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Optimal bidding strategy of wind power producers in pay-as-bid power markets

机译：按需竞价电力市场中风电生产商的最优竞价策略

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This paper presents a method to determine the optimal bidding strategy of the wind power producers with market power for a strategic presence in the day-ahead market with pay as bid method. Since the wind power producer is not capable of exact prediction of his power production, he has to trade the difference between the amount won in the day-ahead market and the actual production value in the balancing market. Uncertainties related to power generation is modeled by likely scenarios. However in order to model the punitive effect of trade in balancing market, the balancing market price is considered as a factor of the day-ahead market's clearing price. In the proposed model, optimal bidding strategy is formulated via a bi-level problem including the upper-level and lower-level sub-problems. The purpose of the upper-level sub-problem is to maximize the wind power producer's earning while the purpose of the lower-level sub-problem is to clear the day-ahead market. To solve both upper-level and lower-level problems, particle swarm optimization algorithm is applied. The results of three-bus test system and IEEE 24-bus RTS shows the efficiency of the proposed method. (C) 2018 Published by Elsevier Ltd.
机译：本文提出了一种确定具有市场支配力的风电生产商的最优竞标策略的方法，该策略可以在日前市场中以付费作为竞标方法。由于风力发电商无法准确预测其发电量，因此他必须在日间市场中赢得的金额与平衡市场中的实际产值之间的差额进行交易。与发电相关的不确定性是通过可能的情况建模的。但是，为了模拟交易对平衡市场的惩罚作用，平衡市场价格被视为日前市场清算价格的一个因素。在提出的模型中，通过包括上层和下层子问题的两级问题，制定了最优投标策略。上层子问题的目的是最大程度地提高风电生产商的收入，而下层子问题的目的是清理日间市场。为了解决上层和下层问题，应用了粒子群优化算法。三总线测试系统和IEEE 24-bus RTS的结果表明了该方法的有效性。 （C）2018由Elsevier Ltd.发布

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