ITK's price forecasting service is designed from the outset to forecast prices in the renewable energy dominated NEM. Weather is a basic driver of price and the half-hourly focus shows the resulting volatility clearly. Weather sensitivities are a basic service with clients able to choose a weather outlook.

Our model runs quickly, focusses on providing information useful for decision-making, and client sensitivity cases are easily handled. Rather than having the model drive the technology mix based on required investment returns, we treat supply as exogenous, notionally as policy-driven, and look at the resulting price.

We take a merit order approach, the model sequentially fitting supply to demand. It outputs generation mix, flat load price and state of charge for energy storage systems for each NEM state and in every half-hour period for the forecast time horizon.

• Annual flat load price 2025-2035.
• Annual fuel-weighted prices.
• Explore the impact of weather on forecasts. Underlying weather-driven volatility is exposed and not hidden in period averages.
• Storage average spreads separately for long and shorter duration storage.
• Prices by half hour for an average day in a year.
• Price setting frequency by fuel.
• Explore the price setting process with ability to see the forecast price for any given half hour between now and 2035 and what fuels will be used.

• 13 modelled weather years based on AEMO ISP half-hourly weather traces.
• Prices are shown for median, good, and bad VRE years by default, but results for all weather years can be accessed.
• Users can select their own weather year and see the impact on price.

Input a time period from a given day in the year up to the full 20-year forward forecast, choose a region, choose a weather year, and look at the price outcomes. For example, explore the impact on prices in Winter 2027 of good weather or bad weather.

Run your own supply case. Don't like ITK supply assumptions? Then put your assumptions in a spreadsheet template we supply, send it back to us, and get your own custom price forecasts in no time. Our highly-efficient model is capable of outputting multi-year forecasts modelled on an array of historical weather reference years in a matter of minutes.

Our modified merit order model has up to 4 bid bands per fuel. Fuel bid band quantities and bid prices are adjusted as estimates of market conditions change.

Forward-looking weather and time-aware intelligent battery storage buy and sell price algorithm.

The model has been tuned to historical experience and also tuned to the futures curve, but only for the purpose of validation.

Model outcomes are considered against machine learning modelled outcomes.

Supply inputs are exogenous and informed by policy and analysis. This enables calculations to be efficient and timely with fuel-weighted price outcomes checked against LCOE requirements.

Our small team cares about our clients and aims to assist clients individually and promptly. Many years of market analysis and deep contacts within the industry mean we can quickly and efficiently inform clients of the implications of news and policy changes as it impacts the electricity industry.

Equally, we have a deep understanding of the electricity industry in Australia, not just generation, but network regulatory economics, retailer profit models, and the standard analysis tools such as Porter analysis, net present values, required rates of return, as well as intangibles such as industry structure, history, and likely motivations. Our breadth of knowledge, together with the data science capabilities and access to a full range of data, provides us with the tools that clients can depend on for sound advice, time after time.