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Net-Metering Benefits for Residential Customers: The Economic Advantages of a Proposed User-Centric Model in Italy

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Contrary to many expectations, the development of smart (mini-)grids is slow, notwithstanding drastic improvements in innovative technologies, and the reasons for this are not strictly technical. The problem lies in regulatory barriers. Moreover, the current business models accommodate utilities more so than customers. Net metering is a key enabling factor for smart (mini-)grids. This article addresses the economic benefits of net metering for residential customers. Energy demands for individual apartments and common areas are calculated using the daily energy-consumption behavior of occupants for typical days of each month of the year. Photovoltaic (PV) generation is estimated for a residential building in Italy. The proposed net-metering scheme is applied on the aggregate energy demand of a selected building. The current energy billing without modifications is compared to the case in which net-metering tariffs are applied to the billing. Results show that noticeable savings can be obtained in the net-metering case.
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IEEE Industry Applications Magazine july/AuGuST 2018 1077-2618 /18©20 18IEE E2
CONTR ARY TO MANY EXPECTATIONS, THE DEVELOPMENT
of smart (mini-)grids is slow, notwithstanding drastic improve-
ments in innovative technologies, and the reasons for this are
not strictly technical. The problem lies in regulatory barriers.
Moreover, the current business models accommodate utilities
more so than customers. Net metering is a key enabling factor
for smart (mini-)grids. This article addresses the economic ben-
efits of net metering for residential customers. Energy demands
for individual apartments and common areas are calculated
using the daily energy-consumption behavior of occupants
By Intisar A. Sajjad, Matteo Manganelli,
Luigi Martirano, Roberto Napoli,
Gianfranco Chicco, and Giuseppe Parise
Net-Metering Benefits for
Residential Customers
THE ECONOMIC ADVANTAGES OF A PROPOSED USER-CENTRIC MODEL IN ITALY
xxxxxx
Digital O bject Identif ier 10.1109/MIAS.2017.2740459
Date of publ ication: 12 April 2018
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july/AuGuST 2018 IEEE Industry Applications Magazine 3
for typical days of each month of the year. Photovoltaic
(PV) generation is estimated for a residential building in
Italy. The proposed net-metering scheme is applied on
the aggregate energy demand of a selected building. The
current energy billing without modifications is compared
to the case in which net-metering tariffs are applied to
the billing. Results show that noticeable savings can be
obtained in the net-metering case.
Role of Prosumers
in the Evolving Electrical System
During the last few decades, the power system has been
restructured to achieve different goals. Smart grids are
the practical application of this restructuring process.
Despite bright promises, the smart grid concept has not
developed with the speed that was anticipated, due not
to technical reasons but to weaknesses in the business
and underlying social model. Current business models
are mainly based on the needs of the utility companies,
which can limit the opportunities for consumers despite
the possibilities offered by smart components. Reduced
public investments and the uncertainty about the actual
benefits for users hint toward a different power system
architecture by moving from a top-down model (just
energy from the distributors and just money from the cus-
tomers) to an alternate user-centric model.
In the user-centric model, many consumers will
become prosumers, with generation and control capabili-
ties and the bidirectional f lows of both energy and money.
This transformation requires distributed private invest-
ments. It is also crucial to overcome some fears of the
distributors and create better equilibrium between distrib-
utors and users [1]. The current debate includes different
aspects, up to the extreme scenario in which prosumers
tend to become independent of the grid, using local gen-
eration and storage to supply their electricity needs. This
extreme scenario, called grid defection [2], also depends
on the availability of technologies able to reach the con-
dition of grid parity [3], [4] or a sufficiently high market
value [5]. In any case, massive grid defection is still unlike-
ly to occur in the next years. In addition to incurring
investment and maintenance costs for new equipment, the
prosumer would have to guarantee an appropriate level of
quality of service in the local system, considering internal
faults, and a supply continuity at least comparable with
the one currently provided by the grid connection.
In envisioning a new power system architecture, we
can learn from nature. It is accepted that the more resil-
ient natural complex structures having a greater ability to
survive are the ones with ordinate replication of simple
structures. The same is true for social structures, as an
aggregate of simpler structures (such as families, condo-
miniums, and neighborhoods) and complex technological
networks [6].
With this vision, the power system structure can be
traced back to early aggregations of elementary electrical
subsystems, with each including generation, distribution,
and use inside well-defined small geographical boundar-
ies. Repeated aggregation gives rise to successive micro-
and minigrids, resulting in complex power systems as a
combination of smaller systems, each one in some way
autonomous and responsible. The web of cells [7] concept
has emerged as a way to introduce independent, demand-
side management control schemes, acting on locally
flexible devices, to provide network services such as
dynamic frequency support to the grid. The development
of minigrids will also depend on the evolution and usage
of on-site storage systems [8] and electric vehicles in appli-
cations combined with local generation and strategies for
demand management [9].
The conventional transformation of the energy sector
with a top-down approach has not kept up with expecta-
tions. The European Energy Union has set out an updated
user-centric vision in [10], stressing the need to reduce the
obstacles to consumers and open the sector to aggrega-
tion and collective schemes.
To encourage industrial innovation and reinforce the
sense of community social cohesion, it becomes neces-
sary to get users more involved in energy control, either
directly (if adequately sized) or by interposing small-/
medium-sized companies between users and the distribu-
tors or main producers. This advocates successive levels
of aggregations in the form of mini- and microgrids. Flexi-
bility from aggregate residential users has been addressed
in [11], defining new indicators for the flexibility levels
of aggregate demand to reflect the collective behavior of
groups of consumers and for different aggregation sizes
and time steps using available data. In [12], the aggrega-
tion of residential customers is considered to promote
participation in demand-response programs to obtain
financial benefits from the exploitation of smart appli-
ances and renewable energy sources (RESs). The potential
benefits of residential load aggregations to reduce green-
house gas emissions are discussed in [13] by considering
the combined optimization of costs and emissions.
One of the key aspects affecting customer involvement
in the control of electrical systems is the metering mode
[14]. Unidirectional metering enables separate metering of
the electricity injected into the grid with respect to con-
sumption. With this mode, the consumer does not ben-
efit directly from the control of the local resources aimed
at serving the internal demand from self-production.
Conversely, the net-metering mode (where bidirectional
metering is used on the net power drawn from/injected
into the grid) fully enables customer decision making
on how to deploy locally available resources for electric-
ity production and storage to serve the local load [15].
As shown in [16], the effectiveness of net metering may
depend on the averaging time interval used to gather data
from loads and local generation. Strategies for storage
deployment under a net-metering scheme are illustrated
in [8].
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IEEE Industry Applications Magazine july/AuGuST 20184
Net metering is already implemented in countries such
as Belgium, Canada, Greece, and Japan [17], with different
characteristics, as discussed in the section “Net-Metering
Applications and Standards.” The current definitions of
net metering are one of the important regulatory barriers
that prevent incentivizing end users. The point of com-
mon coupling (PCC) is generally considered at the supply
point, thus limiting the possibility of creating additional
benefits in internal sections of the local system.
Legislation and billing mechanisms affect the econom-
ic benefits of net metering and, thus, the attractiveness of
net-metering policies. This article addresses the economic
benefits of net metering for residential customers and
investigates the opportunity of varying net metering to
fully exploit its economic benefit. Starting from the aggre-
gation level seen at the intake of a microgrid (e.g., a build-
ing with local generation), the article explores how net
metering can be considered for groups of consumers (e.g.,
apartments and offices in a building or groups of houses).
Net metering should be applied at different aggregation
levels and PCCs to incentivize consumers and promote
local generation. Implementing this concept requires the
presence of new aggregators and building energy manag-
ers, whose tasks are to manage the internal billing mecha-
nism, even up to that of individual units.
A residential building in Italy, with 70 apartments
and different occupancy levels, is used as a case study.
The energy demands for the individual apartments and
common areas are calculated, using the occupants’ daily
energy consumption behavior for typical days of each
month of the year and during the summer and winter sea-
sons. PV generation is estimated using the PV Geographic
Information System (PVGIS), which is an online tool pro-
vided by the Joint Research Centre of the European Com-
mission [18] for a residential building. Energy-cost savings
are calculated for individual customers under different
scenarios.
Net-Metering Applications and Standards
Net-Metering Applications
Different regulatory policies, fiscal incentives, and public
financing mechanisms are in place to support renew-
able energy generation and integration in the grid. Net
metering is one of these regulatory policies to incentivize
customers [19], in some cases, with the help of adequate
measures to secure the financial stability of the distri-
bution system operators (DSOs) [20]. Viable options for
making net metering consistent with DSO needs are
under investigation [21]. Net-metering policies are being
used across the globe, such as in the United States,
Europe, Australia, Canada, Japan, South Korea, Brazil,
Pakistan, and India [17]. The savings through net meter-
ing depend on the retail electricity rates along with the
characterization of the local generation and customers
themselves [22]. In the United States, at the end of 2014,
there were approximately 700,000 net-metered custom-
ers, the majority of which were residential [23]; in Febru-
ary 2016, the number of net-metered customers reached 1
million [24].
In Europe, the development of net metering is much
slower. There are a few net-metering programs, with
different impacts on the markets, as well as nonmarket-
based net-metering programs. From the distribution net-
work operator (DNO) point of view [25], nonmarket-based
net-metering schemes lead to the prosumers not paying
the supplier for the actual amount of electricity delivered,
creating higher fees for the other customers and reducing
the profit margins of the DNOs. In Denmark, from 1998
to 2012, an annual net-metering scheme was available
for residential sites with installed PV plants up to 6kWp,
which was very convenient for prosumers and replaced
in 2013 with 1-h net metering. Additional regulations
introduced yearly net metering in Belgium and The Neth-
erlands, daily net metering in Turkey, and net metering
(in some cases, together with other rules, such as feed-in
tariffs and net billing) in Portugal and Italy. Net metering
has been regarded as a valuable program to replace feed-
in tariffs to promote distributed RESs, especially small-
scale PVs (e.g., residential PVs [26]), because of technical
simplicity [27], the possibility of decreasing PV prices,
and the ability to provide a cost-optimum tool to create
a self-sustainable market [28]. However, the presence of
net-metered PVs may cause revenue reductions for the
utilities and subsequent rate increases for the utility-billed
customers [19].
The benefits and attractiveness of net-metering poli-
cies are strongly affected by electricity pricing and leg-
islative constraints that limit the amount of exchanged
energy. The Italian context is summarized in the “Italian
Standards and Regulations for Net Metering” section.
Because of legislative limitations, in the residential case,
net metering applies either to common services only (in
the case of a building) or to a single user (in the case of a
household). Examples of specific households with rooftop
PVs showed the convenience of net metering with respect
to the feed-in tariffs by taking into account household
electricity rates in Cyprus [29] and Spain [30]. In [31],
the authors indicated the opportunity to vary legisla-
tive limitations to better exploit net-metering benefits for
the aggregate demand of an apartment building. Unlike
previous studies, the distinction between the electric-
ity rates used for households and those for the common
services of the buildings is taken into account, creating
cases to which net metering is applied. The present work
is the detailed version of [31] and investigates the oppor-
tunity to apply net metering to an aggregation of users
to take advantage of the aggregate energy demand and
a more beneficial billing mechanism. The Italian eco-
nomic context on net metering and billing, detailed in the
Economic Considerations” section, is considered in the
CaseStudy Application” section.
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july/AuGuST 2018 IEEE Industry Applications Magazine 5
Italian Standards and Regulations for Net Metering
Italian low-voltage (LV) power bills are based on market-
based contracts between customers and utilities or on
authority-regulated tariffs. In the latter case, an authority
body [i.e., Autorità per l’Energia Elettrica il Gas e il Siste-
ma Idrico (AEEGSI)] makes the decisions [32]. Tariffs and
charges are set by the authority body within the functions
of customer characteristics (residential versus nonresiden-
tial, contractual power, on-peak versus off-peak hours,
and consumption cluster). Newly installed PV systems
may benefit from a choice of net billing or a simplified
purchase and resale arrangement.
In net billing, the user, depending on the power bal-
ance in time, either purchases energy from the grid or
sells it to the grid for a refund based on net shared energy
and a compensation of grid service charges. In the sim-
plified purchase and resale arrangement case, the user
may sell energy only to the grid in return for a minimum
guaranteed price equal to the market-based average zonal
price under the present regulation.
Net metering is regulated via decisions by AEEGSI
[33]–[35] and applies to RES power plants (up to 20kW)
and high-efficiency combined heat and power (up to
200kW). The Energy Service Manager [Gestore dei Servizi
Energetici (GSE)] is in charge of supervising net metering
and disburses a net-billing grant, which partially refunds
the user for paid power charges. The grant assessment by
GSE is based on power plant and power contract specifi-
cations by considering
the minimum between the price of purchased energy
(paid by the user) and the value of energy delivered by
the user (assessed by GSE)
shared energy (i.e., the minimum between purchased
and delivered energy) times a unitary compensation
(taking into account grid service charges and some
duti es)
compensation for excess energy (in case delivered
energy exceeds purchased energy), valued based on
market price.
In the current Italian regulation for a residential build-
ing, if an RES power plant is installed, the remuner-
ated energy is limited only to the common services of
the buildings. The users in the apartments cannot benefit
from the net-metering options.
Building Energy Demand Calculations
Power Demand for Apartments
The overall electric power demand of a building is the
sum of demand for all individual appliances during a
period of time. The use of appliances in time is driven by
the users’ needs and behavior and by environmental con-
ditions. Ultimately, individual loads statistically add up,
based on typical patterns and taking into account random
time shifts. Power demands can be aggregated at the unit
(e.g., apartment) and building levels (Figure 1).
Let us consider a period of time for the analysis par-
titioned into time steps and denote the generic time
step with
.s
The load pattern of each appliance can be
expressed as
,Ps Pp s
jN
jj
=
^^hh
(1)
where the subscript
j
refers to the
j
th appliance,
Ps
^h
is the active power at the sth time step,
PN
is the rated
power, and
is the power normalized with respect to
PN
.
The building load
PB
can be calculated by superimpos-
ing the loads of the
,,Ii 1f=
units, which, in turn, result
from superimposed
,,Jj 1
i
f=
appliance loads within
each unit
.
Ps Ps
Ps
B
i
I
i
i
I
j
J
ij
11
1
i
==
===
///
^^ ^hh h
(2)
The time evolution of the load patterns for an apartment
depends on the behavior of the occupants, the type of
appliances inside the apartment, and seasonal variations
[36]. In this article, different apartment models presented
in [36] and [37] are used. Some load patterns for different
apartment models on a typical winter day are shown in
Figure 2.
Demand for Common Services Inside the Building
The common areas for demand calculation are the ele-
vators, general building lighting, and general building
auxiliary services (such as thermal, hydro, alarms, and
communications) and spaces for common activities like
halls for indoor sports, meeting rooms, outdoor/indoor
swimming pools, and so forth. The calculation method for
elevator and lighting demand is briefly explained in the
following sections.
Elevator Demand
For residential buildings across Europe, geared technol-
ogy is generally used [38]. We consider geared traction
elevators for our calculations. The energy consumption for
the elevators depends on many factors, e.g., motor power,
load, elevator rise height, and speed. Generally, the aver-
age energy is measured in terms of standby energy con-
sumption and the actual consumption during ascending
and descending operation. To measure these parts, the
standard procedure described in [38] and [39] is used.
Building
Unit 1
Appliance
1.1
Appliance
1.j1
Appliance
i.1
Appliance
i.ji
Unit i
Building
Unit
Appliance
FIGURE 1. The aggregation levels of power demands.
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IEEE Industry Applications Magazine july/AuGuST 20186
Mathematically, the annual energy consumption can be
calculated using
,
,E
En
E
1000
21MLFTDF BF
s
cycle
#### #
=
-
+
^h
(3)
where
,
,,
,
Ev
Hn
P
8760 3600 1000
TDF
ss
#
#
##
=-
cm
(4)
MLF
is average motor load factor,
TDF
is average travel
distance factor,
E
cycle
is energy for a single elevator opera-
tion cycle (Wh),
BF
is balancing factor,
n
is the number of
annual trips,
Es
is standby energy (kWh),
H
is rise height
of the elevator (m),
v
is elevator speed (m/s), and
Ps
is the
standby power (W).
The average values used in the previous equations to
calculate the annual energy consumption of the elevator
are listed in Table 1. These values are useful for a general-
ized analysis on a number of buildings. Actual values are
needed for a precise analysis. For a simplified calculation
of daily energy consumption, we assume that the daily
load profile of the elevators is the same throughout the
year [31] using a typical summer or winter day and typical
elevator usage.
Lighting Demand
In buildings, most of the energy consumption is
from lighting. To simplify the calculation, we take an
approximate lighting load of 150 W/floor for a stair. It is
assumed that the lighting load is switched on and off via
available sunlight. The availability of sunlight is taken
from the measurements of PV generation for each month
of the year, and the resultant daily demand pattern
for a residential building with nine floors is depicted in
Figure 3.
Total Lo ad
The total demand for the common services of a building
can be calculated by adding the demand of an elevator,
lighting load, and auxiliary services. Figure 4 shows the
energy pattern of common services of a building with one
elevator and lighting for a single nine-floor stairway.
Estimation of PV Generation
Building-applied and building-integrated PVs are typical
cases of dispersed generation, especially within urban
areas. The PV generation at a time-step duration of 15
min is calculated for a building in Rome, Italy, using the
PVGIS. Table 2 contains the PV system parameters. The
parameters for average daily solar irradiance are calculat-
ed for the grid-connected PV systems. These parameters
include global irradiance on a fixed plane G
^h
and the
average daytime temperature profile .Td
^ h
The ac power generated by the PV system is calcu-
lated by using the following expressions, also taking into
account the various sources of losses to characterize the
PV system efficiency [39]:
,000
./
,PP
GT
09211
nc
acac dc
T
hc
=-
-
^h
(5)
NOCT
/,
TT G25 20 800
cd
T
=-+-
^^ hh
(6)
4
3
2
1
004 812162
02
4
Time (h)
(a)
Power Demand (kW)
4
3
2
1
004 812162
02
4
Time (h)
(b)
Power Demand (kW)
Economy Standard Luxury
FIGURE 2. A comparison of load patterns for different apartment
models for a typical winter day in (a) family apartments and
(b)couple apartments.
0 2 4 6 8 10 12 14 16 18 20 22 24
0
500
1,000
1,500
Time (h)
Energy (Wh)
Jan.
Feb.
Mar.
Apr.
May
June
July
Aug.
Sept.
Oct.
Nov.
Dec.
FIGURE 3. The daily energy demand pattern of lighting load for a
single stairway with nine floors.
Table 1. The average characteristics for residential
geared elevators [37], [38]
Parameters Values Parameters Values
MLF
0.35
n
62,300
TDF
0.3
H
17 m
E
cycle50.4 Wh
v
1 m/s
BF
0.5
Ps
163.8 W
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july/AuGuST 2018 IEEE Industry Applications Magazine 7
where
Pn
is the rated power of the PV system (e.g., from 1
to 100 kWp),
NOCT
is the normal operating cell tempera-
ture (45 °C),
acdc
h
- is the inverter efficiency (95%), and
c
is the power temperature coefficient (0.007). The power
generated by the PV system is shown in Figure 5.
Balance
For a grid-connected building, the power grid can be
used as a buffer entity, toward a net-zero energy building.
Conceptually, it can be considered a smart (mini-)grid.
Economic Considerations
The energy bill calculation procedure suggested by
AEEGSI, used by all of the DSOs in Italy with slight
modifications, is followed here. In general, the main com-
ponents of an energy bill include fixed, contract power,
energy, and network costs; costs related to the compo-
nents called A (covering the general costs for the electrical
system) and UC (covering further costs of the electrical
se rvice); and taxes [revenue/excise tax and value-added
tax (VAT )].
There are different tariffs for different types of custom-
ers with respect to the contract power agreements. In this
article, the case study includes a residential building, so
the related energy billing tariffs are briefly presented.
Two-Level Tariff for Small Residential Customers
On 1 January 2012, AEEGSI introduced the application
of two-level tariffs to residential customers who have
installed smart meters. The price of electricity varies,
depending on the time it is used during the day. The
level F1 corresponds to the energy price in peak hours
(between 8:00 a.m. and 7:00 p.m. Monday through Friday,
other than national holidays), and F23 corresponds to the
energy price during off-peak hours (between 7:00 p.m.
and 8:00 a.m. Monday through Friday and the entire day
for weekends and national holidays).
The two-level tariff is further categorized into two
tariffs, D2 and D3. The D2 tariff is for residential users
whose contract power agreement is lower than or equal
to 3 kW, and the D3 tariff is for residential customers
with more than 3 kW contract power or for nonresidential
customers having contract power lower than or equal to
3 k W.
Tariffs for Other LV Customers
These tariffs are designed for the customers other than
individual residential users connected to the LV sys-
tem, e.g., building services, garages, and small-business
buildings. This tariff is termed BTA (an Italian acronym
for “LV other uses”) and is divided into six subcategories
(BTA1–BTA6) based on the contract power. The energy
demand for the common services of a residential building
is billed using the BTA4 tariff. Three different time-of-
use rates (F1–F3) for a day are normally used to calculate
the billing amount. In other cases, if the energy meter
installed on the premises is capable of storing time-of-use
energy consumption, then there is a uniform rate for a
complete day. For the sake of simplicity, we have used the
uniform energy billing rate for BTA tariffs.
Apart from the common services energy-cost calcula-
tions, in this article, the BTA6 tariff is used to estimate
the energy cost for the aggregate demand of the complete
building that includes demand for all of the apartments
and common services. The details for each component of
the energy bill can be found at the AEEGSI website [41].
0 2 4 6 8 10 12 14 16 18 20 22 24
0
500
1,000
1,500
2,000
Time (h)
Energy Demand (Wh)
Jan.
Feb.
Mar.
Apr.
May
June
July
Aug.
Sept.
Oct.
Nov.
Dec.
FIGURE 4. The daily energy demand pattern for common services of
a building.
Table 2. The PV system parameters for calculating
generation
Number Parameters Value
1PVGIS radiation database Classical PVGIS
2PV technology Crystalline silicon
3Estimated system losses 14%
4PV module mounting
position and tilt angle
Freestanding
with 34
c
024681012141618202224
0
10
20
30
40
50
60
Time (h)
PV Generation (kW)
Jan.
Feb.
Mar.
Apr.
May
June
July
Aug.
Sept.
Oct.
Nov.
Dec.
FIGURE 5. The PV generation profile for a typical day of each month
of the year for a PV system with 100 kWp.
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IEEE Industry Applications Magazine july/AuGuST 20188
In general, the total annual energy cost can be writ-
ten as
=,CCCCCCtaxost
,fpenAUC
++++ +
(7)
where
C
f is the fixed annual cost for sales and network
services (€/client);
Cp
is the annual cost for contract power
(€/kW);
Ce
is the annual energy cost (€/kWh);
Cn
is the
network charges for metering, transmission, and distribu-
tion services for annual energy (€/kWh);
C,AUC
is the cost
for RES promotion, research and development, decommis-
sioning of nuclear power plants, quality of service, and so
forth (€/client, €/kWh); and tax is the revenue/excise (€/
kWh) and VAT (percentage of the sum of all of the above
components, including revenue/excise).
Tariff for Net Billing
The economic benefit of net billing is paid by GSE
because of the annual balance between the amount of
energy taken from the grid and the amount fed into the
grid. The annual economic contribution (premium) paid
by GSE for the exchange in energy is calculated as
,,
minOC CU EpremiumiEE sf s
#
=+
^h
(8)
where
OE
is the price of energy purchased from the grid
(calculated using the single national price),
CEi
is the
price of energy fed into the grid (calculated using the
regional day-ahead market price),
CU
sf is the unit price
for exchanged energy, calculated based on the network
energy charges and A and UC components of the energy
purchase tariff (e.g., D2, D3, BTA4), and
Es
is the amount
of exchanged energy, equal to the minimum energy taken
from the grid and fed into the grid.
Annual Net Energy Cost and Savings
The annual net balance for energy cost is calculated as
the difference between the cost of energy taken from the
grid and the premium received for the energy fed into the
grid, i.e.,
net balancecostpremium
=- . (9)
Case Study Application
In this section, we present a case study based on a cus-
tomer’s energy usage data for a residential building with
different types of apartments and PV generation.
Case Study
The building under study is located in Rome, Italy, and
has nine f loors and 70 residential apartments. The resi-
dential units are subdivided into four types with respect
to the occupancy. The descriptions of the types and num-
ber of apartments and other facilities for the building are
presented in Table 3.
The individual and aggregate demand patterns for
each apartment and the combination of all apartments for
Table 3. The composition of the apartments in
the building
Apartment
Type
Apartment
Class
Number of
Apartments
Contract
Power (kW)
Family
apartment
Economy 73
Standard 19 3
Luxury 9 6
Couple
apartment
Economy 17 3
Standard 83
Luxury 10 6
Common
area
Lighting
and elevator
Three stair-
ways with
three elevators
10
Apartment
Type
Apartment
Class
Unitary Annual
Energy Consumption
(kWh)
Measured
apartment 3,332
Family apartment Economy 2,673
Standard 4,429
Luxury 8,195
Couple apartment Economy 2,090
Standard 3,441
Luxury 5,855
Common services
(stairway lighting
and elevators)
8,355 (per unit
stairway-elevator)
Table 4. A comparison of annual energy consumption
per apartment or common service
3
2
1
0
Apartment Power Demand (kW)
0612 18 24
Time (h)
Simulation (11.4 kWh/d)
Measurement (11.4 kWh/d)
FIGURE 6. A comparison of simulated and measured apartment load
patterns.
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july/AuGuST 2018 IEEE Industry Applications Magazine 9
each month of the year are generated from the procedure
in [36]. The total energy consumption for the aggrega-
tion of each apartment type in one year is summarized in
Table 4.
The building has three stairways with three elevators
that jointly account for the common services demand.
The demand for the elevators and the lighting of com-
mon areas, e.g., stairways, is calculated as indicated in the
Demand for Common Services Inside the Building” sec-
tion, using the rise height
H27m=
for each elevator and
the corresponding values
. ,v15m/s
=
,E75 Wh
cycle
=
,,n78 514
= and the other values taken from Table 1.
PV generation from 10 to 100 kWp is used to assess the
economic benefits. PV generation patterns are deduced as
explained in the “Estimation of PV Generation” section for
the site in Rome, Italy.
The energy cost for the individual apartments is calcu-
lated using the tariff D2 or D3 in accordance with the con-
tract power, as mentioned in Table 3. The energy cost for
common area consumption is calculated using the tariff
BTA4. The energy cost presented in this article includes
the revenue/excise and 10% VAT.
The average energy cost in the summer is less than
in the winter due to the absence (or partial presence) of
boiler load. However, for luxury apartments, the situation
is different due to the presence of air-conditioning load
during the summer.
Experimental Activity
Measurements
To validate the simulation results, an
experimental review of an apartment
inhabited by a family with young
children was conducted. The total
energy consumption of the apart-
ment was sampled every quarter
hour for 33 days in the last months
of 2016 via an energy meter with
64-b resolution (minimum definition
0.1 Wh) and class 1 accuracy
installed in the apartment switch-
board.
Data Processing
From the sampled energy consump-
tion, the load pattern (with a time
step of one-quarter hour), load dura-
tion curve (LDC), and energy con-
sumption were calculated for one
month. Figure 6 shows an exam-
ple comparison of the simulated
and measured load pattern. Figure
7 shows the monthly LDC for the
measured apartment together with
the LDCs of simulated apartments
of different types. The characteristics of the measured
apartment are similar to the family economy apartment
class. This is confirmed by the results shown in Figure 8
for the monthly energy consumption for measured and
simulated apartments. The energy consumption of the
measured apartment is compared in the winter case to
the consumption of the family apartments. Additional
confirmation comes from the annual energy consumption
(Table 4) and daily energy consumption per apartment
4
3
2
1
Month LDC (kW)
00255075 100
Time (%)
Measured
Family Economy
Family Standard
Family Luxury
Couple Economy
Couple Standard
Couple Luxury
Measure
d
Famil
y
Econom
y
Famil
y
S
tandar
d
Famil
y
Luxur
y
C
ouple Econom
y
C
ouple
S
tandar
d
C
ouple Luxur
y
FIGURE 7. The one-month load duration curves for measured and
simulated apartments.
800
600
400
Monthly Energy
Consumption (kWh)
200
0Measured Eco. Std. Lux.
800
600
400
Monthly Energy
Consumption (kWh)
200
0Eco. Std. Lux.
800
600
400
Monthly Energy
Consumption (kWh)
200
0Eco. Std. Lux.
800
600
400
Monthly Energy
Consumption (kWh)
200
0Eco. Std. Lux.
(a) (b)
(c) (d)
FIGURE 8. The monthly energy consumption for measured and simulated apartments in (a)
winter, family apartments; (b) winter, couple apartments; (c) summer, family apartments; and
(d) summer, couple apartments. Eco.: economy, Std.: standard; Lux.: luxury.
This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.
IEEE Industry Applications Magazine july/AuGuST 201810
(Table 5), where the annual consumption of the measured
apartment is taken from the bill. The comparison of the
average energy bills for summer and winter is shown in
Figure 9. All costs are expressed in monetary units (m.u.).
Scenarios
To demonstrate the effect of net metering, two case study
scenarios are considered: case 1, demand metering (with-
out PV generation), and case 2, net billing using local PV
generation. Case 2 is further divided into the following:
case 2a, considering only common ser vices demand
case 2b, considering only common services demand
but apartment demand is billed at aggregation level,
i.e., a nonresidential tariff applies to collective con-
sumption
case 2c, considering the aggregate demand of the
whole building, again with a nonresidential tariff that
applies to the overall consumption (apartments and
common services).
Current Italian regulations are considered in case 2a
(used as the base case for comparison). A simple modifi-
cation of the net-billing concept is used in cases 2b and
2c (i.e., net billing is applied on the aggregate demand of
the whole building rather than the demand for common
services only). The BTA6 tariff is used to calculate the
cost of energy taken from the grid to serve the aggregate
demand of the building.
The comparisons of the results for the case study sce-
narios are shown in Figures 10 and 11 and Table 6.
Discussion
A reduction in the net energy expenditures for the sce-
narios presented in case 2 (with PV generation) in com-
parison with case 1 (without PV generation) is indicated
in Figure 10. Case 2a presents the net balance for the
expenditures under current Italian regulations for energy
billing. There is a reduction in the net balance for the
expenditures up to a certain limit for the installed capac-
ity of the PV generator, but, after that, there are no eco-
nomic benefits because the present regulation restricts
the sale of energy to the grid to a maximum given by the
annual energy taken from the grid. An alternative to over-
come this situation may be local storage, but this option
is not considered in this article. Another important aspect
is the average annual savings for the individual customers
(apartments) to install PV generators. Figure 11 demon-
0
50
100
150
200
250
Energy Cost (m.u.)
Winter Summer
Season
Family Economy
Family Standard
Family Luxury
Couple Economy
Couple Standard
Couple Luxury
FIGURE 9. A comparison of average monthly energy costs for
different apartment types during summer and winter.
Apartment
Type
Apartment
Class
Unitary Daily Energy
Consumption (kWh)
Measured
apartment
9.1
Family
apartment
Economy 7.3
Standard 12.1
Luxury 22.5
Couple
apartment
Economy 5.7
Standard 9.4
Luxury 16.0
Table 5. A comparison of daily energy consumption
per apartment
700
600
500
400
300
200
100
0
Annual Savings/
Apartment (m.u.)
0102030405060708090 100
PV Installed Capacity (kWp)
Case 2a Case 2b Case 2c
FIGURE 11. A comparison of annual savings for each apartment with
different PV system capacity.
020406080 100
PV Installed Capacity (kWp)
100,000
80,000
60,000
40,000
20,000
0
Net Energy
Expenditures (m.u.)
Case 1 Case 2a
Case 2cCase 2b
FIGURE 10. A comparison of net energy cost for different case
studies.
This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.
july/AuGuST 2018 IEEE Industry Applications Magazine 11
strates this parameter. For case 2a, the maximum savings
are about €50/year per customer, not accounting for the
costs related to the installation and maintenance of the
PV system. Therefore, in practice, this solution does not
exhibit actual convenience.
For case 2b, if net metering is applied by considering
only the demand of common services and the apartment
demand is metered and billed on the aggregate level,
there are savings of about €220–270/year for individual
customers. Even if no PV generator is installed, there is a
benefit of about €220/year for each apartment. However,
the problem with this kind of scenario is the limit for the
installed capacity of PV generation (as indicated in case
2a).
The third and most important option is net metering
for the aggregate demand of the whole building. Figures
10 and 11 show that this option is most beneficial for
customers with savings higher than in previous scenarios
(present regulations). Also, PV generation with higher
capacity can be installed to benefit customers. The only
additional costs are PV system installation and mainte-
nance. In this case, the savings for individual customers
are about €270–570/year. Table 6 summarizes the results
of the comparison of savings with respect to case 2a. The
net savings are with the minimum value of 8.52 times that
of case 2a for case 2b and 9.21 times that of case 2a for
case 2c. If we consider a PV system with installed capacity
of 100 kWp, then the savings increases to 11.20 times that
of case 2a.
Conclusions
Net metering is vital to the goals of future power systems
and will highly benefit customers and local generation.
The economic advantage of implementing net metering in
residential buildings was assessed in this article. An ener-
gy and economic model of a case study household (simu-
lating power demand, PV generation, and billing and
net-metering policies) was used to calculate energy expen-
ditures and savings. Several scenarios were considered,
depending on the presence of PV generation and the
level of net metering implemented. A sensitivity analysis
has been performed with respect to PV installed capacity,
considering the no-PV case as the reference scenario.
The results show that, under present Italian regula-
tions, as PV capacity increases, net metering is beneficial
up to a point because of regulatory energy constraints.
Implementing billing at the aggregate level (still consid-
ering only common service demand) allows for a much
greater benefit (approximately 75% of the expenditure
and a tenfold annual saving), even when there is no PV.
Still, a limit to effective PV capacity exists. Implementing
net metering for the whole building is the most beneficial
option, allowing for the greatest PV capacity (stretching to
half the expenditure and a 20-fold annual savings). The
authors suggest advanced net-metering policies as a key
factor to move toward the goals of smart grid develop-
ment.
Author Information
Intisar A. Sajjad is with the University of Engineering
and Technology, Taxila, Pakistan. Matteo Manganelli
(matteo.manganelli@uniroma1.it), Luigi Martirano, and
Giuseppe Parise are with Sapienza University of Rome,
Italy. Roberto Napoli and Gianfranco Chicco are with
the Politecnico di Torino, Italy. Sajjad and Manganelli are
Members of the IEEE. Napoli is a Life Memeber of the
IEEE. Martirano is a Senior Member of the IEEE. Chicco is
a Fellow of the IEEE. Parise is a Life Fellow of the IEEE.
This article first appeared as “Net Metering Benefits for
Residential Customers” at the 2015 IEEE 15th International
Conference on Environment and Electrical Engineering.
This article was reviewed by the IAS Renewable and Sus-
tainable Energy Conversion Systems Committee.
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... The Big-M method [44,75] is utilized in (13b) and (13c) to linearize the bi-linear term appearing in (13a). The solar power generation is modeled in this work using the approach presented in [46,76,77]. Daily PV generation profiles for the considered scenarios at a time step duration of 1-min were calculated using the global irradiance and the daytime temperature profile for the selected location at Oak Ridge, Tennessee, USA. ...
... Eqs. (14)(15)(16)) are used to model the PV system power profile ( , ( )) for scenario ' ' at time ' ', where (14) relates the , ( ) with the per unit PV generation estimate ( , ( ))-which is modeled using (15) and (16) [76,77]-and its rated capacity [46]. Moreover, this paper assumes that full sunlight exposure is possible by installing the solar modules at open locations where shading from buildings, trees, and other objects does not reduce the PV system power generation [78]. ...
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... The Big-M method [44,75] is utilized in (13b) and (13c) to linearize the bi-linear term appearing in (13a). The solar power generation is modeled in this work using the approach presented in [46,76,77]. Daily PV generation profiles for the considered scenarios at a time step duration of 1-min were calculated using the global irradiance and the daytime temperature profile for the selected location at Oak Ridge, Tennessee, USA. ...
... Eqs. (14)(15)(16)) are used to model the PV system power profile ( , ( )) for scenario ' ' at time ' ', where (14) relates the , ( ) with the per unit PV generation estimate ( , ( ))-which is modeled using (15) and (16) [76,77]-and its rated capacity [46]. Moreover, this paper assumes that full sunlight exposure is possible by installing the solar modules at open locations where shading from buildings, trees, and other objects does not reduce the PV system power generation [78]. ...
... The input parameters related to BESS and PV generation system are taken from [2,76] and are presented in Table 2. ...
Article
Full-text available
This paper presents mixed integer linear programming (MILP) formulations to obtain optimal sizing for a battery energy storage system (BESS) and solar generation system in an extreme fast charging station (XFCS) with the objective of reducing annualized total cost. The proposed model characterizes a typical year with eight representative scenarios and obtains the optimal energy management for the station and BESS operation to exploit the energy arbitrage for each scenario. Contrasting extant literature, this paper proposes a constant power constant voltage (CPCV) based improved probabilistic approach to model the XFCS charging demand for weekdays and weekends. This paper also accounts for the monthly and annual demand charges based on realistic utility tariffs. Furthermore, BESS life degradation is considered in the model to ensure no replacement is needed during the considered planning horizon. Different from the literature, this paper offers pragmatic MILP formulations to tally BESS charge/discharge cycles using the cumulative charge/discharge energy concept. McCormick relaxations and the Big-M method are utilized to relax the bi-linear terms in the BESS operational constraints. Finally, a robust optimization-based MILP model is proposed and leveraged to account for uncertainties in electricity price, solar generation, and XFCS demand. Case studies were performed to signify the efficacy of the proposed formulations.
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... These results point to a scenario in which there would be a surplus that would be passed on to the grid as determined by current legislation [28]. However, focusing on the research objectives, a scenario that goes against the law, the surplus would be sold to the grid (Figure 11), differently the research by Benalcázar et al. [17], Orioli and Di Gangi [23], Cadavid et al. [24] and Sajjad et al. [25]. ...
... With the purchase and sale of energy for the grid, a positive balance was created in this way, as shown in Figure 12b and Figure 14 [23][24][25]. In Table 2, it was possible to see the annual variation of a gain scenario, in which there would be a minimum of US$ 1,244.64 (50%) and a maximum of US$ 2,489.28 ...
... Secondly, the estimates obtained were compared with the actual energy savings achieved in previous years in the same geographical area following the installation of photovoltaic systems. Thirdly, reference was made to the scientific literature on the subject (for example, the works of ref. [61] and ref. [62]) to validate the estimated values. For the representative building, the percentage savings in the bill determined by photovoltaics varies between 2% and 10%. ...
... The central value (6%) was assumed to be the most probable. This result is in line with the study conducted by ref. [62] in the Italian context. ...
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... 259,200 with a payback period of 4.63 years. In Sajjad et al. (2018), net metering was applied on a residential building with 70 apartments. The solar PV generation was estimated using PVGIS. ...
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... 259,200 with a payback period of 4.63 years. In Sajjad et al. (2018), net metering was applied on a residential building with 70 apartments. The solar PV generation was estimated using PVGIS. ...
... Smart metering infrastructures (SMI) and net metering schemes in Europe are also widely used, and their policies are also considered while tacking the under-mind weather of the most European countries [40,41]. The economic evaluation of a series of net metering policies from a consumer bill, from the perspective of Greece, is also performed to evaluate policies provided by the government [42,43]. ...
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Chapter
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