Two-stage stochastic unit commitment model including non-generation resources with conditional value-at-risk constraints

    Authors

    Y. P. Huang; Q. P. Zheng;J. H. Wang

    Comments

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    Abbreviated Journal Title

    Electr. Power Syst. Res.

    Keywords

    Stochastic unit commitment; Demand response; Energy storage; Conditional; value-at-risk; Benders decomposition; Sensitivity analysis; TRANSMISSION NETWORK EXPANSION; WIND POWER-GENERATION; ENERGY-STORAGE; DEMAND RESPONSE; SYSTEM; UNCERTAINTIES; OPTIMIZATION; MANAGEMENT; ALGORITHM; RESERVE; Engineering, Electrical & Electronic

    Abstract

    This paper presents a two-stage stochastic unit commitment (UC) model, which integrates non-generation resources such as demand response (DR) and energy storage (ES) while including risk constraints to balance between cost and system reliability due to the fluctuation of variable generation such as wind and solar power. This paper uses conditional value-at-risk (CVaR) measures to model risks associated with the decisions in a stochastic environment. In contrast to chance-constrained models requiring extra binary variables, risk constraints based on CVaR only involve linear constraints and continuous variables, making it more computationally attractive. The proposed models with risk constraints are able to avoid over-conservative solutions but still ensure system reliability represented by loss of loads. Then numerical experiments are conducted to study the effects of non-generation resources on generator schedules and the difference of total expected generation costs with risk consideration. Sensitivity analysis based on reliability parameters is also performed to test the decision preferences of confidence levels and load-shedding loss allowances on generation cost reduction. (c) 2014 Elsevier B.V. All rights reserved.

    Journal Title

    Electric Power Systems Research

    Volume

    116

    Publication Date

    1-1-2014

    Document Type

    Article

    Language

    English

    First Page

    427

    Last Page

    438

    WOS Identifier

    WOS:000342267700046

    ISSN

    0378-7796

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