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As the electric energy in the non-connected islands is mainly produced by oil-fueled power plants, the unit cost is extremely high due to import cost. The integration of renewable resources in the energy mix is essential for reducing the financial and environmental cost. In this work, various energy resources (renewable and fossil fuels) are evaluated using technical, environmental and economic criteria with an emphasis to biomass, pumped hydro storage and replacement of oil power plants. Finally, a synthesis is presented as a toy-model in an Aegean island that satisfies the electric energy demand including base and peak electric loads.
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Available online at www.sciencedirect.com
Available online at www.sciencedirect.com
ScienceDirect
Energy Procedia 00 (2017) 000–000
www.elsevier.com/locate/procedia
1876-6102 © 2017The Authors. Published by Elsevier Ltd.
Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling.
The 15th International Symposium on District Heating and Cooling
Assessing the feasibility of using the heat demand-outdoor
temperature function for a long-term district heat demand forecast
I. Andrića,b,c*, A. Pinaa, P. Ferrãoa, J. Fournierb., B. Lacarrièrec, O. Le Correc
aIN+ Center for Innovation, Technology and Policy Research -Instituto Superior Técnico,Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal
bVeolia Recherche & Innovation,291 Avenue Dreyfous Daniel, 78520 Limay, France
cDépartement Systèmes Énergétiques et Environnement -IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France
Abstract
District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the
greenhouse gas emissions from the building sector. These systems require high investments which are returned through the heat
sales. Due to the changed climate conditions and building renovation policies, heat demand in the future could decrease,
prolonging the investment return period.
The main scope of this paper is to assess the feasibility of using the heat demand outdoor temperature function for heat demand
forecast. The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of 665
buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district
renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were
compared with results from a dynamic heat demand model, previously developed and validated by the authors.
The results showed that when only weather change is considered, the margin of error could be acceptable for some applications
(the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation
scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered).
The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the
decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and
renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the
coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and
improve the accuracy of heat demand estimations.
© 2017 The Authors. Published by Elsevier Ltd.
Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and
Cooling.
Keywords: Heat demand; Forecast; Climate change
Energy Procedia 125 (2017) 425–434
1876-6102 © 2017 The Authors. Published by Elsevier Ltd.
Peer-review under responsibility of the scientic committee of the European Geosciences Union (EGU) General Assembly 2017 – Division
Energy, Resources and the Environment (ERE).
10.1016/j.egypro.2017.08.089
10.1016/j.egypro.2017.08.089 1876-6102
© 2017 The Authors. Published by Elsevier Ltd.
Peer-review under responsibility of the scientic committee of the European Geosciences Union (EGU) General Assembly
2017 – Division Energy, Resources and the Environment (ERE).
Available online at www.sciencedirect.com
ScienceDirect
Energy Procedia 00 (2017) 000000
www.elsevier.com/locate/procedia
1876-6102 © 2017 The Authors. Published by Elsevier Ltd.
Peer-review under responsibility of the scientific committee of the European Geosciences Union (EGU) General Assembly 2017
Division Energy, Resources and the Environment (ERE).
European Geosciences Union General Assembly 2017, EGU
Division Energy, Resources & Environment, ERE
Creating the electric energy mix in a non-connected island
Maria Chalakatevakia, Paraskevi Stamoua,*, Sofia Karalia, Vasiliki Daniila, Panayiotis
Dimitriadisa, Katerina Tzoukaa, Theano Iliopouloua, Demetris Koutsoyiannisa, Panos
Papanicolaoua and Nikos Mamassisa
aNational Technical University of Athens, Heroon Polytechniou 9, Zografou 15780, Greece
Abstract
As the electric energy in the non-connected islands is mainly produced by oil-fueled power plants, the unit cost is extremely high
due to import cost. The integration of renewable resources in the energy mix is essential for reducing the financial and
environmental cost. In this work, various energy resources (renewable and fossil fuels) are evaluated using technical, environmental
and economic criteria with an emphasis to biomass, pumped hydro storage and replacement of oil power plants. Finally, a synthesis
is presented as a toy-model in an Aegean island that satisfies the electric energy demand including base and peak electric loads.
© 2017 The Authors. Published by Elsevier Ltd.
Peer-review under responsibility of the scientific committee of the European Geosciences Union (EGU) General Assembly 2017
Division Energy, Resources and the Environment (ERE).
Keywords: renewable energy resources; stochastic simulation; Hurst parameter; hybrid energy system
1. Introduction
Most of the Greek islands are not connected to the electricity network of the mainland. The production of electric
energy relies on local oil fuel plants, which have a high cost due to the import cost of oil (compared to the import and
distribution cost on the mainland and the import-free use of renewable energy resources) and also, a high
environmental impact. During the last years, there has been a significant effort to replace the energy produced from
oil fuel with renewable resources either partly or entirely. The continuous advances in renewable energy resources
technology along with the gradual installation-cost reductions pave the way towards a wider adaptation of renewable
energy resources worldwide.
*Corresponding author. Tel.: +302107722831; fax: +302107722831.
E-mail address: stamou.paraskevi@gmail.com
Available online at www.sciencedirect.com
ScienceDirect
Energy Procedia 00 (2017) 000000
www.elsevier.com/locate/procedia
1876-6102 © 2017 The Authors. Published by Elsevier Ltd.
Peer-review under responsibility of the scientific committee of the European Geosciences Union (EGU) General Assembly 2017
Division Energy, Resources and the Environment (ERE).
European Geosciences Union General Assembly 2017, EGU
Division Energy, Resources & Environment, ERE
Creating the electric energy mix in a non-connected island
Maria Chalakatevakia, Paraskevi Stamoua,*, Sofia Karalia, Vasiliki Daniila, Panayiotis
Dimitriadisa, Katerina Tzoukaa, Theano Iliopouloua, Demetris Koutsoyiannisa, Panos
Papanicolaoua and Nikos Mamassisa
aNational Technical University of Athens, Heroon Polytechniou 9, Zografou 15780, Greece
Abstract
As the electric energy in the non-connected islands is mainly produced by oil-fueled power plants, the unit cost is extremely high
due to import cost. The integration of renewable resources in the energy mix is essential for reducing the financial and
environmental cost. In this work, various energy resources (renewable and fossil fuels) are evaluated using technical, environmental
and economic criteria with an emphasis to biomass, pumped hydro storage and replacement of oil power plants. Finally, a synthesis
is presented as a toy-model in an Aegean island that satisfies the electric energy demand including base and peak electric loads.
© 2017 The Authors. Published by Elsevier Ltd.
Peer-review under responsibility of the scientific committee of the European Geosciences Union (EGU) General Assembly 2017
Division Energy, Resources and the Environment (ERE).
Keywords: renewable energy resources; stochastic simulation; Hurst parameter; hybrid energy system
1. Introduction
Most of the Greek islands are not connected to the electricity network of the mainland. The production of electric
energy relies on local oil fuel plants, which have a high cost due to the import cost of oil (compared to the import and
distribution cost on the mainland and the import-free use of renewable energy resources) and also, a high
environmental impact. During the last years, there has been a significant effort to replace the energy produced from
oil fuel with renewable resources either partly or entirely. The continuous advances in renewable energy resources
technology along with the gradual installation-cost reductions pave the way towards a wider adaptation of renewable
energy resources worldwide.
*Corresponding author. Tel.: +302107722831; fax: +302107722831.
E-mail address: stamou.paraskevi@gmail.com
426 Maria Chalakatevaki et al. / Energy Procedia 125 (2017) 425–434
2 Chalakatevaki et al. / Energy Procedia 00 (2017) 000000
Since the late 1970s, the idea of a so called Hybrid Energy System (HES) that combines wind, solar and diesel
generators, as well as battery tanks, has been developed. In recent years, the integration of other renewable resources,
such as pumped hydro storage, wave energy and biomass, is also evolving. According to [1] combining HES with
wave energy converters to create the energy mix of a non-connected island could lead to a much higher renewable
fraction. However, so far, the combination of all renewables towards an autonomous grid is still at an early stage [1].
In this work, all six renewable resources are examined (solar, wind, marine, hydropower, biomass and geothermal) in
order to create the energy mix for a non-connected island. In this respect, we note that the uncertainty that dominates
the associated natural processes and the energy demand is considerable and requires the use of a stochastic approach
in order to achieve effective planning of the energy system.
For our case study the selected the area for the toy-model analysis is Astypalaia, which is a Greek island, part of
Dodecanese, an archipelago of twelve major islands in the south-eastern Aegean Sea (Figure 1). The island has about
1300 inhabitants and it extends in an area of 97 km2. Astypalaia has more than 20 000 visitors per year which makes
tourism the main industry.
Fig. 1. Location of Astypalaia (36◦5390̍N 26◦3131̍E).
Today the electric energy demand is satisfied by an oil-fuelled thermal station because the island is not connected to
the electricity system of the mainland and there are no renewable energy sources installations in the area.
According to records from 2014 to 2015, the islands mean annual demand was 6250 MWh. The peak hourly
demand was 2.2 MWh (occurred on 14/08/2015 at 21.00) and the minimum was 0.23 MWh. In Figure 2, the hourly
energy demand of Astypalaia for the 2014-2015 period is shown. As expected, it exhibits high values during the
summer touristic period and low values during the rest of the year.
Fig. 2. Historical 2014-2015 hourly electric energy demand (MWh).
14/08/2015
21:00, 2.25
0.00
0.50
1.00
1.50
2.00
2.50
Demand (MWh)
Athens
Greece
Astypalaia
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Chalakatevaki et al. / Energy Procedia 00 (2017) 000000 3
2. Data
2.1. Existing data for renewable energy resources
First, the possibility of hydropower production in the island is examined. The mean annual precipitation in
Astypalaia is estimated as 680 mm/yr. According to hydrological modelling of the only water basin in the island
(Livadi), the mean annual runoff is estimated to be 100 mm/yr. The existing dam located downstream of the Livadi
basin area (8 km2) has a storage capacity of 875 000 m3 and receives approximately 800 000 m3/yr inflows on a yearly
basis. The dam is 32 m high. Today, the reservoir serves only domestic and agricultural water uses.
Furthermore, the potential of deploying the agricultural field residues (remains) in order to produce energy from a
biomass station is investigated. The agricultural residues of the island are 105 tonnes, 40% of which are oat crops,
21% barley crops, 17% wheat crops, 6% corn crops, 6% trees (pruning), 9% olive trees and 1% vineyards. Considering
mean calorific value of 18.5 MJ/kg according to [2] and the efficiency of the power plant as 0.35, the expected electric
energy production is 190 MWh/yr. There is a potential to cultivate energy crops with low irrigation demands that can
cover a maximum area of 500 ha.
Taking into account that Astypalaia is located at the Volcanic Arc of southern Aegean Sea, the potential of using
geothermal energy is also considered. Although no measurements have been implemented, we assume that there is
exploitable geothermal energy of a minimum of 0.5 MW. For reference, according to [3], in the Aegean islands of
Milos and Nisyros units of 2 MW and 3 MW respectively have the potential to be installed.
Since there is a high wind, solar and wave energy potential in the island, the installation of wind turbines, solar
panels and wave energy converter is encouraged. In Figure 3, the histogram of wind speed at 75 m height and wave
height are shown, as well as the monthly distribution of solar radiation in the island.
a b
c
Fig. 3. Histogram of (a) wind speed at 75 m height; (b) wave height; (c) monthly solar radiation.
0
50
100
150
200
250
300
350
1 2 3 4 5 6 7 8 9 10 11 12
Solar radiation (W/m2)
Month
0
5
10
15
20
25
30
Frequency (%)
Wind speed (m/s)
0
10
20
30
40
50
0.5 11.5 22.5 33.5 44.5 5
Frequency (%)
Wave height (m)
0
50
100
150
200
250
300
350
1 2 3 4 5 6 7 8 9 10 11 12
Solar radiation (W/m
2
)
Month
428 Maria Chalakatevaki et al. / Energy Procedia 125 (2017) 425–434
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2.2. Generation of synthetic data for energy production simulation
Considering the electric energy demand simulation implemented in [4], a synthetic time series of hourly electric
demand is produced and evaluated for a 100 year period using the measurements from 2014 to 2015 as seen in Figure
2. The resulting synthetic demand time series shows that the annual demand is 6265 MWh/yr and the peak demand is
2.6 MWh/yr.
Regarding the hydroclimatic conditions presented in [5], 100 years of rainfall data and mean monthly temperature
are generated. The time series produced are based on historic hydrometeorological data from June 2009 to February
2017. The hourly synthetic data are generated through Castalia model [6]. The model preserves the essential statistical
characteristics (marginal and joint distributions) of historical data at three time scales (annual, monthly, daily), as well
as the long-term persistence (Hurst-Kolmogorov dynamics), periodicity and rainfall intermittency.
As for the solar, wind and wave data, the mean-hourly synthetic time series for 100 years are produced using the
methodology developed by [7] which is suitable for double periodic processes. Particularly, this methodology
preserves the double cyclostationarity (i.e. diurnal and seasonal) of a process though the hourly-monthly marginal
distributions, including intermittent characteristics such as probability zero values, as well as the dependence structure
of the processes through the climacogram (variance of scaled process) rather than the autocovariance or the power
spectrum [8]. For the dependence structure we apply a Hurst-Kolmogorov model based on the empirical climacogram
of each process. Finally, for the generation scheme we use the CSAR algorithm (cyclostationary sum of finite
independent AR(1) processes according to [9]) capable of generating any length time series following an Hurst-
Kolmogorov, or various other processes, and with arbitrary distributions of each internal stationary process of the
double cyclostationarity process. The methodology for producing the wind and wave time series is detailed in [10],
for the solar irradiance time series in [11] and for the cross-correlations between several involved hydroclimatic
processes (temperature, dew-point, precipitation and wind speed) in [12].
More detailed information on the timeseries generation can be found at the provided references. All timeseries
reproduce exceptionally well the essential characteristics of the marginal distribution and the dependence structure of
each process. Although the methodologies do not explicitly focus on reproducing the extremes behavior, they exhibit
a satisfactory reproduction of the extremes by preserving the skewness in all cases, or even the kurtosis as well, as in
timeseries produced according to [7]. However, we note that the design and simulation of the energy system is not
particularly sensitive to the extreme events of each timeseries due to technical limitations of the infrastructure, e.g.
when exceeding the reservoir capacity leads to spilling.
3. Simulation of energy system
3.1. Suggested infrastructure
Given the existing and the synthetic data for our case study, possible infrastructure for the exploitation of each
energy resource is suggested below.
An upgrade of the existing dam into a hydroelectric dam is the basis of the proposed solution. A turbine of 0.08
MW is proposed in order to produce 25 MWh per year [5].
Regarding the marine energy, using the 100 years of hourly wave height (and produced energy) synthetic time
series, overtopping wave energy converters are proposed to produce energy collecting the incoming waves through
overtopping and wave run-up into deposit reservoirs, and feeding the water to a low head Kaplan turbine of 0.3 MW
with a capacity factor of 0.32. According to this scenario, 835 MWh per year are produced [10].
With regard to the solar power, the features of the proposed photovoltaic park are presented in Table1.
Table 1. Proposed photovoltaic park.
Power (MW) Total area of
panels (m2)
Total area of
park (m2)
Panel efficiency
(%)
Expected energy production
per park(MWh/yr)
Capacity
factor
0.1 754 11 000 13.4 162 0.16
Maria Chalakatevaki et al. / Energy Procedia 125 (2017) 425–434 429
Chalakatevaki et al. / Energy Procedia 00 (2017) 000000 5
The proposed wind turbine of 0.5 MW has total height of 75 m and diameter of 54 m. The cut-in and cut-out wind
speed is 2.5 m/s and 25 m/s respectively [13] and assuming a capacity factor of 0.5, 2 233 MWh per year are produced.
The features of the proposed biomass station [13] are summarized in Table 2.
Table 2. Energy produced from biomass power station.
Area (ha)
Calorific
value
(MJ/kg)
Production
(t/yr)
Expected
energy
production
(MWh/yr)
Power
(MW)
Capacity
factor
Existing crops
50
18.5
100
190
1 0.35
Cultivated energy
crops
100 18 1 000 1 750
With regard to the infrastructure suggested, several possible energy mix scenarios are simulated below and their
performance is evaluated in terms of providing adequately the energy needed at all times (i.e., total energy production,
hourly failure, mean annual surplus and deficit).
More detailed information on the suggested infrastructure can be found at the provided references.
3.2. Weather-related renewable energy resources
As a first step to create the energy mix, we explore the possibility of designing an energy system based only on
weather-related renewable energy resources. However, as shown in Figure 4 the weather-related renewable resources
cannot reach the peak demand. For energy autonomy, using only wind turbines, we estimate a high percentage of
failure (38 % in 3 MW installed capacity) in an hourly scale (Figure 4a). Using only solar panels (Figure 4b) leads to
a higher percentage of failure than the wind-only scenario (65% in 3 MW installed capacity). Finally, combining wind
and solar energy (Figure 4e) results in a quite smaller percentage of failure (40% in a 3 MW installed capacity of
which 2 MW is wind power and 1 MW is solar power). A great amount of energy surplus is produced in all cases
which cannot be used and controlled because it is not synchronized with the energy demand (Figure 4c, 4d, 4f).
a b
0
10
20
30
40
50
60
70
80
90
100
0 0.5 1 1.5 2 2.5 3
Failure (%)
Total installed MW of wind turbines
0.5 MW per turbine
0
10
20
30
40
50
60
70
80
90
100
0 1 2 3 4
Failure (%)
Total installed MW of solar panels
0.1 MW per panel
430 Maria Chalakatevaki et al. / Energy Procedia 125 (2017) 425–434
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c d
e f
Fig. 4. Evaluation of weather related renewable: (a) wind probability of failure (%); (b) solar probability of failure (%); (c) wind mean annual
surplus-deficit (MWh); (d) solar mean annual surplus-deficit (MWh); (e) wind-solar probability failure (%); (f) wind-solar deficit of demand (%).
As shown in Figure 4, wind and solar energy cannot provide sufficient energy to satisfy the peak demand.
Therefore, marine and hydro are added in order to examine their contribution to the energy mix. The energy system
is simulated at an hourly scale for a 100 years period. Several scenarios with combinations of renewable resources are
examined in Table 3.
For each energy mix scenario, we calculate the probability of failure when satisfying the peak hourly energy
demand, the mean annual deficit of energy and the mean annual surplus of energy, as shown in Figure 5. All seven
scenarios present an hourly failure between 30% and 53% (Figure 5a). Taking into cosideration that the installed
capacity in each successive scenario increases, the propability of failure decreases but the the produced surplus
increases (Figure 5b). Both at the hourly (Figure 5c) and annual (Figure 5d) scale, high energy deficit and surplus are
produced.
0
1000
2000
3000
4000
5000
6000
7000
1 2 3 4 5 6 7 8
Energy [Mwh]
Number of groups of 5 solar panels
Annual energy produced Mean annual surplus Mean annual deficit
0
2000
4000
6000
8000
10000
12000
14000
1 2 3 4 5 6
Energy [Mwh]
Number of wind turbines
Annual energy produced Mean annual surplus Mean annual deficit
1
1.5
2
2.5
0
5
10
15
20
25
30
35
40
45
50
1
1.5
2
2.5
Failure (%)
1
1.5
2
2.5
0
5
10
15
20
25
30
35
40
1
1.5
2
2.5
Deficit as percentage of demand (%)
Maria Chalakatevaki et al. / Energy Procedia 125 (2017) 425–434 431
Chalakatevaki et al. / Energy Procedia 00 (2017) 000000 7
Table 3. Proposed scenarios of combined renewable resources.
Scenarios Hydro (MW) Solar (MW) Wind (MW) Marine (MW) Installed capacity (MW)
1 0.08 0.5 1.0 0.3 1.9
2 0.08 0.5 1.5 0.3 2.4
3 0.08 1.0 1.0 0.6 2.7
4 0.08 1.0 1.5 0.6 3.2
5 0.08 1.5 1.5 0.6 3.7
6 0.08 1.5 1.5 0.9 4.0
7 0.08 2.0 1.5 0.6 4.2
a b
c d
Fig. 5. Weather related energy mix scenarios : (a) Hourly failure (%); (b) Energy produced and energy demand (MWh/yr); (c) Max hourly deficit-
surplus (MWh); (d) Annual surplus-deficit (MWh/yr).
3.3. Adding governable energy resources
Governable renewables (i.e., biomass, geothermal, pumped-storage system) are added to the energy mix in order
to provide an installed capacity equal to 2.6 MW, to satisfy the peak hourly deficit, cover the annual deficits (1-2
GWh/yr) and manage the energy surplus (2-6 GWh/yr).
0
20
40
60
80
100
123456 7
Scenarios
Hourly failure (%)
0
5000
10000
15000
1 2 3 4 5 6 7
Scenarios
Energy demand (MWh/y) Energy produced (MWh/y)
-4000
-2000
0
2000
4000
6000
8000
1 2 3 4 5 6 7
Scenarios
Annual surplus (MWh) Annual deficit (MWh)
0
20
40
60
80
100
1 2 3 4 5 6 7
Scenarios
Hourly failure (%)
-4
-2
0
2
4
1 2 3 4 5 6 7
Scenarios
Max Hourly deficit (MWh) Max Hourly surplus (MWh)
432 Maria Chalakatevaki et al. / Energy Procedia 125 (2017) 425–434
8 Chalakatevaki et al. / Energy Procedia 00 (2017) 000000
In case a geothermal process exists (capable of electric energy production), it is expected to operate with a small
capacity factor as indicated from relevant cases in neighboring islands. Regarding the energy produced from biomass,
all available agricultural residues will be deployed and cultivated energy plants will be used to cover the remaining
deficit.
The use of governable renewable resources could satisfy the peak hourly and the annual deficit, but the amount of
surplus energy is still significant. Hence, a pumped-storage system could be used to store electric energy surplus, as
well as to satisfy the peak deficits.
4. Towards an energy mix
Considering all the above analysis (governable and weather related-renewables), we analyze two proposed
scenarios with the following installed power (Table 4).
Table 4. Proposed scenarios.
Source Power (MW)
Case 1
Case 2
Wind turbine
1
1
Solar panels
0.5
0.5
Hydroelectric dam
0.08
0.08
Wave energy converters
0.3
0.6
Geothermal power station
0.5
-
Pump storage system
-
1
Biomass power station
2.1
1.6
Total
4.5
4.8
In both cases there is a requirement for cultivation of energy plants which cover about 20 ha.
A pumped-storage system, that uses sea water to store energy, is considered. For the Case 2 scenario the reservoir
volume and the installed power of the hydro-turbine will be determined after the optimization of the systems
performance. The available net head is 400 m (Figure 6a) and the efficiency of pumped-storage cycle is assumed to
be 75%.
a b
Fig. 6. Pump strorage system: (a) location; (b) installed power-produced energy-reservoir volume.
Maria Chalakatevaki et al. / Energy Procedia 125 (2017) 425–434 433
Chalakatevaki et al. / Energy Procedia 00 (2017) 000000 9
The pumped storage system is simulated to calculate the energy production for various upper reservoir volumes
and installed hydro turbines. All the proposed combinations (Figure 6b) produce about the same amount of energy,
therefore the combination requiring the minimum installed power and reservoir volume is selected. A scheme of 1
MW hydro-turbine and a 0.5 hm3 upper reservoir volume will produce 1245 MW/yr (70% of the surplus) but there
will still be a deficit of 513 MWh/yr. The use of pumped-storage is a convenient way to store electric energy surplus
from other sources. The existence of a reservoir also contributes to the satisfaction of peak deficits.
5. Discussion
In this study, all six renewable energy sources are examined to create the electric energy mix of a non-connected
small island. The common advantage of the six resources is the free and renewable fuel and the fact that there is no
need for fuel import. The energy production from weather-related sources (i.e., wind, sun, sea, water) is completely
uncontrollable and does not synchronize with the demand, resulting in high values of energy deficit and surplus. In
the case of hydropower, the use of reservoirs can control the production and can additionally store the surplus energy
from other sources through pumped-storage facilities. The other two sources (i.e., biomass, geothermal) are
governable and therefore, capable of satisfying the peak electric energy demand when needed. Combining all the
above, could result in a sufficient energy mix for a small island or a larger area.
In recent years, a considerable number of wind turbines and solar power plants have been installed in many Greek
islands as a result of the financial aid provided by the European Union and the benefits of the ‘green’ energy autonomy.
Hydroelectric dams are broadly used in the mainland and their technology could be easily adjusted to small island
reservoirs. The installation of biomass, geothermal and wave energy power stations is at an early stage but attracts a
lot of scientific and technological interest regarding near future installations.
However, the use of renewable energy sources is not free of obstacles. We note that in the case of the biomass, the
fuel must be thoroughly collected and transported before its use whereas, in the case of the geothermal process, the
corresponding fields with a high exploitable -for electricity production- enthalpy are located in a few places around
the world. The implementation of a renewable energy mix results in a high installed capacity -as the installed power
of each renewable does not always synchronize with the demand- and a rather high installation cost especially for
small islands (regarding the current situation of the renewable infrastructure). For example, the Case 2 scenario
includes two wind turbines of 75 m height, 3800 m2 of photovoltaic panels, two wave converter installations, a small
hydro turbine to the existing dam, a biomass installation that must be fed with 180 t/yr of cultivated biomass, and a
pumped storage system that includes a reservoir with a 0.5 hm3 volume, a 2 km penstock and a hydro turbine
installation. The total installed power of the system will be 4.8 MW (while the peak demand is 2.6 MW) and the total
cost will be much more than 10 M€ according to estimated costs for each energy unit as shown in Table 5.
Table 5. Economic analysis.
Source Estimated cost
(M€/MW)
Power (MW)
Case 1
Case 2
Wind turbine
1.5-2
1
1
Solar panels
2-3
0.5
0.5
Hydroelectric dam
1
0.08
0.08
Wave energy converters
3-4
0.3
0.6
Geothermal power station
1-2
0.5
-
Pump storage system
1.5-2
-
1
Biomass power station
2-3
2.1
1.6
Total
4.5
4.8
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On the other hand, the energy demand (peak and annual) of the island could be easily covered by a common thermal
station [14] with installed power less than 3 MW. The quantity of fossil fuel required to cover the annual electric
energy demand is estimated to be approximately 1 300 toe (tonnes of oil equivalents) per year. In case that the fuel is
oil, the estimated annual cost is about 0.5 M€, considering high import cost but low installation and operational cost.
The decision for the energy mix must be taken after considering financial, environmental and societal issues.
Additionally, the examined solutions still have a high implementation and maintenance cost, and therefore, it is
reasonable that the thermal stations fed with transported oil are broadly used in non-connected islands. In the near
future, it is expected that the cost of renewable resources will be further reduced and the proposed solutions will be
more attractive from a financial, societal as well as environmental point of view.
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Vienna, 19, EGU2017-16919, European Geosciences Union.
... A pilot application in the island of Astypalaia indicates that by creating an energy mix through stochastic investigation, including all the aforementioned renewable energy sources, in combination with the fuel oil could cover efficiently the energy demand of the island (Chalakatevaki et al., 2017). However, taking into consideration that none of the aforementioned energy sources has a constant and adequate reservoir, another alternative solution could be applied. ...
... The potential of deploying agricultural residues, as well as cultivating energy crops with low irrigation demands for biomass energy production, was investigated. Taking into account that Astypalaia is located at the Volcanic Arc of southern Aegean Sea the potential of implementing measurements in order to verify the possibility of geothermal energy was also considered (Chalakatevaki et al., 2017). ...
... It is noted that when designing an energy system based only on weather-related renewable energy resources, i.e. solar, wind, marine and hydroelectric energy, the peak hourly demand is not satisfied and a great amount of energy surplus, uncontrollable and unsynchronized with the demand, is produced. Therefore, biomass and geothermal resources were added to the energy mix in order to cover the remain-ing deficit and a pumped-storage system was used to store electric energy surplus and satisfy the peak deficits (Chalakatevaki et al., 2017). The implementation of the renewable energy mix resulted in a high installed capacity (since the installed power of each renewable does not always synchronize with the demand) and a rather high installation cost. ...
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The ever-increasing energy demand has led to overexploitation of fossil fuels deposits, while renewables offer a viable alternative. Since renewable energy resources derive from phenomena related to either atmospheric or geophysical processes, unpredictability is inherent to renewable energy systems. An innovative and simple stochastic tool, the climacogram, was chosen to explore the degree of unpredictability. By applying the climacogram across the related timeseries and spatial-series it was feasible to identify the degree of unpredictability in each process through the Hurst parameter, an index that quantifies the level of uncertainty. All examined processes display a Hurst parameter larger than 0.5, indicating increased uncertainty on the long term. This implies that only through stochastic analysis may renewable energy resources be reliably manageable and cost efficient. In this context, a pilot application of a hybrid renewable energy system in the Greek island of Astypalaia is discussed, for which we show how the uncertainty (in terms of variability) of the input hydrometeorological processes alters the uncertainty of the output energy values.
... Therefore, the integration of renewable resources in the energy mix is essential for reducing the financial and environmental cost. A pilot investigation of how various energy resources (renewable and fossil fuels) can be evaluated using technical, environmental and economic criteria in order to create the appropriate electric energy mix for a non-connected island can be seen in Chalakatevaki et al. (2017). Particularly, six basic renewable resources are examined (solar, wind, marine, hydropower, biomass and geothermal) for the energy mix in a non-connected island at the Aegean Sea (Astypalea, Greece). ...
... Table 20.2 summarizes the outcomes from two case scenarios, based on a preliminary (but indicative) analysis where each source has to be harvested according to the energy demand (Mavroyeoryos et al. 2017) and economic analysis (Karakatsanis et al. 2017) for the selected case. Therefore, a separate stochastic and cost analysis was first employed for solar energy (Koudouris et al. 2017), wind and marine energy (Moschos et al. 2017), hydropower with a pumped storage system (Papoulakos et al. 2017), biomass and geothermal energy (Chalakatevaki et al. 2017). The second case that was finally selected includes two wind turbines of 75 m height, 3800 m 2 of photovoltaic panels, two wave converter installations, addition of a small hydro turbine to the existing dam, a biomass facility fed with 180 t/year of cultivated biomass, and a pumped storage system that includes a reservoir with storage capacity of 0.5 hm 3 , a 2 km penstock and a hydro turbine installation. ...
Chapter
The fundamental concepts in the field of water-energy systems and their historical evolution with emphasis on recent developments are reviewed. Initially, a brief history of the relation of water and energy is presented and the concept of the water-energy nexus in the 21th century is introduced. The investigation of the relationship between water and energy shows that this relationship comprises both conflicting and synergistic elements. Hydropower is identified as the major industry of the sector and its role in addressing modern energy challenges by means of integrated water-energy management is highlighted. Thus, the modelling steps of designing and operating a hydropower system are reviewed, followed by an analysis of theory and physics behind energy hydraulics. The key concept of uncertainty, which characterises all types of renewable energy, is also presented in the context of the design and management of water-energy systems. Subsequently, environmental considerations and impacts of using water for energy generation are discussed, followed by a summary of the developments in the emerging field of maritime energy. Finally, present challenges and possible future directions are presented.
... In their work, Chalakatevaki et al. (2017) evaluated various energy resources using technical, environmental and economic criteria with emphasis to biomass, pumped hydro storage and replacement of oil power plants. ...
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Due to the intermittent and fluctuating nature of wind and other renewable energy sources, their integration into electricity systems requires large-scale and flexible storage systems to ensure uninterrupted power supply and to reduce the percentage of produced energy that is discarded or curtailed. Storage of large quantities of electricity in the form of dynamic energy of water masses by means of coupled reservoirs has been globally recognized as a mature, competitive and reliable technology; it is particularly useful in countries with mountainous terrain, such as Greece. Its application may increase the total energy output (and profit) of coupled wind-hydroelectric systems, without affecting the availability of water resources. Optimization of such renewable energy systems is a very complex, multi-dimensional, non-linear, multi modal, nonconvex and dynamic problem, as the reservoirs, besides hydroelectric power generation, serve many other objectives such as water supply, irrigation and flood mitigation. Moreover, their function should observe constraints such as environmental flow. In this paper, we developed a combined simulation and optimization model to maximize the total benefits by integrating wind energy production into a pumped-storage multi-reservoir system, operating either in closed-loop or in open-loop mode. In this process, we have used genetic algorithms as the optimization tool. Our results show that when the operation of the reservoir system is coordinated with the wind farm, the hydroelectricity generation decreases drastically, but the total economical revenue of the system increases by 7.02% when operating in closed-loop and by 7.16% when operating in open-loop mode. We conclude that the hydro-wind coordination can achieve high wind energy penetration to the electricity grid, resulting in increase of the total benefits of the system. Moreover, the open-loop pumped-storage multi-reservoir system seems to have better performance, ability and flexibility to absorb the wind energy decreasing to a lesser extent the hydroelectricity generation, than the closed-loop.
... A common approach, widely adopted in the literature, is to optimally design a hybrid energy system, which combines renewable electricity generation (e.g., wind, solar) with conventional power sources (e.g., diesel generators) and storage technologies [31][32][33]. Among these latter, several studies explore pumped-storage as a solution to maximize renewable energy sources (RES) penetration and consequently improve the overall sustainability of the water-energy system [34][35][36]. This well established technology allows the exploitation of RES electricity surplus by pumping water to be stored in an upstream reservoir and released for hydropower when electricity is needed [37]. ...
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Small Mediterranean islands are remote, off-grid communities characterized by carbon intensive electricity systems coupled with high energy consuming desalination technologies to produce potable water. The aim of this study is to propose a novel dynamic, multi-objective optimization approach for improving the sustainability of small islands through the introduction of renewable energy sources. The main contributions of our approach include: (i) dynamic modelling of desalination plant operations, (ii) joint optimization of system design and operations, (iii) multi-objective optimization to explore trade-offs between potentially conflicting objectives. We test our approach on the real case study of the Italian Ustica island by means of a comparative analysis with a traditional non-dynamic, least cost optimization approach. Numerical results show the effectiveness of our approach in identifying optimal system configurations, which outperform the traditional design with respect to different sustainability indicators, limiting the structural interventions, the investment costs and the environmental impacts. In particular, the optimal dynamic solutions able to satisfy the whole water demand allow high levels of penetration of renewable energy sources (up to more than 40%) to be reached, reducing the net present cost by about 2–3 M€ and the CO2 emissions by more than 200 tons/y.
... However, natural processes with HK behaviour abound in literature. For example, turbulent processes exhibit such long-term persistent behaviour (e.g., Dimitriadis et al., 2016a, and references therein), recently in ecosystem variability (Pappas et al., 2017) as well as most geophysical processes as verified in several cases (Koutsoyiannis, 2003;O'Connell et al., 2016;, and specifically in key hydrometeorological processes such as: river discharge and stage (Hurst, 1951;Koutsoyiannis et al., 2008;Markonis et al., 2017); solar radiation and wind speed Tsekouras and Koutsoyiannis, 2014;Koudouris et al., 2017); precipitation Dimitriadis et al., 2016a;; paleoclimatic temperature reconstructions ; temperature and dew point Lerias et al., 2016) and thus, humidity; potential evapotranspiration which can be adequately evaluated only by temperature and deterministic extraterrestrial radiation (Tegos et al., 2017) and therefore, a similar Hurst parameter as in temperature is expected; but also other renewable-energy related processes, such as wave energy and period , as well as processes used in energy production and management (Chalakatevaki et al., 2017;Papoulakos et al, 2017;Mayrogeorgios et al., 2017), but also weather finance models (Karakatsanis et al., 2017). Interestingly, in most of the aforementioned processes (if treated properly within a robust physical and statistical framework, e.g. by adjusting the process for sampling errors as well as discretization and bias effects) the Hurst parameter is estimated at the range 0.8 to 0.85, as indicated by Hurst ...
Thesis
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The high complexity and uncertainty of atmospheric dynamics has been long identified through the observation and analysis of hydroclimatic processes such as temperature, dew-point, humidity, atmospheric wind, precipitation, atmospheric pressure, river discharge and stage etc. Particularly, all these processes seem to exhibit high unpredictability due to the clustering of events, a behaviour first identified in Nature by H.E. Hurst in 1951 while working at the River Nile, although its mathematical description is attributed to A. N. Kolmogorov who developed it while studying turbulence in 1940. To give credits to both scientists this behaviour and dynamics is called Hurst-Kolmogorov (HK). In order to properly study the clustering of events as well as the stochastic behaviour of hydroclimatic processes in general we would require numerous of measurements in annual scale. Unfortunately, large lengths of high quality annual data are hardly available in observations of hydroclimatic processes. However, the microscopic processes driving and generating the hydroclimatic ones are governed by turbulent state. By studying turbulent phenomena in situ we may be able to understand certain aspects of the related macroscopic processes in field. Certain strong advantages of studying microscopic turbulent processes in situ is the recording of very long time series, the high resolution of records and the controlled environment of the laboratory. The analysis of these time series offers the opportunity of better comprehending, control and comparison of the two scientific methods through the deterministic and stochastic approach. In this thesis, we explore and further advance the second-order stochastic framework for the empirical as well as theoretical estimation of the marginal characteristic and dependence structure of a process (from small to extreme behaviour in time and state). Also, we develop and apply explicit and implicit algorithms for stochastic synthesis of mathematical processes as well as stochastic prediction of physical processes. Moreover, we analyze several turbulent processes and we estimate the Hurst parameter (H >> 0.5 for all cases) and the drop of variance with scale based on experiments in turbulent jets held at the laboratory. Additionally, we propose a stochastic model for the behaviour of a process from the micro to the macro scale that results from the maximization of entropy for both the marginal distribution and the dependence structure. Finally, we apply this model to microscale turbulent processes, as well as hydroclimatic ones extracted from thousands of stations around the globe including countless of data. The most important innovation of this thesis is that, to the Author’s knowledge, a unique framework (through modelling of common expression of both the marginal density distribution function and the second-order dependence structure) is presented that can include the simulation of the discretization effect, the statistical bias, certain aspects of the turbulent intermittent (or else fractal) behaviour (at the microscale of the dependence structure) and the long-term behaviour (at the macroscale of the dependence structure), the extreme events (at the left and right tail of the marginal distribution), as well as applications to 13 turbulent and hydroclimatic processes including experimentation and global analyses of surface stations (overall, several billions of observations). A summary of the major innovations of the thesis are: (a) the further development, and extensive application to numerous processes, of the classical second-order stochastic framework including innovative approaches to account for intermittency, discretization effects and statistical bias; (b) the further development of stochastic generation schemes such as the Sum of Autoregressive (SAR) models, e.g. AR(1) or ARMA(1,1), the Symmetric-Moving-Average (SMA) scheme in many dimensions (that can generate any process second-order dependence structure, approximate any marginal distribution to the desired level of accuracy and simulate certain aspects of the intermittent behaviour) and an explicit and implicit (pseudo) cyclo-stationary (pCSAR and pCSMA) schemes for simulating the deterministic periodicities of a process such as seasonal and diurnal; and (c) the introduction and application of an extended stochastic model (with an innovative identical expression of a four-parameter marginal distribution density function and correlation structure, i.e. g(x;C)=λ/[(1+|x/a+b|^c )]^d, with C=[λ,a,b,c,d]), that encloses a large variety of distributions (ranging from Gaussian to powered-exponential and Pareto) as well as dependence structures (such as white noise, Markov and HK), and is in agreement (in this form or through more simplified versions) with an interestingly large variety of turbulent (such as horizontal and vertical thermal jet of positively buoyancy processes using laser-induced-fluorescence techniques as well as grid-turbulence generated within a wind-tunnel), geostatistical (such as 2d rock formations), and hydroclimatic processes (such as temperature, atmospheric wind, dew-point and thus, humidity, precipitation, atmospheric pressure, river discharges and solar radiation, in a global scale, as well as a very long time series of river stage, and wave height and period). Amazingly, all examined physical processes (overall 13) exhibited long-range dependence and in particular, most (if treated properly within a robust physical and statistical framework, e.g. by adjusting the process for sampling errors as well as discretization and bias effects) with a mean long-term persistence parameter equal to H ≈ 5/6 (as in the case of isotropic grid-turbulence), and (for the processes examined in the microscale such atmospheric wind, surface temperature and dew-point, in a global scale, and a long duration discharge time series and storm event in terms of precipitation and wind) a powered-exponential behaviour with a fractal parameter close to M ≈ 1/3 (as in the case of isotropic grid-turbulence).
... From this procedure, we obtained 2100 scenarios of the daily operation of the system for 100 simulated years, by setting a backup storage constraint up to 50 000 m 3 . The key quantity of interest is the mean daily hydropower production, which was used for creating the energy mix of the island [8]. This variable should be represented by a bounded distribution, due to the existence of the head constraint. ...
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We present a stochastic approach accounting for input uncertainties within water-energy simulations. The stochastic paradigm, which allows for quantifying the inherent uncertainty of hydrometeorological processes, becomes even more crucial in case of missing or inadequate information. Our scheme uses simplified conceptual models which are subject to significant uncertainties, to generate the inputs of the overall simulation problem. The methodology is tested in a hypothetical hybrid renewable energy system across the small Aegean island of Astypalaia, comprising a pumped-storage reservoir serving multiple water uses, where both inflows and demands are regarded as random variables as result of stochastic inputs and parameters.
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A time series generator is presented, employing a robust three-level multivariate scheme for stochastic simulation of correlated processes. It preserves the essential statistical characteristics of historical data at three time scales (annual, monthly, daily), using a disaggregation approach. It also reproduces key properties of hydrometeorological and geophysical processes, namely the long-term persistence (Hurst–Kolmogorov behaviour), the periodicity and intermittency. Its efficiency is illustrated through two case studies in Greece. The first aims to generate monthly runoff and rainfall data at three reservoirs of the hydrosystem of Athens. The second involves the generation of daily rainfall for flood simulation at five rain gauges. In the first emphasis is given to long-term persistence – a dominant characteristic in the management of large-scale hydrosystems, comprising reservoirs with carry-over storage capacity. In the second we highlight to the consistent representation of intermittency and asymmetry of daily rainfall, and the distribution of annual daily maxima.
Investigation of the stochastic nature of temperature and humidity for energy management, European Geosciences Union General Assembly
  • E Hadjimitsis
  • E Demetriou
  • K Sakellari
  • H Tyralis
  • P Dimitriadis
  • T Iliopoulou
Hadjimitsis E., Demetriou E., Sakellari K., Tyralis H., Dimitriadis P., Iliopoulou T., and Koutsoyiannis D. (2017) Investigation of the stochastic nature of temperature and humidity for energy management, European Geosciences Union General Assembly 2017, Geophysical Research Abstracts, Vol. 19, Vienna, 19, EGU2017-10164-5, European Geosciences Union.