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On electricity market equilibria with storage: Modeling, uniqueness, and a distributed ADMM

机译:具有存储的电力市场均衡:建模,唯一性和分布式ADMM

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摘要

We consider spot-market trading of electricity including storage operators as additional agents besides producers and consumers. Storage devices allow for shifting produced electricity from one time period to a later one. Due to this, multiple market equilibria may occur even if classical uniqueness assumptions for the case without storage systems are satisfied. For models containing storage operators, we derive sufficient conditions that ensure uniqueness of generation and demand. We also prove uniqueness of the market equilibrium for the special case of a single storage operator. Nevertheless, in case of multiple storage operators, uniqueness fails to hold in general, which we show by illustrative examples. We conclude the theoretical discussion with a general ex-post condition for proving the uniqueness of a given solution. In contrast to classical settings without storage systems, the computation of market equilibria is much more challenging since storage operations couple all trading events over time. For this reason, we propose a tailored parallel and distributed alternating direction method of multipliers (ADMM) for efficiently computing spot-market equilibria over long time horizons. We first analyze the parallel performance of the method itself. Finally, we show that the parallel ADMM clearly outperforms solving the respective problems directly and that it is capable of solving instances with more than 42 million variables in less than 13 min. (C) 2019 Elsevier Ltd. All rights reserved.
机译:我们认为电力的现货市场交易包括生产者和消费者以外的其他代理商,包括存​​储运营商。存储设备允许将产生的电力从一个时间段转移到另一个时间段。因此,即使满足没有存储系统的情况的经典唯一性假设,也可能会出现多个市场均衡。对于包含存储运算符的模型,我们得出足够的条件来确保生成和需求的唯一性。我们还证明了单个存储运营商的特殊情况下市场均衡的独特性。但是,在有多个存储操作员的情况下,通常无法保持唯一性,我们通过示例说明了这一点。我们以一般事后条件结束理论讨论,以证明给定解决方案的唯一性。与没有存储系统的经典设置相比,市场均衡的计算更具挑战性,因为存储操作会随着时间的推移耦合所有交易事件。因此,我们提出了一种量身定制的并行乘法器和分布式交替乘数方向方法(ADMM),以有效地计算长期内的现货市场均衡。我们首先分析该方法本身的并行性能。最后,我们证明了并行ADMM明显优于直接解决相应问题的方法,并且它能够在不到13分钟的时间内解决具有超过4,200万个变量的实例。 (C)2019 Elsevier Ltd.保留所有权利。

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