Modeling flexibility using artificial neural networks

Energy Informatics Förderer et al. Energy Informatics 2018, 1(Suppl 1):21 https://doi.org/10.1186/s42162-018-0024-4 R ESEA R CH Open Access Modeling flexibility using artificial neural networks Kevin Förderer1* , Mischa Ahrens2 , Kaibin Bao2 , Ingo Mauser2 and Hartmut Schmeck1,2 From The 7th DACH+ Conference on Energy Informatics Oldenburg, Germany. 11-12 October 2018 *Correspondence: 1 FZI Research Center for Information Technology, Haid-und-Neu-Str. 10-14, 76131 Karlsruhe, Germany Full list of author information is available at the end of the article Abstract The flexibility of distributed energy resources (DERs) can be modeled in various ways. Each model that can be used for creating feasible load profiles of a DER represents a potential model for the flexibility of that particular DER. Based on previous work, this paper presents generalized patterns for exploiting such models. Subsequently, the idea of using artificial neural networks in such patterns is evaluated. We studied different types and topologies of ANNs for the presented realization patterns and multiple device configurations, achieving a remarkably precise representation of the given devices in most of the cases. Overall, there was no single best ANN topology. Instead, a suitable individual topology had to be found for every pattern and device configuration. In addition to the best performing ANNs for each pattern and configuration that is presented in this paper all data from our experiments is published online. The paper is concluded with an evaluation of a classification based pattern using data of a real combined heat and power plant in a smart building. Keywords: Smart Grid, Modeling, Flexibility, Distributed energy resources, Demand side management, Machine learning Introduction Traditionally, electricity has almost exclusively been produced in large power plants connected to electricity transmission grids. The growing share of distributed energy resources (DERs) connected to distribution grids makes reliable grid operation increasingly challenging. Since solar and wind energy are volatile in nature, generation by DERs that use these energy sources is intermittent. To limit the extent of necessary grid and energy storage expansion, the exploitation of the already existing flexibility of DERs like battery energy storage systems (BESSs) and combined heat and power (CHP) plants is essential. Aside from conventional measures of demand response (DR), newer approaches to achieve a comprehensive demand side management (DSM) (Palensky and Dietrich 2011) have been proposed, including hierarchical (Molderink 2011; Anders et al. 2014; Toersche et al. 2015), distributed (Callaway and Hiskens 2011; Hinrichs and Sonnenschein 2017), decentralized (Bremer and Lehnhoff 2017; Rohbogner et al. 2014) and cellular systems (Mauser et al. 2017b; Waffenschmidt 2017). A common necessity for all these approaches is the need to at least model and often communicate flexibility. © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Förderer et al. Energy Informatics 2018, 1(Suppl 1):21 The flexibility of a particular DER or an aggregate of multiple DERs can be described as the set of all feasible load profiles for a given time frame. Feasible in the context of DER load profiles refers to load profiles that can be realized based on the current state while providing all necessary services (Mauser et al. 2017a). In this paper we pick up the idea of representing and communicating flexibility with artificial neural networks (ANNs) presented in (Förderer et al. 2018): A single ANN implicitly learns a flexibility model for one or multiple aggregated DERs using generated or measured load profiles and state data relating to the corresponding DERs as training data. It is important to note that a single (measured) load profile does generally give only few clues about the available flexibility. In order to derive an adequate description of the flexibility in a given state, a sufficient number of measured load profiles with comparable initial states is required. Since the ANNs can be trained to consider these initial states, they should be able to deduce the actual flexibility. The ANNs are trained locally and then transmitted to third parties to offer flexibility information and act as surrogate models. Depending on the chosen training pattern, such an ANN could, e.g., evaluate if a given load profile is feasible for a particular DER. This approach enables the abstraction and communication of distributed flexibility regardless of the type, configurations and sizes of the considered DERs. Since a trained ANN may factor in the current state of the corresponding DERs, it is sufficient to only communicate states rather than a complete model once an ANN is known. This paper aspires to evaluate the idea of using ANNs as surrogate models for flexibility by testing the effectiveness of different ANN types and topologies in conjunction with the patterns presented in (Förderer et al. 2018). By explaining the particularly good or bad results achieved for a given pattern and DER configuration, we aid future research in designing better ANNs that represent energy flexibility. An additional evaluation is conducted using real-world CHP data for one of these patterns. Related work As mentioned before, this paper is based on the ideas outlined in (Förderer et al. 2018). Due to the variety of possible applications for the concept and patterns, the presented results are related to a multitude of previous publications. In this section we give a brief overview of findings motivating the concept and point out important distinctions from other related work. Regardless of using direct or indirect mechanisms for controlling DERs (Mauser et al. 2017a), it is necessary to employ some sort of model to determine flexibility. For example, customers may respond diversely to time-of-use tariffs (Faruqui and George 2005; Faruqui and Sergici 2010) and even these responses are heavily influenced by local parameters (Jargstorf et al. 2015). Hence detailed models and information are needed. Recently, machine learning has been shown to be beneficial in energy applications like power system monitoring (Malbasa et al. 2017) and non-intrusive load monitoring (Batra et al. 2014). Artificial neural networks, in particular, have been used for diverse tasks including forecasting of consumption (Rodrigues et al. 2014), solar power (Abuella and Chowdhury 2015), prices (Severini et al. 2015), as well as estimating the duration a heating device is able to provide a requested change in power (MacDougall et (...truncated)