Strategic behaviour among market participants may distort intraday prices beyond that of the physical market conditions assessed in Section 3.1 and Section 3.2. This section seeks to explain how such strategic behaviour may depend on the market participants’ available price information
24For example, if consumption is scheduled day-ahead to be lower than usual, while the updated weather forecasts show that the temperature will be fairly low too, it might indicate that scheduled consumption will not represent actual consumption.
at the time of decision making (Section 3.3.1) and to what extent the participants may exhibit market power (Section 3.3.2).
3.3.1 Available Price Information
As intraday trading happens continuously, market participants may be expected to consider information about Elspot prices, the previous Elbas prices and/or the regulating prices when they make their trading decisions. Botnen Holm (2017) finds that prices in Elbas correlate with Elspot prices, though Elbas price spikes were not necessarily reflected in the day-ahead market.
Pape et al. (2016) also include the day-ahead price in their fundamental model for determining the intraday price, and they find that doing so improves the model compared to a model where the day-ahead price is left out. Hence, they suggest that using day-ahead price information is beneficial when forecasting the intraday price. In our models, this can be done by including the aforementioned Elspot spot prices for each bidding area and the system price.
Past Elbas price information may also be of relevance. In GIME, including previous intraday prices is found to improve model performances (Kiesel & Paraschiv, 2017; Pape et al., 2016).
Price information from previous intraday auctions are also important in predicting the next MIBEL intraday price (Andrade et al., 2017; Monteiro et al., 2016). Information on past Elbas prices can be incorporated in our models in two ways, namely the Elbas volume-weighted prices for previous hours of power delivery — that are available to participants at the time of prediction
— and theElbas volume-weighted prices for trades already settled for the hour of power delivery in question at the prediction time.
Imbalance costs for a given delivery hour are not available ex-ante, but both Scharff and Amelin (2016) and Mauritzen (2013) explain how expected imbalance costs are related to market par-ticipants’ incentives to trade in Elbas to adjust their deviations. There is reason to believe that these expectations about future imbalance costs are, at least partially, based on the costs for previous delivery hours that market participants observe during the course of the day. However, by comparing intraday prices to regulating prices, Scharff and Amelin find that it may be hard for market participants to predict the direction of regulating prices. Thus, the way previous imbalance costs affect the intraday price is not definite. Still, due to the strong interaction between the Elbas and the regulating market, we consider it relevant to include regulating price data for previous hours of power delivery available at the time of prediction.
3.3.2 Market Power
Though electricity is a homogeneous product, and the Nordic and Baltic electricity market con-sists of over 370 producers (Nord Pool, 2018f), a few companies constitute larger shares than others (NordREG, 2014).25 In addition, as transmission bottlenecks may constrain the possi-bilities of importing electricity from other bidding areas, these producers may constitute even larger shares in their local markets (Fridolfsson & Tanger˚as, 2009; Tanger˚as & Mauritzen, 2014).
Joint ownership of power plants is also common.26 Furthermore, there exist both economic and political barriers to entering the market (Tanger˚as & Mauritzen, 2014). As such, it is relevant to consider the degree to which market power may be present in Elbas.27 Dominating players may be able to influence prices so as to obtain a profit margin, in addition to covering their power plant’s marginal and ramping costs (Hagemann, 2013; Hagemann & Weber, 2013). For example, they may apply trading strategies such as retention of capacity or offering it at non-competitive prices to maximise profit. This may ultimately drive up intraday prices.
In particular, Tanger˚as and Mauritzen (2014) analyse to what extent producers may exercise market power in Elspot and Elbas, and find that Elbas prices tend to exceed Elspot prices during weekdays, while the relationship is reversed during weekends. The authors discuss how the day-ahead planned power production, and consequently also the transmission constraints, tend to be higher during weekdays than weekends, leaving less capacity for the Elbas market in these periods. Consequently, the observed price relationships may be due to producers exercising seller power in Elbas during weekdays.
It is challenging to fully account for the effects of participants exercising market power on the Elbas price in our models, as each individual participant is likely to behave based on unobservable factors such as the participant’s market share and marginal production cost. As we do not know which participants are involved in a given Elbas trade, these factors are difficult to estimate.
However, if market power to some degree is the explanation for the price effects observed by Tanger˚as and Mauritzen (2014), another important aspect related to a participant’s ability to influence prices is highlighted; market power may not be constant, but rather depend on temporal effects. The possibility of producers exercising market power during peak hours where
25In terms of Nordic generation capacity, Vattenfall holds a market share of 19%, Fortum and Statkraft 12%
each, and E.ON. 7% (NordREG, 2014).
26For example, Vattenfall, Fortum and E.ON jointly own all Swedish nuclear power (Tanger˚as & Mauritzen, 2014).
27In fact, Hagemann and Weber (2015) find that Elbas trading in Denmark is best described by an oligopolistic market model.
the market operates close to its capacity constraints, is also brought up in the context of the UK market (Hagfors et al., 2016). On the other hand, the argument can be made thatoff-peak hours are exposed to the use of market power. Section 3.1.1 explained how trading activity may affect intraday prices in the context of available production capacities. In addition, competition is likely reduced in these periods as smaller companies do not necessarily participate in the market. With fewer participants, opportunities for dominating participants to exercise market power may increase (Hagemann & Weber, 2013).28 Given these temporal patterns, the way in which we may incorporate — at least partially — the effects of market power is through the aforementioned production and transmission capacity variables (Section 3.1) and the dummies forhour-of-day and day-of-week effects.