Trading wind energy on the basis of probabilistic forecasts both of wind generation and of market quantities

Abstract

Wind power is not easily predictable and non‐dispatchable. Nevertheless, wind power producers are increasingly urged to participate in electricity market auctions in the same manner as conventional power producers. The aim of this paper is to propose an operational strategy for trading wind energy in liberalized electricity markets and to assess its performance. At first, the so‐called optimal quantile strategy is revisited. It is proved that without market power, i.e. under the price‐taker assumption, this strategy maximizes expected market revenues. Forecasts of wind power production, of day‐ahead and real‐time market prices and of the system imbalance are inputs to this strategy. Subsequently, constraining of the bid that maximizes the expected revenues is proposed as a way to overcome the strategy's disregard of practical limitations and, at the same time, of risk. Two constraining techniques are introduced: constraining in the decision space and in the probability space. Finally, the trade of a wind power producer is simulated in a test case for the Eastern Danish (DK‐2) price area of the Nordic Power Exchange (Nord Pool) during a 10 month period in 2008. The results of the test case show the financial benefits of the aforementioned strategy as well as the consequent interaction with the electricity market. This study will support a demonstration in the framework of the EU project ANEMOS.plus. Copyright © 2012 John Wiley & Sons, Ltd.

Optimal Offering Strategies for Wind Power in Energy and Primary Reserve Markets

“…The literature on optimal offering strategies for wind power producers in the day-ahead market while accounting for potential balancing costs has been flourishing over the last few years. This includes a number of studies (assuming that wind power producer act as a price-taker) on expected utility maximization strategies [13], [14], additional consideration on risk-aversion and temporal dependencies [15], extension to LMP markets [16] and multi-period setting to adjust contracted offerings [17], appraisal of uncertainties on both wind and market quantities [18], bidding under one-price and two-price system [19], generalized opportunity cost bidding [20], as well as minimizing imbalance costs accounting for wind power predictions and imbalance prices [21], among others. Although it is not the goal of the present paper to work on optimal strategies assuming that the wind power producer acts as a price-maker, readers are encouraged to consult these recent works [22]- [27] for detailed information.…”

“…Because of this independence, and the fact that all prices enter linearly in the expressions below, all calculations depend only on the expected mean prices, rather than their full distribution. This reduction follows from certainty equivalent theory [36], and removes the need for a full stochastic description of prices using, e.g., scenarios [18]. In the following, we will refer to the sum of λ sp E * and λ cap P * as the expected inflow.…”

Economics of control reserve provision by fluctuating renewable energy sources

“…Previous publications on the provision of control reserve were made by the author of this doctoral thesis on several occasions (e.g. (Brauns et al, 2014;Hennig et al, 2014;Jansen, 2014, Jansen & Speckmann, 2013a, 2013bJansen, Speckmann, Harpe et al, 2013;Rohrig et al, 2013) Source: Own analysis based on (Andersen et al, 2012;Braun, 2009;Consentec, 2011;Consentec et al, 2011;Frunt et al, 2009;Gesino, 2011;Kapetanovic et al, 2008;Kirby et al, 2010;Morales et al, 2010;Papaefthymiou et al, 2015;Pinson, 2006;Pinson et al, 2007;Saiz-Marin, GarciaGonzalez et al, 2012;Speckmann, 2016;Tuohy et al, 2012;Zhang et al, 2013;Zugno et al, 2010) (Spitalny et al, 2012, p. 5). It was also concluded that the delivery of positive control reserve (primary, secondary and tertiary) is economically and ecologically inefficient, also due to the FIT paid to these units (Spitalny et al, 2012, p. 5).…”

A New Ensemble Probabilistic Method for Short-Term Photovoltaic Power Forecasting

“…The outputs of deterministic methods are single values of power, and no further information on the uncertainty of the prediction is provided. Nowadays, the development of probabilistic tools is strongly encouraged, since they completely address the unavoidable uncertainties related to wind and solar source; this facilitate the operators' decisions, especially in risk-related tasks such as electrical market bidding [16,17]. Two subcategories of probabilistic methods can be distinguished: the first is based on an underlying deterministic model and provides the uncertainty of the error usually expressed in terms of prediction intervals or quantiles, while the second is based on a direct approach that directly provides the predictive probabilistic representation of the wind or PV power (i.e.…”