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Energy Systems and Technologies for the Coming Century ...

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Figure 6: EWEA's 20 year Offshore Network Development Master Plan <strong>and</strong> Europe'soffshore wind power development <strong>and</strong> concession zones [extract, EWEA (2009)]The third point is of a more general nature: providing system reserves could be providedin a cheaper way by storages than by having additional fast-reacting <strong>the</strong>rmal capacityinstalled. In a meshed offshore grid, however, <strong>the</strong>se reserves can only be provided to aneighbouring country if <strong>the</strong> line towards <strong>the</strong> country is not fully utilised. In o<strong>the</strong>r words,an offshore storage will never be able to provide up-regulating reserves <strong>for</strong> <strong>the</strong>neighbouring country where dem<strong>and</strong> is most scarce (reflected by high power prices <strong>and</strong>power import at <strong>the</strong> full capacity of <strong>the</strong> interconnector).4 Model <strong>and</strong> dataThe WILMAR planning tool (Wind Power Integration in Liberalised ElectricityMarkets) optimizes <strong>the</strong> operation of a power system with a focus on fluctuating windenergy. The time resolution is hourly, <strong>and</strong> <strong>the</strong> calculation period is up to a year. For windpower production <strong>for</strong>ecasts, a perfect <strong>for</strong>ecast without <strong>for</strong>ecast error, a single <strong>for</strong>ecast orseveral <strong>for</strong>ecasts per region can be part of <strong>the</strong> optimization. The latter cases are based on<strong>the</strong> Scenario Tree Tool of <strong>the</strong> model, which calculates wind power production <strong>for</strong>ecasts,load <strong>for</strong>ecasts <strong>and</strong> dem<strong>and</strong> <strong>for</strong> positive minute reserves as scenario trees. The JointMarket Model uses <strong>the</strong>se data as input <strong>for</strong> <strong>the</strong> power system optimization. Thegeographical extension can be Europe-wide; <strong>for</strong> fur<strong>the</strong>r details, see e.g. Barth et al.(2006), Tuohy et al. (2009) or Meibom et al. (2011). In this paper <strong>the</strong> model is run with ageographical scope covering <strong>the</strong> North Sea countries, namely Norway, Sweden,Denmark, Germany, <strong>the</strong> Ne<strong>the</strong>rl<strong>and</strong>s, Belgium <strong>and</strong> <strong>the</strong> United Kingdom. Sweden issubdivided into 2 zones, whereas Germany is divided into 3 zones (Northwest,Nor<strong>the</strong>ast, Central-South) to represent bottlenecks due to transmission networkconstraints (Dena, 2011). The offshore hub in <strong>the</strong> North Sea is defined as a singlecountry. Forecast errors are reflected deterministically, which is estimated to reflectcurrent practice most appropriately. The assumptions <strong>for</strong> electricity dem<strong>and</strong>,interconnector capacities etc. are largely in line with EWIS study assumptions <strong>for</strong> 2015,installed wind power is according to best estimate. Fuel prices are set at <strong>the</strong>comparatively high World <strong>Energy</strong> Outlook 2008 levels, with slight regional differences<strong>for</strong> transportation costs. An AA-CAES unit has in principle <strong>the</strong> same characteristics asRisø International <strong>Energy</strong> Conference 2011 Proceedings Page 4

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