Abstract
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.
Highlights
With the growing use of variable renewable energy, like wind and solar power, influencing electricity demand becomes increasingly relevant for balancing electricity supply and demand
Distributed energy resources (DERs), such as battery storage systems (BESSs) and combined heat and power plants (CHP plants), are sources of flexibility that may be used by a demand side manager (DSMgr) to steer electricity demand
The primary goal of the approach presented in this paper is to enable a DSMgr to plan and steer the behavior of diverse distributed energy resources (DERs) that are managed by a Förderer and Schmeck Energy Informatics (2019), 2(Suppl 1): 18 local energy management system (EMS)
Summary
With the growing use of variable renewable energy, like wind and solar power, influencing electricity demand becomes increasingly relevant for balancing electricity supply and demand. The goal of the approach presented in this paper is to allow some external party, namely the DSMgr, to explore and select load profiles that are likely to be feasible This is achieved by explicitly estimating the state of the represented DERs at any considered point in time. It is possible to encode additional information in the description (Nieße et al 2016) and use the models to train an aggregated model (Bremer and Lehnhoff 2017; 2018) Another approach for identifying feasible load profiles, which could be used to describe the flexibility of DERs, is to use a cascade of overlapping classifiers (Neugebauer et al 2015; Neugebauer et al 2016; Neugebauer et al 2017). For generating feasible load profiles, the current state as well as the latest forecasts
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
