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Why Wind Is Not Coal: On the Economics of Electricity

Author

Listed:
  • Hirth, Lion
  • Ueckerdt, Falko
  • Edenhofer, Ottmar

Abstract

The economics of electricity is shaped by its physics. A well know example is the non-storability of electricity that causes its price to fluctuate widely. More generally, physical constraints cause electricity to be a heterogeneous good along three dimensions - time, space, and lead-time. Consequently, different generation technologies, such as coal and wind power, produce different economic goods that have a different marginal economic value. Welfare maximization or competitiveness analyses that ignore heterogeneity deliver biased estimates. This paper provides an analytical welfare-economic framework that accounts for heterogeneity for unbiased assessments of power generators. The framework offers a rigorous interpretation of commonly used cost indicators such as ‘levelized electricity costs’ and ‘grid parity’. Heterogeneity is relevant for all generators, but especially for variable renewables such as wind and solar power. We propose a definition of ‘variability’, derive the opportunity costs of variability, and link that concept to the ‘integration cost’ literature. A literature review shows that variability can reduce the value of wind power by 20-50%. Thus it is crucial that economic analysis accounts for the physics of electricity.

Suggested Citation

  • Hirth, Lion & Ueckerdt, Falko & Edenhofer, Ottmar, 2014. "Why Wind Is Not Coal: On the Economics of Electricity," Energy: Resources and Markets 172433, Fondazione Eni Enrico Mattei (FEEM).
  • Handle: RePEc:ags:feemer:172433
    DOI: 10.22004/ag.econ.172433
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    Cited by:

    1. Wilhelm Kuckshinrichs, 2021. "LCOE: A Useful and Valid Indicator—Replica to James Loewen and Adam Szymanski," Energies, MDPI, vol. 14(2), pages 1-8, January.
    2. Hirth, Lion & Ueckerdt, Falko & Edenhofer, Ottmar, 2015. "Integration costs revisited – An economic framework for wind and solar variability," Renewable Energy, Elsevier, vol. 74(C), pages 925-939.
    3. Lion Hirth, 2015. "The Optimal Share of Variable Renewables: How the Variability of Wind and Solar Power affects their Welfare-optimal Deployment," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    4. Miranda, Raul & Simoes, Sofia & Szklo, Alexandre & Schaeffer, Roberto, 2019. "Adding detailed transmission constraints to a long-term integrated assessment model – A case study for Brazil using the TIMES model," Energy, Elsevier, vol. 167(C), pages 791-803.
    5. Tapia-Ahumada, Karen & Octaviano, Claudia & Rausch, Sebastian & Pérez-Arriaga, Ignacio, 2015. "Modeling intermittent renewable electricity technologies in general equilibrium models," Economic Modelling, Elsevier, vol. 51(C), pages 242-262.
    6. Jacques Després & Patrick Criqui & Silvana Mima & Nouredine Hadjsaid & Isabelle Noirot, 2014. "Analysing the interactions between Variable Renewable Energies, electricity storage and grid in long term energy modelling tools," Post-Print hal-01279461, HAL.
    7. Després, Jacques & Mima, Silvana & Kitous, Alban & Criqui, Patrick & Hadjsaid, Nouredine & Noirot, Isabelle, 2017. "Storage as a flexibility option in power systems with high shares of variable renewable energy sources: a POLES-based analysis," Energy Economics, Elsevier, vol. 64(C), pages 638-650.
    8. Jeffrey C. Peters & Thomas W. Hertel, 2017. "Achieving the Clean Power Plan 2030 CO2 Target with the New Normal in Natural Gas Prices," The Energy Journal, , vol. 38(5), pages 39-66, September.

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    Keywords

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    JEL classification:

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

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