In the deregulated Norwegian electricity market a zonal transmission pricing system is used to cope with network capacity problems. In this paper we will illustrate some of the problems that the zonal pricing system, as implemented in Norway, has. With the use of small network examples we illustrate the difficulties involved in defining the zones, the redistribution effects of the surplus that a zonal pricing system has, as well as the conflicting interests concerning zone boundaries that are present among the various market participant. We also show that a zone allocation mechanism based on nodal prices does not necessarily lead to a zone system with maximal social surplus. Finally, we formulate an optimization model that when solved yields the zone system that maximizes social surplus given a pre-specification of the number of zones to be used.,QWURGXFWLRQA zonal approach to managing congestion has been adopted in the Norwegian scheduled power market. The trading process works approximately as follows:1) Based on the supply and demand schedule bids given by the market participants, the market is cleared while ignoring any grid limitations. This produces a system price S of energy.2) If the resulting flows induce capacity problems, the nodes of the grid are partitioned into zones.3) Considering the case with two zones defined, the zone with net supply is defined as the low-price area, whereas the net demand zone is determined the high-price area. 4) Net transmission over the zone-boundary is fixed when curtailed to meet the violated capacity limit.

In this paper, we investigate methods for managing congestion on the grid in the Nordic power market. Specifically, we have considered the differences between using counter purchases as opposed to pricing out the transmission constraints of the grid. We show that the specific method used for congestion management greatly affects prices and therefore the surplus of the various agents, including the system operator. This means that the market agents may have preferences for one method, and take actions in order to influence which method is to be used. Based on this, we have studied the incentives and possibilities of “moving” capacity constraints, and the effect this has on system performance. We have also looked into the differences between various pricing schemes, i.e. optimal nodal prices versus optimal zonal prices. The effects that are demonstrated by the examples in this paper are especially relevant when designing coordination mechanisms and regulation for integrated markets, like the (emerging) European electricity market.

The issue of finding market clearing prices in markets with non-convexities has had a renewed interest due to the deregulation of the electricity sector. In the day-ahead electricity market, equilibrium prices are calculated based on bids from generators and consumers. In most of the existing markets, several generation technologies are present, some of which have considerable non-convexities, such as capacity limitations and large start up costs. In this paper we present equilibrium prices composed of a commodity price and an uplift charge. The prices are based on the generation of a separating valid inequality that supports the optimal resource allocation. In the case when the sub-problem generated as the integer variables are held fixed to their optimal values possess the integrality property, the generated prices are also supported by non-linear price-functions that are the basis for integer programming duality.

Regulators of electricity distribution networks have typically applied Data Envelopment Analysis (DEA) to cross-section data for benchmarking purposes. However, the use of panel data to analyse the impact of regulatory policies on productivity change over time is less frequent. The main purpose of this paper is to construct a Malmquist productivity index to examine the recent productivity change experienced by Norwegian distribution companies between 2004 and 2007. The Malmquist index is decomposed in order to explore the sources of productivity change, and to identify the innovator companies that pushed the frontier forward each year. The input and output variables considered are those used by the Norwegian regulator. In order to reflect appropriately the exogenous conditions where the companies operate, the efficiency model used in this paper incorporates geography variables as outputs of the DEA model. Unlike the model used by the regulator, we included virtual weight restrictions in the DEA formulation to correct the biases in the DEA results that may be associated to a judicious choice of weights by some of the companies.

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