By Christoph Weber
Around the realm, liberalization and privatization within the electrical energy have result in elevated pageant between utilities. while, utilities at the moment are uncovered greater than ever to danger and uncertainties, which they can't cross directly to their clients via expense raises as in a regulated surroundings. specially electricity-generating businesses need to face risky wholesale costs, gas rate uncertainty, constrained long term hedging chances and enormous, to a wide volume, sunk investments.
In this context, Uncertainty within the electrical energy undefined: equipment and types for choice Support goals at an integrative view at the determination difficulties that energy businesses need to take on. It systematically examines the uncertainties energy businesses are dealing with and develops versions to explain them – together with an cutting edge process combining primary and finance types for cost modeling. The optimization of iteration and buying and selling portfolios below uncertainty is mentioned with specific specialise in CHP and is associated with chance administration. right here the idea that of imperative profits in danger is built to supply a theoretically sound mix of worth in danger and revenue in danger ways, tailored to actual marketplace constructions and industry liquidity. additionally equipment for aiding long term funding judgements are provided: know-how evaluation in response to event curves and operation simulation for gasoline cells and a true suggestions method with endogenous electrical energy costs.
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Additional info for Uncertainty in the Electric Power Industry: Methods and Models for Decision Support
At longer time horizons, the impact of current values of the stochastic variables on the future prices is rather limited, as can be both explained fbndamentally (stability of weather conditions, duration of plant outages) and observed empirically (cf. section 2). g. Monte-Carlo-simulations of demand variations, plant outages etc. But even if these processes could be modeled bottom-up perfectly, one should not expect to describe the full range of fluctuations observed on the Chapter 4 markets: As demonstrated for the financial markets (cf.
Using the (log-)likelihood-fimction. g. prices and log of prices) cannot be done by applying the quality statistics directly to the models. Instead, the model outputs have to be transformed to the same explained variable in order to achieve a meaningful comparison. For prescriptive models, quality measurement is easier on the one hand, since with the objective function a "natural" quality criterion exists. But as stated before, quality can here always only be measured in relation to some other decision model.
A concise discussion why the AIC (or at equal model size the log-likelihood) is an adequate criterion of model quality is provided by Chi, Russel (1999). A general introduction to maximum likelihood estimation in econometrics is provided inter alia by Greene (2000). The likelihood is usually only calculated on the estimation sample, but the extension to the out-of-sample case is straightforward. e. using the (log-)likelihood-fimction. g. prices and log of prices) cannot be done by applying the quality statistics directly to the models.
Uncertainty in the Electric Power Industry: Methods and Models for Decision Support by Christoph Weber