Assessment of on-site steady electricity generation from renewable energy sources in Chile

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Energy Procedia 158 Energy Procedia 00(2019) (2017)1099–1104 000–000 www.elsevier.com/locate/procedia

10th International Conference on Applied Energy (ICAE2018), 22-25 August 2018, Hong Kong, 10th International Conference on Applied Energy China(ICAE2018), 22-25 August 2018, Hong Kong, China

Assessment of on-site steady electricity generation from renewable AssessmentTheof15th on-site steady electricity generation from renewable International Symposium on District Heating and Cooling energy sources in Chile energy sources in Chile Assessing the feasibility of using the heat demand-outdoor a,b Luis Ramirez Camargoa,b*, Javier Valdesaa, Yunesky Masip Maciacc,Wolfgang Dorneraa Luis Ramirez Camargo *, Javier , Yunesky Masip heat Maciademand ,Wolfgangforecast Dorner temperature function for aValdes long-term district Institute for Applied Informatics, Technische Hochschule Deggendorf, Freyung 94078, Germany a

a Planning, Environmental Planning and Land Rearrangement, University of Natural Resources and Life Sciences, Institute of Spatial Institute for Applied Informatics, Technische Hochschule Deggendorf, Freyung 94078, Germany a,b,c a Planning and aLand c c b Vienna 1190, Austria b University of Natural Resources Institute of Spatial Planning, Environmental Rearrangement, and Life Sciences, c Vienna 1190, Austria Escuela de Ingeniería Mecánica, Facultad de Ingeniería, Pontificia Universidad Católica de Valparaíso, Quilpue, Chile. c a Escuela IngenieríaTechnology Mecánica, and Facultad Ingeniería, Pontificia Universidad Católica de Valparaíso, Quilpue,Lisbon, Chile. Portugal IN+ Center for de Innovation, PolicydeResearch - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c Abstract Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France b

I. Andrić

*, A. Pina , P. Ferrão , J. Fournier ., B. Lacarrière , O. Le Corre

Abstract Chile has an immense renewable energy potential but their integration in the energy system constitutes a major challenge. The Chile hastopography an immenseofrenewable energy potentialofbut integration in grids the energy system constitutes a majorenergy challenge. The complex the country, the isolation thetheir different electric and the variability of renewable sources, complex topography of the from country, the isolation of variable the different electricpower gridsgeneration and the variability sources, Abstract makes energy transmission locations with high renewable potentialof to renewable the demandenergy centres not an makes energy with variable renewable power generation to storage the demand centres not an easy task. The transmission present studyfrom aimslocations at assessing thehigh potential of combining solar power, windpotential power and systems to provide easy task. Thealready present study aims at assessing the potential of literature combining wind andbetween storage variable systems to provide steady loads from theare source. Such aaddressed combination should take advantage complementarity renewable District heating networks commonly in the assolar one power, ofofthe most power effective solutions for decreasing the steady loads already the source. Such a combination should takerequire advantage complementarity between variable renewable sources and serves tofrom simplify their in the These energy system. The analysis relies on anwhich optimization model to size hybrid greenhouse gas emissions from theintegration building sector. systems highof investments are returned through the heat sources andenergy serves tochanged simplify their integration in and the energy system. The relies an optimization model totosize hybrid renewable systems and climate 10 years of weather data from the brand new analysis ERA-5 reanalysis. Necessary system sizes constantly sales. Due to the conditions building renovation policies, heatondemand in the future could decrease, renewable systems and 10for years of weather data from the brand new ERA-5 reanalysis. for Necessary system sizes to constantly generate 1 energy MWh of electricity every hour during the period 2008-2017 are calculated all possible locations across the prolonging the investment return period. generate 1 scope MWh ofshow electricity every hour during the 2008-2017 areprovide calculated for energy all possible locations across the country. The results that the generation and storage capacities to a steady output are for veryheat high even The main of this paper isfor tonecessary assess the feasibility of period using the heat demand – outdoor temperature function demand country. The results show that therenewable necessary energy generation and storage capacities to provide a steady energy output are very high even for areas with exceptionally high potential. forecast. The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of 665 for areas with exceptionally high renewable energy potential. buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district Copyright © 2018 Elsevier Ltd. All rights reserved. renovation scenariosPublished were developed (shallow, © 2019 The Elsevier Ltd. intermediate, deep). To estimateththe error, obtained heat demand values were Copyright ©Authors. 2018 Elsevierunder Ltd. by All rights reserved. Selection and peer-review responsibility of the scientific committee of the 10 International Conference on Applied Energy This is an open the CC license (http://creativecommons.org/licenses/by-nc-nd/4.0/) compared withaccess resultsarticle from aunder dynamic heatBY-NC-ND demand model, previously developed and validated by the authors. Selection and peer-review under responsibility of the scientific committee of the 10th International Conference on Applied Energy (ICAE2018). Peer-review responsibility of the scientific committee of ICAE2018 – The of 10th International Conferencefor onsome Applied Energy. The results under showed that when only weather change is considered, the margin error could be acceptable applications (ICAE2018). (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation Keywords: renewable energies, ERA-5, system sizing optimization, spatiotemporal modelling, Chilean energy transition scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). Keywords: renewable energies, ERA-5, system sizing optimization, spatiotemporal modelling, Chilean energy transition 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. * Corresponding author. Tel.: +49-(0)8551-91764-28; fax: +49-(0)8551-91764-69. * E-mail Corresponding Tel.: +49-(0)8551-91764-28; fax: +49-(0)8551-91764-69. address:author. [email protected] Keywords: Heat demand; Forecast; Climate change E-mail address: [email protected]

1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. 1876-6102and Copyright © 2018 Elsevier Ltd. All of rights reserved. committee of the 10th International Conference on Applied Energy (ICAE2018). Selection peer-review under responsibility the scientific Selection and peer-review under responsibility of the scientific committee of the 10th International Conference on Applied Energy (ICAE2018). 1876-6102 © 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. 1876-6102 © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of ICAE2018 – The 10th International Conference on Applied Energy. 10.1016/j.egypro.2019.01.266

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1. Introduction In order to exploit its massive potential of renewable energy sources (RES), a key concern for Chile is, how to integrate the variability of the potential new generation resources to supply demand, optimize reliability level and cost impacts. Chile has a target to generate 20% of its electricity from renewable sources by 2025[1]. The challenge ahead is tremendous as the country has a unique topography, shaped by the presence of the Andes Mountains and extreme climatic differences ranging from desert in the North to the Southern Patagonian Ice Field. The large latitudinal extension of the country and its characteristic topography is reflected by the configuration of the electricity system, divided in multiple independent grid systems either with limited or without interconnections. Although the country has recently improved the level of interconnections by linking the two main grid systems through a 500kV line in 2017 and modernizing the energy grid [2], the existing and planned grid extensions are seen as the main barrier to RES deployment and are expected to be insufficient given the increasing growth of the electricity demand [3]. Chile has high quality spatiotemporal data concerning the monthly and yearly potential of RES and there are multiple studies, evaluating potentials of RES based on yearly balances [4]. However, long time series of high temporal resolution data (at least hours) are only available for the public from satellite imagery derived data and reanalysis data with global coverage. Studies concerning compensation of the variability of RES for system integration for the country are still scarce. McPherson et al. [5] for example, proposed a characterization framework for the integration of variable RES, including recommendations for Chile, but from a more general approach. Their study covers the whole South America territory and relies on MERRA reanalysis data, having a resolution of around 50 km x 50 km. Taking advantage of the brand new ERA-5 global reanalysis from the European Centre for Medium Range Weather Forecast (ECMWF), with a spatial resolution of 30 km x 30 km and temporal resolution of one hour, the present study contributes to the discussion on the integration of high shares of RES in the Chilean electric system by assessing the on-site potential of hybrid PV-wind-battery systems to provide stable amounts of energy at all time. An optimization model to size hybrid systems able to produce 1MWh at every hour during certain period is proposed. The model is applied for locations in the entire country, using the whole time series of the ERA-5 reanalysis that were available at the beginning of 2018 (i.e. 2008-2017). The results are presented in the form of maps covering Chile, where for every pixel with a resolution of 30 km x 30 km, the required PV, small wind turbines and storage capacities sizes are displayed The rest of this article is structured as follows: the next section describes the methodology and data. Section 3 presents and discusses the results of the optimization model. Finally, section 4 presents the conclusions. 2. Methods and data The methodology consists of three consequent steps. The first step is related to modelling on-site renewable electricity generation, where data sets of PV and Wind power potential are created based on ERA-5 reanalysis data. Secondly, a mixed linear-integer program (MILP), relying on these data, serves to define PV, small wind turbines and electric storage system sizes. Finally, the MILP is applied to every location in the country (in a 30 km x 30 km grid). PV, wind turbines as well as battery system sizes maps are calculated for two different optimization objectives (Scenarios O1 and O2): O1) where the installation cost of the hybrid systems is minimized and scenario O2) where the size of the battery is minimized. 2.1. Data: PV and wind energy generation This study is based on the first ten years of data from the brand new ERA-5 global reanalysis from ECMWF, which were made available to the public until the beginning of 2018. These data sets correspond to the period 2008-2017 with a temporal resolution of one hour. The retrieved variables are summarized in table 1. Based on this data, a simplified PV model for each data point is calculated, following [6], including global solar radiation (ssrd from ERA5 [7]) and ambient temperature (2t from ERA5) data as well as technical parameters of a free-standing PV module. The latter include module efficiency, operation temperature and output variation factors due to temperature changes. The technical details are adopted from [8] and the output is calculated using equation 1.



Luis Ramirez Camargo et al. / Energy Procedia 158 (2019) 1099–1104 Author name / Energy Procedia 00 (2018) 000–000

𝑂𝑂𝑂𝑂𝑂𝑂𝑝𝑝𝑝𝑝,𝑡𝑡 (𝐺𝐺) = 𝐺𝐺 ∗ 𝜂𝜂𝑃𝑃𝑃𝑃 ∗ [1 + 𝛼𝛼𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 ((𝑇𝑇𝑡𝑡𝑎𝑎𝑎𝑎𝑎𝑎 + 𝑘𝑘 𝑇𝑇 𝐺𝐺/𝐴𝐴) − 𝑇𝑇0 )]

1101 3

(1)

Where 𝑂𝑂𝑂𝑂𝑂𝑂𝑝𝑝𝑝𝑝,𝑡𝑡 is the photovoltaic power output per kWp at time t; G is ssrd from ERA5; 𝜂𝜂𝑃𝑃𝑃𝑃 is the photovoltaic panel efficiency, in this case 0.21; 𝛼𝛼𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 is the temperature correction factor, in this case -0.0045; 𝑇𝑇𝑡𝑡𝑎𝑎𝑎𝑎𝑎𝑎 , represents the ambient air temperature, equal to 2t from ERA5; 𝑘𝑘 𝑇𝑇 is the reduction factor due to installation type, in this case 0.035; A is the PV plant area in meters, in this case 4.8 m2 are necessary for every kWp and; 𝑇𝑇0 is the nominal operating temperature, in this case 25°C. This model has been used in multiple studies estimating PV potential (see e.g. [9]) and it is also part of the widely validated Python library PVLIB [10]. The potential power output of small wind turbines is calculated using wind speed from ERA-5 and a power curve for an average small wind turbine. The U and V component of the ERA5 reanalysis wind data (10u and 10v) at a height of ten meters above ground (Table 1) are used to calculate instantaneous wind speed. The power curve of an average small wind power turbine with nominal power of 10.5 kW (generated from three similar wind turbines [11,12]) is used to determine wind power from the previously calculated wind speeds. The wind power generation is calculated following equation 2. 𝑂𝑂𝑂𝑂𝑂𝑂𝑤𝑤,𝑡𝑡 =

(𝑝𝑝ℎ𝑖𝑖,𝑡𝑡 −𝑝𝑝𝑙𝑙𝑙𝑙,𝑡𝑡 )∗ 𝑠𝑠𝑡𝑡 +𝑠𝑠ℎ𝑖𝑖,𝑡𝑡 ∗ 𝑝𝑝𝑙𝑙𝑙𝑙,𝑡𝑡−𝑠𝑠𝑙𝑙𝑙𝑙,𝑡𝑡 ∗𝑝𝑝ℎ𝑖𝑖,𝑡𝑡

(2)

𝑠𝑠ℎ𝑖𝑖,𝑡𝑡 −𝑠𝑠𝑙𝑙𝑙𝑙,𝑡𝑡

Where, 𝑠𝑠𝑙𝑙𝑙𝑙,𝑡𝑡 and 𝑠𝑠ℎ𝑖𝑖,𝑡𝑡 represent respectively the wind speeds below and above a certain wind speed (𝑠𝑠𝑡𝑡 ) in time step t; 𝑝𝑝𝑙𝑙𝑙𝑙,𝑡𝑡 represents the wind power generation below a certain speed (𝑠𝑠𝑡𝑡 ) and; 𝑝𝑝ℎ𝑖𝑖,𝑡𝑡 the wind power generation above a certain speed (𝑠𝑠𝑡𝑡 ). The full time series of available ERA-5 data were kept for the analysis so that the optimization model sizes systems following actual hourly weather conditions for a period of ten years. This long time series allow to dimension systems that provide a reliable supply under all combinations of solar radiation and wind speed that occurred at every location. This large number of alternative operative conditions is a factor that is usually ignored, when sizing of hybrid systems is performed with short time series, using averaged or typical weather data. Table 1. Overview of the ERA-5 variables used for the PV and wind power potential estimation ERA-5 Variable

Units

Wind velocity in u direction at ten meters height (10u)

m/s

Wind velocity in v direction at ten meters height (10v)

m/s

Surface shortwave radiation downwards (ssrd)

J/m2

Ambient temperature at two meters height (2t)

K

2.2 Optimization model The optimization model is an extension of the linear model to optimize PV-battery system sizes for Electricity selfsufficient family houses proposed in [6]. The extension consists of the inclusion of the output of wind turbines as source to supply a demand as well as the inclusion of an integer variable in the objective function that represents the number of necessary wind turbines. This transforms the original linear program into a MILP that minimizes the total installation costs of a hybrid PV-wind-battery system. The objective function aiming at minimizing system’s installation costs is: Min( 𝐶𝐶𝑝𝑝𝑝𝑝 ∗ 𝑆𝑆𝑝𝑝𝑝𝑝 + 𝐶𝐶𝑤𝑤 ∗ 𝑆𝑆𝑤𝑤𝑤𝑤 + 𝐶𝐶𝑏𝑏 ∗ 𝑆𝑆𝑏𝑏 ∗ 𝜃𝜃 )

(3)

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Where 𝐶𝐶𝑝𝑝𝑝𝑝 , 𝐶𝐶𝑤𝑤 𝑎𝑎𝑎𝑎𝑎𝑎𝐶𝐶𝑏𝑏 are the costs associated to PV, wind and the battery respectively; 𝑆𝑆𝑝𝑝𝑝𝑝 , 𝑆𝑆𝑤𝑤 𝑎𝑎𝑎𝑎𝑎𝑎 𝑆𝑆𝑏𝑏 are the sizes of the installed capacity for each technology; and 𝜃𝜃 is a replacement factor for the batteries, taking into account the shorter life expectancy of current storage systems compared with the life time expectancy of the PV and Wind installations. The model simulates how much installed capacity would be necessary to produce at least 1 MWh in every hour of the year for all available years. The optimization model is restricted to three balancing conditions necessary to comply with permanent electricity self-sufficiency. The first balancing condition is the parity between energy supply and demand, given by the following equation: 𝐷𝐷𝑡𝑡 = ∑ 𝑈𝑈𝑖𝑖,𝑡𝑡 + B𝑑𝑑𝑑𝑑𝑑𝑑𝑡𝑡 ∗ 𝜆𝜆, ∀𝑡𝑡

(4)

∑(𝑆𝑆𝑖𝑖 ∗ 𝑂𝑂𝑂𝑂𝑂𝑂𝑖𝑖,𝑡𝑡 ) = ∑ 𝑈𝑈𝑖𝑖,𝑡𝑡 + ∑ 𝑆𝑆𝑆𝑆𝑖𝑖,𝑡𝑡 + ∑ 𝐸𝐸𝐸𝐸𝑖𝑖,𝑡𝑡 , ∀𝑡𝑡

(5)

Where 𝐷𝐷𝑡𝑡 is the linear demand, equal to at least 1MW per time step t; 𝑈𝑈𝑖𝑖,𝑡𝑡 corresponds to the part of the energy generation that can be directly used per time step from each source i and; 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑡𝑡 is the output of the electric storage system, decreased by the discharge efficiency of the storage system, 𝜆𝜆, which is assumed to be a constant. The second balancing condition concerns the electricity generation:

Electricity generation per technology i and time step t, can be either directly used (𝑈𝑈𝑖𝑖,𝑡𝑡 ) or it can be stored (𝑆𝑆𝑆𝑆𝑖𝑖,𝑡𝑡 ). The part of the generation that cannot be used or stored due to the 1MWh restriction, should be subject of curtailment and is called 𝐸𝐸𝐸𝐸𝑖𝑖,𝑡𝑡 . The sum of these three components is always equal to the output of all the energy generation installations, represented in the left side of the equation. The total output consists of the sum of the electricity outputs of each kWp in one time step 𝑡𝑡 for each technology i (𝑂𝑂𝑂𝑂𝑂𝑂𝑖𝑖,𝑡𝑡 ) calculated in the step one (1) and (2) of the methodology multiplied by the size of the respective installation (𝑆𝑆𝑖𝑖 ).

The third balancing condition is related to the state of charge of the electric storage system (B𝑐𝑐ℎ𝑡𝑡 ), which can be calculated as follows 𝐵𝐵𝐵𝐵ℎ𝑡𝑡 = 𝛺𝛺 ∗ 𝐵𝐵𝐵𝐵ℎ𝑡𝑡−1 + 𝜌𝜌 ∗ ∑ 𝑆𝑆𝑆𝑆𝑖𝑖,𝑡𝑡 − 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑡𝑡 , ∀𝑡𝑡

(6)

𝐵𝐵𝐵𝐵ℎ𝑡𝑡 ≤ 𝑆𝑆𝑏𝑏 , ∀𝑡𝑡

(7)

∑ 𝑆𝑆𝑆𝑆𝑖𝑖,𝑡𝑡 ≤ 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿, ∀𝑡𝑡

(8)

where, 𝛺𝛺 , is a constant that represents the storing efficiency and is multiplied by the state of charge of the corresponding storage systems in the previous period 𝐵𝐵𝐵𝐵ℎ𝑡𝑡−1 ; 𝜌𝜌 is the constant charging efficiency and is multiplied by the total stored electricity generation of that time step (∑ 𝑆𝑆𝑆𝑆𝑖𝑖,𝑡𝑡 ) and; 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑡𝑡 is the storage discharge of the respective time step t and is bound to the state of charge in the previous period 𝐵𝐵𝐵𝐵ℎ𝑡𝑡−1 . The highest 𝐵𝐵𝐵𝐵ℎ𝑡𝑡 serves to determine the size of the storage system 𝑆𝑆𝑏𝑏 : Additional constraints for the stored electricity generation (∑ 𝑆𝑆𝑆𝑆𝑖𝑖,𝑡𝑡 ) and the storage discharge (𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑡𝑡 ) on that time step, related to the capacity of the storage system to charge and discharge (𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿), are: 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑡𝑡 ≤ 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿, ∀𝑡𝑡

(9)

A first scenario, O1, is calculated assuming a total cost of 56,000 EUR per small wind turbine, 800 EUR per kWh battery and 1200 EUR per kWp PV. In a second scenario, O2, the price of the storage system is assumed to be 1,000 times higher than in O1 so that the resulting hybrid system has the minimum required storage system. In both scenarios, the expected lifetime of the battery is ten years and the Wind turbines and PVs are expected to last as long as the 20 years’ lifetime horizon of the entire hybrid system.



Luis Ramirez Camargo et al. / Energy Procedia 158 (2019) 1099–1104 Author name / Energy Procedia 00 (2018) 000–000

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The model was implemented in GAMS and solved with CPLEX. The model was run for every single pixel of the ERA-5 data set inside of the administrative boundaries of Chile using Python, numpy and xarray. 3. Results and discussion The results presented in Fig. 1. correspond to scenario O1 that minimizes total installation costs based on actual prices of the different technologies. Each map represents the necessary battery sizes, number of wind turbines as well as PV sizes necessary to produce constantly 1MWh. As it could be expected from a country with such variate range of climates, the results are quite mixed and the only general result is that, independent of the location, the necessary installed capacity of the different technologies is several times larger, than it would be necessary, if traditional power plants would be installed to steadily generate 1MWh. The latter not only leads to high costs, but also to a very high overproduction or required curtailment. PV and batteries sizes increase with the latitudinal distance to the equator, due to lower solar radiation in Southern areas. The north of the country, from the XV to IV regions, shows a very high potential of PV decreasing the necessary number of installations. In these locations, where the solar radiation is very high, the required PV installation capacities are small and the resulting hybrid systems consist mainly of PVs and batteries. The same occurs in the X and XI regions and in general terms on the Andes Mountains, but with a higher requirement of PV and battery sizes. In the other hand, some locations on the central and southern part of the country, where solar radiation is lower, wind installations complement PV installations decreasing total system costs and reducing storage requirements. These exceptions are presented mainly for the regions V to the XIV and more concentrated in the valley region between the Andes and the coastal mountains. In these cases, although the installation of wind turbines reduces PV requirements, it does not significantly reduce the storage requirements, which are relatively high, compared to the north of the country.

Fig 1. Maps of number of small wind turbines, PV and battery system sizes for steady generation of 1MWh for every hour in the period 2008-2017 (results from O1), as well as administrative division of Chile (maps order from left to right).

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Ramirez Camargo et al./ Energy Procedia 00 (2018) 000–000 Luis Ramirez Camargo et al. / Energy Procedia 158 (2019) 1099–1104

The maps generated for the second scenario (O2) are not presented here but are available on request. The results of O2 showed that it would be possible to reduce the required battery capacity to a quarter compared to O1. Nevertheless, such gains are obtained at the expense of a massive increases in the required PV installation sizes. A further consequence of the minimization of storage systems is the disappearance of the positive effects of the complementarity between solar and wind resources. In Scenario O2, for almost all locations in the country, the only selected renewable energy generation technology to steadily produce 1MWh, is PV. 4. Conclusions and future work The evaluation of the potential of on-site 1MWh steady electricity generation from a hybrid renewable energy system consisting of PV, micro-generation wind turbines and battery systems shows mixed results for the Chilean case. Only specific regions possess weather conditions that allow complementarity between solar and wind resources. In regions with very high solar radiation, such as the Atacama Desert and the Andes Mountains along the country, there is no apparent advantage of combining PV and wind power. Under those climatic conditions, systems, consisting on PV and battery systems, are preferred both from the size as from the cost perspective. The results show that, as the cost of the necessary generation and storage capacities to provide and steady energy output are very high even for areas with high RES potential, the deployment of renewable energies technologies would not be enough to ensure reliability of supply for the country. Nevertheless, an option to reduce the overall cost of the installation would be to complement the deployment of renewable energies and storage technologies with flexibility measures such as Demand Side Management (DSM). Under DSM schemas such as demand response, which better align the demand to the possible supply, the hybrid system would be able to react better to critical situations with low availability of RES, reducing the total required installed capacity. The integration of DSM options in the modelling approach presented here is part of planned future work. Acknowledgements The study was conducted within the project INCREASE “Increasing renewable energy penetration in industrial production and grid integration through optimized CHP energy dispatch scheduling and demand side management” (grant number BMBF150075) funded by the German Federal Ministry of Education and Research (BMBF) and the Chilean National Commission for Scientific Research and Technology (CONICYT). References [1] [2] [3]

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