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Integrated Electricity Generation Expansion and Transmission Capacity Planning: An Application to the Central European Region

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  • Andreas Schröder
  • Maximilian Bracke

Abstract

This article presents an integrated electricity dispatch and load flow model with endogenous electricity generation capacity expansion. The target is to quantify generation capacity requirements for 2030 and where within Central Europe it shall be ideally placed when taking into account the projected grid structure. We explicitly model the interdependence between grid operation and power plant placing as we investigate the contribution of centralized power plant placement on reducing the need for grid expansion. The application focuses on Germany and its neighbors and reference is made to recently published plans on grid expansion (TSO 2012). We adopt the perspective of a welfare maximizing system planner and thus determine capacity expansion levels as first-best benchmark. Results show that optimal capacity expansion levels are much lower than previous studies indicate (e.g. dena (2008); EC (2011); EWI et al. (2010); Maurer et al. (2012)). We also show that the need for grid expansion can be reduced by the appropriate placing of just a few Combined Cycle Gas Turbine (CCGT) power plants as well as the use of storage and Demand-Side-Management. The presence of intra-national HVDC lines as proposed in the Grid Development Plan of 2012 (TSO 2012) is found to significantly reduce overall congestion and the need for back-up power plants. However, the contribution of the proposed HVDC lines varies greatly from project to project, calling for a prioritization of plans.

Suggested Citation

  • Andreas Schröder & Maximilian Bracke, 2012. "Integrated Electricity Generation Expansion and Transmission Capacity Planning: An Application to the Central European Region," Discussion Papers of DIW Berlin 1250, DIW Berlin, German Institute for Economic Research.
  • Handle: RePEc:diw:diwwpp:dp1250
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    References listed on IDEAS

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    1. van der Weijde, Adriaan Hendrik & Hobbs, Benjamin F., 2012. "The economics of planning electricity transmission to accommodate renewables: Using two-stage optimisation to evaluate flexibility and the cost of disregarding uncertainty," Energy Economics, Elsevier, vol. 34(6), pages 2089-2101.
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    4. Fleten, Stein-Erik & Näsäkkälä, Erkka, 2010. "Gas-fired power plants: Investment timing, operating flexibility and CO2 capture," Energy Economics, Elsevier, vol. 32(4), pages 805-816, July.
    5. Scorah, Hugh & Sopinka, Amy & van Kooten, G. Cornelis, 2012. "The economics of storage, transmission and drought: integrating variable wind power into spatially separated electricity grids," Energy Economics, Elsevier, vol. 34(2), pages 536-541.
    6. Nagl, Stephan & Fürsch, Michaela & Lindenberger, Dietmar, 2012. "The costs of electricity systems with a high share of fluctuating renewables - a stochastic investment and dispatch optimization model for Europe," EWI Working Papers 2012-1, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).
    7. van der Weijde, A.H. & Hobbs, B.F., 2011. "Planning electricity transmission to accommodate renewables: Using two-stage programming to evaluate flexibility and the cost of disregarding uncertainty," Cambridge Working Papers in Economics 1113, Faculty of Economics, University of Cambridge.
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    Cited by:

    1. McInerney, Celine & Bunn, Derek W., 2017. "Optimal over installation of wind generation facilities," Energy Economics, Elsevier, vol. 61(C), pages 87-96.
    2. Rajesh, K. & Bhuvanesh, A. & Kannan, S. & Thangaraj, C., 2016. "Least cost generation expansion planning with solar power plant using Differential Evolution algorithm," Renewable Energy, Elsevier, vol. 85(C), pages 677-686.

    More about this item

    Keywords

    electricity; network planning; renewable energies; electricity markets; capacity investment;

    JEL classification:

    • D61 - Microeconomics - - Welfare Economics - - - Allocative Efficiency; Cost-Benefit Analysis
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources

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