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A Unit Commitment and Economic Dispatch Model of the GB Electricity Market – Formulation and Application to Hydro Pumped Storage

Author

Listed:
  • Chi Kong Chyong

    (EPRG, CJBS, University of Cambridge)

  • David Newbery

    (Faculty of Economics, University of Cambridge)

  • Thomas McCarty

    (EPRG, CJBS, University of Cambridge)

Abstract

We present a calibrated unit commitment dispatch model of the GB electricity market applied to an economic analysis of the four existing hydro pumped storage (PS) stations in GB. More variable renewable electricity (VRE) increases PS profit by an amount dependent on the generation mix. With a large share of coal, a 1 percentage point (p.p) increase in VRE increases total PS profit by an average of 2.3 p.p. With a more flexible system, to achieve a similar profitability, the share of wind and solar should rise to more than 60% of supply. Inflexible coal with high VRE increases price volatility that drives PS arbitrage revenue. Higher system flexibility smooths VRE variability and limits PS price arbitrage, increasing the role of PS balancing and ancillary service revenue. Between 2015 and 22 PS stations were inactive in managing transmission constraints but provided 18% of fast reserve, response and other reserve services in 2022. Stacking up the modelled revenue from price arbitrage and 2022 balancing and ancillary services revenues against the ongoing fixed costs suggests that the four existing PS stations are quite profitable. However, the revenues will not be enough to cover capex and opex of a new 600 MW PS station without more balancing and ancillary services market opportunities.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Chi Kong Chyong & David Newbery & Thomas McCarty, 2019. "A Unit Commitment and Economic Dispatch Model of the GB Electricity Market – Formulation and Application to Hydro Pumped Storage," Working Papers EPRG1924, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    • Handle: RePEc:enp:wpaper:eprg1924
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    2. Ghorbani, Narges & Sihvonen, Ville & Gulagi, Ashish & Makian, Hamed & Breyer, Christian & Honkapuro, Samuli, 2025. "Developing a replicable methodology for assessing pumped hydro energy storage potential: Mapping, estimation, and economic analysis," Energy, Elsevier, vol. 334(C).
    3. Jiang, Hou & Zhang, Xiaotong & Yao, Ling & Lu, Ning & Qin, Jun & Liu, Tang & Zhou, Chenghu, 2023. "High-resolution analysis of rooftop photovoltaic potential based on hourly generation simulations and load profiles," Applied Energy, Elsevier, vol. 348(C).

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    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • L98 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Government Policy
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy

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