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.

Not All Doom and Gloom: How Energy-Intensive and Temporally Flexible Data Center Applications May Actually Promote Renewable Energy Sources

“…The recent streams of EI combine -as also highlighted in the call for this special issue -''the perspectives of electrical engineering, energy economics, and information technology'' (Staudt et al 2019). This is in line with Zhang et al (2018), who state that EI analyzes various digital technologies and ''their applications in the energy sectors'' to tackle energyrelated challenges, see, e.g., Förderer et al (2018) or Holly et al (2020). Literature in this field finds that the intelligent management of DCs can introduce positive effects on the stability of such flow network and offer appropriate economic incentives (Thimmel et al 2019).…”

Modelling and Output Power Estimation of a Combined Gas Plant and a Combined Cycle Plant Using an Artificial Neural Network Approach

“…An efciency of more than 60% was achieved by modelling a COGAS power plant using the multilayer perceptron network design [29]. In the construction sector, distributed energy resources have been studied through various neural network topologies for the evaluation of a classifed pattern of a combined heat and power plant [30]. Te heat rate is incorporated as an output parameter using three input parameters, including the fuel gas heat rate (P1), the CO 2 percentage (P2), and the power output (P3), for the training/evaluation processes.…”

Operational Flexibility of Small-Scale Electricity-Coupled Heat Generating Units

“…Another approach for modeling flexibility is proposed by Förderer et al [12]. They argue that a model that can be used for creating feasible load profiles of a distributed energy resource represents a potential model for the flexibility of that particular resource.…”

“…Different from other approaches for quantifying flexibility, such as [27,31], no (optimal) reference schedule needs to be determined for the flexibility calculation, but any operation between two extreme (minimum and maximum) schedules is possible. The calculation of this set of feasible schedules, or operational zone as we refer to it here, does not require computationally expensive machine learning approaches as used in [12,18]. At the same time, it does not require a high level of detail about the modeled units, which makes it easy to implement for new units in practical applications.…”