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Highlights • We analyze the economic value of participating in multi-stage electricity markets. • The party submitting bids is a wind power producer with generic energy storage system. • The market analyzed as a case analysis has eight sub-markets. We condensate them into one. • We consider uncertainty in power production and prices. We propose a common modeling basis. • We analyze a number of scenarios comprising power and price differences. Abstract This paper provides a methodology to compute the optimal bidding by a wind power producer in a multi-stage market. The methodology is not restricted to the two-stage markets often reported in the literature—a day-ahead bid submission followed by an adjustment in the imbalance market. Instead, it allows studying any number of markets operating on the same dispatch hour. Particularly, this paper analyzes part of the Spanish market, covering the day-ahead, the six intraday, and the imbalance market. They are markets with different schedules, but this paper shows that by simply rearranging the market prices into a single equivalent market and employing the increments of power as bids, the calculations are visibly simplified; despite the different scope and gate closures. The methodology also includes a dynamic programming approach that relies on the equivalent market data to provide an optimal bidding sequence and its economic value when (i) uncertain prices and wind power production are considered, and (ii) energy storage is employed. As an application, the proposed methodology is employed to analyze the revenues derived by a wind power producer using ESS in the Spanish market. [ABSTRACT FROM AUTHOR]

This paper discusses an alternative computation method of the levelized cost of energy of distributed wind power generators. Unlike in the conventional procedures, it includes time of commencement as an optimization variable. For that purpose, a methodology from Longstaff and Schwartz’s dynamic program for pricing financial American options is derived, which provides the ability to find the optimum time and value while coping with uncertainty revenues from energy sales and variable capital costs. The results obtained from the analysis of wind records of 50 sites entail that the conventional levelized cost of energy can be broken down into an optimum, minimum (time-dependent) value and a penalty for early exercising, which can be employed to define investment strategies and support policies. [ABSTRACT FROM AUTHOR]