Smart energy systems for smart city districts: case study Reininghaus District

Background

Dense settlement structures in cities have high demands of energy. Usually, these demands exceed the local resource availability. Individually developed supply options to cover these demands differ from place to place and can also vary within the boundaries of a city. In a common sense of European governance, cities are pushed to save energy, increase renewables and reduce import dependency on fossil fuels. There are many innovative concepts and technologies available to tackle these needs. The paper provides a comprehensive methodology for planning and assessing the development of ‘smart’ energy systems leading to complex energy provision technology networks using different on-site as well as off-site resources.
Methods

The use of the P-graph (process-graph) method allows the optimisation of energy systems by using different energy sources for heating, storing and cooling. This paper discusses this method in the development of an urban brown field, the premises of the Reininghaus District, a former brewery in the city of Graz in Austria. The case study is interesting as it combines on-site energy sources (e.g. solar heat and photovoltaic) with nearby industrial waste heat and cooling at different temperatures and grid-based resources such as existing district heating, natural gas, and electricity. The case study also includes the competition between centralised technologies (e.g. large scale combined heat and power and heat pumps with district heating grids) and decentralised technologies (e.g. small scale combined heat and power, single building gas boilers, solar collectors, etc. in buildings).

Ecological assessment with the Energetic Long-Term Analysis of Settlement Structures (ELAS) calculator provides an evaluation of the ecological impact of the developed energy systems.
Results

Different scenarios based on two building standards OIB (low energy house standard) and NZE (passive house standard) as well as different prices for key energy resources were developed for an urban development concept for the Reininghaus District. The results of these scenarios show a very wide spectrum of structures of the energy system with strong variations often caused by small changes in cost or prices. The optimisation shows that small changes in the setup of the price/cost structure can cause dramatic differences in the optimal energy system to supply a smart city district. However, decentralised systems with low-temperature waste heat and decentralised heat pumps in the building groups show the financially most feasible and, compared to alternatives, most ecological way to supply the new buildings.
Conclusions

The planning process for the development of the Reininghaus District is a complex and therefore lengthy process and shall be concretised over the next decades. Optimal energy technology networks and scenarios resulting from the application of the described methods support the framework energy plan. The accumulated knowledge can be used to form smart energy supply solutions as an integral part for the discussion of the stakeholders (investors, city department) to guide the forming of their action plan through the development of the city quarter.