Initial Situation
An energy supplier with a power plant portfolio for the generation of electricity and district heating intends to switch from intrinsic to full value for the valuation in long-term marketing. Simulation based full value evaluations are necessary both for introducing delta hedging rather than hedging by a rolling intrinsic approach and for improving the data basis for a more comprehensive risk management. The vendor software currently used for unit commitment is tailored to short-term schedule calculation. Accordingly, the implementation is to take place through the introduction of new third-party software.
Project Scope
- Analyses of relevant restrictions on the unit commitment problem for the current power plant portfolio with given loads
- Market sounding and execution of the software selection process
- Implementation of the vendor software including functional and non-functional tests regarding the business requirements
- Training on delta hedging and discussion of the associated advantages and disadvantages
- Recommendation for action to switch the hedging strategy from rolling intrinsic to delta hedging
Our Contribution
- Development of a unit commitment prototype tool in Python
- Analyses of potential simplifications in the power plant park evaluation and corresponding effects on the uncertainties in long-term marketing
- Preparation of tender documents in cooperation with the client’s business and the IT department as well as weighting of the business requirements contained therein
- Design and execution of test cases, including comparison with Python prototypes
- Implementation of upload and download interfaces
- Generation of training material and execution of training courses on delta hedging
- Quantitative analysis of the effects on market price risks and transaction costs when changing the hedging strategy
Customer Benefit
With the introduction of third-party software, the extrinsic value of the park value can be measured quantitatively for the first time. Previously it had to be estimated conservatively as part of the planning process. In addition, knowledge of the full value allows for switching to delta hedging and thus reduces the market price risk by hedging the risks associated with the fair value.
Finally, the vendor software’s high performance provides room for additional analyses. It is now possible to quantitatively estimate shape risks caused by model-dependent price structure curves as well as model risks associated with spot price simulations. Thus, risk analysis and reporting are much more meaningful, also regarding P&L attribution, e.g. to district heating contribution effects.
Relevant Skills/Tools
- Python incl. optimisation package Pyomo
- Solver: Gurobi, SCIP
