Building and Urban Energy Systems

Energy hub approach | Future energy concepts for neighborhoods | Optimization at the urban level | Building and urban energy modelling

The energy hub approach - Integration of Decentralized Energy Adaptive Systems for cities

The increasing use of energy from decentralized renewable sources such as solar, wind, or geothermal heat causes an important re-engineering of the energy infrastructure. To efficiently integrate renewables in city quarters or communities, energy systems of the future have to be able to manage fluctuating and distributed power sources, store energy, convert energy from one carrier to another (e.g. electricity to heat, natural gas to heat, thermal solar or bio-mass water heating and hot water storage, etc.) and sufficiently supply electricity, heat, cold, gases or fuels to the community.

The aim is to convert building quarters and cities to self regulating energy systems by introducing the concepts of the urban energy hub, a facility that manages the energy flows within a city quarter or community, and the urban microgrid, a small-scale urban energy system which integrates electrical and thermal local generation, loads and storage.

Simulation models of urban energy hubs and urban microgrids for a cluster of buildings or city quarters are developed and coupled to city energy models, which simulate the energy demand, generation, storage and management of the buildings in the city.

The goal is to study different cases of implementation such as the development plan of a city quarter, a city centre with historic buildings, or rural communities where an integrated energy-adaptive system could be successfully and profitably implemented.

Within the IDEAS4cities project the NEST testing facility, which serves as a real scale model for a neighbourhood will be used for validation and demonstration of the concept.

Adaptation of communities towards SELF-REGULATING ENERGY systems

Integration of an energy hub

NEST testing facility at Empa, Bild: Empa / Gramazio & Kohler, Zürich

Future energy concepts for neighborhoods

Renewable energy integration in the future energy system is seen as the most potent solution to unprecedented challenges like the continuously growing demand for energy, the dependency on depleting fossil fuels, and climate change. Increased environmental awareness and the effort to assist towards the targets of the Swiss Energy Strategy 2050, have led the village of Zernez, in Switzerland, towards the decision to transform into an energy sustainable community. The intention is to generate energy to meet the village buildings’ demands from local, carbon neutral resources, relying on renewable energy, while fully reducing the consumption of fossil fuels by the year 2020. To satisfy this ambitious goal, a research project has been launched (KTI-Project Zernez Energia 2020), which deals with the development of different scenarios for a future energy concept for the village. Renewable energy integration should play a major role in the village’s future energy system; therefore, the potential for the installation of building integrated solar technologies (photovoltaic or solar thermal) is being thoroughly assessed together with the maximum exploitation of local biomass resources and ground energy utilization via ground source heat pumps. Different levels of energy system integration are proposed ranging from standalone solutions for all buildings of the village to utilization and expansion of existing district heating networks and the installation of new, innovative low temperature district heating schemes. To address the complex interactions of multiple decentralized renewable energy sources, advanced conversion and storage technologies the innovative concept of an energy-hub is being further developed. The energy hub concept will be employed at neighborhood scale in order to find the optimal technology portfolio mix that will compose the future energy system of the energy sustainable village of Zernez.

Development of future energy concepts

Optimization at the urban level

Renewable energy generation, building technologies and urban areas frequently exhibit complex behaviour, and require the resolution of trade-offs between conflicting objectives. There is no single solution, but rather a balance of interacting options must be considered. There are temporal and spatial effects that influence performance at many levels. These issues present new challenges for designers, end-users, and ultimately for society. Optimisation, particularly using computational approaches to address multiple design objectives simultaneously, is a powerful tool in addressing such challenges. It has the power to provide rigorous answers to the important issues posed by these new technologies. The overarching question is how to obtain the greatest benefit for the lowest cost. There is a need for better methods and tools for the design of new districts and the renewal of existing ones. These should enable effective use of the resources available (land area, solar gains, air flows) to achieve synergies that benefit both energy use and comfort. These resources form the constraints of a holistic optimization problem; the objectives are the goals of minimizing energy use (CO2 emissions) whilst maximizing comfort (thermal, daylight, perceptions), while constrained by limited resources. Computational optimization processes (for example multi-objective genetic algorithms) are ideally suited to such design space exploration. The solutions identified by these detailed methods for typical urban design problems can be extracted and embedded in fast tools for use in early design stages. Application areas include geometric layout of urban district (accounting for microclimatic effects), district energy system sizing and operation, as well as retrofit decision-making. The integration of optimisation into urban design process in practice is of great importance, including the extraction, analysis and visualisation of optimisation results within tools that aid in exploring the urban design space.

CO2-optimisation in dependence of cost

Building and urban energy modelling

Building simulation | Modelling renewable energy sources

Building simulation

Building energy simulation tools and methodologies have been widely applied and adapted to facilitate decision making processes to improve building system efficiency. A similar effort is now emerging for the application of urban energy systems. Like buildings, urban energy models require a large subset of spatial and temporal data and can vary greatly depending on the desired outcome of the analysis. Keeping this in mind, the focus of this research is to accurately predict building energy demands for select urban districts within Switzerland. The demand profiles will be used to evaluate implementation potential of decentralized renewable energy systems at the building and district level.

Simulation approaches focus on improving the representation of occupancy and user behavior patterns to develop accurate demand profiles at the urban scale. The research incorporates building energy demand simulation methodologies for different scales of complexity. These methodologies include detailed building simulation tools applied to the urban scale, integrating existing city simulation tools, archetype modeling and clustering approaches as well as statistical models.

Modelling renewable energy sources

Cities are expected to play an important role in the widespread adoption of renewable energy sources via diverse forms of distributed, local generation. Advantages of local renewable energy generation include the integration with existing structures (no additional land or material use) and reduction of transmission losses. Determining the potential and calculating the performance of renewables in an urban environment is important to effectively design future cities and retrofit existing structures, and accurate modelling is essential to achieve this.

Related projects:

CCEM – IDEAS4cities
KTI – Zernez Energia 2020
SCCER – Future energy efficient buildings & districts

Modelling building systems and networks

Co-simulation platform

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