Modeling and Communicating Flexibility in Smart Grids Using Artificial Neural Networks as Surrogate Models

Increasing shares of renewable energies and the transition towards electric vehicles pose major challenges to the energy system. In order to tackle these in an economically sensible way, the flexibility of distributed energy resources (DERs), such as battery energy storage systems, combined heat and power plants, and heat pumps, needs to be exploited. Modeling and communicating this flexibility is a fundamental step when trying to achieve control over DERs. The literature proposes and makes use of many different approaches, not only for the exploitation itself, but also in terms of models. In the first step, this thesis presents an extensive literature review and a general framework for classifying exploitation approaches and the communicated models. Often, the employed models only apply to specific types of DERs, or the models are so abstract that they neglect constraints and only roughly outline the true flexibility. Surrogate models, which are learned from data, can pose as generic DER models and may potentially be trained in a fully automated process. In this thesis, the idea of encoding the flexibility of DERs into ANNs is systematically investigated. Based on the presented framework, a set of ANN-based surrogate modeling approaches is derived and outlined, of which some are only applicable for specific use cases. In order to establish a baseline for the approximation quality, one of the most versatile identified approaches is evaluated in order to assess how well a set of reference models is approximated. If this versatile model is able to capture the flexibility well, a more specific model can be expected to do so even better. The results show that simple DERs are very closely approximated, and for more complex DERs and combinations of multiple DERs, a high approximation quality can be achieved by introducing buffers. Additionally, the investigated approach has been tested in scheduling tasks for multiple different DERs, showing that it is indeed possible to use ANN-based surrogates for the flexibility of DERs to derive load schedules. Finally, the computational complexity of utilizing the different approaches for controlling DERs is compared

State-based load profile generation for modeling energetic flexibility

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

Distribution Grid Monitoring Based on Widely Available Smart Plugs

During the last few years, smart home devices have become increasingly popular. Smart plugs, smart lights, and smart switches are now found in as many as 37 percent of German households, and the popularity of these devices is rising. Smart devices sometimes also integrate sensors for measuring voltage and current. The increase in renewable generation, e-mobility and heat pumps lead to scenarios for which the distribution grid was not originally designed. Moreover, parts of the distribution grid are only sparsely instrumented, which leaves the distribution grid operator unaware of possible bottlenecks resulting from the introduction of such loads and renewable generation. To overcome this lack of information, we propose a grid monitoring that is based on measurements of widely available smart home devices, such as smart plugs. In the present paper, we illustrate the collection and utilization of smart plug measurements for distribution grid monitoring and examine the extent and effect of measurement inaccuracy. For this evaluation, we analyze the measurements of multiple commercially available smart plugs and test the effect of measurement errors on the monitoring when using a single smart plug.Comment: 8 pages

Automated generation of models for demand side flexibility using machine learning – an overview

Flexibility in consumption and production provided by distributed energy resources (DERs) is a key to the integration of renewable energy sources into the energy system. However, even for identical DERs, the flexibility can vary widely, based on local constraints and circumstances. Therefore, handcrafting models can be labor-intensive and automating the generation of models could help increasing the volume of controllable flexibility in smart grids. Depending on the underlying mechanism for controlling demand side flexibility, there are various ways how an automation can be achieved. In this paper, we discuss fundamental concepts relevant to the automated generation of models for demand side flexibility, give an overview of different approaches, and point out fundamental differences. The main focus lies on model generation by means of machine learning techniques

A Concept for Standardized Benchmarks for the Evaluation of Control Strategies for Building Energy Management

Given the expected high penetration of renewable energy production in future electricity systems, it is common to consider buildings as a valuable source for the provisioning of flexibility to support the power grids. Motivated by this concept, a wide variety of control strategies for building energy management has been proposed throughout the last decades and especially for the previously mentioned components. However, these algorithms are usually implemented and evaluated for very specific settings and considerations. Thus, a neutral comparison, especially of performance measures, is nearly impossible. Inspired by recent developments in reinforcement learning research, we suggest the use of common environments (i.e. benchmarks) for filling this gap and finally propose a general concept for standardized benchmarks for the evaluation of control strategies for building energy management

Modeling flexibility using artificial neural networks

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

Definition von Flexibilität in einem zellulär geprägten Energiesystem

Bereits existierende nationale und internationale Definitionen des Begriffs Flexibilität sind zu unpräzise und werden den Anforderungen, die sich bei der Beschreibung von Flexibilität innerhalb eines zellulären Energiesystems ergeben, nicht gerecht. Diese Arbeit benennt und definiert Flexibilitätsbegriffe, um ein gemeinsames Verständnis zu schaffen. Zunächst werden die technisch-ökonomischen Flexibilitätsbegriffe Fahrkurve, Flexibilitäts-bereitstellung, Flexibilitätserbringung, Quantifizierbarkeit, Prognostizierbarkeit sowie explizite und implizite Flexibilität beschrieben. Im Anschluss werden Begriffe beschrieben, die mit der Ansteuerung und dem Abruf von Flexibilität einhergehen. Darunter fallen die aktive und passive Flexibilitätserbringung, der zustands- und kommunikationsgesteuerte Flexibilitätsabruf sowie der direkte und indirekte Flexibilitätsabruf. Zuletzt wird eine Unterscheidung anhand des Verwendungszwecks in system-, netz- und marktdienliche Flexibilität vorgenommen. Das Ergebnis sind exakte Begriffsdefinitionen und Begriffsabgrenzungen, auf deren Basis Flexibilitätsprodukte beschrieben und kategorisiert werden können, die für die Konzeption und Demonstration eines zellulären geprägten Energiesystems unabdingbar sind

Smart Meter Gateways: Options for a BSI-Compliant Integration of Energy Management Systems

The introduction of Smart Meter Gateways (SMGWs) to buildings and households creates new opportunities and challenges for energy management systems. While SMGWs provide interfaces for accessing recorded information and enable communication to external parties, they also restrict data access to protect the privacy of inhabitants and facility owners. This paper presents an analysis of options for integrating automated (Building) Energy Management Systems (EMSs) into the smart meter architecture based on the technical guidelines for SMGWs by the German Federal Office for Information Security (“Bundesamt für Sicherheit in der Informationstechnik„, BSI). It shows that there are multiple ways for integrating automated EMSs into the German smart metering architecture, although each option comes with its own advantages and restrictions. By providing a detailed discussion of trade-offs, this paper supports EMS designers that will be confronted with differing freedoms and limitations depending on the integration option