Optimal offering and allocation policies for wind power in energy and reserve markets

Tiago Soares, Tue Vissing Jensen, Nicolo Mazzi, Pierre Pinson, Hugo Morais

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    Abstract

    Proliferation of wind power generation is increasingly making this power source an important asset in designs of energy and reserve markets. Intuitively, wind power producers will require the development of new offering strategies that maximize the expected profit in both energy and reserve markets while fulfilling the market rules and its operational limits. In this paper, we implement and exploit the controllability of the proportional control strategy. This strategy allows the splitting of potentially available wind power generation in energy and reserve markets. In addition, we take advantage of better forecast information from the different day-ahead and balancing stages, allowing different shares of energy and reserve in both stages. Under these assumptions, different mathematical methods able to deal with the uncertain nature of wind power generation, namely, stochastic programming, with McCormick relaxation and piecewise linear decision rules are adapted and tested aiming to maximize the expected revenue for participating in both energy and reserve markets, while accounting for estimated balancing costs for failing to provide energy and reserve. A set of numerical examples, as well as a case study based on real data, allow the analysis and evaluation of the performance and behavior of such techniques. An important conclusion is that the use of the proposed approaches offers a degree of freedom in terms of minimizing balancing costs for the wind power producer strategically to participate in both energy and reserve markets.
    Original languageEnglish
    JournalWind Energy
    Volume20
    Issue number11
    Pages (from-to)1851-1870
    Number of pages20
    ISSN1095-4244
    DOIs
    Publication statusPublished - 2017

    Keywords

    • Energy and reserve markets
    • Offering strategies
    • Piecewise linear decision rules
    • Stochastic Programming
    • Wind power

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