A review of assessment methods for the urban environment and its energy sustainability to guarantee climate adaptation of future cities

The current climate change is calling for a drastic reduction of energy demand as well as of greenhouse gases. Besides this, cities also need to adapt to face the challenges related to climate change. Cities, with their complex urban texture and fabric, can be represented as a diverse ecosystem that does not have a clear and defined boundary. Multiple software tools that have been developed, in recent years, for assessment of urban climate, building energy demand, the outdoor thermal comfort and the energy systems. In this review, we, however, noted that these tools often address only one or two of these urban planning aspects. There is nonetheless an intricate link between them. For instance, the outdoor comfort assessment has shown that there is a strong link between biometeorology and architecture and urban climate. Additionally, to address the challenges of the energy transition, there will be a convergence of the energy needs in the future with an energy nexus regrouping the energy demand of urban areas. It is also highlighted that the uncertainty related to future climatic data makes urban adaptation and mitigation strategies complex to implement and to design given the lack of a comprehensive framework. We thus conclude by suggesting the need for a holistic interface to take into account this multi-dimensional problem. With the help of such a platform, a positive loop in urban design can be initiated leading to the development of low carbon cities and/or with the use of blue and green infrastructure to have a positive impact on the mitigation and adaptation strategies.

Modeling and Assessment of Urban Energy Systems

Amarasinghage Tharindu Dasun Perera

Improving the energy sustainability of our cities involves the integration of multiple renewable energy technologies into existing energy infrastructure, stretching the capabilities of traditional energy systems to the limit. To consider the transition take place in distributed energy system sector, complex cyber physical interactions need to be adequately modelled, which is not possible with currently used white box models. Furthermore, the volatility of climate conditions and frequent extreme climate events due to climate change as well as climate phenomena at urban scale make it essential to improve the robustness and resilience of energy systems. Again, white box models do not allow sufficient flexibility in the modelling approach. To address these limitations, thesis seeks to optimize distributed energy system design with the help of grey- and black-box techniques. A grey box model based on fuzzy logic is introduced to consider the dispatch strategy when designing electrical hubs. The grey box model shows better performance when optimizing electrical hubs. It has been shown that the method can achieve a renewable energy integration level of up to 80%. However, the grey box model fails to handle complex energy flows within the energy system. Therefore, a black-box method based on reinforcement learning is introduced to consider complex energy systems catering multi-energy services. Reinforcement learning based on a fully connected neural network (FNN), outperforms the grey box model by improving the objective function values by 60%. A convolution neural network improves the objective function values further (by up to 20% compared to FNN). The results reveal that black box models are competent when conducting optimization for complex energy systems. Distributed optimization is introduced to move from a single energy system to an energy internet consisting of several interacting energy systems. The energy internet is optimized considering fully cooperative and non-cooperative scenarios. The optimization algorithm shows a good capability to reach the Epsilon-Nash equilibrium when conducting the optimization. Finally, supervised and transfer learning methods have been introduced when conducting energy system optimization at the regional and national scale, which reduced the computation time by 84%. Stochastic and robust programing methods are introduced to improve the climate flexibility of energy systems. A hybrid stochastic-robust optimization algorithm is developed by extending the novel approach to consider both climate uncertainty and extreme events. A regional climate model provides climate scenarios for the stochastic optimization. The results of the study show that renewable energy technologies such as solar PV and wind can be used to cater 50% of the annual energy demand while guaranteeing a robust operation of the energy system during extreme climate events. The model is then further extended by integrating computational models for urban energy simulation considering urban climate. Results of the analysis show that a performance gap of up to 40 % can be observed when neglecting the influences of urban climate in the design of urban energy systems. In the final part of this thesis, a multi-criterion decision making technique is introduced into the energy system optimization model. This helps decision makers to weight a number of conflicting objectives and to consider impacts at the urban scale.

EPFL2019

Spatial clustering for district heating integration in urban energy systems: application to geothermal energy

Stéphane Joost, François Maréchal, Stefano Moret, Jérémy Michel Pierre Unternährer

Given the challenges related to climate change and dependency from fossil fuels, modification of the energy systems infrastructure to increase the share of renewable energy is a priority in urban energy planning. The high heating density in cities makes it more economically competitive to deploy district heating (DH), which is essential for large-scale integration of renewable energy sources. Combining georeferenced data with district heating design methods allows to improve the quality of the system design. However, increasing the spatial resolution can lead to intractable model sizes. This paper presents a methodology to spatially assess the integration of DH networks in urban energy systems. Given georeferenced data of buildings, resource availability and road networks, the methodology allows the identification of promising sites for DH deployment. First, an Integer Linear Programming (ILP) model divides the urban system into spatial clusters (of buildings). Graph theory and routing methods are then used to optimally design the DH configuration in each cluster considering the road network in the routing algorithm. A Mixed-Integer Linear Programming (MILP) model is formulated in order to economically evaluate the DH integration over the whole urban area. The proposed methodology is applied to an example case study, evaluating the use of geothermal energy (deep aquifer) for direct heat supply. The results of the optimization show the interest of deploying geothermal DH in some of the clusters. The profitability of DH integration is strongly affected by the spatial density of the heating demand.

2017

Bioclimatic Design of Sustainable Campuses using Advanced Optimisation Methods

Silvia Coccolo

Cities occupy 0.5% of the earth surface, but they consume 75% of worldwide energy, and they are responsible of 50% to 80% of CO2 emissions. Cities are directly responsible for the climate change. However, they are the key for providing solutions to this problem. More specifically, a city comprises a very large number of microclimates, according to their urban and environmental design. A sustainable and liveable urban planning could well improve the urban environmental conditions by mitigating the energy fluxes of the city. The objective of this thesis is to address the energy fluxes within the urban environment, in time and space. The study focuses on the improvement of the energy demand of buildings as well as the outdoor human comfort. To do so, we try to establish a new bridge between the biometeorology and the architecture and to find a simplified approach to bring this research into practice. More specifically, the human comfort is well addressed in the research domain but, due to its complexity, it is quite difficult to use it in the real practice. In order to overcome this problem, we introduce three new modules in the software CitySim Pro. CitySim is an urban energy modelling tool which is able to quantify, dynamically, the energy demand from a building scale to the city scale. A first module, developed in this doctoral thesis, focuses on the quantification of the outdoor human comfort by the Index of Thermal Stress (ITS) and the COMFA* budget. The second module aims to understand the radiative environment by the calculation of the Mean Radiant Temperature (MRT). The third module focuses on the cooling potential of the vegetation and evaluates the shadings and the evapotranspiration provided by greenings. Based on the modules, Comfort Maps are designed, representing an important instrument to bring the research into practice: these maps are proposed as an effective way to share information between architects and municipalities, providing indications on the urban microclimatic conditions. Finally, the developed modules are used to optimize, using the hybrid CMA-ES/HDE evolutionary algorithm, the energy demand and the outdoor human comfort of two campuses: EPFL campus in Lausanne (Switzerland), and the Swiss International School (SISD) campus in Dubai (United Arab Emirates). On site monitoring, realized in the SISD campus, underlined the impact of the built environment, as well as the shadowing strategies, by punctual monitoring in five locations of the campus. The results show that i) we should not limit an architectural design to a single building, but it is important to think and design at the district/ city scale. There is ii) a strong relationship between the energy demand of buildings and the outdoor human comfort, consequently both of them should be jointly addressed by architects and urban planners, focusing on the building and the "space between buildings" design. Finally, iii) a sound urban design should derive from the bioclimatology, transforming the climatic adversities into design opportunities. Finally, a list of practical recommendations is defined, providing a support for a sound architectural design in time, and space.