Matthias Strobbe

Assessment and mitigation of voltage violations by solar panels in a residential distribution grid

Distributed renewable electricity generators, such as solar cells and wind turbines introduce bidirectional energy flows in the low-voltage power grid, possibly causing voltage violations and grid instabilities. The current solution to this problem comprises automatically switching off some of the local generators, resulting in a loss of green energy. In this paper we study the impact of different solar panel penetration levels in an residential area and the corresponding effects on the distribution feeder line. To mitigate these problems, we assess how effective it is to locally store excess energy in batteries. A case study on a residential feeder serving 63 houses shows that if 80% of them have photo-voltaic (PV) panels, 45% of them would be switched off, resulting in 482 kWh of PV-generated energy being lost. We show that providing a 9 kWh battery at each house can mitigate some voltage violations, and therefor allowing for more renewable energy to be used.

Novel Applications Integrate Location and Context Information

New applications and services aim to adapt themselves to the users context and thus require platforms that can collect, distribute, and exchange contextual information. The Context-Aware Service Platform (CASP) can help, as exemplified here in three different use cases.

Hybrid reasoning technique for improving context-aware applications

With the rapid adoption of GPS enabled smart phones and the fact that users are almost permanently connected to the Internet, an evolution is observed toward applications and services that adapt themselves using the user’s context, a.o. taking into account location information. To facilitate the development of such new intelligent applications, new enabling platforms are needed to collect, distribute, and exchange context information. An important aspect of such platforms is the derivation of new, high-level knowledge by combining different types of context information using reasoning techniques. In this paper, we present a new approach to derive context information by combining case-based and rule-based reasoning. Two use cases are detailed where both reasoners are used to derive extra useful information. For the desk sharing office use case, the combination of rule-based and case-based reasoning allows to automatically learn typical trajectories of a user and improve localization on such trajects with 42%. In both use cases, the hybrid approach is shown to provide a significant improvement.

Interest based selection of user generated content for rich communication services

The last few years, we have witnessed an exponential growth in available content, much of which is user generated (e.g. pictures, videos, blogs, reviews, etc.). The downside of this overwhelming amount of content is that it becomes increasingly difficult for users to identify the content they really need, resulting into considerable research efforts concerning personalized search and content retrieval. On the other hand, this enormous amount of content raises new possibilities: existing services can be enriched using this content, provided that the content items used match the users personal interests. Ideally, these interests should be obtained in an automatic, transparent way for an optimal user experience. In this paper two models representing user profiles are presented, both based on keywords and with the goal to enrich real-time communication services. The first model consists of a light-weight keyword tree which is very fast, while the second approach is based on a keyword ontology containing extra temporal relationships to capture more details of the users behavior, however exhibiting lower performance. The profile models are supplemented with a set of algorithms, allowing to learn user interests and retrieving content from personal content repositories. In order to evaluate the performance, an enhanced instant messaging communication service was designed. Through simulations the two models are assessed in terms of real-time behavior and extensibility. User evaluations allow to estimate the added value of the approach taken. The experiments conducted indicate that the algorithms succeed in retrieving content matching the users interests and both models exhibit a linear scaling behavior. The algorithms perform clearly better in finding content matching several user interests when benefiting from the extra temporal information in the ontology based model.

Distributed multi-agent algorithm for residential energy management in smart grids

Distributed renewable power generators, such as solar cells and wind turbines are difficult to predict, making the demand-supply problem more complex than in the traditional energy production scenario. They also introduce bidirectional energy flows in the low-voltage power grid, possibly causing voltage violations and grid instabilities. In this article we describe a distributed algorithm for residential energy management in smart power grids. This algorithm consists of a market-oriented multi-agent system using virtual energy prices, levels of renewable energy in the real-time production mix, and historical price information, to achieve a shifting of loads to periods with a high production of renewable energy. Evaluations in our smart grid simulator for three scenarios show that the designed algorithm is capable of improving the self consumption of renewable energy in a residential area and reducing the average and peak loads for externally supplied power.

Distributed Reasoning for Context-Aware Services through Design of an OWL Meta-Model

A growing number of applications start using semantic Web technologies. The base concept in this technology is the use of ontologies, allowing first-order logic reasoning engines to execute a number of semantic tasks, such as the validation and consistence checking of the underlying ontology. Because applications work in a distributed environment, e.g. the retrieval of sensor values, resulting in ever increasing amounts of data, there is a clear need for this reasoning process to be distributed. Additional concepts to enable this distributed reasoning are therefore needed. In this paper, we present a meta-model based on the same Semantic Technology. To facilitate distributed reasoning, an architecture supporting this meta-model has been developed as well. The paper will show that the proposed solution offers significant improvement in terms of the reasoning process response times.

Quantifying flexibility in EV charging as DR potential: Analysis of two real-world data sets

The increasing adoption of electric vehicles (EVs) presents both challenges and opportunities for the power grid, especially for distribution system operators (DSOs). The demand represented by EVs can be significant, but on the other hand, sojourn times of EVs could be longer than the time required to charge their batteries to the desired level (e.g., to cover the next trip). The latter observation means that the electrical load from EVs is characterized by a certain level of flexibility, which could be exploited for example in demand response (DR) approaches (e.g., to balance generation from renewable energy sources). This paper analyzes two data sets, one from a charging-at-home field trial in Flanders (about 8.5k charging sessions) and another from a large-scale EV public charging pole deployment in The Netherlands (more than 1M sessions). We rigorously analyze the collected data and quantify aforementioned flexibility: (1) we characterize the EV charging behavior by clustering the arrival and departure time combinations, identifying three behaviors (charging near home, charging near work, and park to charge), (2) we fit statistical models for the sojourn time, and flexibility (i.e., non-charging idle time) for each type of observed behavior, and (3) quantify the the potential of DR exploitation as the maximal load that could be achieved by coordinating EV charging for a given time of day t, continuously until t + Δ.

Ontology Based and Context-Aware Hospital Nurse Call Optimization

In this paper, the focus is on how context information can be efficiently modeled with an ontology. This ontology can than be used by reasoning algorithms which are based on this context information. This is illustrated with a use case which studies the evolution from a place oriented to a person oriented nurse call system. An ontology was designed which holds the necessary context information. A nurse call algorithm that uses this information was constructed. The CASP Context framework was extended to implement the use case. This framework is bases on an OSGi framework. Rules are formulated to implement the algorithm. OWL was applied to integrate the ontology into the framework. A Web service interface was designed which allows to insert new information into the knowledge base or extract information from it. At last a simulation was set up to show the advantages of the person oriented approach. The results of a performance study are shown as well.

Design of a semantic person-oriented nurse call management system

Context information is becoming increasingly important in a world with more and more wireless devices that have to be in touch with the environment around them. In this paper, we focus on how this context information can be efficiently modelled by employing an ontology. This ontology can then be used by reasoning algorithms (e.g., Rules) which are based on this context information. This way, the algorithm is more sensitive to the varying conditions of the environment. This is illustrated with a use case which studies the transition from a place-oriented to a person-oriented nurse call system. The Context-aware Service Platform (CASP) context framework (Strobbe et al., 2007; 2006) was extended to implement the use case. A web service interface was designed which allows the insertion or extraction of new information into the Knowledge Base. Finally, a simulation was set up to illustrate the advantages and the performance of the new person-oriented approach.

Real-world user flexibility of energy consumption: two-stage generative model construction

Since the inception of smart grids, a substantial amount of research has focused on the development of scalable Demand Response (DR) approaches. For example, to flatten peak load, or to balance renewable energy production. A crucial assumption in DR is that at least some portion of the load is flexible, i.e., can be shifted in time. While the flexibility potential of smart devices has been analyzed extensively based on the device characteristics, little effort has been devoted to establishing potential factors in their owners behavior. In this paper, we focus on sharpening the analysis of flexibility in residential user load and contribute with: (1) a quantitative specification of such flexibility, (2) a systematic methodology to derive a generative model for user flexibility behavior from data, (3) application of the methodology on a real-world data set from a field trial with smart appliances, and (4) analysis of factors determining that flexibility.