Urban Building Energy Planning With Space Distribution and Time Dynamic Simulation

The building sector is currently responsible of 40% of global final energy consumption, influencing the broader energy system in terms of new electricity and heat capacity additions, as well as distribution infrastructure reinforcement. Current building energy efficiency potential is largely untapped, especially at the local level where retrofit interventions are typically enforced, neglecting their potential synergies with the entire energy system. To improve the understanding of these potential interactions, this paper proposes a methodology that links dynamic building simulation and energy planning tools at the urban scale. At first, a detailed bottom-up analysis was conducted to estimate the current and post-retrofit energy demand of the building stock. The stock analysis is further linked to a broader energy system simulation model to understand the impact of building renovation on the whole urban energy system in terms of cost, greenhouse gas emission, and primary energy consumption up to 2050. The methodology is suited to analyze the relationship between building energy demand reduction potential and clean energy sources’ deployment to shift buildings away from fossil fuels, the key priority for decarbonizing buildings. The methodology was applied to the case study city of Torino, Italy, highlighting the critical role of coupling proper building retrofit intervention with district-level heat generation strategies, such as modern district heating able to exploit low-grade heat. Being able to simulate both demand and supply future alternatives, the methodology provides a robust reference for municipalities and energy suppliers aiming at promoting efficient energy policies and targeted investments.

An evidence-based framework for designing urban green infrastructure morphology to reduce urban building energy use in a hot-humid climate

Urban form is an important factor affecting urban energy. However, the design of urban form and energy mostly belong to two separate disciplines and fields, and urban energy planning research rarely considers their mutual relationship. The available space intensity (ASI) of solar energy is formed on the basis of energy planning and urban design; the objective of this research is to evaluate the impact of urban form on the ASI of solar energy and to propose strategies for planning of the space that is available for solar energy so as to improve the efficiency of urban energy utilization and achieve sustainable urban development. Methodologically, this study firstly proposes a model to quantify the ASI of solar energy using three indicators: solar radiation intensity (SRI), solar installation intensity (SII), and solar generation intensity (SEGI). Then, we quantitatively calculate the solar ASI of nine types of typical urban blocks in a sub-center of Wuhan City, Nanhu. Correlation analysis and multiple linear regression analysis are then used to analyze the correlation between the form indicators and solar ASI, as well as the degree of influence. The results show that the differences in SRI, SII, and SEGI amongst the nine types of city blocks were as high as 114.61%, 162.50%, and 61.01%. The solar ASI was mainly affected by three form indicators: the building coverage ratio, the average building height, and the volume-to-area ratio. Reducing the building coverage ratio and increasing vertical development at the same time can effectively improve the ASI of solar energy. The results of this study and the established method provide an important reference and rapid calculation tool for urban energy planning and design, reducing the data and time usually required for solar analysis at the block scale.

Rational planning and the use of renewable energy are effective means to reduce urban carbon emissions. In view of the few urban renewable energy planning cases and unclear methods, the paper takes the Sino-Singapore Tianjin Eco-City renewable energy planning project as a case to provide a renewable energy planning method under the guidance of carbon constraints. Based on scenario analysis, the energy demand of urban buildings, industry and transportation is analyzed and predicted. On the basis of meeting the needs of terminal energy use, with the goal of reducing carbon emissions, the renewable energy planning scheme from 2021 to 2050 under the low-carbon scenario has been formulated, including the promotion of energy-efficient buildings, the utilization of renewable energy in buildings, the electrification of terminal energy use, and the application of large-scale municipal renewable energy. It is planned that, by 2050, the overall renewable energy utilization rate of the Sino-Singapore Tianjin Eco-City will reach 76.76%. It will use renewable energy to heat about 60 million square meters, generate about 766 million kWh of electricity, save about 0.723 million tons of standard coal and reduce 1.287 million tons of carbon dioxide every year, which will have a good effect of energy conservation and emission reduction. In this paper, the renewable energy planning method under the guidance of carbon constraint is established, which can achieve the purposes of saving resources, protecting the environment and driving sustainable development. The Sino-Singapore Tianjin Eco-City is an international co-creation city, which will receive extensive attention and provide theoretical guidance and demonstration cases for urban renewable energy planning in the context of carbon peak and carbon neutrality in the new era.

Prospects for key technologies of new-type urban integrated energy system

Urban microclimate and its impact on building performance: A case study of San Francisco

Rapid urbanization, climate change, and aging infrastructure pose significant challenges to achieving sustainability and resilience goals in urban building energy use. Although retrofitting offers a viable solution to mitigate building energy use, there has been limited analysis of its effects under various weather conditions associated with climate change in urban building energy use simulations. Moreover, certain parameters in energy simulations necessitate extensive auditing or survey work, which is often impractical. This research proposes a framework that integrates various datasets, including building footprints, Lidar data, property appraisals, and street view images, to conduct neighborhood-scale building energy use analysis using the Urban Modeling Interface (UMI), an Urban Building Energy Model (UBEM), in a coastal neighborhood in Galveston, Texas. Seven retrofit plans and three weather conditions are considered in the scenarios of building energy use. The results show that decreasing the U-value of building envelopes helps reduce energy use, while increasing the U-value leads to higher energy consumption in the Galveston neighborhood. This finding provides direction for coastal Texas cities, like Galveston, to update building standards and implement retrofit measures.

The growth of urban population as a result of economic and industrial development has transformed our habitat from a prosperous place to one of careless resource consumption. On the other hand, long-term climate change, i.e., global warming, has had an adverse impact on our resources. Some resources are on the verge of being depleted due to climate change and indiscriminate consumption of resources, unless serious measures are implemented immediately. Building sector whose contribution to urban energy consumption Significantly higher, is a key player that may successfully solve the problem. In this paper, the impact of climate change on building energy demand and thermal comfort in buildings has been examined. Currently, most countries are grappling with the dual challenge of reducing greenhouse gases and climate change in the building sector. However, their efforts in addressing this challenge and stabilizing climate change. The building sector has tremendous potential to reduce greenhouse gas emissions at relatively low costs. Long-term climate change (e.g., global warming) is driving an increase in energy consumption for cooling demand, which contributes much more to the total energy consumption of buildings than heating demand. Therefore, to conserve its resources, urban energy planning and management should focus on creating an appropriate framework of guidelines on how to reduce cooling loads in building energy consumption patterns. It was also shown that in summer and spring, entry into the thermal stress zone is external, while in cold seasons, entry into the thermal comfort zone is external.

Urban form and building energy use: A systematic review of measures, mechanisms, and methodologies

Impact of urban wind environment on urban building energy: A review of mechanisms and modeling

Methods for reducing large speed differences in traffic streams

Recently it has become important to establish the local environmental preservation level based on the Agenda 21 including the local energy system In this study we examined the improvement techniques on an urban energy system in snowy cold region. We surveyed the possibility of new urban energy system like waste heat from incineration plants and sewage plants. We analyzed the results using fuzzy utility function and structural modeling. We simulated several scenarios on the urban new energy system which can be introduced and evaluated by using cost-performance with environmental cost. We proposed the rational energy system in a residential area in Sapporo practically.

Co-optimization of urban morphology and distributed energy systems is key to curb energy consumption and optimally exploit renewable energy in cities. Currently available optimization techniques focus on either buildings or energy systems, mostly neglecting the impact of their interactions, which limits the renewable energy integration and robustness of the energy infrastructure; particularly in extreme weather conditions. To move beyond the current state-of-the-art, this study proposes a novel methodology to optimize urban energy systems as interconnected urban infrastructures affected by urban morphology. A set of urban morphologies representing twenty distinct neighborhoods is generated based on fifteen influencing parameters. The energy performance of each urban morphology is assessed and optimized for typical and extreme warm and cold weather datasets in three time periods from 2010 to 2039, 2040 to 2069, and 2070 to 2099 for Athens, Greece. Pareto optimization is conducted to generate an optimal energy system and urban morphology. The results show that a thus optimized urban morphology can reduce the levelized cost for energy infrastructure by up to 30%. The study reveals further that the current building form and urban density of the modelled neighborhoods will lead to an increase in the energy demand by 10% and 27% respectively. Furthermore, extreme climate conditions will increase energy demand by 20%, which will lead to an increment in the levelized cost of energy infrastructure by 40%. Finally, it is shown that co-optimization of both urban morphology and energy system will guarantee climate resilience of urban energy systems with a minimum investment.

Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow

In response to the growing need for sustainable urban development, energy systems modelling must provide long-term carbon-neutral solutions at the city scale while balancing competing criteria. This work introduces a multi-objective optimization approach addressing technical, economic, and environmental criteria for urban smart energy systems designed to achieve 100% renewable energy integration. The analysis incorporates climate change impacts on both energy demand and production. Two optimization strategies are evaluated using Bozen-Bolzano, Italy, as a case study. Specifically, the energy systems were modelled using EnergyPLAN, integrated with Python for automation. Grid search and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) were adopted as optimization methods to compare the advantages and limitations of two different approaches. The results show that both methods produce similar solutions on the Pareto front, with the grid search slightly outperforming due to the consideration of extreme input ranges. However, NSGA-II generated a significantly larger number of Pareto solutions, demonstrating its effectiveness in exploring the solution space more comprehensively. This study underscores the importance of incorporating climate change into multi-objective optimization for robust decision-making in the design of smart urban energy systems for sustainable development. • Optimal techno-economic and environmental design of 100% renewable systems. • Comparison of optimization approaches in urban energy systems modelling. • Impact of climate scenarios on energy demand and renewable integration.