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Perfect competition vs. strategic behaviour models to derive electricity prices and the influence of renewables on market power

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Abstract

A variety of fundamental modelling approaches exist using different competition concepts with and without strategic behaviour to derive electricity prices. To investigate the quality and practicability of these different approaches in energy economics, a perfect competition model, a Cournot model and a Bilevel model are introduced and applied to different situations in the German electricity market. The three electricity market approaches are analysed with respect to their ability to represent electricity prices and the possibility of market power abuse. Market prices are taken as a benchmark for model validity. As a result, the perfect competition model fits best to today’s market situation in most hours of the year. The Bilevel approach explains prices in high load hours sometimes better than the competition model. But complexity and calculation time increase disproportionately. In addition to the analysis of model quality, we use three scenarios to quantify how a high renewable feed-in influences the ability to abuse market power. Results show that the ability to address market power strongly depends on the amount of installed capacities.

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Notes

  1. The year 2012 is chosen as present year, as relevant data for power markets are available.

  2. In this paper, the players are utilities.

  3. Also, in the United States, especially after the California energy crisis in 2001 and 2002, an intense academic debate concerning whole sale electricity prices in California started and the interested reader is referred to e.g. Bushnell et al. (2002), Joskow and Kahn (2002), Mansur et al. (2002) and Kim and Knittel (2006). Most of these papers use empirical methods to analyse the abuse of market power and find indications for the abuse of market power during this crisis.

  4. For example, the concentration ratio is used by the German monopoly commission to test structural market power (independent from energy markets).

  5. For a short overview about market power indicators for electricity markets see e.g. Möst and Genoese (2009).

  6. Market power has been an issue in the years 2004–2007 and several publications exist for this period, especially in Germany. Since 2008–2010, wholesale electricity prices have been quite low and very competitive, so that currently no publications claim that market power is observable. But household consumer electricity prices are one of the highest in Europe, especially due to the renewable energy act surcharge and taxes.

  7. Of the European Energy Exchange—https://www.eex.com/.

  8. In electricity markets, electricity demand is often assumed to be inelastic. In this contribution, we take two different elasticities into account: one nearly inelastic and one slightly more elastic. Slope and intersection are determined by: \(b(h)=\frac{p_{\mathrm{eex}}(h)}{\varepsilon \mathrm{res}_l(h)}\), \(a(h) = p_{\mathrm{eex}}(h)-b(h)\mathrm{res}_l(h)\) \(\forall h\). In case the residual load is zero slope b(h) and intersection a(h) are also zero \(\forall h\).

  9. The interested reader is referred to Martinez (2008), who contributes to the formulation and implementation of production cost models for the modelling of liberalised electricity markets by addressing issues associated with the level of detail in the representation of the underlying power system, the accuracy of the results and the modelling effort.

  10. \(\forall s_f,f\) indicates that this condition holds for every power plant \(s_f\) of firm f and for all elements of f (all firms).

  11. Operating costs include fuel costs, costs for CO\(_2\) emissions (both dependent on fuel/emission prices and the efficiency of the plant) and further operating costs.

  12. On the one hand, Big M should be large enough, so that the model could be solved with big M method. But the solver may fail citing numerical difficulties or an ill-conditioned basis. Such events can be caused by poor scaling, which means that M is too big for the solver. For more details, the interested reader is referred to Spreen and McCarl (1968).

  13. Nonlinear optimisation problems can be solved depending on the type of model; however, nonlinear complementarity models applied to real market data can hardly be solved.

  14. A reduction of hours per year could be an option (e.g. using typical day structures), but as demand and renewable feed-in vary from hour to hour and hourly resolution gets more and more important with higher renewable penetration. For a good modelling of the power plant dispatch and the analysis of market power, an hourly representation is preferable (Möst and Genoese 2009). The interested reader is also referred to Staffell et al. (2014), which shows how representative hours can be selected for a system with a high penetration of renewables.

  15. The Bilevel problem is non-convex due to linearization of the nonlinearity. The obtained solutions can usually only be guaranteed to be local solutions. We do modelling in the same way as in Gabriel and Leuthold (2010) and Barroso et al. (2006), who conclude that the solution is the Nash equilibrium. Additionally, we have made some checks with different starting solutions and come up with the conclusion that the solution is the global one.

  16. This means that nearly the same amount of conventional capacities is necessary, but resulting in significantly less full load hours and hence the question arises how these capacities can cover their fixed costs.

  17. 2013 cannot be used as reference year, as (some) data are only available for 2012 at the earliest.

  18. The electricity market in Germany was deregulated and nearly no market power was exercised in 2001 (cf. Möst and Genoese 2009).

  19. The year 2033 was selected as the net development plan for Germany (Netzentwicklungsplan 2013 2 (NEP13), cf. NEP 2013) is a common base in scientific work. The scenario 2033 B was chosen in this paper for modelling.

  20. Be aware that load curve in 2033 may look different to that of 2012 due to the electrification of heat and transport as well as changes in the macro-economy. Due to simplicity, we assumed today’s load curve for 2033.

  21. Hourly fuel prices can be used for modelling. However, Wozabal and Graf (2013) states that ... ‘However, the prices of electricity do not react to daily changes on the corresponding commodity markets, but rather to long term trends.’ They propose to smoothen hourly values, but for reasons of simplicity we directly refer to monthly values. The interested reader is referred to Martinez (2008), who examined in detail, which data input has which impact on model outcome and quality.

  22. Market shares of the four utilities depend on the method of calculation and of system borders. The market share is often referred to different dimensions, e.g. installed capacities, energy production, or sales to final customers, etc. Taking capacity values as basis, EON has a market share of approximately 26 %, RWE 25 %, Vattenfall 14 % and EnBW 10 % (see Ellersdorfer 2009).

  23. These companies have the biggest share in conventional generation capacities in the year 2012. So the influence and the interest to influence prices on the electricity market is assumed to be higher compared with other market participants.

  24. Please notice that this is a common approach in electricity market modelling. Power plants are grouped together by energy carrier and divided into three different groups (per energy carrier) depending on their vintage, respectively, their efficiency. The efficiency factors depend on type, vintage, modernization, etc. and are taken from the database of the Chair of Energy Economics. Average availability factors are used to model (unplanned and planned) non-availabilities. For further details on modelling non-availabilities the interested reader is referred to Weber (2004).

  25. It is implicitly assumed that import-/export profiles will not significantly change between the scenarios. Of course, this is a an assumption, but the increase in total interconnector capacity in Germany until 2033 is very small taking the planned projects in the Ten-Year-Network-Development plan of the EU (2014) as reference. Besides, it is very difficult to estimate the change in import-/export profiles as it depends on a variety of factors (e.g. development of national capacities and merit orders, interconnector capacities, etc.).

  26. The price duration curve is the sorted hourly price curve of 1 year.

  27. Because the Lerner-indices are negative for most years (except for the year 2012 with perfect competition) this could be used as a supportive argument that power plants are not profitable on average since market prices remain below marginal costs and that perfect competition is the most accurate description of the market structure.

  28. Very low prices occur as renewable generation is provided with marginal costs at zero and the compensation of renewables is paid outside the market, while a low demand occurs resulting in low prices. This phenomenon of low prices in combination with renewable feed-in is also called merit-order effect of renewables. The interested reader is referred to Sensfuß et al. (2008).

  29. In the scenario future, capacities are assumed based on the grid development plan, which is currently a reference document for the development of capacities in Germany. However, it is unclear who will invest in these capacities and whether these capacities will be really installed. It has to be mentioned that prices should be in equilibrium, meaning that capacities will (maybe) be scarcer when prices are too low.

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Correspondence to Susanne Koschker.

Appendix: Social welfare calculation

Appendix: Social welfare calculation

The consumers surplus and the producers surplus added together is called the social welfare or the gain from trade and is a standard measure of market efficiency (Gabriel et al. 2013, p. 88). The consumers surplus is calculated by

$$\begin{aligned} \text{ consumers } \text{ surplus } = \int _0^{q^*} f_d^{-1}(q')dq'-p^*q^* \end{aligned}$$
(45)

whereas \(*\) stands for the equilibrium quantity and the resulting price. Since \(q = \sum _{h,s}g(h,s)\) it is analogue \(q^* = \sum _{h,s}dp^*(h,s)\) where \(dp^*(h,s)\) is the produced quantity of power plant s in equilibrium. The symbols are declared in Sect. 3. The producer surplus is calculated by

$$\begin{aligned} \text{ producers } \text{ surplus } = p^*q^*-\sum _{h,s}\mathrm{oc}(h,s^*)dp^*(h,s). \end{aligned}$$
(46)

Therefore, the social welfare will be obtained by

$$\begin{aligned} \text{ social } \text{ welfare }&= \int _0^{q^*}f_d^{-1}(q')dq'-\sum _{h,s}\mathrm{oc}(h,s^*)dp^*(h,s) \nonumber \\&= a(h)q^* + \frac{1}{2}b(h){q^*}^2 - \sum _{h,s}\mathrm{oc}(h,s^*)dp^*(h,s). \end{aligned}$$
(47)

In Sect. 3.1, the quantity \(q^*\) is found by maximisation of the social welfare.

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Koschker, S., Möst, D. Perfect competition vs. strategic behaviour models to derive electricity prices and the influence of renewables on market power. OR Spectrum 38, 661–686 (2016). https://doi.org/10.1007/s00291-015-0415-x

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