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Scope of this paper is to deliver a complete techno-economic model for the economic assessment of lithium-ion battery energy storage systems in the framework of the nearly zero energy buildings (NZEB). The proposed model simulates the combined operation of photovoltaics, solar thermal generators, heat pump generators, electrical and thermal storage devices in order to represent efficiently the typical characteristics of NZEBs. The model takes into account the thermal and electrical needs of the building, typical electricity prices, household electrical consumption profiles, weather data and typical costs for lithium battery energy storage systems. Using these inputs, an optimization procedure is applied and the optimal size, in terms of net present value, for the lithium-ion battery energy storage system is derived.
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Economic Assessment of Lithium-Ion Battery
Storage Systems in the Nearly Zero Energy
Building Environment
Angelos I. Nousdilis, Eleftherios O. Kontis Georgios C. Kryonidis,
Georgios C. Christoforidis, and Grigoris K. Papagiannis
Abstract—Scope of this paper is to deliver a complete techno-
economic model for the economic assessment of lithium-ion
battery energy storage systems in the framework of the nearly
zero energy buildings (NZEB). The proposed model simulates the
combined operation of photovoltaics, solar thermal generators,
heat pump generators, electrical and thermal storage devices
in order to represent efficiently the typical characteristics of
NZEBs. The model takes into account the thermal and electrical
needs of the building, typical electricity prices, household elec-
trical consumption profiles, weather data and typical costs for
lithium battery energy storage systems. Using these inputs, an
optimization procedure is applied and the optimal size, in terms
of net present value, for the lithium-ion battery energy storage
system is derived.
Index Terms—Electrical storage, lithium-ion batteries, nearly
zero energy buildings, photovoltaics, thermal storage.
European Union’s target for 2030 include the transformation
of the existing building stock to Nearly Zero Energy Buildings
(NZEBs) [1], [2]. NZEBs are characterized by reduced net-
energy demand, since the major part of their thermal and
electrical energy needs are covered locally by renewable
energy sources (RESs), especially photovoltaics (PVs).
Consequently, in the following years, a considerable amount
of intermittent solar generators will be connected in the
existing distribution grids, posing new technical challenges
for distribution system operators (DSOs). The most important
of them include overvoltages, protection, stability and con-
gestion issues. An efficient solution to tackle these technical
Angelos I. Nousdilis, Eleftherios O. Kontis, Georgios C. Kryonidis, and
Grigoris K. Papagiannis are with the Power Systems Laboratory, School
of Electrical and Computer Engineering, Aristotle University of Thessa-
loniki, Thessaloniki, Greece, GR 54124, (e-mail:, kontislef-;;
Georgios C. Christoforidis is with the Western Macedonia University of
Applied Sciences, Kozani, Greece, (e-mail: ).
This work has been co-funded by the European Union and National Funds
of the participating countries through the Interreg-MED Programme, under
the project "PV-ESTIA - Enhancing Storage Integration in Buildings with
The work of Georgios. C. Kryonidis and Eleftherios. O. Kontis was
conducted in the framework of the act "Support of PhD Researchers" under the
Operational Program "Human Resources Development, Education and Life-
long Learning 2014-2020", which is implemented by the State Scholarships
Foundation and co-financed by the European Social Fund and the Hellenic
challenges is to store locally the excess of PV energy, using
energy storage systems (ESS) [3].
Residential ESS are mainly based on battery technologies.
Among the available solutions, lead-acid batteries are the dom-
inant technology for small scale applications [4]. Compared to
other technologies, lead-acid batteries present high reliability,
low self-discharge as well as low maintenance and investment
cost. On the other hand, they have short lifetime and low
energy and power density. Thus, it is expected that in the
near future lead-acid batteries will be replaced by lithium-ion
ones, which present higher energy efficiency and better aging
characteristics [5], [6].
Therefore, there is a strong need to evaluate the profitability
of residential ESSs based on lithium-ion batteries. Towards
this objective, in [7] the economic viability of second use
electric vehicle batteries for energy storage application in
the residential sector is evaluated, while in [8] and [9] the
profitability margins of adding lithium-ion battery SSs (BSSs)
to existing residential grid-connected PV plants is investigated.
The corresponding results reveal that the present costs of
lithium-ion batteries are too high to allow economically viable
investments. However, the above-mentioned studies do not
take into account the impact of thermal storage systems (TSS),
solar thermal (ST) and heat pump (HP) generators on the
optimal size of the electrical BSS.
Concerning the above-mentioned issue, the scope of the
paper is to develop a complete techno-economic model to
evaluate the economic viability of electrical ESSs based on
lithium-ion batteries in the NZEB environment. The combined
operation of PVs, HP and ST generators, TSS and BSS is
simulated by a full set of equations, describing the coupled
behavior of each component in the NZEB context [10]. The
proposed model receives as inputs: i) technical data related
with the building energy systems and the building shell, ii)
economical data such as the typical cost of lithium-ion batter-
ies and electricity prices, iii) typical electricity consumption
profiles related with the electrical power consumed for lighting
and household appliances, and iv) weather data. Initially, the
model calculates the total thermal and electrical needs of the
building, using the available climate data. Afterwards, the
optimal size, in terms of net present value (NPV), for the
lithium-ion BSS is determined by applying an optimization
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The adopted building model [10] takes into account PV
systems, ST and HP generators, TS and BS systems. The
mathematical description of these components as well as the
control strategies, used for the charge/discharge of TSSs and
BSSs are described in detail in the following subsections.
A. Heating and Cooling Load
The thermal power required at each time instant for the
heating of the building, Pth,H , can be estimated as:
Pth,H =Pth,des,H 1
Text Tdes,H
Toff,H Tdes,H !(1)
where Pth,des,H is the thermal power, required for the heating
of the building under the designed conditions, while Tdes,H
is the external temperature, used for the design of the heat-
ing system. Moreover, Toff,H is the nominal temperature at
which building losses and gains are balanced and the heating
system is switched off. Finally, ¯
Text is the effective outdoor
temperature based on the characteristics of the building and
the actual external temperature Text. The ¯
Text temperature is
defined as the simple moving average of the previous ¯
of Text. Where ¯
φis the nearest integer of the effective time
shift of the building, defined as:
i=1(U A)iφi
i=1(U A)i+Hv e
where φiand (UA)iare the characteristic time shift, as
defined by EN ISO 13786:2007 [11], and the surface thermal
transmittance of the i-th external wall, respectively, while Hve
is the equivalent ventilation-thermal transmittance based on air
change rate.
The thermal power required for the cooling of the building,
Pth,C , is calculated as:
Pth,C =Pth,des,C 1
ext T
Toff,C T
des,C !(3)
where Tis the so-called "sol-air temperature" [12], Toff,C
is the nominal temperature in which the cooling system is
deactivated, while ¯
ext is the simple moving average of the
previous ¯
φvalues of T
B. Heating and Cooling System Terminal
In this paper, a radiant floor is considered as the heat ter-
minal unit. Water and floor temperature evolution is simulated
through a simplified resistance model [10], as presented in
TRF =Tw,RF Tin =
TRF,nom Pth
SRF KRF,nom !
nRF (4)
Tf,RF,H =Tw,RF,H Pth,H
Tf,RF,C =Tw,RF,C +Pth,C
where Tw,RF and Tin is the temperature of the water within
the radiant floor and the internal air temperature of the build-
ing, respectively. Please note that in this paper, the internal
air temperature is considered equal to the corresponding set-
point value. Additionally, TRF is the nominal difference
between the mean temperature of the water and the internal
air, while nRF is the emitter exponent of the radiant floor
and KRF,nom is the nominal thermal output per surface unit.
Moreover, Tf,RF stands for the floor temperature, while SRF
is the total surface of the radiant floor and Uwf is the thermal
transmittance between the circulating water and the flood
C. Domestic Hot Water Production
The power required for the production of domestic hot water
(DHW) can be evaluated as:
PDHW =dwcw(TD HW Taqua )(7)
where dwis the supply of domestic hot water in Kg/s, while cw
is the specific heat capacity of the water. TDHW is the desired
temperature of the DHW, whereas Taqua can be considered
equal to the outdoor temperature.
D. PV System
The power produced by the PV system can be efficiently
calculated using the following set of equations [10]:
PP V =nP V ηinvηP V SP V Isol (8)
ηP V =ηP V,ref [1 βPV (TP V Tref,P V )] (9)
TP V Text = (219 + 819Kt)N OC T 20
800 (10)
where, ηinv is the efficiency of the inverter, nPV is the number
of the installed PV modules, while SP V is the surface of each
module. Moreover, ηPV is the actual efficiency of the PV
modules at the operational temperature TP V , while ηPV ,ref is
the efficiency of the PV modules at the reference temperature
Tref,P V . Additionally, βPV is a temperature penalization factor
depending on the PV technology and NOCT denotes the
nominal operating cell temperature. Finally, Isol is the clear
sky irradiation and Ktis the clearness index.
E. Heat Pump Generator
In this paper, air to water electrically-driven HPs are con-
sidered. To ensure that the thermal load of the building is
covered under any conditions, the nominal capacity of the
HPs is chosen to be equal with the peak thermal load. The
performance of the HPs can be evaluated using the following
Tcond Teva
Tcond Teva
The HP efficiency ηHP can be considered with sufficient
accuracy as a constant value [10]. Additionally, in this paper,
the same ηHP is adopted for both the heating mode and the
production of DHW. The values of Tcond and Teva depend
on the provided service [10]. At the indoor of the HP heat
exchanger (water side), a temperature droop equal to 5 K
is considered. Moreover, a drop of 10 Kis assumed at the
outdoor of the HP heat exchanger (air side).
F. Solar Thermal Generator
The operation of the ST generator is simulated using the
following mathematical model [10]:
Pth,ST =nST ηS T SST Isol (13)
ηST =FR(τ α)nAB(14)
A= (1 b0(1/cosθ 1)) (15)
B=FRUL(TST ,in Text)
In the above equations, nST and ηST denote the number of
solar collectors and their efficiency, respectively. SST is the
surface of each collector, while FRis the solar thermal removal
factor. Furthermore, (τα)nis the transmittance-absorptance
product for normal incidence irradiance, while b0is the inci-
dence angle modifier coefficient for single-cover solar thermal
collectors, and θis the angle between the beam radiation and
the normal to the solar thermal collectors. Finally, ULis solar
thermal frontal losses and TST ,in is the temperature of the
thermal storage.
G. Thermal Storage System
For the adopted TSS, the thermal power balance equation
at the j-th time step of the analysis can be written as:
VT S ρwcw(Tj
T S Tj1
T S ) =Pth,S T +Pth,HP,T S
Pth,DHW Pth,T S,H
Pth,T S,ls
where VT S is the total volume of the TSS, while ρwis
the density of the water. Moreover, Tj
T S and Tj1
T S are the
temperatures of the water at the TSS at the jand j1time
step of the analysis, respectively. Pth,ST and Pth,H P,T S are the
powers provided by the ST generator and the HP to the TSS,
respectively. On the other hand, Pth,T S,H denotes the power
exported from the TSS for heating purposes, while Pth,DHW is
the power used for the production of DHW. Finally, Pth,T S,ls
stands for the power losses of the TSS and can be calculated
Pth,T S,ls =ST S
λT S
sT S
(TT S Text,T S )(18)
In the above equation, STS ,λT S and sT S are the total
surface, the thermal conductivity, and the thickness of the
TSS. Moreover, Text,TS is the TSS room temperature. Further
information concerning the control strategy of the TSS can be
found in [10].
Algorithm 1 Pseudocode for the control strategy of the BSS
1: Define upper/lower SOC limits, i.e. SOCuand SOCl.
2: if PP V > Pload && SOC < SOCu
3: Battery charges
4: elseif PP V > Pload && SOC SOCu
5: No action
6: elseif PP V < Pload && SOC > SOCl
7: Battery discharges
8: elseif PP V < Pload && SOC SOCl
9: No action
10: end if
H. Battery Storage System
The electrical power balance equation can be written as:
PP V +Pgrid +Pbat =Pload (19)
Pload =PHP +Pel (20)
In the above equations, Pgrid denotes the electrical power
imported from the main utility grid, Pbat is the electrical power
of the BSS, and Pel is the electrical power, used for lighting
and household appliances. Finally, PHP is the electrical power
of the HP, which can be easily computed using the following
PHP,H =Pth,H
COP (21)
PHP,C =Pth,C
EER (22)
Concerning the control strategy of the BSS, a simple scheme
that maximizes the self-consumption ratio is adopted. More
specifically, in case the PV power exceeds the total load
demand, the BSS starts the charging procedure by absorbing
the surplus of the generated power up to the maximum
permissible value. On the other hand, if the PV generated
power is lower than the load demand, the BSS discharges.
During this process, the upper and lower limit of the state of
charge (SOC) of the BSS is taken into account. The adopted
control strategy is analytically presented in Algorithm 1.
Nowadays, the majority of PV installations in the residential
sector are operated under net-metering (NeM) schemes [13],
[14]. In these policies, prosumers can offset the imported
energy from the grid with electrical energy generated from
a local PV system. The prosumers are then charged for a
certain billing period according to their net energy consumed.
The time period during which the produced PV energy can
be netted against the imported energy is characterized as
netting period [14]. In case the generated energy exceeds the
consumed throughout the netting period, a compensation may
be provided for the excess energy at a certain sale price (sp).
In this paper, the economic viability of lithium-ion BSS
is investigated assuming a full NeM scheme [14]. In the
examined scheme, the netting period is considered equal to
one hour. This paper examines two cases in which excess
energy is either compensated using the system marginal price
(SMP) of the Greek electricity market or not compensated at
all. Moreover, the billing is performed assuming two distinct
charge categories: i) The fixed cost (fc), that includes trans-
mission and distribution systems constant charges, standing
fees, etc, and ii) the netted-cost (nc), which is the prosumer
charge calculated using the net energy consumed during the
billing period.
A 20-year economic analysis is performed based on the
following procedure: Initially, the exported energy to the grid
(Eexp) and the netted energy (Enet ) are calculated. For a
typical day, these energies can be defined by the aid of Fig. 1
as follows:
exp =B+D(23)
net =A+F+EBD(24)
exp =B(25)
net =A+FB(26)
Eq. (23) - (24) stand for the case in which only the PV system
is considered, while Eq. (25) - (26) refer to the case of an
integrated PV-BSS.
For a specific year of investment (t), the NPV of the
installed BSS (npvt) is computed as:
in cft,n
(1 + i)ncapexB(1 + a)t1(27)
Here Ndenotes the lifetime of the lithium-ion batteries. A
typical system with a depth of discharge equal to 80% can
perform more than 8000 cycles of operation [15]. Therefore,
assuming a full operation cycle during each day of the year, N
is considered equal to 22 years and no replacement is foreseen
for the batteries during the analysis period. Moreover, in (27),
iis the discount rate, while astands for the inflation rate.
in and cft,n
out are the cash in and out flows, respectively.
Finally, capexBis the capital investment cost of the BSS.
The cash flow out is related with the operation and mainte-
nance costs of the BSS (opexB) and is calculated according to
(28). On the other hand, the cash flow in represents the profit
from the use of the BSS and can be derived using (29).
out =opexB(1 + a)t1(1 + a)n1(28)
Fig. 1. Typical household consumption and generation profiles.
Model inputs
Building model,
Eq. (1) (22)
S Smax
Battery size (S)
S=0.5 kWh
npv calculation, Eq. (27)
Optimal BSS size
Step 1
Step 2
Step 3
Step 4
Step 5
Fig. 2. Proposed techno-economic model.
in = (ct,n
P B )(prt,n
P B )(29)
Where, ct,n
Pis the electricity cost of the base scenario,
where only the PV system is considered, while ct,n
P B is the
corresponding cost when an integrated PV-BSS is assumed.
These values are obtained using (30) and (31), respectively.
Moreover, prt,n
Pand prt,n
P B correspond to prosumer profit due
to the compensation for excess energy and are obtained using
(32) and (33), respectively.
net nct) + fct#(1 + a)n1(30)
P B ="K
(Ek,P B
net nct) + fct#(1 + a)n1(31)
exp spt)#(1 + a)n1(32)
P B ="K
(Ek,P B
exp spt)#(1 + a)n1(33)
Here, Kis the last billing period of year n. For different
investment periods, all values, i.e., nc,fc, and sp can be
calculated as:
[nctfctspt] = [nc fc sp](1 + a)t1(34)
The proposed techno-economic model is presented in Fig. 2
by means of a flowchart. The model consists of 5 main steps,
described in detail below:
Step-1:.Initialization phase. In this step, several technical
and economical data are provided as inputs to the model. More
specifically, the required inputs include: i) technical parameters
related with the building shell and the energy systems of
the building, ii) economical parameters such as the typical
cost of lithium-ion batteries and electricity prices, iii) typical
electricity consumption profiles related with the electrical
power consumed for household appliances and lighting, and
iv) historical weather data, used to evaluate the thermal energy
Building Cell Radiant Floor PV Generator
A=300 m3TRF,nom=20 K SP V =1.5 m2
U=3.56 W/(m2K) SRF =80 m2nP V =40
φi=8 h KRF,nom=60 W/(m2)ηinv =0.85
Hve=0 W/(m2K) nr f =1.1 ηPV ,ref =0.13
Tdes,H =-3 oCU=6 W/(m2K) βT,P V =0.004 1/K
Tdes,C =47 oCToff ,H =17 oCTref ,P V =25 oC
Pth,des=3.75 kW Toff ,C =26 oCN OC T =45 oC
TSS ST Generator HP Generator
VT S =0.5 m3sST =3 m2PH P,max=4.125 kW
ST S =3.5 m2nST =1 nH P,H =0.45
TT S,set=50 oCFR=0.8 nH P,C =0.35
TT S,up=60 oC (τ a)n=0.7 BSS
TT S,down=42 oCUL=5 W/(m2K) S OCu=90%
TT S,max=90 oCb0=0.1 SOCl=20%
λT S =0.04 W/(mK) θ=0.3839 costbattery =600 e/kWh
sT S =0.08 m costinverter =200 e/kW
Economical Parameters
a=2% nc1=0.174 e/kWh fc=35 e/year
i=5% nc2=0.165 e/kWh
needs of the building. Weather data and consumption profiles,
used in this paper, are presented in Fig. 3. Technical and
economical parameters are summarized in Table I. Concerning
electricity costs, typical values of the Greek System are used.
Based on the netted energy (whether it is over or below 2000
kWh), the netted-cost receives two separate values, namely
nc1and nc2, respectively. Finally, in the presented analysis
opexBis neglected [16].
Step-2:.Evaluation of thermal and electrical energy needs.
In this step, the thermal and electrical energy needs of the
building are calculated on a 15 minute basis using the profiles
of Fig. 3. Thermal needs are estimated using (1) - (18), while
electrical needs are calculated from (19) - (22) assuming a
specific size Sfor the BSS. Concerning the first algorithm
iteration, it is assumed that Sis equal to 0.5 kWh. During the
first iteration, the base scenario, where only the PV system is
installed, is also simulated in order to evaluate (30).
Step-3:.npv calculation. Here, (23) - (26) are computed
and the npvtof the investment is derived using (27). The
corresponding result is stored to a vector, named as P Ss.
Step-4:.Stopping criterion. If the size Sof the BSS
is greater than a maximum user-defined value (Smax), the
algorithm proceeds to Step 5. Otherwise, the procedure moves
back to Step 2, by setting S=S+0.5.
Step-5:.Optimal BSS size. In this step, a search algorithm
is used to determine the maximum value of P Ss. This value
actually corresponds to the optimal size of the BSS.
0 4 8 12 16 20 24
Irradiation (kW/m2)
0 4 8 12 16 20 24
Temperature (oC)
0 4 8 12 16 20 24
Pel (kW)
Fig. 3. Typical daily: a) irradiation, b) temperature, and c) consumption
In this Section, indicative results for the proposed model are
presented. More specifically, a 20-year economic analysis is
performed assuming a typical three-phase house installation,
located in the central Greece. The total floor area of the house
is considered equal to 100 m2, while its height is assumed
0 2.5 5 7.5 10 12.5 15 17.5 20
Battery size (kWh)
npv1 (euros)
100 euros
200 euros
400 euros
600 euros
Fig. 4. NPV under a full net metering scheme. Excess energy is compensated
with SMP.
0 2.5 5 7.5 10 12.5 15 17.5 20
Battery size (kWh)
npv1 (euros)
100 euros
200 euros
400 euros
600 euros
Fig. 5. a) NPV and b) IRR under a full net metering scheme. Excess energy
is not compensated.
equal to 3 m. The internal air temperature of the house is
assumed to be constant and equal to 26 oC and 20 oC during
the cooling and the heating mode, respectively. The supply
for DHW is considered constant and equal to 10 kg/min. The
temperature of the DHW is 45 oC.
Indicative results for the two examined NeM policies are
presented in Figs. 4 and 5, respectively. As shown, under
the examined NeM policies and with the current prices,
the installation of BSS is not profitable for the prosumer.
Therefore, a parametric analysis for different BSS prices
is conducted. The analysis reveals that the installation of
BSSs can become profitable if the corresponding prices are
reduced. Additionally, it is worth noticing that the profitability
is considerably increased for prosumers operated under NeM
policies in which the excess of energy is not compensated.
In this paper, a techno-economic model is developed to
facilitate the economic assessment of lithium-ion BSSs in the
framework of NZEBs. The proposed model simulates the com-
bined operation of PVs, thermal and battery storage systems
as well the operation of ST and HP generators to represent
efficiently the typical characteristics of NZEBs. It receives as
inputs several technical and economical parameters, typical
consumption and weather data. Initially, using these inputs,
the model evaluates the thermal and electrical energy needs of
the building. Afterwards, an optimization procedure is applied
and the optimal size for the lithium-ion BSS is derived.
Using the proposed model, the economic viability of
lithium-ion BSSs for a typical household installation, located
in the central Greece, is evaluated. The simulation results
reveal that with the current market prices, the installation of
BSSs is not recommended, since standalone PV systems can
generate more profits. However, the conducted analysis reveals
that as the BSS prices decrease, the profitability is increased,
outperforming the NPVs of standalone PVs. Therefore, in the
next few years a high number of BSSs is expected in the
building environment. In this context, the proposed method can
be a valuable tool for the economic analysis and the optimal
sizing of household BSSs.
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164, pp. 175 – 187, 2016.
... Nousdilis et. al [6] carry out an economic analysis of the profitability of Li-ion BESS in a building microgrid in the presence of thermal loads and a TESS. The weather profile of one day is used to represent all days of a season of the year. ...
... TCLs have been widely deployed for providing frequency regulation and demand following schemes in [16]- [18]. The works of [4]- [6] assume a rigid load profile. In contrast, Williams et. ...
... The usage pattern and operating conditions of the BESS have significant effect on the its health. The long-term cost of battery degradation is neglected in [6]- [8], [22]- [25], and oversimplified in [26]- [29]. The degradation caused by non-regular battery usage profiles can be analyzed by the Rainflow Counting Algorithm (RCA) [30], [31]. ...
This paper investigates an optimal sizing strategy for an islanded building microgrid. The microgrid composites a rooftop Photovoltaic (PV) system, a Battery Energy Storage System (BESS), an ice-Thermal Energy Storage System (ice-TESS), and loads. The loads are divided into two sets based on their ability to participate in demand response: i) Plugged Loads (PL) such as lights, and ii) Cooling Loads (CL) such as air-conditioners. The microgrid is islanded and loads must be supplied with local generation resources. Therefore, the BESS is deployed to offset the PV output’s variability, and the absence of PV power supply at night time. However, relying only on the BESS incurs high stress and shortens the BESS’s lifetime. Hence, we propose an optimal sizing strategy of the microgrid constituents, where the BESS coordinates with the ice-TESS to maintain the balance between generation and load in the microgrid. Nevertheless, the dispatch commands cannot swing freely between the two ESSs because of the difference in the type of energy delivery. Specifically, the BESS stores electric energy and can supply both PL and CL. On the other hand, the TESS can only supply the CL. Hence, the BESS-TESS coordination is also aided by a customer-friendly shifting and curtailment mechanism of CL. The design incorporates the effect of weather uncertainty on the PV output. Weather variations are imitated using Recurrent Neural Networks trained on 19-years of contiguous hourly weather data. After optimizing the sizes of the microgrid constituents, the optimal sizes are used in a detailed dynamic model of the system for a real-time simulation on the OPAL-RT platform. The validation results demonstrate the successful coordinated operation of the microgrid constituents which are cost-effective in sizing.
... Due to the above-mentioned support policies, an important number of PVs have been installed during the last two decades in low-voltage (LV) distribution grids [8], while a significant amount is also expected to be connected in the near future to facilitate the conversion of the existing building stock to NZEBs [9]. Nevertheless, the increased penetration of PVs in LV distribution grids may introduce several technical challenges for distribution system operators [10] - [12]. ...
... The NPV ( ) of an investment in combined PV and BES system at year (t) is calculated using (8) where and are the cash inflow and outflow of the n-th year, respectively. The total investment cost, oci t , represents the sum of capital expenditures for the PV system, t pv capex , and BES system, t bat capex as defined in (9). ...
... . (9) The capital investment expenses are calculated for the investment at a specific period t, by the aid of (10), assuming an inflation rate a. ...
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During the last two decades, the use of residential photovoltaic systems (PVs) has been widely promoted by governments through various support mechanisms such as feed-in-tariffs, net-metering, net-billing, etc. These support schemes have developed a secure investment environment, increasing the penetration level of PVs in low-voltage distribution grids. Nonetheless, increased PV integration may introduce several technical problems regarding the secure operation of distribution grids. Battery energy storage (BES) systems can mitigate such challenges, but the high capital cost is one of the most important limiting factors towards the widespread use of these systems. In fact, the financial viability of integrated PV and BES systems under different support schemes remains an open issue. In this paper, the profitability of PV and BES systems is evaluated through an advanced techno-economic model, that provides the optimal size of PV-BES system in terms of net present value, based on the electricity production and consumption profile of the installation, PV and BES systems costs, and electricity charges. The proposed model may be a useful tool for prosumers, grid operators and policy makers, to assess the impact of various incentive policy schemes and different BES operation strategies on the economic viability of PV-BES systems.
... Most have a cathode of lithium oxide (LiMO 2 , LiCoO 2 , LiNiO 2 ), a graphite pure carbon anode and lithium salt electrolyte organic solvent (LiPF6). When compared to other types of batteries, lithium-ion batteries have a longer life cycle (500-2000 cycles) and are more energy efficient (99-100%) [25]. Lithium is the lightest compared to all metals, with the highest electrochemical potential and the highest energy density in battery designs (up to 250 Wh/kg) [22]. ...
... Communication equipment, medical devices and power tools are among the most common applications for this battery type. Although they are well suited for renewable energy systems, they are rarely used due to their high initial cost, since they are more expensive-up to four times more than lead-acid and two times more than lithium-ion batteries [25]. ...
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The current EU energy and climate policy targets a significant reduction of carbon dioxide emissions in the forthcoming years. Carbon pricing, embedded in the EU emissions trading system, aims at achieving emission reductions in a more evenly spread way and at the lowest overall cost for society, compared with other environmental policy tools, such as coal or electricity taxes, or incentives such as subsidies on renewables. Still, the implementation of the decarbonization policy depends on several technical parameters such as the type, size and connectivity of the energy system as well as economic restrictions that occur. Within this paper, an optimization tool will be presented, focusing on cleaner energy production and on the control and reduction of environmental impact regarding energy storage solutions. Various types of batteries are examined and evaluated towards this direction. Emphasis is given to setting new criteria for the decision-making process, considering the size of battery storage and the selection of the battery type based on the environmental impact assessment parameter. The objective function of the system is formulated so as to evaluate, monitor and finally minimize environmental emissions, focusing mainly on carbon emissions. Optimization is carried out based on mixed integer nonlinear programming (MINLP). Two of the main battery types compared are lead–acid and lithium-ion; both of them result in results worth mentioning regarding the replacement impact (seven times during system lifetime for lead–acid) and the total environmental impact comparison (lithium-ion may reach a 60% reduction compared to lead–acid). Case studies are presented based on representative scenarios solved, which underline the importance of choosing the appropriate scope for each case and demonstrate the potential of the tool developed, as well as the possibilities for its further improvement.
... The authors in [34] presented a sizing scheme of TESS, BESS and a photovoltaic system to balance the generation and demand, minimize the operational cost of microgrid components, and achieve peak load shaving considering user comfort. The study in [35] analyzed the economic benefits of using BESS in coordination with TESS in the nearly zero energy building environment. ...
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Energy storage system (ESS) plays a key role in peak load shaving to minimize power consumption of buildings in peak hours. This paper proposes a novel energy management unit (EMU) to define an optimal operation schedule of ESSs by employing metaheuristic and mathematical optimization approaches. The proposed EMU uses a thermal energy storage system (TESS) and a battery energy storage system (BESS) to store the energy in off-peak periods and discharge it in high load demands. We formulate the charging/discharging schedule of TESS and BESS as an optimization problem. Then, particle swarm optimization (PSO) is employed to obtain the optimal schedule due to its computational time efficiency. The mathematical approach is also applied to prove the convexity of the problem and the uniqueness of the solution. Due to the different characteristics of the building loads, this paper divides the total load into shiftable and fixed loads. Moreover, to model the building components and loads, grey-box modeling is adopted. Results show that employing a combination of TESS and BESS achieves peak load shaving while reducing 42.2% of the required BESS capacity compared with the case where the BESS is only used. In addition, the results indicate the effectiveness and robustness of the proposed algorithm.
... Distributed generators could be solar panels or wind generators. ESS could be lead-acid batteries or lithium-ion batteries, which can reduce net-energy demand from main grids by storing excess renewable energies locally and are very important for implementing nearly-zero energy buildings in the future [20]. At present, ESS costs are very high (e.g., around 450$/kWh), which means that installing ESS in a smart home is not very economical. ...
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In this paper, we investigate an energy cost minimization problem for a smart home in the absence of a building thermal dynamics model with the consideration of a comfortable temperature range. Due to the existence of model uncertainty, parameter uncertainty (e.g., renewable generation output, non-shiftable power demand, outdoor temperature, and electricity price) and temporally-coupled operational constraints, it is very challenging to design an optimal energy management algorithm for scheduling Heating, Ventilation, and Air Conditioning (HVAC) systems and energy storage systems in the smart home. To address the challenge, we first formulate the above problem as a Markov decision process, and then propose an energy management algorithm based on Deep Deterministic Policy Gradients (DDPG). It is worth mentioning that the proposed algorithm does not require the prior knowledge of uncertain parameters and building thermal dynamics model. Simulation results based on real-world traces demonstrate the effectiveness and robustness of the proposed algorithm.
... Distributed generators could be solar panels or wind generators. ESS could be lead-acid batteries or lithium-ion batteries, which can reduce net-energy demand from main grids by storing excess renewable energies locally and are very important for implementing nearly-zero energy buildings in the future [20]. At present, ESS costs are very high (e.g., around 450$/kWh), which means that installing ESS in a smart home is not very economical. ...
In this paper, we investigate an energy cost minimization problem for a smart home in the absence of a building thermal dynamics model with the consideration of a comfortable temperature range. Due to the existence of model uncertainty, parameter uncertainty (e.g., renewable generation output, non-shiftable power demand, outdoor temperature, and electricity price) and temporally-coupled operational constraints, it is very challenging to determine the optimal energy management strategy for scheduling Heating, Ventilation, and Air Conditioning (HVAC) systems and energy storage systems in the smart home. To address the challenge, we first formulate the above problem as a Markov decision process, and then propose an energy management strategy based on Deep Deterministic Policy Gradients (DDPG). It is worth mentioning that the proposed strategy does not require the prior knowledge of uncertain parameters and building thermal dynamics model. Simulation results based on real-world traces demonstrate the effectiveness and robustness of the proposed strategy.
The current climate and energy policies of the European Union aim at achieving carbon dioxide emissions reduction and the promotion of clean energy. The priorities set concentrate on decarbonizing the energy sector, mainly by promoting renewables. At the same time, it is of great importance to effectively manage the energy generated in-situ within a community. That community can be approached as a micro-grid able either to be connected to the main grid or to be independent. In addition to this, storage of electricity seems the unavoidable solution for the effective energy management with the micro-grid. Due to the technological developments and the reduction in production costs that are expected to decrease even further in the coming years (50% reduction), batteries are a crucial factor for the effective integration of renewables on a residential scale. Thereafter, the proper size of a battery system plays an important role for the total minimization of system's cost during its lifetime. The purpose of the paper is to present a mathematical tool, able to manage the energy produced by residential photovoltaic panels, the energy stored in the batteries and the energy purchased from the main grid. Continuing with the energy management, the framework should come up with an optimized life cycle cost solution, regarding both the energy management within the grid and the optimum size of energy battery system. Main findings of the paper indicate that storage is a feasible option, whenever selling electricity to the main grid is not applicable, as for that case the battery capital cost should decrease to 400 (€/kWh); this is a 20% cost reduction compared to current prices and 30 (€/kWh).
Direct electrical energy storage can play a pivotal role in the efficient grid integration of renewable energy resources and compensating temporary power surpluses and shortages, however it is needed to move beyond the single sector focus and electricity’s conversion to other storable forms of energy should not be neglected in efforts to find the optimal solutions for maintaining the momentary balance between electricity supply and demand. This paper discusses the benefits of storing electricity as thermal energy over direct electricity storage. A community located in a hot climate region is considered as a case study and the performance of cold thermal storage and direct electricity storage are compared for it. For this purpose, the hourly performance of the storage systems during each year is modeled and particle swarm method is used to solve the optimization problem. Results show that storing electricity as thermal energy shows a much better performance over direct electricity storage. It requires much less investment, has a higher net present value and results in a flatter load profile. The paper also investigates the effects of electricity pricing structure, economies of scale and battery cost reduction on the economic performance of both thermal and electricity storage systems.
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Rooftop photovoltaic systems integrated with lithium-ion battery storage are a promising route for the decarbonisation of the UK’s power sector. From a consumer perspective, the financial benefits of lower utility costs and the potential of a financial return through providing grid services is a strong incentive to invest in PV-battery systems. Although battery storage is generally considered an effective means for reducing the energy mismatch between photovoltaic supply and building demand, it remains unclear when and under which conditions battery storage can be profitably operated within residential photovoltaic systems. This fact is particularly pertinent when battery degradation is considered within the decision framework. In this work, a commercially available coupled photovoltaic lithium-ion battery system is installed within a mid-sized UK family home. Photovoltaic energy generation and household electricity demand is recorded for more than one year. A comprehensive battery degradation model based on long-term ageing data collected from more than fifty long-term degradation experiments on commercial Lithium-ion batteries is developed. The comprehensive model accounts for all established modes of degradation including calendar ageing, capacity throughput, ambient temperature, state of charge, depth of discharge and current rate. The model is validated using cycling data and exhibited an average maximum transient error of 7.4% in capacity loss estimates and 7.3% in resistance rise estimates for over a year of cycling. The battery ageing model is used to estimate the cost of battery degradation associated with cycling the battery according to the power profile logged from the residential property. A detailed cost-benefit analysis using the data collected from the property and the battery degradation model shows that, in terms of utility savings and export revenue, the integration of a battery yields no added benefit. This result was, in-part, attributed to the relatively basic control strategy and efficiency of the system. Furthermore, when the cost of battery degradation is included, the homeowner is subject to a significant financial loss.
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We examine the economic viability of second use batteries from electric vehicles for load shifting and peak shaving in residential applications. We further investigate the expected impact of a growing number of residential storage systems on the electricity market. For the analysis a simulation model of a private household with integrated PV- storage system is used that is parametrized for an electricity demand of three people and a location in southern Germany. The conditions for which investments in second use batteries are profitable are examined for three scenarios. The central scenario S2 tackles an expected net increase in the electricity price by 4% per year. Upward and downward deviations from this price trajectory are covered by scenarios S1 and S3. For scenario S1, we find that investments in storage systems are profitable for all Li-ion battery costs assumed. In scenario S2, the breakeven battery price is found to be 107 €/kWh, whereas in scenario S3 with the lowest electricity price growth the battery price has to be equal or lower than 73 €/kWh to maintain economic viability.
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This paper deals with the cost-optimal sizing of solar technologies for thermal and electrical needs of residential or tertiary nearly Zero-Energy buildings. The proposed design procedure is based on lifetime simulation of building loads and energy systems; therefore, according to proper cost-optimality considerations, it is possible to find the best sizing of both heat and electricity generators in the context of high-efficiency buildings (e.g. number of solar thermal and PV modules). The paper is divided in two parts. In this first part, we describe general features and principles of the methodology, together with the physical models of building-plant system. Building requirements of thermal and electrical energy are evaluated according to internal loads and external climate, while energy system operation is simulated by a full set of equations reproducing the coupled behavior of each piece of equipment. A preliminary application example referring to a nearly Zero-Energy Building is also illustrated: In the second part of the work, we will apply and discuss the overall simulation-based optimization procedure. Results show the notable benefits of the proposed design approach with respect to traditional ones, in terms of both energy and economic savings. Besides, the proposed methodology can be successfully applied in the more general framework of Net Zero Energy Buildings (NZEBs) in order to fulfill recent regulatory restrictions and objectives in building energy performances.
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The photovoltaic market has recently experienced an enormous expansion, mainly due to the generous Feed-in-Tariffs (FiTs) adopted by many countries. However, in the recent years FiTs have been considerably reduced or even disappeared as their role in the PV deployment has ended. One of the alternatives is the Net-Metering (NEM) policy, which has attracted the interest of stakeholders as it provides a basis for the efficient collaboration between generation and the consumption profiles of the consumer. Currently, there is a lack of a universal policy harmonizing the respective legislations of the E.U. member countries. This paper proposes a novel generalized methodology for the techno-economic assessment of different NEM policies in terms of profitability for the prosumer. The methodology is tested in a formulated case study based on the current NEM policy in Greece. The method proposed uses as inputs the averaged load profiles constructed from real measurements collected from 31 consumers in the Thessaloniki area and evaluated PV production. The current NEM policy and four alternatives are examined, using as additional input the average system marginal prices of the year 2013. The results show that the proposed methodology is capable of evaluating a wide variety of NEM policies and can lead to suggestions for policy adaptation in order to establish a win-win contract between all interested stakeholders.
Several parts of the Mediterranean basin are experiencing grid parity nowadays, which is a sign that Photovoltaic (PV) technology has become competitive to conventional energy sources and may continue to develop without support. The net-metering policy takes advantage of the grid parity situation and is considered a step away from fading Feed-in Tariff schemes. Nevertheless, several policy variations make the decision about a specific net-metering scheme a complicated task. Therefore, the aim of this paper that deals with residential PV systems is two-fold. The first aim is to present a methodology that identifies the appropriate general net-metering scheme given the particularities and local conditions. The second goal is to examine the parity situation and compose net-metering policy recommendations in 6 Mediterranean countries. The current net-metering policy in Cyprus, which sets a grid charge on PV prosumers, is used as a case study under 3 scenarios. The first simulates the current policy in Cyprus, the second simulates self-consumption and different partial-netting schemes, and the third a partial-netting scheme with variable netted network charges. The results reveal that partial-netting policies with shorter timeframes of rolling credits and higher grid charges offer advantages that can be utilised for effective net-metering policies.
The use of batteries in combination with PV systems in single homes is expected to become a widely applied energy storage solution. Since PV system cost is decreasing and the electricity market is constantly evolving there is marked interest in understanding the performance and economic benefits of adding battery systems to PV generation under different retail tariffs. The performance of lead-acid (PbA) and lithium-ion (Li-ion) battery systems in combination with PV generation for a single home in Switzerland is studied using a time-dependant analysis. Firstly, the economic benefits of the two battery types are analysed for three different types of tariffs, i.e. a dynamic tariff based on the wholesale market (one price per hour for every day of the year), a flat rate and time-of-use tariff with two periods. Secondly, the reduction of battery capacity and annual discharge throughout the battery lifetime are simulated for PbA and Li-ion batteries. It was found that despite the levelised value of battery systems reaches up to 28% higher values with the dynamic tariff compared to the flat rate tariff, the levelised cost increases by 94% for the dynamic tariff, resulting in lower profitability. The main reason for this is the reduction of equivalent full cycles performed with by battery systems with the dynamic tariff. Economic benefits also depend on the regulatory context and Li-ion battery systems were able to achieve internal rate of return (IRR) up to 0.8% and 4.3% in the region of Jura (Switzerland) and Germany due to higher retail electricity prices (0.25. CHF/kW. h and 0.35. CHF/kW. h respectively) compared to Geneva (0.22. CHF/kW. h) where the maximum IRR was equal to -0.2%.
For future energy supply systems the effects and benefits of battery storage systems in households with photovoltaic (PV) generators and the effects on distribution and transmission grids need to be identified and analyzed. The development of grid relieving management strategies for the storage system in due consideration of self-consumption is a necessary step forward in order to analyze the potential of private home battery storage systems to reduce stress on the power supply system. A MATLAB-based model of a lithium-ion storage system has been developed. The model is applicable for a wide range of PV generator sizes, different battery storage systems and diverse management strategies. In order to identify the potential of grid relieving forecast strategies, without discharging the storage into the grid, a management strategy based on persistence forecasts of solar radiation and household load demand has been implemented and analyzed. To minimize forecast uncertainties a proportional plus integral controller has been developed. The persistence forecast management strategy is applicable in real-life PV-battery-systems and due to the simple forecast it is easy to equip existing systems with such a management system with only low effort. As a result it will be shown that a storage system management based on forecasts has a significantly higher potential to relieve the grid than a system that only maximizes self-consumption as it is usually used nowadays. Besides, such a management strategy is able to unload the grid more than a static power reduction to 70% of the nominal power rating according to the current German Renewable Energy Sources Act (EEG). At the same time, the self-consumption can be retained at nearly the same level as by using a management strategy to purely maximize the self-consumption. Even less energy is wasted then with the feed-in limitation. See:
The penetration of renewable sources (particularly wind power) in to the power system network has been increasing in the recent years. As a result of this, there have been serious concerns over reliable and satisfactory operation of the power systems. One of the solutions being proposed to improve the reliability and performance of these systems is to integrate energy storage devices into the power system network. Further, in the present deregulated markets these storage devices could also be used to increase the profit margins of wind farm owners and even provide arbitrage. This paper discusses the present status of battery energy storage technology and methods of assessing their economic viability and impact on power system operation. Further, a discussion on the role of battery storage systems of electric hybrid vehicles in power system storage technologies had been made. Finally, the paper suggests a likely future outlook for the battery technologies and the electric hybrid vehicles in the context of power system applications.