RT info:eu-repo/semantics/bachelorThesis
T1 Estudio de la variación de la demanda energética en las Islas Canarias con el cambio climático
A1 Orribo Morales, Juan
K1 Demanda Energética
K1 Canarias
K1 Cambio climático
AB In the study presented below, an analysis of the variability of the increase in energy demand in theCanary Islands due to meteorological causes has been carried out. The seasonal and daily conditions ofenergy demand in the period 2013-2019 have been studied. Then, some future estimations were made.The variability of the energy demand has been studied through three standard indexes, monthlyseasonal variation index (MSVI), daily seasonal variation index (DSVI) and hourly seasonal variationindex (HSVI).The results confirm the seasonal increase in energy demand in the long summers of all the CanaryIslands. This variability is greater in the non-capital islands, probably due to a greater flow of residentsduring the summer period. The hourly and daily variability of the energy demand is also veryremarkable in all the islands, which has been reflected by the imposition of different quotas in severaltime slots by the Spanish Government and the Electricity Companies. The energy consumed duringweekdays remains fairly constant throughout the week, decreasing on Saturday and, even more so, onSunday. Even in some islands, such as Fuerteventura or La Gomera, the drop is only really noticeable onSundays.The direct relationship between the increase in demand and the increase in temperatures, through thecooling degree-days, CDD, has been proven. This gives rise to quite disparate values, with particularitiesbeing observed on each island. For this purpose, the comfort temperatures of the different islands havebeen calculated, using a degree 3 polynomial fit of the energy demand data versus the averagetemperature of each island, once demand time series have been detrended. Establishing in this way abase level of energy consumption of cooling systems from which temperature changes involve anincrease in energy demand. Both by the use of refrigeration systems giving rise to the CDD, and heatingsystems that give rise to the so-called Heating degree-days, HDD. The latter having a relatively lowerweight in the Canary Islands due to climatic conditions. The time series for the mean temperature ofeach island were calculated from the ERA5 reanalysis data, averaging all those grid nodes thatcorrespond to land and that are below 1000 masl. In this way, the possible bias produced by the lowtemperatures at higher elevations, which are not significant for the relationship with electricityconsumption, since it does not occur in those areas, is reduced.After the analysis of the historical 2013-2019 period, the study has been extended to the analysis ofpossible future scenarios of the evolution of electricity demand and its variability due to climatechange.Regionalized climate projection data were provided by Grupo de Observación y la Atmósfera (GOTA),that you belong to the Universidad de La Laguna (ULL). These projections were made using the WRFmesoscale model and different boundary conditions provided by three global climate models(GFDL-ESM2M, IPSL-CM5A-MR and MIROC-ESM) were used to simulate two future periods under study:2030-59 and 2070-99. In addition, two possible socio-economic scenarios of greenhouse gas emissions,the RCP4.5 (Representative Concentration Pathway) scenario, a more hopeful scenario, and the RCP8.5,a more catastrophic one, were taken into account. They correspond to additional 4.5 and 8.5 W/m2radiative forcings by 2100, respectively. Data from the WRF simulations, which have a much higher resolution, were aggregated to create a grid equivalent to that of the ERA5 and the same process wasapplied to calculate the mean temperature time series for each island. Furthermore, a bias correctionmethod was applied to these time series, using the scaled distribution mapping (SDM) technique, whichoutperforms previous methods based on quantile mapping and preserves raw climate model projectedchanges to meteorological variables such as temperature and precipitation.An increase in CDD was predicted in both cases, in the RCP4.5 scenario in a more moderate andassumable way with a stabilization of the values. On the other hand, in the RCP8.5 scenario, theincrease in CDD is exponential and without stabilization, reaching values of almost 5 more CDD in thesummers of the 2070-2099 period. In addition, in the first three months of the year, when there arecurrently very few days with non-zero CDD values, the energy demand for cooling in the future could besignificant. At the end of the century and in the least favorable scenario, the corresponding CDD couldtake values between 2 and 5 during those first months.For future work, it would be interesting to have more disaggregated data on energy demand; it wouldbe interesting to study the relationship between energy demand and temperature in differentsocioeconomic environments: rural, residential areas, industrial or commercial areas, etc. In addition,the use of projections of technological and socioeconomic evolution, that allow us to estimate a futuretrend in the use of cooling systems and their efficiency, would make it possible to translate projectresults, currently based on CDD, into estimates of future energy demand. This approximation would bemuch more appropriate than simply assuming that energy uses and technologies remain unchanged.
YR 2021
FD 2021
LK http://riull.ull.es/xmlui/handle/915/25728
UL http://riull.ull.es/xmlui/handle/915/25728
LA es
DS Repositorio institucional de la Universidad de La Laguna
RD 26-nov-2024