A method for generating synthetic hourly solar radiation data for any location in the south west of Western Australia, in a world wide web page

Laslett, Dean; Creagh, Christine; Jennings, P.J.

doi:10.1016/j.renene.2014.01.015

Cited by 15 publications

(8 citation statements)

References 46 publications

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“…It was assumed that the cost of the BAU scenario does not change, although it may go up because of increasing fuel costs. The framework to model these scenarios has previously been developed to model 100% renewable energy (RE) scenarios for the SWIS on an hourby-hour basis [33][34][35]. Hourly power generation profiles were synthesised for each scenario, based on the installed capacity of each renewable energy technology and statistical profiles of wind speed and solar radiation.…”

Section: Methodsmentioning

confidence: 99%

Can high levels of renewable energy be cost effective using battery storage? Cost of renewable energy scenarios for an isolated electric grid in Western Australia

Laslett

2020

Renew. Energy Environ. Sustain.

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Many simulations of very high or 100% renewable energy electricity systems rely on existing or expanded capacity of utility scale power technologies with long construction lead times, such as hydro power or pumped hydro power. However, globally, the shorter lead time and more distributed technologies of wind power, solar PV, and batteries are expanding rapidly, and costs are falling. Can a grid get to high levels of renewable energy with these technologies alone, along with energy efficiency improvements, at reasonable cost? To address this question, scenarios of partial (<100%) renewable electricity supply were simulated for the South-West Interconnected System (SWIS) in the southwest of Western Australia. The SWIS is isolated from other grids, so power balance between supply and demand must be maintained completely within the grid, and there is no significant hydropower capacity to fall back on. Even with no improvement in cost and no carbon price, the partial renewable energy scenarios were found to be less expensive than a fossil fuel "business as usual" scenario up to about 70% renewable generation. With carbon prices of $24/tonne and $70/tonne, the same scenarios were less expensive up to around 80% and 96% renewable generation respectively. Hence at current costs, using solar PV, wind, energy efficiency and battery storage technologies are cost effective up to very high levels of renewable energy, but not 100%. However the cost of these technologies are falling rapidly. A simple way to include these continuous cost improvements into the levelised cost of energy calculation was developed, and it was found that if the costs of solar, wind and battery technologies continue to improve at current global rates, then the break even level with conventional generation increases significantly, up to 99% or above with a carbon price of $70/ tonne and current Australian installed capacity growth rates. Hence a battery based system operating at almost 100% renewable energy which is no more expensive than a conventional fossil system is foreseeable for the SWIS grid, and perhaps other grids as well.*

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Section: Methodsmentioning

confidence: 99%

Can high levels of renewable energy be cost effective using battery storage? Cost of renewable energy scenarios for an isolated electric grid in Western Australia

Laslett

2020

Renew. Energy Environ. Sustain.

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“…wind power for all seasons and for any location in the South-West of Western Australia have been developed previously by the authors [41] [42]. A simple model for solar thermal power generation with thermal storage was developed in this study, as it is considered to be a low emission technology that has rapid response rates.…”

Section: Methodsmentioning

confidence: 99%

“…The statistical properties of the cloudiness values were adjusted for location by calibration to measured data at 31 separate meteorological stations, and also adjusted to reflect seasonal changes. See Laslett et al [41] for a detailed explanation of these parameters. The average Mean Bias Error (MBE) between the synthetic and measured irradiance falling on a horizontal plane was found to be -0.81%, indicating slightly conservative synthetic values.…”

Section: Solar Pv Modelmentioning

confidence: 99%

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A large-scale renewable electricity supply system by 2030: Solar, wind, energy efficiency, storage and inertia for the South West Interconnected System (SWIS) in Western Australia

et al. 2017

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An interactive web tool was created to simulate 100% renewable electricity supply scenarios for the SouthWest Interconnected System (SWIS) in the southwest of Western Australia. The SWIS is isolated from other grids and currently has no available hydropower. Hence it makes a good case study of how supply and demand might be balanced on an hour-by-hour basis and grid stability maintained without the benefit of energy import/export or pumped hydroelectric storage. The tool included regional models for wind and solar power, so that hypothetical power stations were not confined to sites with existing wind farms or solar power stations, or sites with measurements of wind speed and solar radiation. A generic model for solar thermal storage and simple models for energy efficiency, distributed battery storage and power to gas storage were also developed. Due to the urgency of climate change mitigation a rapid construction schedule of completion by 2030, rather than the more common target of 2050, was set. A scenario with high wind generation, and scenarios with varying levels of solar power, wind power, distributed battery storage, energy efficiency improvements and power to gas systems were considered. The battery storage system and PV arrays were configured to provide synthetic inertia to maintain grid stability (with a small loss in capacity for each), and existing synchronous generators were kept spinning with no fuel input, adding a small increase to the electrical load demand. The level of synthetic inertia provided by battery storage was estimated for each scenario. The results indicated that a balanced mix of solar PV, solar thermal, efficiency, and storage were the most feasible to be built on a rapid time scale. The required capacity and build rate of the generation and storage systems would be reduced if energy efficiency improvements were implemented on a more rapid schedule compared to the current global improvement rate. The scenario with very high

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“…For all components, the distance from the coast of the wind farm was an important parameter. In a process similar to Laslett et al [40], a shape map of the Western Australian Coastline was constructed from the GEODATA COAST 100K 2004 data package published by Geoscience Australia [41]. This data set is based on a 1:100,000 scale map sheet.…”

Section: Methodsmentioning

confidence: 99%

A simple hourly wind power simulation for the South-West region of Western Australia using MERRA data

2016

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A simple simulator capable of generating synthetic hourly values of wind power was developed for the South West region of Western Australia. The global Modern Era Retrospective Analysis for Research and Applications (MERRA) atmospheric database was used to calibrate the simulation with wind speeds 50m above ground level. Analysis of the MERRA data indicated that the normalised residual of hourly wind speed had a double exponential distribution. A translated square-root transformation function y n =( √ (1.96+ y e )−1.4)/0.302 was used to convert this to a normal-like distribution so that autoregressive (AR) time series analysis could be used. There was a significant dependency in this time series on the last three hours, so a third order AR model was used to generate hourly 50m wind speed residuals. The MERRA daily average 50m wind speed was found to have a Weibull-like distribution, so a square root conversion was used on the data to obtain a normal distribution. The time series for this distribution was found to have a significant dependency on the values for the last two days, so a second order AR model was also used in the simulation to generate synthetic time series values for the square root of the daily average wind speed. Seasonal, daily, diurnal, and hourly components were added to generate synthetic time series values of total 50m wind speed. To scale this wind speed to turbine hub height, a time varying wind shear factor model was created and calibrated using measured data at a coastal and an inland site. Standard wind turbine power curves were modified to produce an estimate of wind farm power output from the hub-height wind speed. Comparison with measured grid supervisory control and data acquisition (SCADA) data indicated that the simulation generated conservative power output values. The simulation was compared to two other models: a Weibull distribution model, and an AR model with normally distributed residuals. The statistical fit with the SCADA data was found to be closer than these two models. Spatial correlation using only the MERRA data was found to be higher than the SCADA data, indicating that there is still a further source of variability to be accounted for. Hence the simulation spatial correlation was calibrated to previously reported findings, which were similar to the SCADA data. MonthWind speed (m/s) Daily average wind speed (m/s) Normalised frequency

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A method for generating synthetic hourly solar radiation data for any location in the south west of Western Australia, in a world wide web page

Cited by 15 publications

References 46 publications

Can high levels of renewable energy be cost effective using battery storage? Cost of renewable energy scenarios for an isolated electric grid in Western Australia

Can high levels of renewable energy be cost effective using battery storage? Cost of renewable energy scenarios for an isolated electric grid in Western Australia

A large-scale renewable electricity supply system by 2030: Solar, wind, energy efficiency, storage and inertia for the South West Interconnected System (SWIS) in Western Australia

A simple hourly wind power simulation for the South-West region of Western Australia using MERRA data

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