Demo Project 01: Model

Prototyping the future: Using data-driven and integrated decision making for climate neutral cities.

Vision (DP01) in a nutshell

An Integrated Modeling and Decision Support Tool (DST) is used to model the +CityxChange LHC and FC Bold City Visions, citizen engagement activities, and sizing of the energy generation technologies which have been chosen to be implemented within the DPEB. The DST analyses different ‘what-if’ scenarios of technologies to be installed as part of the PEB development, measures to be taken to reduce energy consumption and implications of technologies on the Distribution network. This also takes into account future weather files so that climate change adaptation and climate change mitigation are accounted for. An interactive 3D model lets citizens actively participate in the transformation to a Positive Energy City, e.g. by voting on different technology interventions, providing feedback to different scenarios, and identifying public acceptable and non-acceptable solutions. 

Problem addressed & specific objective

To achieve the necessary energy transition in cities, it is essential to increase energy systems integration and to push energy performance levels beyond the levels of current building codes. In many instances, solutions are developed with a focus on the environmental and economic impact, however the social impact on/of the citizen is often overlooked or not given the level of importance it requires. 

The goal of DP01 is to have an integrated modeling and design decision support tool (DST) which can model primary energy end use and generation, grid power and transmission, EV charge and discharge and citizen interaction in both space and time, while at the same time enabling a citizen-centred approach through associated visualisation tools, to enable citizen participation and ownership of solutions for the transformation towards a positive energy city.

Related +CityxChange solutions

Decision Support Tool:

The Decision Support Tool allows the end user to create a model of a block, district, city etc., and then carry out different analyses for hard measures such as renovation or energy efficiency upgrades. The user is then able to understand the impacts on both energy and carbon reduction targets, as well as the impact on socio-economic factors such as health, job growth, and/or improved GDP.. It also enables  analyses of EV charging stations and local production interacting with the distribution networks, as well as analyses of district heating networks and interactions between district heating networks and electricity networks.. The addition of socio-economic data means that the effect of decreasing carbon emissions can be viewed through the lens of health and economic prosperity.. The resulting visualisations can be tailored to different users (urban planners, building owners, citizens), enabling improved citizen participation and ownership of solutions for the transformation towards a positive energy city.

PED Grid Design toolbox:

The PED Grid Design toolbox is an integrated tool for design, analyses and grid operation of a local energy system including use of storage and grid balancing. The models in the toolbox include reports presented as dashboards/tables with results of calculations. It also includes topology descriptions of the local grid, which is a part of the community grid and/or PEB. The calculated results are easily exported to third parties for further processes and tasks like settlement and invoice. The eMobility is managed as local energy storage and is included as local energy resources with information represented as a time series in the same way as other local resources and/or forecasts.

CityxChange learnings: 

  • For initial baseline / feasibility studies, it is not necessary to use as much data as is required for the more detailed studies. The data collection was often the longest to complete due to the time required to obtain the various data sets, so the more this can be reduced, the more efficient the process will become without a huge trade-off in accuracy;
  • The mapping of data is not as straightforward as expected and energy-related data is difficult to obtain from private energy operators and private companies, as well as state energy providers;
  • Engagement of local stakeholders is critical in delivering a systemic energy planning process at the local level.