A new clustering and visualization method to evaluate urban energy planning scenarios

The spatial visualization is a very useful tool to help decision-makers in the urban planning process to create future energy transition strategies, implementing energy efficiency and renewable energy technologies in the context of sustainable cities. Statistical methods are often used to understand the driving parameters of energy consumption but rarely used to evaluate future urban renovation scenarios. Simulating whole cities using energy demand softwares can be very extensive in terms of computer resources and data collection. A new methodology, using city archetypes is proposed, here, to simulate the energy consumption of urban areas including urban energy planning scenarios. The objective of this paper is to present an innovative solution for the computation and visualization of energy saving at the city scale. The energy demand of cities, as well as the micro-climatic conditions, are calculated by using a simplified 3D model designed as function of the city urban geometrical and physical characteristics. Data are extracted from a GIS database that was used in a previous study. In this paper, we showed how the number of buildings to be simulated can be drastically reduced without affecting the accuracy of the results. This model is then used to evaluate the influence of two set of renovation solutions. The energy consumption are then integrated back in the GIS to identify the areas in the city where refurbishment works are needed more rapidly. The city of Settimo Torinese (Italy) is used as a demonstrator for the proposed methodology, which can be applied to all cities worldwide with limited amount of information.

A new clustering and visualization method to evaluate urban heat energy planning scenarios

Silvia Coccolo, Dasaraden Mauree, Jean-Louis Scartezzini, Sara Torabi Moghadam

Spatial visualization is a very useful tool to help decision-makers in the urban planning process, i) to define future energy transition pathways, ii) to implement energy efficiency strategies and iii) to integrate renewable energy technologies in the context of sustainable cities. There is thus a need to develop new tools to understand the energy consumption patterns across cities. Statistical methods are often used to understand the driving parameters of energy consumption but rarely used to evaluate future urban refurbishment scenarios. Simulating whole cities using energy demand softwares can be very extensive in terms of computer resources and data collection. A new methodology, using city archetypes, is hence proposed to simulate the energy consumption of urban areas and to evaluate urban energy planning scenarios. The objective of this paper is to present a solid framework and innovative solution for the computation and visualization of energy saving at the city scale. The energy demand of cities, as well as the microclimatic conditions, are calculated by using a 3D model designed as function of the real city urban geometrical and physical characteristics. Data are extracted from a GIS database. We demonstrate how the number of buildings to be simulated can be drastically reduced thereby significantly decreasing the computational time and without compromising the accuracy of the results. This model is then used to evaluate the influence of two sets of refurbishment solutions. The energy consumption are then integrated back in the GIS to identify the areas in the city where refurbishment works are needed more rapidly. The city of Settimo Torinese (Italy) is used as a demonstrator for the proposed methodology, which can be applied to medium-sized cities worldwide with limited amount of information.

2019

Establishing links for the planning of sustainable districts

Sébastien Cajot, François Maréchal, Jessen Page, Nils Christian Schüler

Cities can play a key role in mitigating climate change impacts, as they represent two thirds of the world’s energy consumption and greenhouse gas emissions. This paper presents a model-based approach to support cities in the challenge of including energy issues in early stage urban planning. Recognizing that successful planning must rely on highly integrated solutions, this paper aims, through its approach, to better understand and reinforce links between generally fragmented sectors, scales, stages and disciplines. Considering the planning framework of the Swiss canton of Geneva, the main planning instruments are presented and an in-depth analysis of the urban and energetic goals and constraints is carried out. Linking the disciplines of planning and optimization, a Mixed Integer Linear Programing (MILP) model is formulated to identify optimal energy strategies, as well as to provide insights into the urban layout, distribution and size of buildings. Additionally, this approach aims to overcome one of the difficulties of early stage planning, where information regarding energy is scarce. In this regard the proposed methodology generates building information based on available data and constraints. The simultaneous consideration of multiple scales allows to assess the interactions of building scale decisions with overall district energy supply alternatives. The resulting depiction and quantification of trade-offs should allow planners to anticipate optimal energy system configurations and adapt their plans accordingly.

vdf Hochschulverlag AG2016

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.