In modern power systems, small distributed energy resources (DERs) are considered a valuable source of flexibility towards accommodating high penetration of Renewable Energy Sources (RES). In this paper we consider an economic dispatch problem for a community of DERs, where energy management decisions are made online and under uncertainty. We model multiple sources of uncertainty such as RES, wholesale electricity prices as well as the arrival times and energy needs of a set of Electric Vehicles. The economic dispatch problem is formulated as a multi-agent Markov Decision Process. The difficulties lie in the curse of dimensionality and in guaranteeing the satisfaction of constraints under uncertainty. A novel method, that combines duality theory and deep learning, is proposed to tackle these challenges. In particular, a Neural Network (NN) is trained to return the optimal dual variables of the economic dispatch problem. By training the NN on the dual problem instead of the primal, the number of output neurons is dramatically reduced, which enhances the performance and reliability of the NN. Finally, by treating the resulting dual variables as prices, each distributed agent can self-schedule, which guarantees the satisfaction of its constraints. As a result, our simulations show that the proposed scheme performs reliably and efficiently.

中文翻译：

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通过将深度学习与对偶性相结合来管理不确定性下的分布式灵活性


在现代电力系统中，小型分布式能源（DER）被认为是适应可再生能源（RES）高渗透率的宝贵灵活性来源。在本文中，我们考虑分布式能源社区的经济调度问题，其中能源管理决策是在不确定的情况下在线做出的。我们对多种不确定性来源进行建模，例如可再生能源、批发电价以及一组电动汽车的到达时间和能源需求。经济调度问题被表述为多智能体马尔可夫决策过程。困难在于维数灾难以及保证不确定性下约束的满足。提出了一种结合对偶理论和深度学习的新方法来应对这些挑战。特别是，训练神经网络（NN）以返回经济调度问题的最优双变量。通过在对偶问题而不是原始问题上训练神经网络，输出神经元的数量显着减少，从而提高了神经网络的性能和可靠性。最后，通过将生成的双变量视为价格，每个分布式代理可以进行自我调度，从而保证其约束的满足。结果，我们的模拟表明所提出的方案执行可靠且高效。