Jean-Laurent Hippolyte

Ontology-based demand-side flexibility management in smart grids using a multi-agent system

Power distribution network management must integrate with demand side management, alongside distributed energy resources, in order to meet sustainability, resilience, and economic challenges through a smart grid approach. This paper presents an implementation of the Universal Smart Energy Framework (USEF) through a multiagent system and a novel semantic web ontology, which aligns and enriches relevant existing standards. USEF provides a common specification of the market processes and information exchange but does not specify the internal reasoning of the different roles involved. The authors explain the systematic design and development process from the requirements of the energy-flexibility value chain to software implementation. The underpinning ontology formalizes a domain perspective which is coherent with existing standards, and is sufficient for the agent-oriented implementation of the mentioned framework. As well as contributing this model as a web ontology artifact, the presented work utilizes metaprogramming to transform the domain model into a standard agent communication language ontology. The research reported in this paper is expected to lead towards efficient and scalable development of decision support and automation software for smart grids.

Web-based 3D urban decision support through intelligent and interoperable services

The application of information and communications technology to support urban operational decision makers has received vast interest from industry and academia. This has helped to mature several fields of research within the smart city domain, such as the internet of things, cybernetics, and informatics. However, these fields of research remain siloed, which leads to a clear gap in the literature. The paper recognizes the mentioned gap manifesting in a new smart urban area in Wales, UK, and presents a platform which intends to demonstrate the benefits of exploiting the synergies between these fields of research. Following consultation with various stakeholders at the pilot site, the platform utilizes advanced sensing, analytics, interoperability, and visualization components to provide valuable human-machine interactions to facility managers in the district. Delivering this high value knowledge in a timely, engaging, and accessible manner through advanced decision support interfaces. The paper presents the platforms software architecture, before discussing the decision support interface, intelligent web services, and interoperability components in more detail. The solutions key contributions beyond existing internet of things platforms are the use of a 3D game engine, machine learning and optimization web services, and the integration across the knowledge value chain. This knowledge integration is achieved through semantic modelling of the buildings, urban environment, socio-technical systems, and smart devices in the district.

An analytical optimization model for holistic multiobjective district energy management - a case study approach

Efficient management during the operational phase of district energy systems has become increasingly complex due to the various static and dynamic factors involved. Existing deterministic algorithms which are largely based on human experience acquired from specific domains, normally fail to consider the overall efficiency of district energy systems in a holistic way. This paper looks into taking a black box approach by using genetic algorithms (GA) to solve a multiobjectiveoptimization problem conforming to economic, environmental and efficiency standards. This holistic optimization model, takes into account both heat and electricity demand profiles, and was applied in Ebbw Vale district, in Wales. The model helps compute optimized daily schedules for the generation mix in the district and different operational strategies are analyzed using deterministic and genetic algorithm (GA) based combined optimization methods. The results evidence that GA can be used to define an optimum strategy behind heat production leading to an increase in profit by 32% and reduction in CO2 emissions by 36% in the 24 hour period analyzed. This research fits in well with future district energy systems which give priority to integrated and systematic management.

Collaborative Network for District Energy Operation and Semantic Technologies: A Case Study

The growing interest toward renewable energies and alternative energy sources has led to the development of an increasingly complex district energy landscape with multiple agents and systems. In this new prospect, some frameworks such as USEF [1] or holonic multi-agent systems [2] propose new approaches, where, in the way of a Virtual Organisation Breeding Environment (VOBE) [3], diverse organizations cooperate on a long-term basis to run an energy system. This study focuses on the THERMOSS project, an EU-funded project that investigates the efficient operation of district heating and cooling networks, and demonstrates that such organisation can be integrated into the Collaborative Networks (CNs) paradigm. Additionally, a semantic approach is briefly introduced as a mean to support and improve data transfer and communication between the different entities of THERMOSS as a CN.

Moving from targeted acquisition to urban area modelling — increasing the scale of point cloud processing

Handling real world, acquired point cloud data within a sector such as architecture, engineering, and construction (AEC) is currently a difficult task. A highly desirable, future goal is to fully automate the scan-to-BIM process which at this time has a high dependency on manual work effort. Improvements within this workflow will speed up the production of detailed 3d building models and reduce associated costs. By increasing the level of automation in the scan-to-BIM process it becomes possible to speculate the expansion of the typical use case from a single structure, targeted acquisition towards urban area data collection and modelling. The scale and characteristic differences of an urban area point cloud dataset and that of a single structure create opportunities to validate the applicability of novel analytical approaches to process automation. A decrease in process complexity could be achieved by reducing both the depth of prerequisite knowledge and the level of intervention expected from an operator by a modelling platform. This would also provide an alternative perspective and an opportunity to model operator tasks at a higher, more abstract level. There lacks a completeness of modern documentation within preexisting civil structures. Building information modelling of the as-built condition can reduce overheads associated with key areas such as collaboration, maintenance, and future modifications. However, it is often made more difficult by a lack of accurate documentation due to the age of the building. A common trend that can be observed in countries such as the United Kingdom is that much of future building stock is already standing. Therefore, it is apparent the need to record the detailed, as-built condition of structures essentially from scratch will not resolve itself in the near future. This paper will overview a case study of an urban area modelling conducted in Ebbw Vale, Wales and introduces an abstract scan-to-BIM process automation methodology. This will be supported by a review of a selection of applied research literature. This paper is part of the early development stages of a point cloud processing platform, pcl_toolkit. The command line software aims to simplify approaches commonly associated with various point cloud processing tasks and provide a foundation for rapid development in the near future.