Integrating Intermittent Renewable Wind Generation - A Stochastic Multi-Market Electricity Model for the European Electricity Market


In northern Europe, wind energy has become a dominate renewable energy source, due to natural conditions and national support schemes. However, the uncertainty about wind generation affects existing network infrastructure and power production planning of generators, which cannot be fully diminished by wind forecasts. In this paper we develop a stochastic electricity market model to analyze the impact of uncertain wind generation on the different electricity markets as well as network congestion management. Stochastic programming techniques are used to incorporate uncertain wind generation. The technical characteristics of transporting electrical energy as well as power plants are explicitly taken into account. The consecutive clearing of the electricity markets is incorporated by a rolling planning procedure reflecting the market regime of European markets. The model is applied to the German electricity system covering one week. Two different approaches of considering uncertain wind generation are analyzed and compared to a deterministic approach. The results reveal that the flexibility of generation dispatch is increased either by using more flexible generation technologies or by operating rather inflexible technologies under part-load conditions.

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    Baldick (1995) provides a generalized formulation of the unit commitment problem; a recent review of various contributions to the unit commitment literature is given in Padhy (2004).

  2. 2.

    Stochastic models allow for an explicit representation of the sources of uncertainty reflected through multiple scenarios with associated probabilities, whereas in deterministic optimization models all input parameters are assumed to be certain or deterministic. Fundamentals of stochastic optimization can be found in Birge and Louveaux (1997) and Kall and Wallace (1994). With respect to energy, Wallace and Fleten (2003) provide a survey of different stochastic programming models and their application to the energy sector. Herein, stochastic versions of the unit commitment, generation dispatch, as well as optimal power flow are presented and solution methods are discussed. Additionally, an overview of different applications of stochastic programming with a focus on electricity systems is given in Weber (2005), Kallrath et al. (2009), Möst and Keles (2010), and Conejo et al. (2010).

  3. 3.

    Devine et al. (2014) applies a similar concept to replicate the clearing of the UK natural gas markets considering stochastic demand.

  4. 4.

    Moreover, the economic relevance of transmission networks in electricity systems is not limited to the short-term congestion perspective. Jonkeren et al. (2014) investigate the impact of critical infrastructure failures and develop a modeling framework to quantify the economic implications. Furthermore, Abrell and Weigt (2012) analyze the interaction effects between different energy infrastructures. They show that the interaction of congestion in electricity transmission and natural gas pipeline networks can lead to unexpected consequences of energy regulation.

  5. 5.

    We assume perfectly competitive market, thus abstracting from any strategic behavior. E.g. Metzler et al. (2003) and Habis and Csercsik (2014) incorporate different fashions of strategic behavior of generators and load in network-constrained electricity markets.

  6. 6.

    A list of the notation used is given in the appendix.

  7. 7.

    It is important to note that a deterministic dayahead market setup may underestimate the effects of an equivalent stochastic setup. However, a unique dayahead renewable forecast is assumed for two reasons: from a market perspective, marketers of renewable energy have to place a bid in the dayahead market specifying the deliverable amount of electricity. Therefore, the amount of renewable energy may be based on an evaluation of underlying uncertainties, but the offered amount itself is then deterministic. Moreover, from a methodological perspective, the incorporation of uncertain renewable generation in a stochastic modeling setup requires the specification of technology-specific recourse costs for adjusting the first stage decision. These costs may be higher than the direct marginal generation costs due to additional transaction costs as corrective bidding may be required in the intraday market.

  8. 8.

    In general, we continue the notation given above. However, due to the stochastic programming approach, the variables are additionally indexed by the set of nodes in the scenario tree kK.

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    NUTS (Nomenclature of Territorial Units for Statistics) is a hierarchical system for geographic division of the European territory.

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    The expected value of the approximated distribution is always equal to the expected value used in the Changing Forecast case.

  14. 14.

    Conventional generation technologies are grouped into baseload (nuclear, lignite), midload (hard coal), flexible midload (CCGT), and peakload (OCGT, gas steam, CCOT, OCOT, oil steam, and pumped-hydro storage).


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Friedrich Kunz acknowledges financial support of the Mercator foundation and the RWE fellowship program (RWE Studienförderung). The authors thank Christian von Hirschhausen and Hannes Weigt for comments.

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Correspondence to Jan Abrell.

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Abrell, J., Kunz, F. Integrating Intermittent Renewable Wind Generation - A Stochastic Multi-Market Electricity Model for the European Electricity Market. Netw Spat Econ 15, 117–147 (2015).

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  • Electricity markets
  • Unit commitment
  • Stochasticity
  • Renewable energy
  • Transmission network