaining the yearly investment without PV (ID) and the yearly investment in terms of deferred
distribution (IDP), the utility cost can be obtained by dividing the difference between the yearly
investment without PV and the yearly investment with PV by the distribution capacity (CD). This utility
cost can be called the deferred cost per kW of solar. This deferred cost per kW of solar is discounted by
the discount factor (F), multiplied by the solar PV capacity, and summed up over the analysis period to
obtain the avoided distribution capacity cost.
U
C
=I
D
I
DP
C
D
*F*C
PV
( 21 )
V7=∑ UC0
i
( 22 )
2.8. Avoided Environmental Cost (V8):
The three major pollutants that are considered in the calculation of the avoided environmental cost (V8)
[$] are: greenhouse gases (GHGs), pollutants sulfur dioxide, nitrogen oxide, and hazardous particulates
[80].
The two parameters that influences the cost linked to CO2 and other greenhouse gasses’ emission are
the social cost of CO2 and the gas emission factor [81]. With these two variables, the cost of avoided
CO2 can be calculated in dollars and then the real value linked to this cost is obtained by converting the
previously calculated value in current value of dollars. This is done by multiplying the externality cost
of CO2 by the consumer price index (CPI) [82]. The obtained result is then multiplied by the general es-
calation rate for the following years [80]. The cost of CO2 for every year is obtained by multiplying the
previous value by pounds of CO2 per kWh. The same logic is applied to the other pollutants to calculate
the related costs and the cost related to all three categories of pollutant are added up to get the environ-
mental cost (E) [$/MMBtu].
By multiplying the environmental cost by the solar heat rate (HS), the utility cost (UC) is obtained. An
environmental discount factor (FE) is applied to the utility factor. The environmental discount factor
(FE) is defined as follows [83]:
F
E
=1
(1+D
E
)
n
( 23 )
Here, DE is the environmental discount rate taken from the Social Cost of Carbon report [81].
U
C
=E*H
S
*F
E
*O
( 24 )
V8=∑ UC0
i
( 25 )
2.9. Avoided health liability cost (V9):
8
The use of solar PV systems prevents part of the emissions of pollutants from getting into the air. This
can in turn result in great health benefits. The harmful pollutants that greatly impact human health are
NOx and SO2. These two chemicals react with other compounds when they are released in the air to
form a heavy and harmful product that is called particulate matter PM2.5, [84-86]. Particulate matter
PM2.5, can cause diseases such as lung cancer and cardiopulmonary diseases [87]. It is difficult to
evaluate the cost related to the avoided health liabilities and the saved lives. Several works have
investigated the calculation of the cost of human health related to electricity production through fossil
fuels [88-91]. Nevertheless, the most relevant approach is the work of [91] because the methods
accounts for changes of the cost at a regional and plant level. This has been made possible because of
data collected by EPA on the emission level of facilities through the Clean Air Markets Program. The
result obtained by [91] is conservative as it does not include environmental impacts over the long term
(e.g. climate change) [66,68,69,92]. The calculation of the cost of health liability by [91] depends on
the quantity of pollutants emitted [tons/year] during a year, the cost of a unit mass of emission for each
pollutant in [$/tons], and the annual gross load [kWh/year].
The health cost of energy produced by fossil fuel sources (CH) [$/kWh] obtained by [91] are used to
calculate the utility cost. The utility cost (UC) is the product of the health cost by the PV systems output
(O), that is discounted by the environmental discount factor (FE).
UC=CH*O*FE
( 26 )
The avoided health liability cost (V9) [$] is then calculated by:
V
9
=∑ U
C0
i
( 27 )
2.10. Value of solar (VOS)
There are three different ways to represent the value of solar. It can be expressed either as the annual
cost [$] over the analysis period or the lifetime of the installed solar photovoltaic system, or as the cost
per unit of solar PV power installed [$/kW], or finally as the cost of generated electricity by the solar
system [$/kWh] [58]. The most commonly used metric to express the VOS is the cost of electricity
generated by the solar system [$/kWh] because it is user friendly and is the same metric used by utili-
ties on electricity bills [58]. To calculate the levelized value of VOS per kilowatt-hour of electricity
produced, the sum of the value of all the avoided cost is calculated and then divided by the total amount
of energy produced (O) during the analysis period discounted by the discount factor (F).
VOS=V1+V2+V3+V4+V5+V6+V7+V8+V9
0
i
(O*F)
( 28 )
Where:
V1: Avoided O&M fixed cost
V2: Avoided O&M variable cost
V2: Avoided fuel cost
V4: Avoided generation capacity cost
V5: Avoided reserve capacity cost
V6: Avoided transmission capacity cost
V7: Avoided distribution cost
V8: Avoided environmental cost
V9: Avoided health liability cost
O: Output of the solar PV system
9
F: Utility discount factor
3. Sensitivity
The calculation of VOS requires several parameters that come from different sources. Some parameters
are location dependent, while other parameters are state dependent, and there are parameters that are
utility dependent. Many of these parameters can also change from one year to another. As a result, there
are wide differences in the calculation of VOS across the literature [56]. The utility-related parameters
that can change from one VOS calculation to another are the number of years in the analysis period (i),
the utility discount rate (D), the utility degradation rate, the utility O&M fixed, and variable costs, the
O&M cost escalation rate, the hourly heat rate (HP), the heat rate degradation rate (DH), the reserve ca-
pacity margin (M), the transmission capacity cost (UT), the peak load of year 1 (PL1) and year 10
(PL10), the distribution cost (Q), the distribution cost escalation factor (GD), and the distribution capac-
ity (CD). Parameters such as the cost of peaker combustion turbine (IP), the cost of combine cycle gas
turbine (IC), the heat rate of peaker combustion turbine (HCT), and the heat rate of combine cycle gas
turbine (HC) can be either obtained from the utility or from the U.S. Energy Information Agency. The
solar PV fleet (S) can also be obtained from the utility or by simulation using the open source Solar
Advisory Model (SAM) (https://github.com/NREL/SAM ) [45]. Other variables that can affect the
VOS but are not controlled by the utility are the PV degradation rate (DPV), the environmental discount
factor (FE), the environmental cost of conventional energy, the health cost of conventional energy, and
the cost of natural gas on the energy market. Table 1 summarizes high and low estimates of the values
for the variables that are required to perform a VOS calculation and the VOS component they are used
to calculate.
Table 1. Assumptions used for required variables for a VOS calculation
Variable High
estimate
Source Low
estimate
Source VOS components
Degradation rate of PV
(DPV) [%]
1 [93] 0.5 [57,93,
94]
All components
Distribution capacity (CD)
[kW]
429000 [95] 237000 [95] Avoided distribution cost
(V7)
Distribution cost (Q) [$/
kW]
1104 [95] 678 [95] Avoided distribution cost
(V7)
Environment discount rate
(DE) [%]
2.5 [81] 5 [81] Avoided environmental cost
(V8)
Environmental Cost (E)
[$/metric tons of CO2]
[62-89] [81] [12-23] [81] Avoided environmental cost
(V8)
Health cost of natural gas
(CH)[$/kWh]
0.025 [91] 0.025 [91] Avoided health liability cost
(V9)
Heat rate degradation rate
(DH) [%]
0.2 [96] 0.05 [96] Avoided fuel cost (V3)
Avoided environmental
cost (V8)
Heat rate of combined cy-
cle gas (HC) [Btu/kWh]
7627 [97] Avoided generation capac-
ity cost (V4)
Avoided reserve capacity
cost (V5)
Heat rate of peaker com-
bustion turbine (HCT)
[Btu/kWh]
11138 [97] Avoided generation capac-
ity cost (V4)
Avoided reserve capacity
cost (V5)
10
Installation capital cost of
combined cycle gas tur-
bine (IC) [$/kW]
896 [98] Avoided generation capac-
ity cost (V4)
Avoided reserve capacity
cost (V5)
Installation cost of peaker
combustion turbine (IP) [$/
kW]
1496 [98] Avoided generation capac-
ity cost (V4)
Avoided reserve capacity
cost (V5)
Load Growth Rate (K)
[%]
1.17 [99] -0.94 [99] Avoided distribution capac-
ity cost (V7)
Number of years in analy-
sis period
30 [57] 25 PV in-
dustry
war-
ranties
All components
Reserve capacity margin
(M) [%]
36 [100] 13 [100] Avoided reserve capacity
(V5)
Solar Heat Rate (HS) [Btu/
kWh]
8000 [53] Avoided fuel cost (V3)
Avoided generation capac-
ity cost (V4)
Avoided reserve capacity
cost (V5)
Avoided environmental
cost (V8)
Transmission capacity
cost (UT) [$/kW]
130.535 [101] 17.895 [101] Avoided transmission capac-
ity (V6)
Utility Discount rate (D)
[%]
9 [57] 2.18 [57] Avoided plants O&M fixed
cost (V1)
Avoided plants O&M vari-
able (V2)
Avoided generation capac-
ity cost (V4)
Avoided reserve capacity
cost (V5)
Avoided transmission ca-
pacity cost (V6)
Avoided distribution capac-
ity cost (V7)
Utility fixed O&M cost
(UF) [$/kW]
18.86 [95] 7.44 [95] Avoided O&M fixed cost
(V1)
Utility variable O&M cost
(UV) [$/kWh]
0.01153 [95] 0.00216 [95] Avoided O&M variable cost
(V2)
3.1. Number of years in analysis period
The number of years in the analysis period varies and can be as low as 20 years, and as high as 30 years
or more [12,57]. The typical warranty provided by solar panels manufacturer is 25 years. As a result, it
is reasonable to set the lowest value of the analysis period to 25 years. Also, solar modules have proved
to continue to reliably deliver energy 30 years after the installation of the system [57], therefore, 30
years has been set as the higher value of the analysis period in this study. Keyes et al. have pointed out
11
that utility planning is often over shorter time periods (e.g. 10-20 years) [57]. However, economic deci-
sions should be made over the entire life of the physical project not an arbitrary cutoff date [102] and
there are existing methods to estimate the load growth on the utility side as it is usually done for con-
ventional energy generators [53].
3.2. PV system degradation rate
The degradation rate of PV panels overtime depends on the location of operation as well as climate
conditions (temperature, wind speed, dust, etc.). A statistical study conducted by the National Renew-
able Energy Laboratory [93] has found the value of the PV system degradation rate to be comprised be-
tween 0.5% and 1%. These two values are the boundaries that will be used as low and high values for
the sensitivity analysis on the PV system degradation rate.
3.3. Utility discount rate
The discount rate is used to assess the change in money value overtime. This value can change
depending not only on the location, but also, on the utility. A discount rate value as high as 9% can be
used or a value as low as the inflation rate might be used. The discount rate used by utilities are usually
in the high values, but the social discount rate is closer to the inflation rate [57]. As a result, 9% will be
considered as the high-end value of the discount rate while the current inflation rate of 2.18% will be
considered for the lowest value. It is important to note that the value of the inflation rate changes with
time and if this value is chosen as the discount rate it should be updated regularly for new calculations
of the VOS. Also, the value of the inflation rate can be subjected to ongoing events. The value of the
inflation rate of 2.18% was chosen at a date before the coronavirus outbreak in the United States that is
ongoing. The outbreak has brought the inflation rate to as low as 0.25%. This value will not be used to
run a sensitivity analysis because of the special conditions in which it occurred.
3.4. Environmental cost
The environmental cost associated with electricity production through conventional energy sources
depends on the cost associated with the pollution from carbon dioxide (CO2), carbon monoxide (CO),
nitrogen oxide (NOx), and hazardous particulates (PM). The environmental cost of carbon dioxide
dominates the cost of the other components. Different estimates of the CO2 cost are given by the EPA
[81]. The cost of CO, NOx, and PM depends on state laws. The lowest value and highest value used for
the cost of CO, NOx, and PM were chosen from the state of Minnesota [103]. It has been hypothesized
that if conventional energy sources are being used to produce electricity in the future, the effects on
environment are going to worsen (e.g. lower quality fuel, higher embodied energies, etc.), therefore the
environmental cost will be expected to increase. This will be investigated by raising the environmental
cost while analyzing the sensitivity of VOS to the environmental cost. This will show the trend of the
impact of the environmental cost on the VOS and in the future, the values will need to be updated
because the environmental cost is likely to exceed the maximum used value in this study.
3.5. Health liability cost
The health liability cost is a new calculated VOS component introduced by this study. This component
has been mentioned by several studies but was not incorporated in the calculation due to lack of data
for the evaluation [57,66,67,104]. The health and mortality impacts of coal in particular are so severe
an ethical case can be made for the industries elimination [105]. For example, Burney estimated that
26,610 American lives were saved between 2005 and 2016 by a conversion of coal-fired units to
natural gas in the U.S. [106]. More lives as well as non-lethal health impacts would be avoided with a
greater transition from coal to solar [70]. The values used here were obtained from the study of [91]
that found the value of health impact cost of natural gas to be $0.025/kWh. As previously hypothesized,
the use of fossil fuel energy sources in the future will increase the emissions, and the cost of health care
has been escalating faster than inflation [106] thus increasing the cost of derived health liability.
12
Several increase rates will be investigated. Although it should be pointed out the approach taken here
was extremely conservative as the potential for climate/greenhouse gas emission liability [107,108]
was left for future work as discussed below.
3.6. Other parameters
The other parameters are utility related and in case of absence of utility data, generic values from the
U.S. government agencies is used as indicated in Table 1 and run through realistic percent increases or
decreases to determine their effect on the VOS components.
3.7. Sensitivity Analysis
A sensitivity analysis has been run on each of the nine VOS components as well as on the VOS. For
each component, the sensitivity has been analyzed for some of its parameters wherever data was
available. The evaluation of the variability of the VOS components has been performed for each
parameter. The sensitivity of a component to one of its parameters is determined by maintaining an
average value of the other parameters and varying the studied parameter from its lowest value to its
highest value. The different values that are obtained for the VOS component are then plotted to show
its variation according to the parameter studied. A correlation study between the different parameters
has not been conducted because there was no evident relationship between these parameters. Most of
the parameters are set by the utilities and is often not disclosed openly. An interaction study between
the parameters and how their interaction affects the VOS components would be interesting for future
studies where utility data are available.
A similar process has been used for the sensitivity analysis of the main VOS. The main VOS’s
variability has been studied according to the nine VOS components. For each component for which the
sensitivity of the VOS is analyzed, average values of the other components are maintained while the
studied component’s value is varied from its lowest value to its highest value.
4. Results and Discussion
The simulation results are plotted first for each VOS components. For each component, sensitivities on
the different input variables have been investigated. Then the sensitivity of the overall VOS to each of
the VOS components has been analyzed.
4.1. Avoided O&M fixed cost (V1)
Figure 1 shows the results for the avoided O&M fixed cost (V1). The sensitivity has been plotted for
five parameters: the utility O&M fixed cost, the utility O&M cost escalation, the PV degradation rate,
the utility discount rate, and the utility degradation rate. According to the results, the avoided O&M
cost is highly sensitive to the utility O&M fixed cost and O&M cost escalation. When the utility O&M
fixed cost increases, the avoided O&M cost increases accordingly and an increase in the O&M
escalation rate obviously increases the avoided O&M cost because it increases the utility fixed O&M
cost over the analysis period. V1 is also sensitive to the utility discount rate and decreases when the
discount rate increases. This means that using a discount rate close to the social discount rate while
conducting a VOS study will increase the avoided O&M cost while using a higher discount rate will
lower the cost. this is in accordance with the recommendation of [57] that is the use of a discount rate
lower than that of the utility in a distributed solar generation economic calculation. Also, the avoided
O&M fixed cost is not very sensitive to the utility degradation rate or the PV degradation rate.
Nevertheless, its value is slightly reduced when the PV degradation rate increases.
13
Figure 1. Sensitivity of avoided O&M fixed cost (V1) in terms of LCOE (¢/kWh) to its parameters
in percent change.
4.2. Avoided O&M variable cost (V2)
The parameters for which the avoided O&M variable cost’s (V2) sensitivity has been studied are: the
utility O&M variable cost, the utility O&M cost escalation, the PV degradation rate, and the utility
discount rate. The sensitivity of the avoided O&M to its parameters are plotted in Figure 2. Figure 2
shows a similar variation trend of V2 as compared to the case of the avoided fixed O&M cost. It is
highly sensitive to the utility variable O&M cost, and the O&M cost escalation. The avoided variable
O&M cost increases when the variable O&M, or the O&M cost escalation rate is increased but
decreases with the increase of the discount rate, and the PV degradation rate.
14
Figure 2. Sensitivity of avoided O&M variable cost (V2) in terms of LCOE (¢/kWh) to its
parameters in percent change.
4.3. Avoided fuel cost (V3)
In the case of the avoided fuel cost (V3), the variable considered for the sensitivity analysis are the heat
rate degradation rate, the natural gas price fluctuation rate and the PV degradation rate. While the
avoided fuel cost has shown to be not very dependent on the heat rate degradation rate or the PV
degradation rate, this value changes very quickly with a change in the natural gas price as in Figure 3.
This is an important factor that should be carefully considered while conducting a VOS study because
the price of natural gas is not fixed and varies according to several parameters that are not controlled by
the utility such as, the economy, the weather, market supply and demand [109,110]. The equivalent
heat rate degradation rate expresses the degradation of the utility plant’s efficiency over the analysis
period and when the efficiency decreases, there is a slight decrease in the avoided fuel cost. Another
value for which the avoided fuel’s sensitivity could have been studied is the equivalent heat rate for
solar, which was not analyzed in detail here because of the lack of utility data. This is left for future
work.
15
Figure 3. Sensitivity of avoided fuel cost (V3) in terms of LCOE (¢/kWh) to its parameters in
percent change.
4.4. Avoided generation capacity cost (V4)
The sensitivity of the avoided generation capacity cost (V4) has been plotted in Figure 4 for the
discount rate, the utility degradation, and the PV degradation rate. The V4 VOS component does not
have a high variability to the PV degradation rate even though it shows a decreasing trend with the
increase of PV degradation. But it reacts sharply to the utility degradation rate. This is because the
generation capacity of the utility is highly impacted by the utility degradation. Also, as previously
observed, when the discount rate grows far from the social discount rate, the avoided generation
capacity cost decreases.
16
Figure 4. Sensitivity of avoided generation capacity cost (V4) in terms of LCOE (¢/kWh) to its
parameters in percent change.
4.5. Avoided reserve capacity cost (V5)
The avoided reserve capacity cost (V5) expresses the reserve component of the generation capacity;
therefore, it can have a value of zero when there is no reserve capacity planned by the utility as shown
in Figure 5. V5 is highly sensitive to the reserve margin and the result shows that the more generation
capacity is reserved, the more the avoided generation capacity cost increases. On the other hand, the
avoided reserve capacity cost is not very sensitive to the discount rate compared to its sensitivity to the
other parameters. V5’s value goes up when the utility degradation rate increases and goes down when
the PV degradation rate increases.
17
Figure 5. Sensitivity of avoided reserve capacity cost (V5) in terms of LCOE (¢/kWh) to its
parameters in percent change.
4.6. Avoided transmission capacity cost (V6)
Three parameters have been analyzed in the sensitivity study of V6: the discount rate, the transmission
capacity cost, and the PV degradation rate. The parameter it is the most sensitive to is the transmission
capacity cost. Obviously, when the transmission is low cost in a location, the avoided cost associated
will be low. The results shown in Figure 6 make it clear that the avoided transmission capacity cost
does not change with the PV degradation rate or the discount rate. This is because the utility
transmission capacity has been assumed to be constant over the analysis period, and the transmission
capacity degradation rate has not been considered because utility data on this parameter was not
available.
18
Figure 6. Sensitivity of avoided transmission capacity cost (V6) in terms of LCOE (¢/kWh) to its
parameters in percent change.
4.7. Avoided distribution capacity cost (V7)
The avoided distribution capacity cost (V7) is one of the most complicated VOS components to
evaluate. As shown in Figure 7, its sensitivity has been studied for six variables: the load growth rate,
the distribution capacity, the distribution capacity cost, the utility discount rate, the distribution cost
escalation, and the PV degradation rate. But it depends on more than six parameters. The growth rate,
for example is calculated from utility data, mainly, the load for the past ten years of operation [45,111].
Here, the sensitivity has been analyzed on the growth rate directly to be as widely applicable as
possible. Another parameter is the number of deferred years that is also a utility owned data.
The avoided distribution capacity cost naturally increases with the distribution capital cost. Figure 7
shows that the avoided distribution capacity cost does not fluctuate with the distribution capacity at all,
but it is highly sensitive to the discount rate, the distribution cost, and the distribution cost escalation
rate. It can even shift to a negative value when the discount rate is too low. This shows that choosing
the discount during a VOS study must be a trade-off between the social discount rate and the utility
discount rate. It is interesting to note that the avoided distribution capacity cost goes down when the
distribution cost escalation in increasing. A possible explanation for this observation is that when a
utility has enough distribution capacity, it will purchase less power from solar PV systems owners,
therefore the price goes down. The same reasoning can be used to explain the decreases of the cost
when the load growth goes up. Finally, V7 shows a slight decrease with the increase of the PV
degradation rate.
19
Figure 7. Sensitivity of avoided distribution capacity cost (V7) in terms of LCOE (¢/kWh) to its
parameters in percent change.
4.8. Avoided environmental cost (V8)
The second most complicated component of the VOS calculation is the avoided environmental cost
(V8). The sensitivity has been analyzed for the three environmental discount rate scenarios provided by
the EPA [81]. For each scenario, a sensitivity analysis has been conducted on the environmental cost
increase rate. V8 will increase when the chosen environmental discount rate is low but overall, each of
the three EPA scenarios show an increase when the environmental cost increase rate goes up as seen in
Figure 8. This is useful to see how the avoided environmental costs might change in the future.
Environmental externalities are volatile and changing quickly [66]. If it is assumed that in the future,
the environmental impact of conventional energy production technologies will increase, then the costs
of the environmental externalities will increase as well [104]. On the other hand, an increase in
distributed renewable energy generation could lead to a decrease or stabilization of the avoided
environmental cost.
20
Figure 8. Sensitivity of avoided environmental cost (V8) in terms of LCOE (¢/kWh) to its
parameters in percent change.
4.9. Avoided health liability cost (V9)
The avoided health liability cost, V9, depends on three values, the health cost increase rate, the
environmental discount rate, and the PV degradation. This cost does not fluctuate with the PV
degradation rate but is very sensitive to the other two parameters. The environmental discount rate used
here is the same as the environmental discount rate used in the evaluation of the avoided environmental
cost’s sensitivity study. As a result, the avoided health liability cost decreases when the environmental
discount rate goes up as is the case for the avoided environmental cost.
21
Figure 9. Sensitivity of avoided health liability cost (V9) in terms of LCOE (¢/kWh) to its
parameters in percent change.
4.10. VOS
After the sensitivity analysis of each VOS component, the main VOS value has been studied to find out
how the impact of different components compare to one another and which components have more
variability. Figure 10 shows that the VOS is, in decreasing order, sensitive to the avoided
environmental cost (V8), avoided health liability cost (V9), avoided transmission capacity cost (V6),
avoided fuel cost (V3), avoided distribution capacity cost (V7), avoided O&M variable cost (V2),
avoided reserve capacity cost (V5), avoided O&M fixed cost (V1), and avoided generation capacity cost
(V4)
22
Figure 10. Sensitivity of VOS LCOE (¢/kWh) to all the components in this study, in percent
change.
The contribution of each VOS component to the overall VOS depends on the case. The lowest VOS
value calculated with the assumptions used in this study in term of LCOE is 9.37¢/kWh while the
highest value calculated is 50.65¢/kWh. This variation observed in the VOS value comes from the fact
that the parameters values considered from this study are chosen to have the lowest and the highest
value of a VOS. The values of calculated VOS using utility data are highly likely to be located within
this interval. It is also clear based on the values shown in Figure 10, that the VOS exceeds the net
metering rates (when they are even available as shown in Table 2) in the U.S. Thus, it can be concluded
that even when grid-tied solar owners are provided with a full net metered rate for electricity fed back
onto the grid they are effectively subsidizing the electric utility/other customers.
For the low VOS value case shown in Figure 11, the avoided distribution cost (V7), and the avoided
reserve capacity cost (V5) has no contribution in the VOS value. The avoided generation capacity cost
(V4) and the avoided health liability cost (V9) represent most of the VOS value followed by the avoided
environmental cost (V8) and avoided fuel cost (V3).
23
Figure 11. Contribution of each VOS component to the overall VOS LCOE – Low Cost Scenario.
The contribution of the avoided environmental (V8) cost increases with the VOS value as it becomes
the largest contributor to the overall value followed by the health liability (V9) cost as shown in Figure
12 representing a middle VOS value. The avoided generation capacity cost’s (V4) is reduced as well as
the contribution of the avoided fuel cost (V3).
24
Figure 12. Contribution of each VOS component to the overall VOS LCOE Middle Cost
Scenario.
Figure 13 represents the contribution of each of the VOS components to the overall value in the case of
the highest obtained value in the scope of this study. The avoided environmental cost (V8), avoided
health liability cost (V9), and avoided transmission capacity cost (V6) represent 69% of the total cost.
25
Figure 13. Contribution of each VOS component to the overall VOS LCOE – High Cost Scenario.
The evolution of the cost percentage contribution of each VOS throughout Figure 11, Figure 12, and
Figure 13 shows the level of uncertainty of the VOS in respect to the corresponding component.
The lowest and highest LCOE VOS values obtained from the assumptions made in this study are
respectively 9.37¢/kWh and 50.65¢/kWh. The existing VOS studies results fall into this interval. The
sample calculation made by [45] for Minnesota is 13.5¢/kWh while [46] calculated a VOS of
10.7¢/kWh for Austin Energy. These values are in the lower spectrum of the result of this study
because of the considerations made. They incorporate less VOS components than the present study, and
this study focuses on sensitivity, therefore higher values of parameters have been considered. Other
results summarized by [47] have found the VOS to be 33.7¢/kWh in Maine, between 25.6 and
31.8¢/kWh in New Jersey and Pennsylvania [48], and 19.4¢/kWh in Washington DC. In general, the
VOS is much higher than the net metering costs as even the highest costs observed at the residential
level pay [50,62,112]. The residential net metering rates are also the highest as compared to
commercial and industrial rates so the latter two are even more unjustly compensated for installing
solar. Overall, this indicates that utilities are under-compensating customers with grid-connected PV
systems if they are only paying net metering rates, as displayed in Table 2. Table 2 shows a comparison
between VOS rates and net metering rates in the U.S. states mentioned above, wherever data is
available. As only a tiny fraction of utilities (3%) are paying full net metering rates anyway [43], there
is a need for regulators to ensure that solar customers are being adequately compensated for the value
of solar electricity they are sharing with the grid [42]. Substantial future work is needed to ensure that
solar PV owners are not subsidizing non-solar electricity customers.
26
Table 2. Comparison of VOS rates and net metering rates for some U.S. States
State VOS Net Metering
Minnesota 13.5¢/kWh
Austin (Texas) 10.7¢/kWh Approximately 4 – 5¢/kWh
(1.2 – 1.6$/kWh) [113]
Maine 33.7¢/kWh 12.16 – 14.66¢/kWh [114]
New Jersey 25.6 – 28¢/kWh
Pennsylvania 28.2 – 31.8¢/kWh Minimum value of (4¢/kWh) [115]
Washington
D.C.
19.4¢/kWh
5. Future Work
This study has covered a vast number of existing VOS components, but some components were not
included in this study due to the lack of a reliable evaluation methodology. These components include
the economic development cost, the avoided fuel hedge cost, and the avoided voltage regulation cost.
These represent opportunities for future work once the evaluation methodologies have been developed.
Also, there are some parameters sensitivities that would provide insights with multiple utility data sets.
These parameters include the analysis period, the hourly solar heat rate and solar PV fleet, and the 10-
years load profile. Future studies can focus on incorporating the sensitivities of these parameters into
the model or can use the foundation of this model to build on new VOS studies according to a specific
location and available data from utilities. Another limitation to this study is that it does not include the
effect of the load match factor, and loss saving factor.
As the results show the environmental and health costs can dwarf the technical costs and thereby
determine the VOS. There are also second order effects that can be used to obtain a more accurate VOS
values. For example, the negative impact of pollution from conventional fossil fuel electricity
generation on crop yields [106] as well as PV production could also be considered in future work to
give a more accurate V8. In addition, as greater percentages of PV are applied to the grid the avoided
costs will change and there is a need for a dynamic VOS akin to dynamic carbon life-cycle analyses
needed for real energy economics [116]. This complexity will be further enhanced by the introduction
of PV and storage systems [117] as it will depend on size [118] and power flow management and
scheduling [119,120].
Perhaps the most urgent need for future work is accurate estimations of the value of avoided GHG
liability costs because the magnitude of the potential liability [107,108] could overwhelm other
subcomponents of the VOS. This is because as the realities of climate change have become more
established, a method gaining traction to account for the negative externalities is climate litigation
[107,108,121-131]. For utility VOS analysis this is particularly complex as it is difficult to know where
to draw the box around environmental costs. As some studies have concluded there is liability for past
emissions as well as for harm done in other nations [122]. Liability for disastrous events is also
challenging to predict [126]. Combining both other nations and disaster creates liability potential that
could become enormous with prioritization given to victims that are losing their land, culture, and lives
due to climate change [127]. Tort-based lawsuits are already possible from a legal point of view [126],
but there are other legal methods that could be used to reduce climate change such as public nuisance
laws [128]. Some authors have argued a ‘polluters pay principle’ for carbon emissions [129]. Other
studies have concluded that emitters such as conventional fossil fuel power plant operators should be
forced to buy long term insurance in order to cover their share of climate change costs for minimizing
risks in case of insolvencies [130]. Determining what such insurance premiums should be is another
area of substantial future work. Determining what the greenhouse gas liability costs are for
conventional electricity generators (as well as potential avoided insurance costs) that can be avoided
27
with PV is extremely challenging. These estimates will become easier with time as climate change
impact studies become more granular thereby assigning specific costs to specific amounts of emissions.
In addition, realizing these climate liability costs in courtrooms will become more likely. As Krane
points out it is clear that as the negative impacts of climate change grow more pronounced, the fossil-
fuel based electricity industry faces a future that will be less accepting of current practices and that will
increase economic (and maybe even industry existential) risks [131]. Avoiding these risks has real
value, which should be included in the VOS in the future.
6. Conclusions
This study demonstrated a detailed method for valuing the incorporation of solar PV-generated
electricity into the grid and analyzed the sensitivity of each VOS component to its input parameters,
and the overall sensitivity of the VOS to the each of its components. Several components have been
found to be sensitive to the utility discount rate, namely the avoided O&M fixed cost; avoided O&M
variable cost; avoided generation capacity cost, and the avoided distribution capacity cost. Except for
the avoided distribution capacity, the other components’ value decreases with the increase of the utility
discount rate. The distribution capacity is more sensitive to the discount rate than the other
components. It increases with the discount rate and can be negative if the discount rate is very low. This
has shown the necessity of carefully choosing the discount rate for VOS studies. Most of the VOS
values do not have a high variability to the solar PV degradation rate even though its increase slightly
reduces the value of each component, and the overall VOS. The environmental cost and the health
liability cost are sensitive to the cost increase rate that can be tied to the emissions impact of the
conventional energy sources. These two costs are likely to increase in the future with the worsening of
the emission of fossil fuel sources and more information about its effects, which increases potential
emissions liability for utilities. Finally, specific case studies could provide additional sensitivities on
the few areas of the VOS that were not evaluated in this paper to create better VOS models. Overall the
results of this study indicate that grid-tied utility customers are being grossly undercompensated in
most of the U.S. as the value of solar eclipses the net metering rate. The implications of this sensitivity
analysis demand a reevaluation of the compensation for U.S. PV prosumers as the VOS is much higher
than net metering or any lesser compensation schemes. Substantial future work is needed for regulatory
reform to ensure that solar owners are not unjustly subsidizing U.S. electric utilities. In addition, future
work can obtain an even more accurate (and higher) value of VOS by evaluating economic
development costs, the avoided fuel hedge costs, the avoided voltage regulation costs, secondary health
and environmental effects such as increased crop yields from PV-reduced pollution, and accurate
estimations of the value of avoided GHG liability costs or avoided GHG emissions liability insurance.
7. Acknowledgments
This research was supported by the Richard Witte Endowment.
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... Under some of these programs, the electricity produced by residential systems (via PVgenerated electricity) can be sold at a price favorable to power-generating households or set by conventional grids by net metering [22,23]. Germany [24] Gross metering Self owned and • Feed-in-Tariff third party owned Japan [24] Net metering and Self owned and • Capital Subsidy Gross metering third party owned Colorado, Net metering Self owned or • Capital Subsidy (Rebates) USA [25] third party owned • Sales-Tax exemption available to owners • Income Tax Credits • Production Tax Credits California, Net metering Self owned or • Property Tax exemption USA [25] third party owned • California Solar Initiative-fully/partially subsidized PV system for low income households • Performance based incentives to builders New Jersey, Net metering Self owned or third • Sales Tax exemption-Purchaser fills out USA [25] party owned a form instead to paying the tax • Property Tax incentives India [26] Net metering Self owned and • Feed-in-Tariff third party owned China [27] Net metering Self owned and • Feed-in-Tariff third party owned Pakistan [28] Net metering Self owned and • Feed-in-Tariff third party owned For example, the Egyptian Electric Utility & Consumer Protection Regulatory Agency (EgyptERA) had published a net metering scheme for residential buildings in November 2013 and revised it in May 2020 with the contribution of the North Cairo Electricity Distribution Company. In this scheme, the service provider will install a net meter, and the concerned consumer will pay the fees and the net load remaining. ...
... Under some of these programs, the electricity produced by residential systems (via PVgenerated electricity) can be sold at a price favorable to power-generating households or set by conventional grids by net metering [22,23]. Germany [24] Gross metering Self owned and • Feed-in-Tariff third party owned Japan [24] Net metering and Self owned and • Capital Subsidy Gross metering third party owned Colorado, Net metering Self owned or • Capital Subsidy (Rebates) USA [25] third party owned • Sales-Tax exemption available to owners • Income Tax Credits • Production Tax Credits California, Net metering Self owned or • Property Tax exemption USA [25] third party owned • California Solar Initiative-fully/partially subsidized PV system for low income households • Performance based incentives to builders New Jersey, Net metering Self owned or third • Sales Tax exemption-Purchaser fills out USA [25] party owned a form instead to paying the tax • Property Tax incentives India [26] Net metering Self owned and • Feed-in-Tariff third party owned China [27] Net metering Self owned and • Feed-in-Tariff third party owned Pakistan [28] Net metering Self owned and • Feed-in-Tariff third party owned For example, the Egyptian Electric Utility & Consumer Protection Regulatory Agency (EgyptERA) had published a net metering scheme for residential buildings in November 2013 and revised it in May 2020 with the contribution of the North Cairo Electricity Distribution Company. In this scheme, the service provider will install a net meter, and the concerned consumer will pay the fees and the net load remaining. ...
... Under some of these programs, the electricity produced by residential systems (via PVgenerated electricity) can be sold at a price favorable to power-generating households or set by conventional grids by net metering [22,23]. Germany [24] Gross metering Self owned and • Feed-in-Tariff third party owned Japan [24] Net metering and Self owned and • Capital Subsidy Gross metering third party owned Colorado, Net metering Self owned or • Capital Subsidy (Rebates) USA [25] third party owned • Sales-Tax exemption available to owners • Income Tax Credits • Production Tax Credits California, Net metering Self owned or • Property Tax exemption USA [25] third party owned • California Solar Initiative-fully/partially subsidized PV system for low income households • Performance based incentives to builders New Jersey, Net metering Self owned or third • Sales Tax exemption-Purchaser fills out USA [25] party owned a form instead to paying the tax • Property Tax incentives India [26] Net metering Self owned and • Feed-in-Tariff third party owned China [27] Net metering Self owned and • Feed-in-Tariff third party owned Pakistan [28] Net metering Self owned and • Feed-in-Tariff third party owned For example, the Egyptian Electric Utility & Consumer Protection Regulatory Agency (EgyptERA) had published a net metering scheme for residential buildings in November 2013 and revised it in May 2020 with the contribution of the North Cairo Electricity Distribution Company. In this scheme, the service provider will install a net meter, and the concerned consumer will pay the fees and the net load remaining. ...
Article
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In this paper, economic feasibility of installing small-scale solar photovoltaic (PV) system is studied at the residential and commercial buildings from an end-user perspective. Based on given scenarios, the best sizing methodology of solar PV system installation has been proposed focusing primarily on the minimum payback period under given (rooftop) area for solar PV installation by the customer. The strategy is demonstrated with the help of a case study using real-time monthly load profile data of residential as well as commercial load/customers and current market price for solar PVs and inverters. In addition, sensitivity analysis has also been carried out to examine the effectiveness of net metering scheme for fairly high participation from end users. Since Saudi Arabia’s Electricity and Co-generation Regulatory Authority (ECRA) has recently approved and published the net metering scheme for small-scale solar PV systems allowing end users to generate and export energy surplus to the utility grid, the proposed scheme has become vital and its practical significance is justified with figures and graphs obtained through computer simulations.
... A complete levelized cost of electricity LCOE (USD/kWh) calculation usually accounts for the cost of all components of the energy generating systems, in this case the solar modules C PV (USD), the inverters C inv (USD), the balance of system (including cabling and racking cost), labor costs C labor (USD), installers overhead costs C ¿ (USD), land costs C land (USD), as well as sales taxes C tax (USD). The calculation of the levelized cost of electricity also factors in the energy generated throughout the lifetime of the system [60], [65]- [67]. The complete levelized cost of electricity is calculated as follows: ...
... This is because the cost of the inverter and the cost of the cables are parameters that depends on the system design, while the rest of the parameters remain the same for the two systems (Equation ( 0 )). Also, the discount factor is a parameter set by the utilities [60], therefore it is not included in the calculation of the LCOE. ...
Article
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Despite the benefits and the economic advantages of agrivoltaics, capital costs limit deployment velocity. One recent potential solution to this challenge is to radically reduce the cost of racking materials by using existing farm fencing as vertical photovoltaic (PV) racking. This type of fenced-based PV system is inherently electrically challenging because of the relatively long distances between individual modules that are not present in more densely packed conventional solar PV farms. This study provides practical insights for inverter selection and wire sizing optimization for fence-based agrivoltaic systems. Numerical simulation sensitivities on the levelized cost of electricity (LCOE) were performed for 1) distance from the fence to the AC electrical panel, 2) inverter costs, and 3) geographic locations. The results showed that microinverters had better performance when the cross-over fence length was under 30 m or when the system was designed with less than seven solar PV modules, whereas string inverters were a better selection for longer fences. The cross-over number of modules depends significantly on the cost of the inverters, which is a parameter that influences the system's design. The capital costs for a fence retrofit are far less than for any form of conventional PV racking. In addition, the LCOE of the vertical fencing solar agrivoltaic system can be competitive with conventional ground-mounted solar PV for the niche of farmers. Especially, when they are located between the latitudes of 10° and 50° in either the northern or southern hemisphere, and coupled with their ancillary benefits they represent a great alternative for conventional PV systems.
... For grid-tied PV systems that have a lower LCOE than the retail rate of grid electricity (e.g., surpassing grid parity), there is massive interest among consumers because they can save money with lower-cost solar electricity [11]. This is particularly true if real net metering is maintained (where prosumers are credited an equivalent economic amount for the electricity they use and send to the grid even if it is not the full value of solar electricity) [12]. Despite clear lifetime economic benefits, the capital expenditures (CAPEX) of PV systems can be challenging for many non-wealthy consumers, both in developing [13] and developed countries [14]. ...
Article
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Fixed-tilt mechanical racking, consisting of proprietary aluminum extrusions, can dominate the capital costs of small-scale solar photovoltaic (PV) systems. Recent design research has shown that wood-racking can decrease the capital costs of small systems by more than 75% in North America. To determine if wood racking provides enough savings to enable labor to be exchanged profitably for higher solar electric output, this article develops a novel variable tilt angle open-source wood-based do-it-yourself (DIY) PV rack that can be built and adjusted at exceptionally low costs. A detailed levelized cost of electricity (LCOE) production analysis is performed after the optimal monthly tilt angles are determined for a range of latitudes. The results show the racking systems with an optimal variable seasonal tilt angle have the best lifetime energy production, with 5.2% more energy generated compared to the fixed-tilt system (or 4.8% more energy, if limited to a maximum tilt angle of 60°). Both fixed and variable wooden racking systems show similar LCOE, which is only 29% of the LCOE of commercial metal racking. The results of this study indicate that the novel variable tilt rack, whether used as a small-scale DIY project or scaled up to fulfill larger energy demands, provides both the lowest cost option even when modest labor costs are included and also may provide specific advantages for applications such as agrivoltaics.
... Photovoltaic (PV) technology is one of the most promising sources of green and sustainable energy with low levelized cost of energy (LCOE) [2]. As utility-scale PV installations have slowed down in 2020, distributed PV continues to increase, driven by steady interest in self-consumption, the benefits of net metering and technical advantages in improving reliability and reducing network losses [3], [4]. Most distributed PVs are installed behind-the-meter (BTM), with only net load visible to utility operators due to single meter deployment restrictions [5] and power information privacy issues of customers [6]. ...
Article
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Photovoltaic (PV) generation is increasing in distribution systems following policies and incentives to promote zero-carbon emission societies. Most residential PV systems are installed behind-the-meter (BTM). Due to single meter deployment that measures the net load only, this PV generation is invisible to distribution system operators causing a negative impact on the distribution system planning and local supply and demand balance. This paper proposes a novel data-driven BTM PV generation disaggregation method using only net load and weather data, without relying on other PV proxies and PV panels’ physical models. Long Short-Term Memory (LSTM) is employed to build a generation difference fitted model (GDFM) and a consumption difference fitted model (CDFM) derived from weather data. Both difference fitted models are refined by a cross-iteration with mutual output. Finally, considering the photoelectric conversion properties, the disaggregated generation results are acquired by the refined GDFM of changing input. The proposed method has been tested with actual smart meter data of Austin, Texas and proves to increase the disaggregated accuracy as compared to current state-of-the-art methods. The proposed method is also applicable to disaggregate BTM PV systems of different manufacturing processes and types.
... NEM 2.0 includes several variations, most significant being the differentiated retail (π + ) and compensation (π − < π + ) rates. The compensation rate π − , also referred to as the marginal value of DER (VDER 6 ), quantifies the benefits gained or costs avoided due to the BTM DER [7], [11], [12]. Note that, under NEM X, the compensation rate π − only applies to the portion of BTM generation exceeding the customer's consumption, which implies that self-consumed DER production is virtually compensated at the retail rate π + . ...
Preprint
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The rapid growth of the behind-the-meter (BTM) distributed generation has led to initiatives to reform the net energy metering (NEM) policies to address pressing concerns of rising electricity bills, fairness of cost allocation, and the long-term growth of distributed energy resources. This article presents an analytical framework for the optimal prosumer consumption decision using an inclusive NEM X tariff model that covers existing and proposed NEM tariff designs. The structure of the optimal consumption policy lends itself to near closed-form optimal solutions suitable for practical energy management systems that are responsive to stochastic BTM generation and dynamic pricing. The short and long-run performance of NEM and feed-in tariffs (FiT) are considered under a sequential rate-setting decision process. Also presented are numerical results that characterize social welfare distributions, cross-subsidies, and long-run solar adoption performance for selected NEM policy designs.
... Determining the exact value of the electricity potentially generated from such agrivoltaic systems is far beyond the scope of this study, as it would entail not only geographic considerations for the technical aspects (e.g., horizon shading and latitude changes throughout Ontario), but also economic ones that would vary with, for example, the year, the penetration rate of solar, the value of offsetting GHG emissions, etc. Overall, the value of solar (VOS) is a complex topic [85,86] that needs to be calculated for each specific case and is left for future work. In addition, the values shown here do not include the second order effects (e.g., agrivoltaic systems operate cooler than conventional solar farms, which provides about a 1% increase in PV output annually). ...
Article
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Well-intentioned regulations to protect Canada’s most productive farmland restrict large-scale solar photovoltaic (PV) development. The recent innovation of agrivoltaics, which is the co-development of land for both PV and agriculture, makes these regulations obsolete. Burgeoning agrivoltaics research has shown agricultural benefits, including increased yield for a wide range of crops, plant protection from excess solar energy and hail, and improved water conservation, while maintaining agricultural employment and local food supplies. In addition, the renewable electricity generation decreases greenhouse gas emissions while increasing farm revenue. As Canada, and Ontario in particular, is at a strategic disadvantage in agriculture without agrivoltaics, this study investigates the policy changes necessary to capitalize on the benefits of using agrivoltaics in Ontario. Land-use policies in Ontario are reviewed. Then, three case studies (peppers, sweet corn, and winter wheat) are analysed for agrivoltaic potential in Ontario. These results are analysed in conjunction with potential policies that would continue to protect the green-belt of the Golden Horseshoe, while enabling agrivoltaics in Ontario. Four agrivoltaic policy areas are discussed: increased research and development, enhanced education/public awareness, mechanisms to support Canada’s farmers converting to agrivoltaics, and using agrivoltaics as a potential source of trade surplus with the U.S.
... as compared to pontoon-based racking or land-based PV racking [48]. Consequently, future work is needed to experimentally scale up foam-based FPV to investigate the real economic costs and viability of the system to find out the return-on-investment period of a foam-based FPV plant, as well as to compare the value of solar (VOS) [111] of the system to that of conventional PV systems. ...
Article
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This study presents a life cycle analysis (LCA) of a 10 MW foam-based floatovoltaics (FPV) plant installed on Lake Mead, Nevada, U.S.
... Determining the exact value of the electricity potentially generated from such agrivoltaic systems is far beyond the scope of this study as it would entail not only geographic considerations for the technical aspects (e.g. horizon shading and latitude changes throughout Ontario), but also economic ones that would vary with for example the year, the penetration rate of solar, the value of offsetting GHG emissions, etc. Overall the value of solar (VOS) is a complex topic [86], which needs to be calculated for each specific case and is left for future work. In addition, the values shown here also do not include the second order effects (e.g. ...
Preprint
Well-intentioned regulations to protect Canada’s most productive farmland restrict large-scale so-lar photovoltaic (PV) development. The recent innovation of agrivoltaics, which is the co-development of land for both PV and agriculture, makes these regulations obsolete. Burgeoning agrivoltaics research has shown agricultural benefits including increased yield for a wide range of crops, plant protection from excess solar energy and hail, improved water conservation while maintaining agricultural employment and local food supplies. In addition, the renewable electricity generation decreases greenhouse gas emissions while increasing farm revenue. As Canada in general, and Ontario in particular, is at a strategic disadvantage in agricultural without agrivoltaics, this study investigates the policy changes necessary to capitalize on the benefits of using agrivoltaics in Ontario. Land use policies in Ontario are reviewed. Then, three case studies (peppers, sweet corn and winter wheat) are analyzed for agrivoltaic potential in Ontario. These results are analyzed in conjunction with potential policies that would continue to protect the green-belt of the Golden Horseshoe, while enabling agrivoltaics in Ontario. Four agrivoltaic policy areas are discussed: increased research and development, enhanced education/public awareness, mechanisms to support Canada’s farmers converting to agrivoltaics and using agrivoltaics as a potential source of trade surplus with the U.S.
... al found ASHP to be economically competitive to natural gas. Pearce & Sommerfeldt [21] found ASHP with PV in Michigan to compete with natural gas when the systems were net metered (although it should be pointed out the economics would improve if the value of solar (VOS) is used [58] and would decline if lower than market rate is paid for distributed generation). To set the economic scope of this study, the literature search is limited to North America, however similar solar heat pump stud- Fig. 7. TLCC sensitivity to discount rate for the Mid-Efficiency house. ...
Article
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Given the need for decarbonization of the heating sector and the acute need of a replacement for propane in the U.S. Upper Midwest, this study quantifies the techno-economic characteristics of sustainable heating electrification in isolated rural, residential buildings in cold climates without natural gas supply. Archetypal buildings are modeled under four levels of electrification. At each electrification level, a parametric solar photovoltaic (PV) sizing analysis is performed and the total life cycle cost, renewable fraction and greenhouse gas (GHG) emissions are calculated based on the primary energy supply for each building type. Cost optimal solutions are stress-tested with multi-dimensional sensitivity analyses. The results show that the total life cycle cost favors heating electrification in all cases and combining PV with heat pumps can reduce residential building GHG emissions by up to 50% immediately. This effect will grow over time, with over 90% reduction of building emissions if renewable energy targets are met. In using primary energy and emissions along with the multi-dimensional sensitivities, this study unique demonstrates the complex techno-economic interactions of PV and heat pumps. It is concluded that electrification is an economically viable decarbonization method for cold climates both now and in the future.
Article
The rapid growth of the behind-the-meter (BTM) distributed generation has led to initiatives to reform the net energy metering (NEM) policies to address pressing concerns of rising electricity bills, fairness of cost allocation, and the long-term growth of distributed energy resources. This article presents an analytical framework for the optimal prosumer consumption decision using an inclusive NEM X tariff model that covers existing and proposed NEM tariff designs. The structure of the optimal consumption policy lends itself to near closed-form optimal solutions suitable for practical energy management systems that are responsive to stochastic BTM generation and dynamic pricing. The short and long-run performance of NEM and feed-in tariffs (FiT) are considered under a sequential rate-setting decision process. Also presented are numerical results that characterize social welfare distributions, cross-subsidies, and long-run solar adoption performance for selected NEM and FiT policy designs.
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The cost of utility-scale photovoltaics (PV) has declined rapidly over the past decade. Yet increased renewable electricity generation, decreased natural gas prices, and deployment of emissions-control technology across the United States have led to concurrent changes in electricity prices and power system emissions rates, each of which influence the value of PV electricity. An ongoing assessment of the economic competitiveness of PV is therefore necessary as PV cost and value continue to evolve. Here, we use historical nodal electricity prices, capacity market prices, marginal power system emissions rates of CO2 and air pollutants, and weather data to model the energy, capacity, health, and climate value of PV electricity at over 10 000 locations across six U.S. Independent System Operators (ISOs) from 2010 to 2017. On the energy and capacity markets, transmission congestion in some locations and years results in PV revenues that are more than double the median across the relevant ISO. While the marginal public health benefits from avoided SO2, NOx, and PM2.5 emissions have declined over time in most ISOs, monetizing the health benefits of PV generation in 2017 would increase median PV energy revenues by 70% in MISO and NYISO and 100% in PJM. Given 2017 PV costs, electricity prices, and grid conditions, PV breaks even at 30% of modeled locations on the basis of energy, capacity, and health benefits, at 75% of modeled locations with the addition of a 50 $/ton CO2 price, and at 100% of modeled locations with a 100 $/ton CO2 price. These results suggest that PV cost decline has outpaced value decline over the past decade, such that in 2017 the net benefits of utility-scale PV outweigh the cost at the majority of modeled locations.
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The recent shift in the United States from coal to natural gas as a primary feedstock for the production of electric power has reduced the intensity of sectoral carbon dioxide emissions, but—due to gaps in monitoring—its downstream pollution-related effects have been less well understood. Here, I analyse old units that have been taken offline and new units that have come online to empirically link technology switches to observed aerosol and ozone changes and subsequent impacts on human health, crop yields and regional climate. Between 2005 and 2016 in the continental United States, decommissioning of a coal-fired unit was associated with reduced nearby pollution concentrations and subsequent reductions in mortality and increases in crop yield. In total during this period, the shutdown of coal-fired units saved an estimated 26,610 (5%–95% confidence intervals (CI), 2,725–49,680) lives and 570 million (249–878 million) bushels of corn, soybeans and wheat in their immediate vicinities; these estimates increase when pollution transport-related spillovers are included. Changes in primary and secondary aerosol burdens also altered regional atmospheric reflectivity, raising the average top of atmosphere instantaneous radiative forcing by 0.50 W m−2. Although there are considerable benefits of decommissioning older coal-fired units, the newer natural gas and coal-fired units that have supplanted them are not entirely benign. The recent shift in the United States from coal to natural gas for electric power has reduced the carbon dioxide emissions intensity of electric power production, but the other pollution-related impacts of this shift are not yet known. This study finds that, between 2005 and 2016, decommissioning coal-fired plants in the continental US saved an estimated 26,610 lives and 570 million bushels of corn, soybeans and wheat in their vicinities and also changed regional climate.
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As the rapid development of photovoltaic (PV) technology in recent years with the growth of electricity demand, integration of photovoltaic distributed generation (PVDG) to the distribution system is emerging to fulfil the demand. There are benefits and drawbacks to the distribution system due to the penetration of PVDG. This paper discussed and investigated the impacts of PVDG location and size on distribution power systems. The medium voltage distribution network is connected to the grid with the load being supplied by PVDG. Load flow and short circuit calculation are analyzed by using DigSILENT Power Factory Software. Comparisons have been made between the typical distribution system and the distribution system with the penetration of PVDG. Impacts in which PVDG location and size integrates with distribution system are investigated with the results given from the load flow and short circuit analysis. The results indicate positive impacts on the system interconnected with PVDG such as improving voltage profile, reducing power losses, releasing transmission and distribution grid capacity. It also shows that optimal locations and sizes of DGs are needed to minimize the system’s power losses. On the other hand, it shows that PVDG interconnection to the system can cause reverse power flow at improper DG size and location and increases short circuit level.
Technical Report
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This paper summarizes actions now being taken in many states to change rate designs for distributed energy resources (DER) on the customer side of the meter. Net energy metering (NEM) has been the most common rate design used for customers with small-scale generators that provide what is sometimes known as self-service power. Recently, there has been considerable interest in finding alternatives to net metering by legislatures and public utility commissions (PUCs), with some related deliberations underway or recently concluded in at least 48 states and the District of Columbia. Alternative proposals to supplant net metering include rate designs with various combinations of: (a) compensating for energy delivered to the grid at a price other than the retail service rate; (b) increasing fixed charges and sometimes also minimum bills; (c) time-varying rates; and (d) adding demand-charges to bills for customers who did not previously have them. Several states have considered creating a separate rate class for customers with distributed generation (DG), whereas others have made provisions for utility ownership of DG under specific circumstances. Another important factor included in this review is the treatment of customers who entered into NEM arrangements under previous rate designs. There is often some provision for grandfathering, allowing customers to continue operating under a previous rate design for some period after the new rate becomes effective. In some jurisdictions, the proposed or adopted changes affect all residential and small commercial customers, whereas in others the changes apply only to NEM customers. In some jurisdictions, NEM or successor rate designs also apply to customers participating in variations of aggregated, neighborhood, or virtual net metering, which often also includes participants in community-solar projects. The rates for community solar participants are often somewhat different from customers with on-site DG, but they are sometimes considered part of the same overall tariff. This NRRI report includes reviews of changes to NEM or DG program rate designs. The changes resulted from either new legislation that directs state commissions to make changes, or changes that state public utility regulatory commission have already adopted, or both. The review encompasses legislative changes that have occurred since 2014 and regulatory changes that took effect by mid-2018 or earlier.
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Because of its environmental damage and now often being the most expensive source for electricity production, coal use is declining throughout the United States. Michigan has no active coal mining and seemingly supportive legislation for distributed generation (DG) and renewable energy (RE) technologies. However, Michigan still derives approximately half of its power production from large centralized coal plants, despite the availability of much lower cost RE DG technologies. To understand this conundrum, this study reviews how Michigan investor owned utilities utilize their political power to perpetuate utility structures that work toward the financial interests of the utilities rather than the best interests of the state’s electricity consumers, including other firms and residents. Background is provided covering the concept of DG, the cost savings associated with DG, and utility regulatory regimes at the national, regional, state, and local levels. Recent case studies from specific utility strategies are provided in order to illustrate how Michigan utilities manipulate regulatory regimes via policy misinterpretation to deter or hinder the proliferation of DG in favor of maintaining the existing interests in centralized, fossil fuel-based electrical energy production. The results of this study demonstrate how DG proliferation is hindered by Michigan regulated utilities via the exercise of political power within existing legal and regulatory regimes. This highlights the need to think about how utilities may interpret and implement rules when designing energy legislation and policy to maximize the benefits for consumers and society. Policy recommendations and alternate strategies are provided to help enhance the role of energy policy to improve rather than limit the utilization of RE DG.
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In the singular search for profits, some corporations inadvertently kill humans. If this routinely occurs throughout an industry, it may no longer serve a net positive social purpose for society and should be eliminated. This article provides a path to an objective quantifiable metric for determining when an entire industry warrants the corporate death penalty. First, a theoretical foundation is developed with minimum assumptions necessary to provide evidence for corporate public purposes. This is formed into an objective quantifiable metric with publicly-available data and applied to two case studies in the U.S.: the tobacco and coal mining industries. The results show the American tobacco industry kills 4 times more people per year than it employs, and the American coal-mining industry kills more than one American every year for every coal miner employed. The results clearly warrant industry-wide corporate death penalties for both industries in America. Future work is discussed to ensure industries only exist to benefit humanity in all the societies in which they operate.
Chapter
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Decentral self-consumption of renewable electricity has gained relevance in power markets around the world, driven by decreasing technology costs and favourable regulatory conditions. In this chapter, we adopt an economic perspective on the potential role of “prosumage” of renewable electricity for the low-carbon energy transition. We extend the concept “prosumption” (production and consumption) to “prosumage” (production, consumption, and storage): decentral energy storage by batteries enables prosumers to detach the moments of electricity generation and consumption. First, we give an overview of recent literature on the subject, including a brief digression on the role of network charging schemes. Second, we examine arguments in favour of and against increasing prosumage in the context of the low-carbon energy transformation. For comparability, we discuss likely benefits and drawbacks of prosumage against the reference of a centrally optimised electricity system assuming the same renewable generation capacities, and not against a system based on fossil fuels. Third, we present a quantitative, model-based analysis to illustrate possible effects of increased prosumage on the electricity system. To this end, we apply the open-source electricity system model DIETER to a future German electricity system of the year 2035.
Article
The interest in off-grid solar PV system has grown significantly in recent years. The price of suitable batteries started to decline following the price trajectory of solar PV, the curtailment of solar feed-in-tariff in many countries and the increasing electricity price. These are some important factors for grid defection trends. In this paper, we analyse the reliability and levelised cost of electricity (LCOE) of residential off-grid customers’ PV-battery systems in order to carry out a techno-economic feasibility study of grid-defection. In more detail, this study demonstrates how off-grid customers can trade-off reliability for LCOE of their PV-battery systems, taking into consideration the priority/user-preference of different load types in a household. Consequently, we propose a novel decision making tool to improve the performance of a dynamic home energy management system (HEMS). The HEMS is set up as a mixed-integer linear optimization (MILP) problem which is solved using a rolling-horizon approach for weekly and yearly simulations. Weekly simulations were first performed in order to investigate the optimal range of PV and battery size combinations based on reliability and LCOE, using a week with the lowest solar PV output and the highest demand. Given the optimal PV-battery size combinations, yearly simulations were performed to assess the reliability and LCOE of PV-battery systems of randomly selected residential households in the Ausgrid Solar Home Electricity Data. Simulation results over the critical time period of the year indicate that an off-grid PV-battery system is relatively feasible for customers who are willing to compromize lower reliability in exchange for a lower cost. The result however indicates that the cost is still higher compared with a grid-connected system.