The transformative effect of unscheduled generation by solar PV and wind generation on net electricity demand
This study investigates the transformative effect of unscheduled solar PV and wind generation on electricity demand. The motivations for the study are twofold, the poor medium term predictions of electricity demand in the Australian National Electricity Market and the continued rise in peak demand but reduction in overall demand. A number of factors contribute to these poor predictions, including the global financial crisis inducing a reduction in business activity, the Australian economy’s continued switch from industrial to service sector, the promotion of energy conservation, and particularly mild weather reducing the requirement for air conditioning. Additionally, there is growing unscheduled generation, which is meeting electricity demand. This growing source of generation necessitates the concepts of gross and net demand where gross demand is met by unscheduled and scheduled generation and net demand by scheduled generation. The methodology compares the difference between net and gross demand of the 50 nodes in the Australian National Electricity Market using half hourly data from 2007 to 2011. The unscheduled generation is calculated using the Australian Bureau of Meteorology half hourly solar intensity and wind speed data and the Australian Clean Energy Regulator’s database of small generation units’ renewable energy target certificates by postcode. The findings are that gross demand rather than net demand helps explain both the overall reduction in net demand and the continued increase in peak demand. The study has two main conclusions. Firstly, a requirement for policy to target the growth in peak demand via time of supply feed-in tariff for small generation units. Secondly, modellers of electricity demand consider both net and gross demand in their forecasts. The time of supply feed-in tariffs are intended to promote the adoption of storage technologies and demand side participation and management. Modellers considering both net and gross demand are required to model unscheduled generation. This requirement ensues that more comprehensive solar intensity data be provided by the Bureau of Meteorology and that the Australian National Electricity Market Operator provide data in GIS format of each demand node using the Australian Statistical Geography Standard developed by Australian Bureau of Statistics to enable easier integration of large quantities of geographic data from a number of sources. The applicability of these finding become more relevant to other countries as unscheduled generation becomes more wide spread. This study is instrumental to a range of further research. Other sources of unscheduled generations should be considered to form a more comprehensive concept of gross demand, for instance, solar hot water and small hydro. Replacing electrical hot water heaters with solar hot water reduces the overnight demand, which may provide a considerable transformative effect on net electricity demand. In addition, energy efficiency is meeting demand for electricity; incorporating energy efficiency would form an even more comprehensive concept of gross electricity demand and could help improve longer term electricity demand projections.
|Date of creation:||03 Apr 2013|
|Date of revision:|
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- John Foster & Liam Wagner & Phil Wild & Junhua Zhao & Lucas Skoofa & Craig Froome & Ariel Liebman, 2011. "Market and Economic Modelling of the Intelligent Grid: End of Year Report 2010," Energy Economics and Management Group Working Papers 10, School of Economics, University of Queensland, Australia.
- John Foster & Liam Wagner & Phil Wild & William Paul Bell & Junhua Zhao & Craig Froome, 2011. "Final Report: Market and Economic Modelling of the Intelligent Grid," Energy Economics and Management Group Working Papers 12, School of Economics, University of Queensland, Australia.
- Bell, William & Foster, John, 2012. "Feed-in tariffs for promoting solar PV: progressing from dynamic to allocative efficiency," MPRA Paper 38861, University Library of Munich, Germany, revised 28 Apr 2012.
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