Forecasting and decision-making in electricity markets with focus on wind energy

Research output: ResearchPh.D. thesis – Annual report year: 2012


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This thesis deals with analysis, forecasting and decision making in liberalised
electricity markets. Particular focus is on wind power, its interaction with the
market and the daily decision making of wind power generators. Among recently
emerged renewable energy generation technologies, wind power has
become the global leader in terms of installed capacity and advancement. This
makes wind power an ideal candidate to analyse the impact of growing renewable
energy generation capacity on the electricity markets. Furthermore, its
present status of a significant supplier of electricity makes derivation of practically
applicable tools for decision making highly relevant.
The main characteristics of wind power differ fundamentally from those of
conventional thermal power. Its effective generation capacity varies over time
and is directly dependent on the weather. This dependency makes future production
uncertain and difficult to contract even on a day-to-day basis. Consequently
decisions about market bids for next-day delivery are based on production
forecasts which are bound to come with some uncertainty. Naturally
markets that experience large scale integration of wind power are affected by
these different characteristics. The thesis presents analyses of how this impact
is realised in markets significantly penetrated by wind power. Due to its representation
by forecasts in the supply curve, such predictions are used to describe
their non-linear influence on the market prices.
Methods adequately accounting for this effect in models for day-ahead forecasting
of the prices are also presented in the thesis. Prompted by the volatile
behaviour of electricity markets, considerable focus has been on time-varying
and robust parameter estimates. The models derived are all based on well
know methods from the statistical literature.
The stochastic production of wind turbines prompts the need for alternative
methods for optimally bidding wind power to day-ahead markets. Such bidding
strategies are formulated in this thesis, which utilise the information provided
by the market models. Bids that maximise expected revenues are found
and the possibility of risk averse behaviour is discussed.
Original languageEnglish
PublisherTechnical University of Denmark (DTU)
Number of pages280
StatePublished - 2012
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