We are getting close to a Beta release of our first member of the PUMA Algorithm’s family, and I wanted to update everyone on what to expect and on progress so far.
The PUMA Algorithms are advanced models for the analysis and prognosis of power markets, that take detailed account of future uncertainty in input levels such as hydro inflow, demand, fuel and emissions prices. The first PUMA Algorithm, PUMA SP, is being developed as part of the PUMA Research Project sponsored by Research Council of Norway, and paid for by several major actors in the Nordic power market.
PUMA SP is a fundamental medium term time-frame model that captures the impact of multiple uncertainty drivers such as inflows, availabilities, demand, fuel and CO2 prices. In modelling something as complex as a power market, you have to make trade offs. Speed verses detail. Detailed modelling of hydro verses detailed modelling of CHP. Uncertainty verses perfect foresight. With PUMA SP we have taken the view that the user is best positioned to make these trade-offs, as they can change from analysis to analysis.
PUMA SP is therefore designed with flexibility and ease-of-use in mind, and can be configured to run at whatever level of detail you need. Want to run deterministically? Just one parameter. Uncertain inflows and fuel prices? The same. Add in uncertain demand? No problem. One nice thing – once you have one uncertain parameter, adding new ones does not add much solve overhead. So, if you need “quick-and-dirty”, that’s what you can have, whilst being able to use the same model and data later on to refine and model a fully detailed market response.
All Puma Algorithms are fully integrated with the PUMA Analytic Framework, and are provided as python packages and with a browser-based front-end. PUMA SP is currently in locked alpha testing with our development license group, but we are planning for it to be available to new customers in Beta at the end of 2017. Drop us a line to find out more.