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Electricity Demand Reduction in Sydney and Darwin with Local Climate Mitigation

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Electricity Demand Reduction in Sydney and Darwin with Local Climate Mitigation P. Rajagopalan and M.M Andamon (eds.), Engaging Architectural Science: Meeting the Challenges of Higher Density: 52nd 285 International Conference of the Architectural Science Association 2018, pp.285–293. ©2018, The Architectural Science Association and RMIT University, Australia. Electricity demand reduction in Sydney and Darwin with local climate mitigation Riccardo Paolini UNSW Built Environment, UNSW Sydney, Australia [email protected] Shamila Haddad UNSW Built Environment, UNSW Sydney, Australia [email protected] Afroditi Synnefa UNSW Built Environment, UNSW Sydney, Australia [email protected] Samira Garshasbi UNSW Built Environment, UNSW Sydney, Australia [email protected] Mattheos Santamouris UNSW Built Environment, UNSW Sydney, Australia [email protected] Abstract: Urban overheating in synergy with global climate change will be enhanced by the increasing population density and increased land use in Australian Capital Cities, boosting the total and peak electricity demand. Here we assess the relation between ambient conditions and electricity demand in Sydney and Darwin and the impact of local climate mitigation strategies including greenery, cool materials, water and their combined use at precinct scale. By means of a genetic algorithm, we produced two site-specific surrogate models, for New South Wales and Darwin CBD, to compute the electricity demand as a function of air temperature, humidity and incoming solar radiation. For Western Sydney, the total electricity savings computed under the different mitigation scenarios range between 0.52 and 0.91 TWh for the summer of 2016/2017, namely 4.5 % of the total, with the most relevant saving concerning the peak demand, equal to 9 % with cool materials and water sprinkling. In Darwin, the computed peak electricity demand is of 2 % with respect to the unmitigated condition. Greater savings could be achieved acting on the demand linked to hot and humid conditions. Keywords: Urban Heat Island; Cooling; Energy; Building. 1. INTRODUCTION Global climate change is expected to increase the annual average air temperatures from 1.8 K to 4 K between 1990 and 2100 (IPCC, 2014). Considering only the variation in heating and cooling degree days related to climate change, an increase in per capita electricity demand of 6 % and 11 % during summer and spring, respectively, is predicted by 2100 for New South Wales (Balogun, Morakinyo and Adegun, 2014). However, global climate change will march in hand with an increase in global population and an increased market penetration of air conditioning, with the latter due to an increase in available income and increased frequency of hot spells (Santamouris, 2016). In addition, a local increase in ambient temperature is due to the urban heat island effect (Santamouris, 2015), for which a crescendo is also expected in some areas, given the growing urban population. All these aspects together will boost the electricity consumption and the need of additional power stations. The increase in the frequency and intensity of heatwaves connected to global climate change is also expected to mirror in boosted frequency and intensity of peak electricity demand. An “additional peak capacity costs of up to 180 billion dollars by the end of the century under business-as-usual” is estimated in the USA, with an all year average increase of 2.8 % in consumption (Auffhammer et al. 2017). Data from Canada, Israel, Japan, Thailand, and the United States show an increase in peak electricity demand by 0.45-4.6 % / °C, with an electricity penalty of 21 (± 10.4) W per degree of temperature increase and per person (Santamouris et al., 2015). Considering the local impacts, the urban heat island effect contributes to an additional increase between 0.5 % and 8.5 % / °C. Usually, the threshold temperature above which the electricity demand increases ranges between 18 °C and 24 °C; and it equals 18 °C in the majority of cases (Santamouris, 2014). In tropical climates, the largest fractions of domestic electricity demand are for air conditioning and refrigeration, thus directly related to the ambient temperature. The benefit of local climate mitigation in terms of electricity demand reduction has not been investigated for Australian cities. 286 R. Paolini, S. Haddad, A. Synnefa, S. Garshasbi and M. Santamouris Here, we assess the relation between ambient conditions and electricity demand and we assess the impact of local climate mitigation strategies in the Darwin CBD area and in Western Sydney. These include greenery, cool roofs and cool pavements, water sprinklers, water and greenery, or and water and cool roofs and pavements. 2. METHODS 2.1 Areas of interest The areas considered here considered are the CBD of Darwin, NT (~ 1 km2) and an area of Sydney, NSW (~ 4,500 km2) with approximately 4.2 million residents, where we modelled in total eight precincts in the Local Government Areas of Bankstown, Campbelltown, Canterbury, Holsworthy, Horsley Park, Olympic Park, Penrith and Richmond. We simulated the unmitigated and mitigated microclimates with the 3D model ENVI-metV4.1.3 (Haddad et al., 2018), considering site specific approaches (Table 1). Table 1: Mitigation scenarios. Scenario Darwin Sydney Unmitigated (reference) Albedo: walls, roofs and concrete pavements Albedo: walls, roofs and concrete pavements = 0.2; asphalt pavements = 0.05; soil=0.15. = 0.2; asphalt pavements = 0.05; loamy Greenery < 10% of unbuilt area. soil=0.15. Grass used as greenery. Greenery Increase of grass and trees cover to 30% of Plantation of 192 mature trees per precinct pavements and open spaces Cool materials (roofs and pavements) Global Albedo=0.6, greenery less than 10% Increased global albedo=0.5 by applying of non-building area cool roofs and pavements Water NA 16 water fountains/precinct Greenery and water NA Combination of the two scenarios Cool materials and water NA Combination of the two scenarios Albedo = 0.6, Greenery 30%, and Shading NA Combined (30 % irradiance reduction) 2.2 Electricity data We received the semi-hourly electricity demand data from Power and Water Corporation for the Darwin CBD area (Darwin City) and the Frances Bay area. We focused on the period from February 2016 until December 2017 because of a variation in the metering system, a very sharp population increase in recent years in Darwin, and as the area is very small, the visitors may be a relevant fraction compared to the resident population. For Sydney, we obtained the semi-hourly electricity demand data for the whole NSW from the Australian Energy Market Operator (Australian Energy Market Operator, 2017), considering the summer periods (Dec-Feb) from 2013 to 2017. To determine the relation between environmental conditions and electricity demand, we used the genetic programming software tool Eureqa. Its engine was originally developed by Schmidt and Lipson (2009) and uses artificial intelligence to search a correlation that minimizes the error function given by the discrepancy between the data and the generated model. We used 75 % of the dataset for development and 25 % for validation. 2.3 Weather data In Sydney, we considered the semi-hourly weather data for the unmitigated scenario given by nine weather stations (Table 2) managed by the Bureau of Meteorology (Australian Bureau of Meteorology, 2017a). Lacking long term records of global horizontal solar radiation free of gaps, we considered the extraterrestrial global horizontal radiation from satellite measurements (University of Colorado and NASA, 2017) and we computed the solar position with an high-accuracy algorithm (Reda and Andreas, 2004). The simulated environmental conditions in the precincts showed a very good agreement with the BoM stations, that were directly used. We considered then a population weighted average temperature, with statistical data on population (Geoscience Australia, 2016). Electricity demand reduction in Sydney and Darwin with local climate mitigation 287 Table 2: Weather stations providing the data used in the study. For the stations of the Bureau of Meteorology (BoM) the station code is provided. BoM Station code Station name Lat Long Location 14015 Darwin -12.411 130.878 Darwin, NT 66137 Bankstown -33.918 150.986 Western Sydney, 20-30 km from the 68257 Campbelltown -34.062 150.774 coast 66194 Canterbury -33.906 151.113 66161 Holsworthy -33.993 150.949 67119 Horsley Park -33.851 150.857 66212 Olympic Park -33.834 151.072 66062 Observatory Hill -33.859 151.202 Coastal, NSW 67113 Penrith -33.720 150.678 NSW, 50 km from the coast 67105 Richmond -33.600 150.776 NA Macquarie (radiation) -33.765 151.115 Inner West Sydney, NSW In Darwin, we considered the dry bulb and dew point temperatures, and the solar radiation measured at the airport (Australian Bureau of Meteorology, 2017b) and we installed in the CBD a network of 15 stations that provided semi-hourly temperature data for approximately two months (11/09/2017 - 31/10/2017). Then we found a relation between the urban and airport temperatures and re-created a long-term data series for the CBD. The weather profiles in the mitigated condition were computed considering the ratio of the air temperature in the mitigated scenario in each precinct to the ambient temperature in the unmitigated scenario. In detail, we multiplied the semi-hourly ambient air temperature from the weather station times the mitigation ratio. In Sydney, for Observatory Hill and Northern Beaches areas we considered the same mitigation coefficients derived for Canterbury. 3. RESULTS 3.1 Electricity demand model 3.1.1 Darwin electricity demand model We found a correlation between urban temperatures (Turb) and airport conditions as a function of airport air temperature (T), global horizontal irradiance (GHI), and wind speed (U). Turb = 10.21 + 0.675*sma(T, 3) + 0.002*GHI + 0.055*T*sma(U, 7) - 1.729*sma(U, 7) – + 1.832e-6*GHI2 (1) Where sma (x, n) is the simple moving average of the previous n records of the quantity x.
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