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Top1. Introduction
Today’s needs to reduce the environmental impact of energy use impose dramatic changes for energy infrastructure and existing demand patterns (e.g. buildings) corresponding to their specific context. The analysis of the building conditions such as morphology, typology, heating energy demands, etc. at a local level would allow urban planners and decision makers to target building refurbishment potentials.
In addition, future energy systems are expected to integrate a considerable share of fluctuating power sources and equally a high share of distributed generation of electricity. Energy system models capable of describing such future systems and allowing the simulation of the impact of these developments thus require a spatial representation in order to reflect the local context and the boundary conditions.
Furthermore, simulation through an Agent-Based Modelling (ABM) approach allows for representing the dynamic behaviour of the system over time, in which the different entities of the system (called agents) are represented autonomously and interact with a common environment. This approach can be then used to reflect operational strategies as well as decentralised decision making in distributed energy systems by representing the communication between the agents.
In the urban context multiple interacting systems are not only coupled by their spatial proximity but as functional units (virtual power plants). Intelligent management of resources often pursued in smart city approaches requires integrated capabilities to model interactions among the various urban resource flows.
The research focus on the spatial aspects of energy modelling for distributed energy system. In general, dynamic models provide insights into the process inherent in the evolution of a system, whereas, Geographic Information System (GIS) provide spatial databases and allow for spatial analysis, interpretation and visualization of data. Combining complex dynamic system modelling (e.g. ABM) and GIS will enable to develop a model that is both dynamic and spatially explicit. The applications and advantages of such models are widely discussed in (Despotakis & Giaoutzi, 1996; Hazelton, Leahy, & Williamson, 1992). However, with the increasing maturity of GIS and availability of 2D and 3D granular datasets, 3D GIS has emerged as an essential tool to enhance the dynamic modelling capabilities. The integration of ABM and GIS technology allow developing experimental prototype models for simulating complex spatial systems representing for example urban growth, traffic congestion as well as ecosystems such as bird migration. A detail discussion is available at (Andrew T. Crooks, Hudson-Smith, & Patel, 2010; Heppenstall, Crooks, See, & Batty, 2012).
Energy systems today are facing several challenges, due to the profound paradigm change that the sector is undergoing. Some examples to be mentioned are:
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Two-way communication between the building and responsible entity for load balancing
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Control of lighting, heating, cooling, ventilation, IT, and other energy using systems
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Optimized coordination of energy loads, on-site energy generation and energy storage - based on local or central smartness
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Automatic demand response to dynamic pricing or control signals from the grid and impact on urban energy system
Current energy system models are often static and based on linear approaches, which do not take into account the dynamics of the system. However, for many of the points mentioned above, the representation of the interactions and communication flows is essential. In addition, classical engineering tools for power systems are not always linked to the spatial dimension of the system. Availability of matured GIS technology and granular 3D GIS data impose an added value to the modelling of energy systems.
The aim of this paper is to describe two recent research approaches in the fields of geo-localised simulation of thermal energy needs in cities and spatially explicit agent-based smart grid simulation conducted by the authors at the European Institute for Energy Research (EIFER). Several case studies have been described and showed how these two different approaches can be coupled to model an integrated smart energy system by taking advantage of 3D city models. They also signify the benefits of using semantic 3D city models to urban energy planning for cities.