Bell, William Paul and Wild, Phillip and Foster, John and Michael, Hewson (2015): Wind speed and electricity demand correlation analysis in the Australian National Electricity Market: Determining wind turbine generators’ ability to meet electricity demand without energy storage. Forthcoming in: Economic Analysis and Policy , Vol. 2015, (2015): pp. 1-10.
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Abstract
This paper analyses wind speed and electricity demand correlation to determine the ability of wind turbine generators to meet electricity demand in the Australian National Electricity Market (NEM) without the aid of energy storage. With the proposed increases in the number of windfarms to meet the Large-scale Renewable Energy Target (LRET), this correlation study is formative to identifying price and power stability issues and determining what transmission structure is required to best facilitate the absorption of wind power. We calculate correlations between wind speed and electricity demand data for the years 2010 to 2012 using Weather Research & Forecasting Model (WRF 2015) wind speed data and Australian Energy Market Operator (AEMO) electricity demand data. We calculate state level correlations to identify potential bottlenecks in the interconnectors that link each state’s transmission network. The transmission lines within each state tend to be less of a constraint. We find a small temporal increase in correlation between electricity demand and wind speed. This we attribute to an unwitting renewable energy portfolio effect with the increase in solar PV and solar water heating. Strengthening this portfolio effect is the decline in manufacturing that makes household domestic demand relatively larger. Comparing our study with an earlier correlation analysis by Bannister and Wallace (2011) tends to confirm our initial findings. We find the most advantage from the lack of correlation between wind speed between the NEM’s peripheral states including Queensland, South Australia and Tasmania. Additionally, the correlation between electricity demand and wind speed is strongest between these states. Similarly, we find the most advantage from the lack of correlation between electricity demand in each of these states. The self-interest groups within Victoria and New South Wales and the transmission companies geographically contained within each state hinders the development of optimal interconnector capacity to maximise the benefit of wind power in the peripheral states and the NEM generally.
Item Type: | MPRA Paper |
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Original Title: | Wind speed and electricity demand correlation analysis in the Australian National Electricity Market: Determining wind turbine generators’ ability to meet electricity demand without energy storage |
Language: | English |
Keywords: | Wind speed Electricity demand Correlation Australian National Electricity Market Wind turbine generators Renewable energy Renewable energy portfolio solar PV |
Subjects: | Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy > Q42 - Alternative Energy Sources Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy > Q47 - Energy Forecasting Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q5 - Environmental Economics Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q5 - Environmental Economics > Q53 - Air Pollution ; Water Pollution ; Noise ; Hazardous Waste ; Solid Waste ; Recycling Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q5 - Environmental Economics > Q56 - Environment and Development ; Environment and Trade ; Sustainability ; Environmental Accounts and Accounting ; Environmental Equity ; Population Growth |
Item ID: | 68185 |
Depositing User: | Dr William Paul Bell |
Date Deposited: | 08 Dec 2015 19:54 |
Last Modified: | 27 Sep 2019 01:11 |
References: | AEMO 2010a, 'Appendix F - NEMLink supplementary information', in 2010 National Transmission Network Development Plan. —— 2010b, 'Chapter 5 - NEMLink: a Pre-feasibility Study into High Capacity Backbone', in 2010 National Transmission Network Development Plan. —— 2011a, 'Appendix C – NEMLink Methodology, and Generation and Transmission Development Results', in 2011 National Transmission Network Development Plan, Australian Energy Market Operator. —— 2011b, 'Chapter 6 - NEMLink: Further study results on a high-capacity backbone', in 2011 National Transmission Network Development Plan. —— 2014, Electricity Data: Price and Demand, viewed 16 Mar 2014, <http://www.aemo.com.au/Electricity/Data/Price-and-Demand>. Bannister, H & Wallace, S 2011, 'Increasing Intermittent Generation, Load Volatility and Assessing Reserves and Reliability', paper presented to IES Seminar on Transmission and Intermittency Issues, Sydney, 16 Aug 2011. Bell, WP, Wild, P & Foster, J 2013, 'The transformative effect of unscheduled generation by solar PV and wind generation on net electricity demand', paper presented to 2013 IAEE International Conference, Daegu, Korea, 16-20 June 2013. Bell, WP, Wild, P, Foster, J & Hewson, M 2015, The effect of increasing the number of wind turbine generators on transmission line congestion in the Australian National Electricity Market from 2014 to 2025, Energy Economic and Management Group Working Paper 3-2015, University of Queensland, Brisbane, Australia. Cutler, NJ, Boerema, ND, MacGill, IF & Outhred, HR 2011, 'High penetration wind generation impacts on spot prices in the Australian national electricity market', Energy Policy, vol. 29, no. 2011, pp. 5939-49. Elliston, B, MacGill, I & Diesendorf, M 2013, 'Least cost 100% renewable electricity scenarios in the Australian National Electricity Market', Energy Policy, vol. 59, no. 2013, pp. 270-81. Evans, J, Ekström, M & Ji, F 2012, 'Evaluating the performance of a WRF physics ensemble over South-East Australia', Climate Dynamics, vol. 39, no. 6, pp. 1241-58. Jiménez, P & Dudhia, J 2013, 'On the Ability of the WRF Model to Reproduce the Surface Wind Direction over Complex Terrain', Journal of Applied Meteorology and Climatology, vol. 52, no. 7, pp. 1610-7. Mosek 2014, High performance software for large-scale LP, QP, SOCP, SDP and MIP, viewed 16 Mar 2014, <http://www.mosek.com/>. Skamarock, W, Klemp, J, Dudhia, J, Gill, D, Barker, D, Duda, M, Huang, X-Y, Wang, W & Powers, J 2008, A Description of the Advanced Research WRF Version 3, National Center for Atmospheric Research, Boulder, Colorado, USA. Wild, P, Bell, WP & Foster, J 2015, Australian National Electricity Market (ANEM) model version 1.10, Energy Economic and Management Group Working Paper 2-2015, University of Queensland, Brisbane, Australia. Woo, K, Horowitz, I, Moore, J & Pacheco, A 2011, 'The impact of wind generation on the electricity spot-market price level and variance: The Texas experience', Energy Policy, vol. 39, no. 2011, pp. 3939-44. WRF 2015, The Weather Research and Forecasting Model, viewed 22 Apr 2015, <http://www.wrf-model.org/index.php>. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/68185 |