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

Stéphane Joost, François Maréchal, Stefano Moret, Jérémy Michel Pierre Unternährer
2017
Article

Résumé

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.

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https://infoscience.epfl.ch/record/223971?ln=fr

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Stéphane Joost, François Maréchal, Stefano Moret, Jérémy Michel Pierre Unternährer
2017
Article

Résumé

Official source

https://infoscience.epfl.ch/record/223971?ln=fr

À propos de ce résultat

Concepts associés (20)

Concepts associés

Chargement

Publications associées (48)

Publications associées

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

Silvia Coccolo, Dasaraden Mauree, Vahid Moussavi Nik, Emanuele Naboni, Amarasinghage Tharindu Dasun Perera, Jean-Louis Scartezzini

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.

PERGAMON-ELSEVIER SCIENCE LTD2019

Chargement

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

Chargement

Evaluation of a Combined Cycle Setup for Solar Tower Power Plants

James Spelling

As our awareness of the dangers of global climate change grows, the development of new renewable energy technologies is of primary importance in the effort to reduce emissions of carbon dioxide and other greenhouse gases. Rapid increases in the price of oil and worries about the stability and security of the extraction of fossil fuels have lead to renewed interest in the development of local energy sources, thereby reducing dependence on foreign sources. Amid the plethora of option available, one of the more promising solutions is the increased use of concentrating solar thermal power. Solar thermal power generation shows great potential for supplying the base electricity needs of numerous countries in the sun-belt regions, stretching from the tropics to the Mediterranean. This has lead to a recent renewal in the development of such systems, most notably with the construction of the PS10 and PS20 central receiver power plants in Spain. However, all currently existing solar thermal power plants have been based around the use of Rankine or steam turbine cycles, which are limited in the efficiencies they can achieve. In order to make better use of the Sun’s energy, higher efficiency cycles should be used. Recent developments in the field of high temperature solar receivers [13] have made it possible to imagine the use of solar thermal energy to drive gas turbine units, potentially allowing the use of combined cycle setups in solar thermal power plants. In order to evaluate the potential improvement in efficiency that can be achieved by using combined cycles in solar thermal power plants, a detailed simulation of such a setup will be performed. By taking into account the requirements of thermal energy storage and the design of the necessary heat exchangers, an objective evaluation of the performance of the combined cycle can be obtained. As a key element in the energy conversion process, particular attention will be given to the modelling and simulation of the high temperature receiver. Due to the highly variable nature of the solar flux, the performance of the receiver will be evaluated over a range of operating points. In order to obtain a detailed breakdown of the sources of losses and irreversibilities within the system, a complete exergy balance will be performed on the power plant. Combined with the standard energy balance, this analysis will allow identification of the key areas for improvement in the design of solar thermal power plants. Finally, in order to determine whether such a setup is economically viable, the construction, operation and maintenance costs will be evaluated in order to predict the levelised energy cost associated with electricity production in a combined cycle plant.

2008

Chargement

EPFL2019

Evaluation of a Combined Cycle Setup for Solar Tower Power Plants

James Spelling

2008