ENERGY RISK MANAGEMENT
Besides the opportunities for trading, along with the
company strategies, this expanded ETRM brings an enormous reduction of operational risk due to single point of entry for prices and trades, automation of the processes through all the operational steps from deal entry to optimization risk valuations, right up to nominations and settlement. Supporting the daily business is a crucial point for
the acceptance of any optimization tool. A streamlined and efficient workflow support enormously reduces the time and effort needed for exchanging data and running processes, thus curtailing operative risks and losses. An automation tool box performing the daily routine work by a single mouse click or event driven, cyclically or scheduled provides the important part of a smart expanded ETRM system.
Integrating Profitability & Risk Awareness 1. Classic Optimization Approach There are a number of different approaches and
techniques for the optimization of energy systems, like Dynamic Programming, Mixed Integer Programming, La-grange Relaxation (with/without decomposition) etc. All have their advantages and disadvantages. However, for the optimization of large energy
systems with complex elements, network topology, and different kinds of restrictions, Mixed Integer Programming (MIP) techniques, using standardized solution algorithms, is the natural choice to solve such optimization problems allowing a free parameterization and an evaluation of the quality of solution.
Supporting the daily business is a
crucial point for the acceptance of any optimization tool
The marginal price for each balance node (= Local
Marginal Price, LMP) is determined automatically as dual variable of the balance continuity condition, indicating regions for potential new assets. Moreover, usage of a MIP solver for the balancing of the supply and demand situation guarantees automatically the compliance with FERC regulations (“first supply nodal demand and then trade”). There are many reasonable arguments saying that
this methodology is suitable for finding an optimal scheduling of assets within large energy systems. However, it is not practical when it comes to stochastic optimization because of the high computational times and the extremely large sizes of data making this method unsolvable in practice without reducing modeling accuracy. This also depends on the stochastic approach used. The next section provides an insight into particular stochastic techniques which avoid the difficulties mentioned above.
58 December 2012
2. Handling of Uncertainties Since we live in an ever changing and uncertain
world, no optimal market strategy can be assumed without risk assessment. Most companies having an optimization tool in addition to an ETRM system are able to calculate an optimal asset schedule and transfer it as a fixed strategy to risk management – a simple deterministic result. However, doing this would ignore the possibilities of changing the strategy as changes in the market occur. Optimal strategy becomes static and loses its optimality in this continuously changing environment. The easiest way to overcome this obstacle is
to compare a wide variety of different scenarios with different assumptions about future market developments. The comparison of results of different scenarios after optimization provides, on the one hand, greater planning security and gives an overview of profitability, and on the other, the risks associated with particular decisions. OpenLink’s expanded ETRM offers an integrated
system framework to handle several scenarios of input data including their comparison and with a special focus on:
• ‘What if’ Analysis • Stress Testing • Opportunity Evaluations (e.g. new deals)
User defined scenario rules control the selection
of the time series scenarios. For example, a scenario rule called Cold Winter may include the scenario High Price for all related index and price time series and the scenario High Demand for the corresponding demand curves. For the evaluation of a potential portfolio component (an opportunity, such as a new deal or plant, etc.), a trader can compare the total costs of the original model with the total costs of the scenario model, including the opportunity to be investigated. To study the robustness of the new component, this analysis can be combined with the scenario management for price and volume scenarios. The next logical step in creating tight connections
between optimization and risk tools is the opportunity to use statistical scenarios for volatile generation/ demands and/or prices. These scenarios constitute a sound basis for a Monte Carlo (MC) analysis of the whole optimization portfolio, as well as for a stochastic approach resulting in single, risk-hedged, optimization solution. The MC simulation runs provide a probability
distribution of the P&L results to evaluate the financial risk exposure described by the expected P&L, Profit-at-Risk (PaR), and Earnings-at-Risk (EaR) based on an optimized solution for each Monte Carlo scenario. The number of these scenarios must be sufficiently high to enable a reliably statistical analysis of the distribution function and to reduce
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