Invited Session Wed.3.MA 549

Wednesday, 15:15 - 16:45 h, Room: MA 549

Cluster 18: Optimization in energy systems [...]

Congestion management and pricing

 

Chair: Mette Bjørndal

 

 

Wednesday, 15:15 - 15:40 h, Room: MA 549, Talk 1

Endre Bjørndal
Congestion management in the nordic electricity market

Coauthors: Mette Bjørndal, Victoria Gribkovskaia

 

Abstract:
Presently in the Nordic day-ahead market, zonal pricing or market splitting is used for congestions between a predetermined set of price areas. Intra-zonal congestion is resolved by counter trading or redispatching in the regulation market. We study aggregation choices when simplifying nodal prices into zonal or area prices. We discuss two different aggregation concepts, which we call economic and physical aggregation, and their relation to optimal nodal prices. In a model of the Nordic electricity market we consider an hourly case from winter 2010 and present analyses of the effects of different congestion management methods on prices, quantities, surpluses and network utilization.

 

 

Wednesday, 15:45 - 16:10 h, Room: MA 549, Talk 2

Linda Rud
Nodal versus zonal pricing: Market power in day-ahead versus in balancing services

Coauthors: Endre Bjørndal, Mette Bjørndal

 

Abstract:
Presently in the Nordic day-ahead market, zonal pricing or market splitting is used for congestions between a predetermined set of price areas. Intra-zonal congestion is resolved by counter trading or redispatching based on bids from the regulation power market. In a stylized model, we compare this joint model of handling congestions to the theoretically correct method of nodal pricing. Furthermore, we investigate the implications of both schemes for exercising market power in congested network scenarios.

 

 

Wednesday, 16:15 - 16:40 h, Room: MA 549, Talk 3

Yves Smeers
Stochastic equilibrium in investment models: Capacity expansion in the power

Coauthor: Danny Ralph

 

Abstract:
An investor in power generation assets faces unprecedented uncertainty on the evolution of the sector. The market equilibrium is hence one under uncertainty. Agents can be risk neutral or risk averse. We therefore
insert risk functions in order to account for idiosyncratic risk (risk that is not priced by the CAPM) in
investments. Adding a risk function on the cost in a standard (stochastic) capacity expansion planning model can be done and we retain a convex program but it poses questions on the interpretation.
We structure the discussion the interpretation around market completeness:
In a world of perfect risk trading we can derive a price vector for all instruments from a system risk function. The complete market can be represented in terms of stochastic programming.
The assumption of perfect risk trading is however rather heroic for investments that last 20 to 30 years. We hence relax the assumption of perfect risk trading and allow for different stochastic discount factors. The interpretation becomes more difficult since the incomplete market is no longer amenable to a stochastic programming approach.

 

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