Towards a 3D Spatial Urban Energy Modelling Approach

2014 ◽  
Vol 3 (3) ◽  
pp. 1-16 ◽  
Author(s):  
Jean-Marie Bahu ◽  
Andreas Koch ◽  
Enrique Kremers ◽  
Syed Monjur Murshed
Keyword(s):  
High Resolution ◽  
Distributed Generation ◽  
Energy Demand ◽  
Energy Use ◽  
Energy Systems ◽  
Energy System ◽  
Heat Energy ◽  
3D City Models ◽  
City Models ◽  
Modelling Approach

Today's needs to reduce the environmental impact of energy use impose dramatic changes for energy infrastructure and existing demand patterns (e.g. buildings) corresponding to their specific context. In addition, future energy systems are expected to integrate a considerable share of fluctuating power sources and equally a high share of distributed generation of electricity. Energy system models capable of describing such future systems and allowing the simulation of the impact of these developments thus require a spatial representation in order to reflect the local context and the boundary conditions. This paper describes two recent research approaches developed at EIFER in the fields of (a) geo-localised simulation of heat energy demand in cities based on 3D morphological data and (b) spatially explicit Agent-Based Models (ABM) for the simulation of smart grids. 3D city models were used to assess solar potential and heat energy demand of residential buildings which enable cities to target the building refurbishment potentials. Distributed energy systems require innovative modelling techniques where individual components are represented and can interact. With this approach, several smart grid demonstrators were simulated, where heterogeneous models are spatially represented. Coupling 3D geodata with energy system ABMs holds different advantages for both approaches. On one hand, energy system models can be enhanced with high resolution data from 3D city models and their semantic relations. Furthermore, they allow for spatial analysis and visualisation of the results, with emphasis on spatially and structurally correlations among the different layers (e.g. infrastructure, buildings, administrative zones) to provide an integrated approach. On the other hand, 3D models can benefit from more detailed system description of energy infrastructure, representing dynamic phenomena and high resolution models for energy use at component level. The proposed modelling strategies conceptually and practically integrate urban spatial and energy planning approaches. The combined modelling approach that will be developed based on the described sectorial models holds the potential to represent hybrid energy systems coupling distributed generation of electricity with thermal conversion systems.

scholarly journals Optimal Design on Fossil-to-Renewable Energy Transition of Regional Integrated Energy Systems under CO2 Emission Abatement Control: A Case Study in Dalian, China

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2879
Author(s):  
Xinxin Liu ◽  
Nan Li ◽  
Feng Liu ◽  
Hailin Mu ◽  
Longxi Li ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Optimal Design ◽  
Co2 Emission ◽  
Energy Demand ◽  
Energy Systems ◽  
Energy System ◽  
Mixed Integer ◽  
Total Annual Cost ◽  
Emission Abatement ◽  
Integrated Energy Systems

Optimal design of regional integrated energy systems (RIES) offers great potential for better managing energy sources, lower costs and reducing environmental impact. To capture the transition process from fossil fuel to renewable energy, a flexible RIES, including the traditional energy system (TES) based on the coal and biomass based distributed energy system (BDES), was designed to meet a regional multiple energy demand. In this paper, we analyze multiple scenarios based on a new rural community in Dalian (China) to capture the relationship among the energy supply cost, increased share of biomass, system configuration transformation, and renewable subsidy according to regional CO2 emission abatement control targets. A mixed integer linear programming (MILP) model was developed to find the optimal solutions. The results indicated that a 40.58% increase in the share of biomass in the RIES was the most cost-effective way as compared to the separate TES and BDES. Based on the RIES with minimal cost, by setting a CO2 emission reduction control within 40%, the RIES could ensure a competitive total annual cost as compared to the TES. In addition, when the reduction control exceeds 40%, a subsidy of 53.83 to 261.26 RMB/t of biomass would be needed to cover the extra cost to further increase the share of biomass resource and decrease the CO2 emission.

scholarly journals LIFE CYCLE ASSESSMENT OF TECHNICAL BUILDING SERVICES OF LARGE RESIDENTIAL BUILDING STOCKS USING SEMANTIC 3D CITY MODELS

2020 ◽  
Vol VI-4/W1-2020 ◽  
pp. 85-92
Author(s):  
H. Harter ◽  
B. Willenborg ◽  
W. Lang ◽  
T. H. Kolbe
Keyword(s):  
Life Cycle Assessment ◽  
Life Cycle ◽  
Energy Demand ◽  
Residential Buildings ◽  
Residential Building ◽  
Building Stock ◽  
3D City Models ◽  
Methodical Approach ◽  
Operational Energy ◽  
City Models

Abstract. Reducing the demand for non-renewable resources and the resulting environmental impact is an objective of sustainable development, to which buildings contribute significantly. In order to realize the goal of reaching a climate-neutral building stock, it must first be analyzed and evaluated in order to develop optimization strategies. The life cycle based consideration and assessment of buildings plays a key role in this process. Approaches and tools already exist for this purpose, but they mainly take the operational energy demand of buildings and not a life cycle based approach into account, especially when assessing technical building services (TBS). Therefore, this paper presents and applies a methodical approach for the life cycle based assessment of the TBS of large residential building stocks, based on semantic 3D city models (CityGML). The methodical approach developed for this purpose describes the procedure for calculating the operational energy demand (already validated) and the heating load of the building, the dimensioning of the TBS components and the calculation of the life cycle assessment. The application of the methodology is illustrated in a case study with over 115,000 residential buildings from Munich, Germany. The study shows that the methodology calculates reliable results and that a significant reduction of the life cycle based energy demand can be achieved by refurbishment measures/scenarios. Nevertheless, the goal of achieving a climate-neutral building stock is a challenge from a life cycle perspective.

scholarly journals Effect of Heat Demand on Integration of Urban Large-Scale Renewable Schemes—Case of Helsinki City (60 °N)

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2164
Author(s):  
Vahid Arabzadeh ◽  
Peter D. Lund
Keyword(s):  
Wind Power ◽  
Large Scale ◽  
Energy Use ◽  
Energy Systems ◽  
Energy System ◽  
Heat Pumps ◽  
Electricity Production ◽  
Population Increase ◽  
Building Energy Efficiency ◽  
Heat Demand

Heat demand dominates the final energy use in northern cities. This study examines how changes in heat demand may affect solutions for zero-emission energy systems, energy system flexibility with variable renewable electricity production, and the use of existing energy systems for deep decarbonization. Helsinki city (60 °N) in the year 2050 is used as a case for the analysis. The future district heating demand is estimated considering activity-driven factors such as population increase, raising the ambient temperature, and building energy efficiency improvements. The effect of the heat demand on energy system transition is investigated through two scenarios. The BIO-GAS scenario employs emission-free gas technologies, bio-boilers and heat pumps. The WIND scenario is based on large-scale wind power with power-to-heat conversion, heat pumps, and bio-boilers. The BIO-GAS scenario combined with a low heat demand profile (−12% from 2018 level) yields 16% lower yearly costs compared to a business-as-usual higher heat demand. In the WIND-scenario, improving the lower heat demand in 2050 could save the annual system 6–13% in terms of cost, depending on the scale of wind power.

A Software Optimization of the Solar Energy System Performance

2014 ◽  
Vol 899 ◽  
pp. 199-204
Author(s):  
Lukáš Skalík ◽  
Otília Lulkovičová
Keyword(s):  
Solar System ◽  
Solar Energy ◽  
System Performance ◽  
Solar Collector ◽  
Energy Demand ◽  
Fossil Fuels ◽  
Thermal Protection ◽  
Energy Systems ◽  
Energy System ◽  
Hot Water

The energy demand of buildings represents in the balance of heat use and heat consumption of energy complex in the Slovak national economy second largest savings potential. Their complex energy demands is the sum of total investment input to ensure thermal protection and annual operational demands of particular energy systems during their lifetime in building. The application of energy systems based on thermal solar systems reduces energy consumption and operating costs of building for support heating and domestic hot water as well as savings of non-renewable fossil fuels. Correctly designed solar energy system depends on many characteristics, i. e. appropriate solar collector area and tank volume, collector tilt and orientation as well as quality of used components. The evaluation of thermal solar system components by calculation software shows how can be the original thermal solar system improved by means of performance. The system performance can be improved of more than 31 % than in given system by changing four thermal solar system parameters such as heat loss coefficient and aperture area of used solar collector, storage tank volume and its height and diameter ratio.

scholarly journals Big Data in Smart Energy Systems: A Critical Review

2020 ◽  
Vol 11 (41) ◽  
pp. 11-26
Author(s):  
Keziban Seçkin Codal ◽  
İzzet Arı ◽  
H. Kemal İlter
Keyword(s):  
Big Data ◽  
Critical Review ◽  
Carbon Dioxide Emissions ◽  
Energy Demand ◽  
Renewable Energy Sources ◽  
Energy Systems ◽  
Energy System ◽  
Interdisciplinary Studies ◽  
Smart Energy Systems ◽  
The Impact

Climate change is an undeniable fact. Considering that two-thirds of greenhouse gas emissions originate from the energy sector, it is expected that the world's energy system will be transformed with renewable energy sources. Energy efficiency will be continuously increased. Reducing energy-related carbon dioxide emissions is the heart of the energy transition. Big data in energy systems play a crucial role in evaluating the adaptive capacity and investing more smartly to manage energy demand and supply. Indeed, the impact of the smart energy grid and meters on smart energy systems provide and assist decision-makers in transforming energy production, consumption, and communities. This study reviews the literature for aligning big data and smart energy systems and criticized according to regional perspective, period, disciplines, big data characteristics, and used data analytics. The critical review has been categorized into present themes. The results address issues, including scientific studies using data analysis techniques that take into account the characteristics of big data in the smart energy literature and the future of smart energy approaches. The manuscripts on big data in smart energy systems are a promising issue, albeit it is essential to expand subjects through comprehensive interdisciplinary studies

scholarly journals Indicators for the optimization of sustainable urban energy systems based on energy system modeling

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Christian Klemm ◽  
Frauke Wiese
Keyword(s):  
Greenhouse Gas Emissions ◽  
Greenhouse Gas ◽  
Energy Demand ◽  
Energy Systems ◽  
Energy System ◽  
Energy Sustainability ◽  
Gas Emissions ◽  
Urban Energy ◽  
Absolute Energy ◽  
Final Energy Demand

Abstract Background Urban energy systems are responsible for 75% of the world’s energy consumption and for 70% of the worldwide greenhouse gas emissions. Energy system models are used to optimize, benchmark and compare such energy systems with the help of energy sustainability indicators. We discuss several indicators for their basic suitability and their response to changing boundary conditions, system structures and reference values. The most suitable parameters are applied to four different supply scenarios of a real-world urban energy system. Results There is a number of energy sustainability indicators, but not all of them are suitable for the use in urban energy system optimization models. Shortcomings originate from the omission of upstream energy supply chains (secondary energy efficiency), from limited capabilities to compare small energy systems (energy productivity), from excessive accounting expense (regeneration rate), from unsuitable accounting methods (primary energy efficiency), from a questionable impact of some indicators on the overall system sustainability (self-sufficiency), from the lack of detailed information content (share of renewables), and more. On the other hand, indicators of absolute greenhouse gas emissions, energy costs, and final energy demand are well suitable for the use in optimization models. However, each of these indicators only represents partial aspects of energy sustainability; the use of only one indicator in the optimization process increases the risk that other important aspects will deteriorate significantly, eventually leading to suboptimal or even unrealistic scenarios in practice. Therefore, multi-criteria approaches should be used to enable a more holistic optimization and planning of sustainable urban energy systems. Conclusion We recommend multi-criteria optimization approaches using the indicators of absolute greenhouse gas emissions, absolute energy costs, and absolute energy demand. For benchmarking and comparison purposes, specific indicators should be used and therefore related to the final energy demand, respectively, the number of inhabitants. Our example scenarios demonstrate modeling strategies to optimize sustainability of urban energy systems.

Impact of blocking on low wind events and its representation by high-resolution GCMs: An energy perspective

2020 ◽  
Author(s):  
Paula Gonzalez ◽  
David Brayshaw ◽  
Reinhard Schiemann
Keyword(s):  
High Resolution ◽  
Wind Power ◽  
Energy Demand ◽  
Large Scale ◽  
Weather Conditions ◽  
Energy System ◽  
Total Power ◽  
Near Surface ◽  
Country Level ◽  
Wind Events

<div> <p>With higher penetration of renewable energies and the effort to decarbonize power production there is a strong interest in the objective characterization of wind resource. Over Europe, wind power accounts for around 17% of total power capacity and almost 30% of renewable capacity and is the overall second largest form of generation capacity after gas. </p> </div><div> <p>In addition to the description of mean capacity factors, there is a need to characterize extremes. Low wind events and persistent low wind events (LWE) are of particular interest because during these the energy system needs to rely on ‘backup’ sources such as gas, coal and nuclear. Over the United Kingdom and other parts of Europe, these are often linked to the occurrence of blocking (e.g., Brayshaw et al. 2012, Cannon et al. 2015, Grams et al. 2017), which is the initial focus of this study. Additionally, blocking events have an impact on near-surface temperatures over Europe, which implies an effect in weather-dependent energy demand. </p> </div><div> <p>This study focuses on the impacts of blocking conditions on low wind events and their persistence, and the representation of these effects on the high-resolution (around 25km) global PRIMAVERA models. Our results confirm that blocking events over Europe have a significant impact on the occurrence and duration of low wind speeds at the country level, which is of relevance to the energy sector. In addition to becoming more frequent, LWE are also more persistent under blocking conditions over large areas of Europe. Both effects are in general captured by most of the PRIMAVERA GCMs analysed here, revealing that when the models do get the blocking events, the basic dynamical connection with wind anomalies is present. Nonetheless, the fact that the simulated weather conditions have deficiencies introduces biases in the properties of the events and their joint occurrence.  </p> </div><div> <p>The errors in the models depend on the statistic, the country and the resolution, but some consistent bias patterns can be observed at times (e.g., North-South dipolar structures). No robust improvements in the representation of these effects were observed in the high-resolution versions of the PRIMAVERA models, nor where the highest resolution runs consistently outperforming coarser simulations.  </p> </div><div> <p>Blocking impacts to the energy systems are not only limited to wind power generation, since these large-scale anomalies also have an impact on near-surface temperature and therefore on electricity demand. These effects are also addressed here.</p> </div>

Flexible energy systems for planning the world’s main copper mines considering geographical conditions

2020 ◽  
Author(s):  
Simon Moreno Leiva ◽  
Jannik Haas ◽  
Wolfgang Nowak ◽  
Tobias Junne
Keyword(s):  
Energy Demand ◽  
Energy Systems ◽  
Demand Side Management ◽  
Energy System ◽  
Energy Planning ◽  
Demand Side ◽  
Management Options ◽  
Available Energy ◽  
Copper Mines ◽  
Ore Grade

<p>Energy systems of the future will be highly renewable, but building the required infrastructure will require vast amounts of materials. Particularly, renewable energy technologies are more copper-intensive than conventional ones and the production of this metal is intensive in energy and emissions. Moreover, as mineral resources are being depleted, more energy is required for their extraction, with subsequent increase in environmental impacts. Highly stressed and uncertain water resources only worsen this situation.</p><p>In this work, we will first provide a comprehensive review of the limited available energy planning approaches on copper mines, including transferrable learnings from other fields. Our second contribution is to compare the influence of different geographical locations on the optimal design of energy systems to supply the world’s main copper mines. For this, we use a linear energy system optimization model, whose main inputs are hourly time series for solar irradiation and power demand, and projections for energy technology costs and ore grade decline. Our third contribution is to propose a multi-vector energy system with novel demand-side management options, specific to copper production processes, including water demand management, illustrated on a case study in Chile (where mining uses a third of the nationwide electricity).</p><p>In the first part, the review, we learned that energy demand models in copper mines have only coarse temporal and operational resolutions, and require major improvements. Also, demand-side management options remain unstudied but could promise large potentials. In general, the models applied in copper energy planning seem overly simplistic when contrasted to available energy decision tools.</p><p>For the second part, we observed that in most locations, using local photovoltaic power not only lowers future electricity costs but also compensates for increased energy demand from ore grade decline. Some regions gain a clear competitive advantage due to extremely favorable climatic conditions.</p><p>In the third and final part, regarding the demand-side management, we saw how the geography and the spatial design of the mines strongly influence the available options and their performance. Jointly planning flexible water and energy supply seems to be particularly attractive. Also, there is space for smart scheduling of maintenance of the production lines, the hardness of the rock feed, oxygen production, and the hauling (rock transport) fleet.</p><p>As an outlook,  we highlight the need for consideration of lifecycle impacts as a design goal, and to further develop demand model’s and their flexibility on the mining side. We expect that implementing these smarter approaches will help secure a cleaner material supply for the global energy transition.</p>

scholarly journals Digital Twin-Driven Decision Making and Planning for Energy Consumption

2021 ◽  
Vol 10 (2) ◽  
pp. 37
Author(s):  
Yasmin Fathy ◽  
Mona Jaber ◽  
Zunaira Nadeem
Keyword(s):  
Energy Consumption ◽  
Energy Demand ◽  
Energy Production ◽  
Energy Use ◽  
Synthetic Data ◽  
Average Energy ◽  
Energy System ◽  
Real Dataset ◽  
Digital Twin ◽  
The Impact

The Internet of Things (IoT) is revolutionising how energy is delivered from energy producers and used throughout residential households. Optimising the residential energy consumption is a crucial step toward having greener and sustainable energy production. Such optimisation requires a household-centric energy management system as opposed to a one-rule-fits all approach. In this paper, we propose a data-driven multi-layer digital twin of the energy system that aims to mirror households’ actual energy consumption in the form of a household digital twin (HDT). When linked to the energy production digital twin (EDT), HDT empowers the household-centric energy optimisation model to achieve the desired efficiency in energy use. The model intends to improve the efficiency of energy production by flattening the daily energy demand levels. This is done by collaboratively reorganising the energy consumption patterns of residential homes to avoid peak demands whilst accommodating the resident needs and reducing their energy costs. Indeed, our system incorporates the first HDT model to gauge the impact of various modifications on the household energy bill and, subsequently, on energy production. The proposed energy system is applied to a real-world IoT dataset that spans over two years and covers seventeen households. Our conducted experiments show that the model effectively flattened the collective energy demand by 20.9% on synthetic data and 20.4% on a real dataset. At the same time, the average energy cost per household was reduced by 10.7% for the synthetic data and 17.7% for the real dataset.

scholarly journals On the Reliable Generation of 3D City Models from Open Data

Urban Science ◽  
2020 ◽  
Vol 4 (4) ◽  
pp. 47
Author(s):  
Renoy Girindran ◽  
Doreen S Boyd ◽  
Julian Rosser ◽  
Dhanya Vijayan ◽  
Gavin Long ◽  
...  
Keyword(s):  
High Resolution ◽  
Open Source ◽  
3D Model ◽  
Airborne Lidar ◽  
Data Availability ◽  
Small Subset ◽  
Surface Model ◽  
High Resolution Data ◽  
3D City Models ◽  
City Models

A 3D model communicates more effectively than a 2D model, hence the applications of 3D city models are rapidly gaining significance in urban studies. However, presently, there is a dearth of free of cost, high-resolution 3D city models available for use. This paper offers potential solutions to this problem by providing a globally replicable methodology to generate low-cost 3D city models from open source 2D building data in conjunction with open satellite-based elevation datasets. Two geographically and morphologically different case studies were used to develop and test this methodology: the Chinese city of Shanghai and the city of Nottingham in the UK. The method is based principally on OpenStreetMap (OSM) and Advanced Land Observing Satellite World 3D digital surface model (AW3D DSM) data and use GMTED 2010 DTM data for undulating terrain. Further enhancement of the resultant 3D model, though not compulsory, uses higher resolution elevation models that are not always open source, but if available can be used (i.e., airborne LiDAR generated DTM). Further we test and develop methods to improve the accuracy of the generated 3D models, employing a small subset of high resolution data that are not open source but can be purchased with a minimal budgets. Given these scenarios of data availability are globally applicable and time-efficient for 3D building generation (where 2D building footprints are available), our proposed methodology has the potential to accelerate the production of 3D city models, and thus to facilitate their dependent applications (e.g., disaster management) wherever commercial 3D city models are unavailable.

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