Strategies for an adaptive control system to improve power grid resilience with smart buildings

Low-voltage distribution grids face new challenges through the expansion of decentralized, renewable energy generation and the electrification of the heat and mobility sectors. We present a multi-agent system consisting of the energy management systems of smart buildings, a central grid controller, and the local controller of a transformer. It can coordinate the provision of ancillary services for the local grid in a centralized way, coordinated by the central controller, and in a decentralized way, where each building makes independent control decisions based on locally measurable data. The presented system and the different control strategies provide the foundation for a fully adaptive grid control system we plan to implement in the future, which does not only provide resilience against electricity outages but also against communication failures by appropriate switching of strategies. The decentralized strategy, meant to be used during communication failures, could also be used exclusively if communication infrastructure is generally unavailable. The strategies are evaluated in a simulated scenario designed to represent the most extreme load conditions that might occur in low-voltage grids in the future. In the tested scenario, they can substantially reduce voltage range deviations, transformer temperatures, and line congestions

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

Modeling flexibility using artificial neural networks

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