KU Leuven

Promotor / Supervisor

 Prof. dr. ir. Geert Deconinck

Samenvatting van het onderzoek / Summary of Research

Liberalising the European electricity industry did not naturally produce
its intended results. Network constraints, few dominant sellers ina
relatively small market, complex market designs, price-inelastic
consumers, reductions in generation capacity, unavailability of perfect
information provided in real-time, and portfolio economics and technical
characteristics induced the observed strategic gaming behaviour of

In order to understand the evolution of the
electricity market, dynamic market modelling tools can be applied. Using
such models, all stakeholders can gain insights on the sensitivity of
market design parameters against potential disturbances ormarket
imperfections, and take necessary actions to pro-actively address them.
How the state of an interconnected electrical system evolves after
clearing the day-ahead market as organised under the European Power
Exchange model, subject to strategic gaming behaviour hasbeen studied.
Presented contributions revolve around two research domains.

a novel profit risk hedging offering strategy is presented. It submits
the coordinated dispatch schedule of thermal, hydropower and renewable
power plants to the market operator. The generator pursues a total
profit-maximising objective by simultaneously exercising physical and
economic withholding while explicitlytaking into account underlying
technical constraints and plant economics. Price-responsive demand is
realistically modelled by step-wise decreasing curves. The consideration
of portfolio flexibility to mitigate profit risks is proven to yield
higher total profit than alternative strategies.

Secondly, the
offering strategy is integrated in a newly designed dynamic electricity
market model. Using multi-agent systems, each generator updates its
perception of the market environment by evaluating the performance of
historic decisions on its profit. Four learning and decision processes
have been designed. The first determines the optimal renewable energy
supply quantityto submit with hydropower as reserve, in order to
minimise future self-balancing responsibilities. The second determines
whether to behave competitively or strategically. The third determines
the degree to which the generator can strategically increase its profit.
The last accounts for crossborder exchanges.

Results obtained by
applying the model to case studies illustrate its validity.
Consequently, by explicitly taking into account the most relevant market
design parameters, the agent-based simulation platform is capable of
answering research questions existing electricity market simulation
tools cannot address.

Volledige tekst van het doctoraat / full text

Examencommissie / Board of examiners

  •   Prof. dr. ir. Geert Deconinck (promotor)
  •   Prof. dr. ir. Joseph Vandewalle (voorzitter/chairman)
  •   Prof. dr. Tom Holvoet (secretaris/secretary)
  •   Prof. dr. ir. Ronnie Belmans
  •   De heer Leonardo Meeus
  •   Prof. dr. Poul Erik Morthorst , DTU