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    Optimizing Instantaneous and Ramping Reserves with Different Response Speeds for Contingencies—Part II: Implications (2020)

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    Type of Content
    Journal Article
    UC Permalink
    https://hdl.handle.net/10092/100638
    
    Publisher's DOI/URI
    https://doi.org/10.1109/tpwrs.2020.2984702
    
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    ISSN
    0885-8950
    1558-0679
    Collections
    • Engineering: Journal Articles [1629]
    Authors
    Schipper, Josh
    Wood, Alan
    Edwards, Conrad
    show all
    Abstract

    Part I presents a formulation to optimize reserves for contingent events while explicitly including their response times. Part II highlights the implications of using this formulation in a reserves market. There are four aspects of the formulation that are of interest: (1) the performance of the solver, (2) the importance of inertia and contingency size. These implications are highlighted through two examples. (3) The proposed formulation is compared against a reserve optimization which does not distinguish between different response speeds, but adjusts the reserve requirement to satisfy the same frequency limits. Differentiating between response speeds within the optimization can provide a 23% reduction in total reserve requirement on average, which improves to a reduction of 52% in low inertia conditions. (4) A marginal pricing methodology is developed which prices reserve in a market context, and gives a unique price to each reserve provider depending on response speed. This gives incentive for reserve providers to improve response speed.

    Citation
    Schipper J, Wood A, Edwards C (2020). Optimizing Instantaneous and Ramping Reserves with Different Response Speeds for Contingencies—Part II: Implications. IEEE Transactions on Power Systems. 1-1.
    This citation is automatically generated and may be unreliable. Use as a guide only.
    Keywords
    Reserve Markets; Primary Frequency Control Reserve; Contingency Reserve; Quadratically Constrained Programming; Convex Optimization
    ANZSRC Fields of Research
    09 - Engineering::0906 - Electrical and Electronic Engineering::090608 - Renewable Power and Energy Systems Engineering (excl. Solar Cells)
    09 - Engineering::0906 - Electrical and Electronic Engineering::090607 - Power and Energy Systems Engineering (excl. Renewable Power)
    Rights
    All rights reserved unless otherwise stated
    http://hdl.handle.net/10092/17651

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