Because a heat zoning plan aims to provide all stakeholders – from local residents and companies to investors and policy- makers – with guidance in making their own energy choices, one of the major challenges is to draw up broadly supported heat zoning plans that reflect climate ambitions taking into account local social, demographic and economic interests. Nevertheless, the starting point in the heat zoning process typically lies in mapping the current heat demand, estimating different scenarios about the evolution of that heat demand and making an inventory of potential sustainable sources for meeting the heat (and cold) demand. Using extensive data analyses and simulations, this knowledge is brought together in heat zoning maps. The heat zoning maps are then an interesting tool for various parties such as policy makers, but also energy brokers from cit- ies and municipalities. They enable them to weigh up possible policy choices – which take into account socio-demographic factors in addition to techno-economic factors. But perhaps even more importantly, we see that in practice they mainly make it possible to identify opportunities – e.g. for 5th genera- tion heat networks - to be identified at an early stage. Pathway optimization By identifying such an opportunity, one is already one step closer to realizing a sustainable energy system. However, de- termining a design that meets all geographic and techno-eco- nomic requirements is a complex puzzle with many degrees of freedom, each of which can have a major impact on the feasibility of a project. Many interdependent choices have to be made; which of the mapped sources are linked to which users, via which pipeline route and at which temperature level, at what cost, … Completing such a puzzle has long ceased to be possible manually, and although designers have been using software that supports them for a long time to work out known net-
works in more detail, until recently there was no tool available that quickly and without simplification was able to determine the optimal design given all the preconditions contained in a heat zoning map. An initiative that can change this is PathOpt, a non-linear op- timization environment that determines an optimal network topology for geographically defined zones with known heat demand and possible sources. The non-linear aspect allows to calculate the physical behaviour of such a network without simplifying, while the optimization techniques used guarantee a fast performance. Both aspects together make it possible to calculate all kinds of scenarios in very short periods of time, and these form a crucial extra piece of information for the us- ers of heat zoning maps. Conclusion Successfully and sustainably developing a new district heating project can be a daunting task, as so many actors and infor- mation streams need to come together. Here, a three-step ap- proach was presented that provides a structured workflow. An energy broker helps in the earliest phases, bringing together customers and providers, who often are unaware of the role they could play. With the support of heat zoning maps, they can then zoom in on specific geographical regions that show great potential in term of social, demographic, and economic interest. Finally, software environments bring in the computa- tional power to quickly assess specific scenarios in the selected zones, so that all involved partners can select and weigh differ- ent options in the earliest stage of development and limit the amount of uncertainty involved.
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