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

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

20th European Conference on the Applications of Evolutionary Computation, EvoApplications 2017

The proceedings contain 57 papers. The special focus in this conference is on Applications of Evolutionary Computation. The topics include: Minimization of systemic risk for directed network using genetic algorithm; pricing rainfall based futures using genetic programming; dynamic portfolio optimization in ultra-high frequency environment; integration of reaction kinetics theory and gene expression programming to infer reaction mechanism; improving the reproducibility of genetic association results using genotype resampling methods; characterising the influence of rule-based knowledge representations in biological knowledge extraction from transcriptomics data; application to blood glucose forecasting; genetic programming representations for multi dimensional feature learning in biomedical classification; meta-heuristically seeded genetic algorithm for independent job scheduling in grid computing; analysis of average communicability in complex networks; configuring dynamic heterogeneous wireless communications networks using a customised genetic algorithm; multi-objective evolutionary algorithms for influence maximization in social networks; Lamarckian and lifelong memetic search in agent-based computing; two-phase strategy managing insensitivity in global optimization; avenues for the use of cellular automata in image segmentation; localization on hubs and delocalized diffusion; hybrid multi-ensemble scheduling; driving in TORCS using modular fuzzy controllers; automated game balancing in ms pacman and starcraft using evolutionary algorithms; evolving game specific UCB alternatives for general video game playing; analysis of vanilla rolling horizon evolution parameters in general video game playing and evolutionary art using the fly algorithm