For day ahead and intraday markets, the power plant optimization model tells you what is optimal to do: produce or don’t produce? When prices change due to unforeseen circumstances, it could be optimal to change the dispatch decision on the intraday market.
KyPlant shows which forward transactions are optimal to hedge risks and lock in profits. The user can choose between intrinsic hedging and delta hedging, two strategies to secure profits. It can provide hedge recommendations for the asset alone or for multiple assets together.
The power plant valuation model calculates the fair value of a power station. The model shows which part of the value is intrinsic and which part is extrinsic. To capture the extrinsic value, a more active trading strategy is required.
Extrinsic values are derived from an intuitive and realistic Monte Carlo simulation model. A combination of forward and spot trading strategies is applied to the simulated price scenarios, using dynamic programming and least-squares Monte Carlo. This type of valuation provides a fair assessment of the future value. Backtesting of the model is another feature: it shows how much money you would have made in the past, following a specific trading strategy.
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Most KyPlant users optimize the power stations at individual plant level. But power plant optimization software is also used effectively for a portfolio of plants, for example connected by joint heat obligations and a heat buffer. Detailed technical characteristics of the power stations can be provided as input, including efficiency curves, a range of start types and curves, costs, trips, maintenance, start constraints, etc.
KyPlant is fully embedded in the KYOS Analytical Platform. With automated data feeds, up-to-date plant valuations are always available.
The power plant optimization software can be applied to real physical power plants, but also virtual power plants, spark and dark spread options, and power options.
KyPlant uses advanced Monte Carlo simulation techniques. Important characteristics are cointegrated commodity prices, and a mean-reverting multi-factor model with long-term, short-term and seasonal dynamics. Users can also import their own price simulations or use those of KySim. Optimal dispatch and exercise decisions are based on dynamic programming and least squares Monte Carlo techniques. The volatility term structure and other simulation inputs are easily derived from historical data. Implied option volatilities may be used as well, by overwriting the historical volatility estimates.
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