Introduction of Electricity Storage and Photovoltaics for an Adequate
Self-Sufficiency in Large Building Complexes
EMMANUEL KARAPIDAKIS1, MARINI MARKAKI1, ARIADNI KIKAKI1, SOFIA YFANTI2,
MARIOS NIKOLOGIANNIS3
1Department of Electrical and Computer Engineering,
Hellenic Mediterranean University,
Estavromenos Campus 71410 Heraklion,
GREECE
2Department of Mechanical Engineering,
Hellenic Mediterranean University,
Estavromenos Campus 71410 Heraklion,
GREECE
3Department of Physics,
University of Crete,
Voutes Campus 70013 Heraklion,
GREECE
Abstract: Energy usage in large-scale premises exhibits a distinctive pattern, encompassing both thermal energy
and electricity. As a result of the recent energy crisis, the operational expenditures associated with these
demands have markedly risen. In line with EU Energy Policies, one of the future goals is the transition towards
energy-wise self-sufficient buildings powered by renewable energy sources (RES). Nowadays, a combination
of contemporary energy management systems, electricity storage and RES are proposed to achieve nearly zero
emission-producing energy consumption in buildings. This paper examines the energy consumption patterns of
a hotel situated on the Mediterranean, in order to investigate the potential of RES-induced independence and
forecast future expansion prospects. An algorithm has been introduced to both optimize and enhance the self-
sufficiency of the hotel under consideration. The proposed algorithm successfully enhances the hotel's energy
self-sufficiency, achieving a remarkable 99% rate through the dimensions of PV power and corresponding
battery capacity for all years under examination, yielding the corresponding financial and environmental
conclusions.
Key-Words: - Energy transition, prosumers, self-sufficiency, RES, sustainable hospitality, GHG emissions.
Received: March 27, 2022. Revised: November 12, 2023. Accepted: December 11, 2023. Published: January 15, 2024.
1 Introduction
Sustainability in premises of large scale has
emerged as a significant concern for numerous
nations in recent decades, [1], [2], [3]. The
infrastructure sector ranks among the largest energy
consumers, accounting for approximately 30% to
40% of total energy consumption, [4]. Furthermore,
over a third of all greenhouse gas (GHG) emissions,
[5], a pivotal factor in global warming and climate
change, [6], [7], are produced by building
complexes, contributing to atmospheric imbalance,
[8]. Hence, attaining sustainability in buildings is a
concept supported by multidimensional pillars, such
as environmental, economic, social, ecological,
technical, and technological ones, [5].
To manage the increased operational
expenditures associated with energy demands
during an energy crisis, individuals and businesses
often need to take steps to reduce their energy
consumption, invest in energy-efficient
technologies, explore renewable energy options, and
adapt their operations to the changing energy
landscape, [9], [10], [11]. This may also involve
government and industry collaboration to address
the crisis and finding solutions to stabilize energy
costs.
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DOI: 10.37394/232015.2024.20.5
Emmanuel Karapidakis, Marini Markaki,
Ariadni Kikaki, Sofia Yfanti, Marios Nikologiannis
E-ISSN: 2224-3496
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The energy policies of the European Union aim
at decarbonising the economy, towards energy
transition, [12] and thus outline a future goal of
achieving self-sufficiency in energy through
renewable sources and the construction of buildings
that are close to achieving net-zero energy
consumption, [13], [14], [15].
This concept necessitates that a building's local
installation of renewable energy sources (RES)
should provide sufficient thermal and electrical
energy to meet its needs. In accordance with EU
goals, Greece has conceptualized certain policies
and measures aspiring to achieve similar goals. Such
measures include RES system installation through
the scheme of net metering and virtual net metering,
promotion of renewable energy systems for heating
and cooling and improving the energy efficiency of
existing machinery and installations, [16], [17].
A combination of RES technologies and state of
the art storage systems, which are developed with
the use of green technologies, permits the extension
of a system’s efficiency. A storage system
contributes to the stabilization of RES production
and strives to remove the stochastic aspect of the
process, to ensure the system’s self-sufficiency,
[18], [19].
According to the latest inventory of building
stock conducted the by Hellenic Statistical
Authority in 2011, hotel buildings constitute 1.7%
of Greece’s building stock. Thus, the present study
adopts a Mediterranean hotel’s energy consumption
as the object of research, since the specific
geolocation combined with the financial
approachability of PV technologies, embedded with
a storage system, leads to the successful
introduction of the specific coupling to an energy
system integrated to the local network. The energy
demand until 2030 is forecasted, in an effort to
determine whether a long-term investment is
feasible for potential interested investors.
In order to determine the optimal characteristics
of the coupling to be installed, an algorithm is
implemented that determines the PV power and
storage capacity required to ensure a satisfactory
percentage of self-sufficiency, allowing the potential
investor to maintain a competitive business model in
the hospitality industry. The algorithm calculates the
required annual system expansion after the initial
installation, maintaining the same rate of self-
sufficiency for the entirety of the investment’s time
horizon.
The specified algorithm estimates the annual
needed system expansion required for 99% of self-
sufficiency, with a total expenditure of 16,950,000 €
and a payback period of 15.5 years. The results of
the study are quite discouraging for any interested
party, an obstacle that could be surpassed through
governmental support in the form of subsidies that
would reduce the initial investment needed for such
an investment.
Nonetheless, apart from the financial
investigation of the proposed installation, the
environmental impact is estimated. Despite the
decline in global interest due to the lockdowns
induced by COVID-19 and the disruptions in the
supply chains due to the Ukrainian war, the frequent
heat waves and heavy rainfalls noted in the southern
and middle European countries resurface the
public’s concern, [20].
As both energy production and consumption are
considered crucial factors for the reduction of
greenhouse gas emissions, to succeed in the
sustainability of energy usage and supply, the CO2
footprint calculation is one of the effective ways to
evaluate GHG emissions. The concept of a CO2
footprint originated from the ecological footprint
concept proposed by William in 1992, which refers
to the number of greenhouse gases (GHG) emitted
through production and consumption activities, [21].
According to the literature (C2es.org &
Statista.com), in 2019 electricity and heat were
responsible for 32% of world greenhouse gas
emissions. Therefore, this study will attempt to
calculate CO2 mitigation, thus, outlining a different
important outcome of the proposed algorithm.
2 Materials and Methods
2.1 Data Analysis
In this study, a representative 5-star hotel on the
Crete Island was analysed and its energy needs were
investigated. The specific hotel occupies a surface
area equal to 384,451.4 m2, with a total capacity of
411 rooms.
The period of electricity consumption that has
been used in this study is two years: 2021 and 2022.
Figure 1 presents the total annual electricity demand
and the corresponding power peaks for 2021 and
2022, respectively.
In 2021, the total demand for electricity reached
382MWh with a peak power at 1.53MW. In 2022,
the total demand increased up to 576MWh with
approximately the same peak power at 1.55MW.
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Emmanuel Karapidakis, Marini Markaki,
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Fig. 1: Annual consumption and hourly recorded
max
Taking into account the annual consumption, as
well as the total numbers of rooms, the annual
average consumption per room is calculated at 1,208
kWh/room for 2021 and 1,706kWh/room for 2022.
Furthermore, as expected, the highest monthly
energy consumption values for the specific two-year
period range between April and October. The
following Figure 2 and Figure 3 show the monthly
consumption as well as the maximum hourly
recorded consumption for each month, respectively.
Fig. 2: Monthly consumption of 2021 and 2022
Fig. 3: Demand peaks of 2021 and 2022
By observing the monthly analysis, it is evident
that in August the highest consumption of the whole
year was recorded, a typical case for a hotel located
in the island of Crete, where the peak of traffic
attributed to tourism is notified in the specific
month. In comparison to 2021, the consumption for
each month of the year 2022 has increased,
indicating an annual rise in hospitality levels. In
Figure 4 and Figure 5, the electricity demand, for
both the days of this specific month and the most
demanding week of the month, are depicted.
Fig. 4: Calculation of energy imported from the grid
Fig. 5: Most demanding week of August
Moreover, a notable observation from Figure 4
and Figure 5 is the increase in electricity
consumption between the two years, with variation
in the differences for each week.
2.2 Methodology
The annual pattern of demand is present in both
years, indicating a rise during summer and a
decrease in winter, fluctuations that, as previously
mentioned, remain the same every year. The
seasonality spotted in the time series of the demand
for the two years, along with the increase in the
values of 2022, permit an elementary forecasting
method for the years 2023-2030. For every
forecasted year, the total annual demand is selected
to be equal to that of the year 2022 multiplied by a
factor of value greater than one, leading to a
saturation level, equal to the total visitation capacity
of the hotel, as presented in Figure 6. The
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Emmanuel Karapidakis, Marini Markaki,
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assumption being made is that there will be a rise in
the hotel’s popularity in the coming years. Each
year’s hourly values are multiplied by their
respective factor, allowing the implementation of
the algorithm to be described.
Fig. 6: Estimation of imported electricity from grid
The algorithm's successful implementation
requires the acquisition of two datasets, the hourly
values of the hotel’s annual demand for 2021 and
2022 and the hourly energy output data from a solar
panel situated in Crete. Using these datasets, the
algorithm computes the status of the energy storage
system for each hour, as well as the distribution of
energy derived from photovoltaic production and
the grid's contribution to meeting the energy
demand. Subsequently, overall metrics related to the
system's efficiency can be calculated and utilized to
establish a correlation between these metrics and the
decision variables within the model.
The hourly demand values are represented as
Load[t], where 't' represents the number of hours
after installation, and the normalized PV
(Photovoltaic) output as PV[t]. So, for a specific
hour 't,' the PV output PV_out[t] is equal to:
(1)
here, PV[t] represents the normalized PV output
for that specific hour, and "PV Capacity" denotes
the capacity or potential of the solar panel
installation. The product of these values gives the
actual PV output for that hour.
Before the theoretical installation, it is assumed
that the storage system starts at full capacity upon
integration into the energy system (Storage[0] =
Capacity). Additionally, both output and storage
efficiencies are set at 90%, with the maximum
output determined as 'max(Load[t])' for each hour
from t=1 to t=8760.
Using the collected data, essential hourly values
that characterize the system's behaviour are
computed.
First, the output of the storage system integrated
into the system for each hour, denoted as
"Battery_out[t]," is computed following the
procedure outlined in Figure 7.
Fig. 7: Calculation of battery storage discharge after
each hour
Thereafter, the total amount of energy stored in
the battery at the conclusion of each hour 't' is
determined, as depicted in Figure 8.
Fig. 8: Calculation of battery storage level each hour
In situations where neither the PV production nor
the battery storage is adequate to meet the energy
demand, the system resorts to importing power from
the grid. The calculation of the amount to be
imported is detailed in Figure 9.
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Fig. 9: Calculation of energy imported from the grid
Given the constraints of both storage capacity
and round-trip efficiency, the surplus energy
generated by the photovoltaic system, after
accounting for losses, cannot, always, be entirely
stored. As a result, the system must consider and
calculate rejections, as demonstrated in Figure 10.
Fig. 10: Calculation of rejections of each hour
Furthermore, the losses incurred by the storage
system following each charge or discharge operation
are determined according to the procedures outlined
in Figure 11.
Fig. 11: Calculation of system losses in each hour
Through the hourly attribute calculations
outlined earlier, the system derives the annual
percentages for rejections, network contributions,
and its self-sufficiency.
The percentage of the network's contribution is
calculated as the annual energy imported over the
annual energy requirement:
(2)
The annual rejection percentage is determined by
dividing the annual amount of rejected energy by
the total PV production that was neither discarded
due to the storage system's inefficiency nor
adequately stored by the end of the year:
(3)
The percentage of annual energy successfully
harnessed is calculated by subtracting the combined
amount lost, rejected, and stored at the end of the
year from the annual PV production and full charge
of the storage system. This result is then divided by
the annual energy requirement:
(4)
Through the calculation of the values derived
from equations (2), (3), and (4), it is possible to
ascertain the relationship between annual self-
sufficiency, installed PV power, and storage
capacity. Additionally, the correlation between
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capital expenditure and these variables can be
determined.
2.3 Calculation of CO2 Emission
The dual needs of economic growth and emission
reduction constitute a grand challenge for all
countries, [22], leading, thus, nations, researchers
and scholars on measurements of CO2 footprints and
their evaluation.
Fig. 12: Crete’s Island Energy Balance of 2021
Within this study for the calculation of CO2
emissions, the energy balance of Crete, presented in
Figure 12, will be taken under consideration, as
energy balance data depict the balance between the
supply, transformation (i.e., conversion), and
consumption of specific energy types and sectors,
[22]. Analysing the yearly electricity consumption
values for the years 2022 till 2030 (Table 1), it
becomes apparent that the acquired energy from the
PVs instalment is significant.
Table 1. Record of Electricity consumption per year
Year
Electricity Need
(kWh)
From grid
(kWh)
2022
5,396,550.45
53,051.85
2023
6,399,938.81
63,346.78
2024
6,799,934.98
67,320.39
2025
7,149,931.63
70,823.42
2026
7,399,929.24
73,020.20
2027
7,499,928.29
75,029.68
2028
7,499,928.29
75,029.68
2029
7,499,928.29
75,029.68
2030
7,499,928.29
75,029.68
For the transformation of the above data to CO2
emissions, the EF from the CoM Default Emission
Factors for the Member States of the European
Union for the year 2013 was used and its value was
0.757 tnCO2/MWh, as published in their respective
report of 2017, Σφάλμα! Το αρχείο προέλευσης
της αναφοράς δεν βρέθηκε.. Figure 13 presents the
final annual percentage of the calculation of the CO2
emissions due to electricity with and without the
installation of PVs.
Fig. 13: Calculation of CO2 emissions
The previous Figure 13 highlights the significant
mitigation of CO2 emissions, which are reduced to
negligible, as a result of extensive use of PV and
batteries, which lead to a significant level of self-
sufficiency up to 99% of the annual energy mix of
the hotel’s electricity needs.
3 Results
An immediate result of equations (2), (3) and (4) is
the calculation of the energy system’s self-
sufficiency for a wide range of PV power and
storage capacity. The set which yields at least 99%
of annual self-sufficiency and ensures at the same
time a minimum CAPEX is sought after for each
year. As the energy demand increases annually, so
does the required PV and storage capacity that
provide the same percentage of self-sufficiency,
meaning a requirement for an additional investment
that negatively impacts the annual operational
expenditure. For total installation costs of PV power
and storage capacity equal to 1,000€/kW and
250€/kWh respectively, the total investment after
each year is calculated and presented in Table 2.
Table 2. Methodology results per year till 2030
PV (kW)
Battery (kWh)
CAPEX (€)
7,200
19,800
12,150,000
9,000
22,100
14,525,000
9,200
24,600
15,350,000
9,800
25,400
16,150,000
10,400
25,600
16,800,000
10,400
26,200
16,950,000
10,400
26,200
16,950,000
10,400
26,200
16,950,000
10,400
26,200
16,950,000
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Emmanuel Karapidakis, Marini Markaki,
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In each year, the system’s expansion requires
less increase of PV power and storage capacity for
99% of annual self-sufficiency, due to the declining
pace of visitation growth, as previously forecasted.
Once the hospitality capacity is reached, additional
installation is not required, assuming total
occupancy for the following years.
In the table below, two scenarios are calculated,
one assuming the annual system expansion scheme
and a scheme of total dependency on the network. In
the first scenario, the annual cost is equal to the
annual cost of system expansion plus the cost of
energy imported from the grid. In the second
scenario, the annual cost is the total cost of load
satisfaction through total dependency on the grid.
For the indicative energy cost of 0.15€/kWh
originating from the grid, the total costs are
presented in Table 3.
Table 3. CAPEX and OPEXs of the examined scenarios
Year
PV+Bat cost (€)
Base-line cost (€)
2023
2,384,502.02
959,990.82
2024
835,098.06
1,019,990.25
2025
810,623.51
1,072,489.75
2026
660,953.03
1,109,989.39
2027
161,254.45
1,124,989.24
2028
11,254.45
1,124,989.24
2029
11,254.45
1,124,989.24
2030
11,254.45
1,124,989.24
Total:
4,886,194.43
8,662,417.17
Profit:
3,776,222.74
Comparing the eight-year profit to the initial
CAPEX, the ratio is calculated as:
Concluding that in eight years, the return of
investment is about a third of the initial expenditure,
a discouraging conclusion for a potential investor.
Assuming that for the following years the hotel
maintains the occupancy levels at maximum
capacity, and the grid price remains at 0.15€/kWh,
the initial payback period of the investment will be
in 7.5 years after 2030.
4 Conclusion
The conclusions drawn from the data calculated and
presented in Table 2 and Table 3, could possibly
ward off any entrepreneur willing to maintain
competitiveness in the hospitality industry from
investing in such RES technologies and their
implementation to their energy system as described
in the specific methodology. For the hotel serving as
an object of study, a payback period of 15.5 years
and a total cost of 16,950,000 € were calculated as
attributes of the specific investment, significant
values for the estimated cost imported energy equal
to 0.15€/kWh. Incentives in the form of
government subsidies could lighten the financial
burden of the investor’s CAPEX, in an effort to
direct the sector towards the achievement of EU
climate goals, ensuring its sustainability and
economic growth at the same time.
However, when viewed from the energy’s
environmental variable, the significant mitigation in
carbon dioxide emissions which causes severe
environmental damage and influences a nation’s
sustainable development, adds further value to the
exploitation of environmentally friendly
technologies. Especially in the tourism industry,
where green consumers are increasing.
Despite the discouraging results, the
methodology developed for the purposes of the
study could be altered to fit different, more suitable
market designs, materialized with contemporary
storage technologies, such as hydrogen storage, that
aspire to achieve the same goals of the current
model. The methodology could, also, serve as a
benchmark for alternative models of demand
response analysis to optimize energy systems of
varied energy profile attributes. All these prospects
will occupy the team’s time and resources in the
near future.
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The authors equally contributed to the present
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The authors have no conflicts of interest to declare.
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WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT
DOI: 10.37394/232015.2024.20.5
Emmanuel Karapidakis, Marini Markaki,
Ariadni Kikaki, Sofia Yfanti, Marios Nikologiannis
E-ISSN: 2224-3496
45
Volume 20, 2024