Communicating the energetic flexibility of distributed energy resources (DERs) is a key requirement for enabling explicit and targeted requests to steer their behavior. The approach presented in this paper allows the generation of load profiles that are likely to be feasible, which means the load profiles can be reproduced by the respective DERs. It also allows to conduct a targeted search for specific load profiles. Aside from load profiles for individual DERs, load profiles for aggregates of multiple DERs can be generated. We evaluate the approach by training and testing artificial neural networks (ANNs) for three configurations of DERs. Even for aggregates of multiple DERs, ratios of feasible load profiles to the total number of generated load profiles of over 99% can be achieved. The trained ANNs act as surrogate models for the represented DERs. Using these models, a demand side manager is able to determine beneficial load profiles. The resulting load profiles can then be used as target schedules which the respective DERs must follow.

中文翻译：

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基于状态的负载配置文件生成，可为能源灵活性建模

传达分布式能源（DER）的能量灵活性是使明确和目标明确的请求能够控制其行为的关键要求。本文介绍的方法允许生成可能可行的负载曲线，这意味着负载曲线可以由相应的DER复制。它还允许针对特定的负载配置文件进行针对性的搜索。除了单个DER的负载配置文件之外，还可以生成多个DER集合的负载配置文件。我们通过训练和测试DER的三种配置的人工神经网络（ANN）来评估该方法。即使对于多个DER的集合，也可以实现可行的载荷曲线与所生成载荷曲线总数的比率超过99％。受过训练的ANN充当代表的DER的替代模型。使用这些模型，需求方管理者可以确定有利的负荷曲线。然后，所得的负载曲线可用作各个DER必须遵循的目标计划。