Temperature as a driver of demand<\/em><\/p>\n\n\n\n The first of these factors has always been an issue of major importance to the operation of the system. Figure 1 shows the relationship between temperature and Victoria demand for 2025, based on 2 weather reference years (ie, 2002 and 2019). Higher temperatures lead to higher demand for cooling; lower temperatures lead to high demand for heating. The vee-shape can be observed in all regions of the NEM, although the steepness of the arms of the vee depends on the amount of electrification and the degree of energy efficiency in each region.<\/p>\n\n\n\n Figure 1 – 2025 Victoria demand versus temperature, 2002 and 2019 weather years<\/strong><\/p>\n\n\n\n <\/p>\n\n\n\n Wind and solar irradiance as the drivers of supply<\/em><\/p>\n\n\n\n In contrast, the amount of solar irradiance and wind speeds are growing in their importance to supply in line with higher penetration of renewables. Figure 2 shows the relationship between wind output and price in South Australia over the last 10 years. Each bar shows the average price for each 50 MW bucket of wind output in South Australia. Here we see the rising significance of wind to price, and that when the system is becalmed prices tend to rise as lower merit plant is brought online. Similar results can be shown for solar irradiance, with effects on both the output of rooftop- and large-scale solar.<\/p>\n\n\n\n Figure 2 – Wind output versus price in South Australia over the last 10 years<\/strong><\/p>\n\n\n\n <\/p>\n\n\n\n One of the powerful aspects of market models is that they can project how different weather conditions affect dispatch, and so price. Endgame offers its clients the ability to examine how different weather years affect market outcomes. Figure 3 shows the NSW annual average price as a percentage of FY2026 price for Endgame’s ‘Sunny Side Up’ Scenario, which sees a generation mix dominated by solar and storage. The figure shows 13 different lines: one for each of 13 weather years.<\/p>\n\n\n\n Figure 3 –<\/strong> NSW average annual price as a percentage of FY2026, 13 weather years <\/strong> The critical feature of this modelling is that we can see that from FY2033 onwards, the system becomes highly weather dependent after the projected closure of a coal-fired power station. We have highlighted three years (ie, 2015, 2017, and 2019) to show just how wide the spread in prices becomes. We note the following:<\/p>\n\n\n\n The use of individual weather reference years for pricing assessments overlooks a major part of the story of future price outcomes \u2013 ie, that the system is becoming increasingly volatile. There are three important implications of this:<\/p>\n\n\n\n Analysis of multiple weather years is inherently more complicated. But without the additional insights that come from looking at the shape of the distribution, we are only seeing one dimension of the future picture. The system we are building is stochastic, and so our modelling needs to be as well.<\/p>","protected":false},"excerpt":{"rendered":" As a provider of market outlooks and price forecasts, we are constantly being asked to provide projections of the future. We are always asked to consider and incorporate different cost assumptions: fuel, capex, and fixed costs. In contrast, only a few investors and market participants are interested in understanding stochastic factors such as the weather. At the same time, our government and market body clients are increasingly asking for analysis that can help them understand the role of the weather, and so the resilience of the grid. The transformation of the power system to a high penetration renewable world means that these stochastic factors and aspects of our methodology are […]<\/p>\n","protected":false},"author":4,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","footnotes":""},"categories":[8,25],"tags":[52,42,36,54,53,32,27,37,35,33,31,39,28,43,38,56,41],"topic":[15],"industry":[20],"class_list":["post-897","post","type-post","status-publish","format-standard","hentry","category-article","category-blog","tag-arts","tag-assumptions","tag-beginner","tag-carbon","tag-dark","tag-electricity","tag-energy","tag-guide","tag-market","tag-modelling","tag-nem","tag-power","tag-prices","tag-renewable","tag-system","tag-technology","tag-weather","topic-analysis","industry-energy"],"acf":[],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t![\"\"](\"https:\/\/www.endgameanalytics.com.au\/wp-content\/uploads\/2025\/07\/image-1024x632.jpg\")
<\/figure>\n\n\n\n![\"\"](\"https:\/\/www.endgameanalytics.com.au\/wp-content\/uploads\/2025\/07\/image-1-1024x632.jpg\")
<\/figure>\n\n\n\nWeather is a major driver of price in the 2030s<\/h3>\n\n\n\n
<\/p>\n\n\n\n
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We must start to think about the distribution of prices as a function of weather<\/h3>\n\n\n\n
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