Bottom-up Energy Model Research Articles

Towards 2050: Evaluating the Role of Energy Transformation for Sustainable Energy Growth in Serbia

This paper aims to investigate the outlook of energy generation by means of transformation within the context of sustainable energy development. An analysis is conducted to assess the stability of energy systems so to implement cutting-edge energy production models at the national level, with a focus on a contemporary approach to energy modeling. Considering the energy transition and the existing constraints within the energy industry, the model assesses the feasibility of the practical advancement of renewable energy sources. The bottom-up energy model was used to determine how the components of energy development sustainability can be applied until the year 2050. To perform comparison testing with the reference state scenario, the LEAP energy model was used. This instrument was selected because of its ability to provide flexible and advanced options for selecting suitable parameters for energy transformation prediction. A progressive reduction in environmental pollution can be achieved by the deployment of current methods of energy generation by transformation until the year 2050 in Serbia, as indicated by the findings. The research highlights the significance of utilizing green energy sources for the continuing development of energy and the gradual reduction in environmental pollution through value co-creation.

Transferring energy signatures across space and time to assess their viability for rapid urban energy demand estimation

Energy signatures offer a rapid bottom-up energy modelling tool that relates thermal building space heating and cooling demand to ambient temperature. While simplified from a building physics perspective, energy signatures can easily exploit existing buildings’ operational datasets for their calibration, fitting individual buildings or archetypes. To assess their viability in modelling buildings’ heating and cooling demand at urban or larger scales, it is important to assess whether the same building model expressed as an energy signature could be used under different weather conditions, through spatial (location) or temporal (future weather) transfer. As energy demand depends on environmental factors other than temperature, a systematic error quantification from transferring energy signatures across different climatic contexts is required. These errors are assessed by comparing signature-based estimates to EnergyPlus simulations of a single-family home and an office building archetype exposed to current and future climate across eight US climate zones. Energy signatures are confirmed to be a valid approach if sufficient heating and cooling data is used for their identification. On an annual basis, signature-based estimates result in an error of approximately ± 30 % if the signature is spatially transferred, which can exceed 75 % for unsuitable source locations. When energy signatures are temporally transferred, an annual error of only ± 5–10 % is expected. At a daily resolution, the inherent error from applying the energy signature method is significantly larger than the additional error introduced through a spatiotemporal transfer of the energy signature.

A Parametric Modelling Approach for Energy Retrofitting Heritage Buildings: The Case of Amsterdam City Centre

The city of Amsterdam has ambitious goals to achieve a 95% reduction in carbon emissions by 2050 and to phase out natural gas by 2040. Disconnecting the building stock from natural gas requires well-ventilated and well-insulated buildings and a switch to renewable energy sources, making optimal use of heat pumps and sustainable heating solutions available locally. Most buildings in the historical city centre are protected and often insufficiently insulated, leading to increased energy use and a poor thermal environment. Standard retrofitting interventions may be restricted, requiring new approaches to balancing the need for energy efficiency and the preservation of heritage significance. With the case of the Amsterdam City Centre, the goal of this research is to present a parametric modelling approach for energy retrofitting heritage buildings and to identify minimum requirements for preparing the residential stock to lower temperature heat (LTH). Using parametric design and bottom-up energy modelling, the research estimates that a 69.1% of natural gas reduction could be achieved when upgrading the buildings to lower temperature (LT). Results of this paper also demonstrate how the applied approach can be used to guide decisions on the improvement in energy performance of the historic built environment.

Policy Design for Electricity Efficiency: A Case Study of Bottom-Up Energy Modeling in the Residential Sector and Buildings

Energy models play a crucial role in the domain of energy policy by serving as essential instruments for decision-making. However, a significant limitation of numerous bottom-up energy models (BUEMs) is their empirical design, which hinders their ability to effectively inform policy design. This study presents a structured framework that can be used to improve the effectiveness of behavior, understanding, and engagement measures in the development of BUEMs for enhancing energy end-use efficiency. The model selected for this case study was provided by the Mexican Commission for the Efficient Use of Energy (CONUEE), and it examines the impact of regulatory instruments on the residential sector and residential buildings. The benefits of the proposed framework were successfully demonstrated through a quantitative comparison of real energy models, using and without using the said framework, revealing the advantages of its use. The framework significantly decreases the time required for model generation in various aspects by 59.43%. The obtained results highlight the effectiveness of the framework, and it could enhance the existing knowledge in the sector.

Balancing heat saving and supply in local energy planning: Insights from 1970-1989 buildings in three European countries

This study investigates the cost balance between heat energy savings through building envelope retrofits and supply from low-carbon decentralised and centralised technologies in a generic urban district, composed of residential and non-residential buildings from the 1970–1989 construction period. For generalisability, the district is analysed in three European countries (Bulgaria, Germany, Finland), each with distinct weather conditions and price levels. Using bottom-up energy modelling and adopting a societal perspective that includes external costs, the study finds the cost-effectiveness of retrofits to be context-specific. In Bulgaria, retrofits prove largely cost-effective, whereas in Germany and Finland, high labour and material costs pose challenges. Heat pumps, whether decentralised in buildings or centralised in district heating systems, emerge as key options for heat supply, even in cold climates. The study underscores the importance of integrated energy planning in line with the ‘energy efficiency first’ principle and corresponding incentive structures to promote sustainable urban energy systems.

Potential pathways to reach energy-related CO2 emission peak in China: analysis of different scenarios.

The prevalence of global unilateralism and the shock of COVID-19 brought considerable uncertainty to China's economic development. Consequently, policy selection related to the economy, industry, and technology is expected to significantly impact China's national economic potential and carbon emission mitigation. This study used a bottom-up energy model to assess the future energy consumption and CO2 emission trend before 2035 under three scenarios: a high-investment scenario (HIS), a medium-growth scenario (MGS), and an innovation-driven scenario (IDS). These were also used to predict the energy consumption and CO2 emission trend for the final sectors and calculate each sector's mitigation contribution. The main findings were as follows. Firstly, under HIS, China would achieve its carbon peak in 2030, with 12.0 Gt CO2. Moderately lowering the economic growth rate to support the low-carbon transition of the economy by boosting the development of the low-carbon industry and speeding up the employment of key low-carbon technologies to improve energy efficiency and optimize energy structure in the final sectors, the MGS and the IDS would achieve carbon peak approximately in 2025, with a peak of 10.7 Gt CO2 for the MGS and 10.0 Gt CO2 for the IDS. Several policy recommendations were proposed to meet China's nationally determined contribution targets: instigating more active development goals for each sector to implement the "1+N" policy system, taking measures to accelerate the R&D, boosting the innovation and application of key low-carbon technologies, strengthening economic incentives, forming an endogenous driving force for market-oriented emission reduction, and assessing the climate impacts of new infrastructure projects.

Can a shift to electric vehicles fast track Africa’s energy transition?

Recently in <i>iScience</i>, Dioha et al. used a bottom-up energy modeling framework to generate insights on future electric vehicle penetration and their potential contribution to Africa's energy transition. In this preview, we discuss electric vehicles within the African context and what it means for Africa's future energy in the midst of a prevailing energy access crisis.

The contribution of forest-based bioenergy in achieving deep decarbonization: Insights for Quebec (Canada) using a TIMES approach

This study assesses the contribution of various forest-based bioenergy technologies when transitioning to a low carbon economy. A detailed modeling of different forest-based bioenergy pathways is provided following a techno-economic (bottom-up) approach. As an illustration, these pathways are implemented in NATEM-Québec, a detailed bottom-up energy model for Québec, Canada. It is the first time different primary and secondary forest-based bioenergy technologies are being modeled in such a detailed bottom-up energy system model like TIMES. A detailed analysis of forest-based bioenergy potential in Quebec is also provided under different greenhouse gas (GHG) emission reduction scenarios. Main insights are as follows. The transportation sector is the primary contributor to GHG emissions over the time horizon in all scenarios, except for the most stringent GHG reduction scenario (GHGB) in 2050. The industrial sector is the main emitter by 2050 in GHGB, indicating the difficulties to decarbonize heavy industry. Furthermore, an extensive electrification is required to reach the GHG reduction targets. The bioenergy share is expected to increase considerably in the transportation and industrial sectors, cutting down on the need to reduce GHG emissions. Forest-based bioenergies such as cellulosic bioethanol, biobased heat, FT diesel and electricity as a co-product can effectively support this energy transition. The present study discerns forest-based bioenergies as an attainable decarbonization pathway for the province of Quebec and envisages a greater penetration of bioenergy than in the 2030 Plan for a Green Economy proposed by the government of Québec. Other world regions with a declining trend for traditional forest products should also consider such a strategy.

The Contribution of Bottom-Up Energy Models to Support Policy Design of Electricity End-Use Efficiency for Residential Buildings and the Residential Sector: A Systematic Review

Bottom-up energy models are considered essential tools to support policy design of electricity end-use efficiency. However, in the literature, no study analyzes their contribution to support policy design of electricity end-use efficiency, the modeling techniques used to build them, and the policy instruments supported by them. This systematic review fills that gap by identifying the current capability of bottom-up energy models to support specific policy instruments. In the research, we review 192 publications from January 2015 to June 2020 to finally select 20 for further examination. The articles are analyzed quantitatively in terms of techniques, model characteristics, and applied policies. The findings of the study reveal that: (1) bottom-up energy models contribute to the support of policy design of electricity end-use efficiency with the application of specific best practices (2) bottom-up energy models do not provide a portfolio of analytical methods which constraint their capability to support policy design (3) bottom-up energy models for residential buildings have limited policy support and (4) bottom-up energy models’ design reveals a lack of inclusion of key energy efficiency metrics to support decision-making. This study’s findings can help researchers and energy modelers address these limitations and create new models following best practices.

Analysis and Evaluation of the Feasibility of Positive Energy Districts in Selected Urban Typologies in Vienna Using a Bottom-Up District Energy Modelling Approach

This article investigates the potential of selected urban typologies in Vienna to reach the state of Positive Energy Districts (PED) by achieving a positive annual energy balance. It follows the EU initiative for implementing at least 100 PED in Europe by 2025. Four urban typologies have been assessed using the bottom-up energy modelling tool MAPED that enables a simplified energy demand-supply analysis at the district scale. Considering relevant urban typologies in different construction periods, the analysis focused on converting the allocated building stocks into PED by employing comprehensive thermal refurbishment and energy efficiency measures, electrification of end-uses and fuel switching, exploitation of local renewable energy potential, and flexible interaction with the regional energy system. The results reveal that a detached housing district can achieve a positive annual energy balance (for heat and power) of 110% due to the fact that there are sufficient surfaces (roofs, facades, open land) available for the production of local renewable energy, whereas the remaining typologies fail to achieve the criteria with an annual balance ranking between 61% and 97%, showing additional margins for improvement to meet the PED conditions. The presented concept offers a practical approach to investigate the PED suitability of urban typologies. It will help the Austrian Ministry for Climate Action and Environment to identify appropriate strategies for the refurbishment of existing urban areas towards the PED standard.

The M-LED platform: advancing electricity demand assessment for communities living in energy poverty

Globally about 800 million people live without electricity at home, over two thirds of which are in sub-Saharan Africa. Planning electricity access infrastructure and allocating resources efficiently requires a careful assessment of the diverse energy needs across space, time, and sectors. Because of data scarcity, most country or regional-scale electrification planning studies have however assumed a spatio-temporally homogeneous (top-down) potential electricity demand. Poorly representing the heterogeneity in the potential electricity demand across space, time, and energy sectors can lead to inappropriate energy planning, inaccurate energy system sizing, and misleading cost assessments. Here we introduce M-LED, a Multi-sectoral Latent Electricity Demand geospatial data processing platform to estimate electricity demand in communities that live in energy poverty. The platform shows how big data and bottom-up energy modelling can be leveraged together to represent the potential electricity demand with high spatio-temporal and sectoral granularity. We apply the methodology to Kenya as a country-study and devote specific attention to the implications for water-energy-agriculture-development interlinkages. A more detailed representation of the demand-side in large-scale electrification planning tools bears a potential for improving energy planning and policy.

Optimal building retrofit pathways considering stock dynamics and climate change impacts

Deep energy retrofit across the European building stock would require decades during which boundary conditions will change. This study identifies a range of retrofit pathways, using a dynamic stock model, a bottom-up energy model and an optimization model for different climate scenarios. We consider 1.1 million different retrofit options in the Swiss residential building stock for different economic/environmental objectives until 2060. Despite the replacement of old by new buildings, energy demand and greenhouse gas (GHG) emissions in the reference scenario without deep energy retrofitting are likely to decrease by only about 25%, while accounting for investments of 2–3 billion CHF/a. Partial energy retrofitting or an investment-minimized pathway are neither cost-effective nor sufficient to get close to the net zero targets. In contrast, the highest GHG-saving pathway leads to very high emission reduction of 90%, but requires investment cost of 9 billion CHF/a, which leads to specific cost of 180 CHF/t CO2eq. The cost-optimal pathway shows moderate trade-offs for investment cost and could reach GHG savings of 77% with specific cost of −140 CHF/t CO2eq. Hence, early and deep energy retrofit is cost-effective and allows deep GHG emission reductions by making full use of the synergies between GHG and cost savings.

Techno-economic and environmental characterization of industrial technologies for transparent bottom-up energy modeling

Techno-economic and environmental characterization of industrial technologies for transparent bottom-up energy modeling

Domestic energy mapping to enable area-based whole house retrofits

A long-standing challenge for area-based mass retrofits has been the ability to rapidly and accurately target appropriate dwellings for energy improvements. This paper demonstrates the application of a data-driven localised Geographical Information System (GIS)-based domestic energy mapping approach to create house-by-house baseline energy models and predict the potential for whole house energy retrofits in a case study of 431 dwellings in Oxford (UK). Top-down spatial datasets on energy, housing, socioeconomics and fuel poverty are combined with bottom-up energy modelling underpinned by actual dwelling details gathered through questionnaire surveys by the local community group. Multiple routes to identify suitable dwellings were tested such as grouping dwellings with high energy use, those with high levels of fuel poverty and by common dwelling characteristics. About 300 dwellings were found to be suitable for a whole house retrofit package, equating to 89–94% mean energy reduction over baseline. While the most common dwelling typology, 1930s semi-detached had high retrofit need, it fell in an area with low annual household income. The second most common dwelling typology, 1930s terrace, was dominant in areas with median level of household income. Funding programmes will need to be customised for different household segments to increase the take-up of energy retrofits.

Evaluating long-term greenhouse gas mitigation opportunities through carbon capture, utilization, and storage in the oil sands

Carbon capture, utilization, and storage (CCUS) has been identified as the only option for substantially reducing greenhouse gas (GHG) emissions from fossil fuel-based processes. There has been limited research into the long-term GHG reduction potential of integrating CCUS into unconventional oil extraction processes. We developed a CCUS-oil sands market penetration model and integrated it with a bottom-up energy model (LEAP-Canada) to conduct a cost-benefit case study for the Canadian oil sands. Scenarios were developed considering currently available CCUS technologies and were investigated under different carbon pricing policies. Cumulative GHG emission mitigation potential from the CCUS applications ranged from 3 to 232 million tonnes (Mt) with marginal costs of $-28–42/tCO2e. Carbon pricing led to an average 3% increase in 2050 market shares of the technologies, resulting in additional GHG abatement of 4.9% (172 Mt). CCUS was found to be the costliest GHG emission mitigation option compared to increased integration of cogeneration and energy efficiency measures. The maximum cumulative oil sands GHG emission mitigation achieved in this study was 7%. This research ultimately provides insights into applications of CCUS technologies into the oil sands industry that might be useful to industry planners and policymakers and support the sustainable development of oil sands resources.

Greenhouse gas emission abatement potential and associated costs of integrating renewable and low carbon energy technologies into the Canadian oil sands

The purpose of this research is to evaluate the greenhouse gas reduction potential and associated costs of integrating renewable energy technologies into Canada’s oil sands . There is very limited analysis in the literature focused on the integration of renewable technology options in oil sands processes. This research addresses the gap by developing a novel framework that applies market penetration and bottom-up energy modeling techniques. A total of 27 long-term scenarios across oil sands extraction, upgrading, and electricity generation were investigated. The results show that up to 84 million tonnes of greenhouse gas emissions abatement (2.3% of oil sands GHG emissions) at a marginal greenhouse gas abatement cost of $1.3/tCO2e is available under a $30/tonne carbon incentive. Scenarios with no carbon incentive resulted in 69 million tonnes less greenhouse gas emissions abatement and a marginal greenhouse gas abatement cost increase of $36/tCO2e. Nuclear-based scenarios mitigated the largest amount of greenhouse gases and had the lowest marginal abatement costs. Geothermal energy, bioenergy, and hydropower were cost effective in certain scenarios but had limited greenhouse gas emission reduction potential. Wind and solar technologies failed to displace existing processes in the scenarios considered. The results from this study may be of value to government policy makers and industry representatives aiming to reduce greenhouse gases.

Heat demand in the Swedish residential building stock - pathways on demand reduction potential based on socio-technical analysis

A transition to a more efficient heat energy system requires the consideration of drivers covering behavioural change, upgrades of the building stock and substitution or improvements in technologies in use. Sweden has set the target to reduce total energy demand per heated area in buildings by 50% by 2050 compared to 1995. This study aims to estimate the potential for reducing heat energy demand in the Swedish residential building stock taking into account behavioural, structural and technological categories of drivers. A combination of bottom-up energy modelling with scenario methodology informed by socio-technical analysis of barriers was used. Our results show that the target can be achieved by combining at least two of the categories of drivers. However, it is noteworthy that the technological category, which has by far the lowest level of barriers, almost reaches the target largely owing to the high impact for single-family houses, showing the crucial role of changes in the technology mix. However, as the same drivers have different demand reduction potential in the two main building types in Sweden, single and multi-family houses, this calls for policymakers to lead on initiatives that foster a combination of technological, behavioural and structural improvements for the latter.

Energy Planning and Modelling for Gadhinglaj Block using Leap

Clean and affordable supply of energy is one of the goals among the 17 sustainable development goals. In developing nations like India the fast development in clean and sustainable energy would allow creating evaluation of the Integrated Sustainable Energy Model scenarios in the context of energy a win-win situation for ensuring the rural energy security, mitigating the impact of rising fossil-fuel cost on the economy and avoid the negative implications of exhausting natural resources and ensure the rural energy security. Therefore, there is substantial scope for the exploitation of renewable energy technologies in bridging this gap by providing affordable and clean energy to the poor to meet their lighting, cooking, and thermal needs. The huge potential for the renewable energy mainly lies in local projects, so there’s need to adopt bottomup energy modelling approaches. Energy modelling at block level is highly influential rather than modelling for one village as the block consists of approximately 80-90 villages. Hence the current study emphasizes on energy planning and scenarios modelling for Gadhinglaj block which consists of 93 villages using LongRange Energy Alternatives Planning system (LEAP) tool. The main objective of the study is to develop Integrated Sustainable Energy Model scenarios for Gadhinglaj block also requirement, Social cost and benefit, and their environmental impact. Survey method is used to collect the data. The main output of this research is the development of alternative renewable energy options under different conditions for Gadhinglaj up to 2030.

Modeling global and regional potentials for building-integrated solar energy generation

With the Paris Agreement coming into force, global efforts will need to maximize opportunities through energy efficiency and renewable energy generation. Zero energy/carbon initiatives are mushrooming worldwide, but it has not been fully understood which building types in which climates and under which conditions can potentially be built to net zero energy standards. In order to inform these efforts, a new model was developed to estimate the technical potential for building—integrated solar energy (BISE, the name of the model) generation in a high resolution regional, climate and building typology breakdown., The BISE model also evaluates the opportunities for potential net zero energy buildings based on the BISE findigns, combining these with the findings of two global low-energy building models. The BISE model has a very high resolution in terms of geographic regions, climate types, building types and vintages. Moreover, the model combines methods for bottom-up energy modeling and geospatial analysis. The thermal building energy demand estimation is based on the 3CSEP-HEB model and the plug load scenarios are based on the BUENAS model. Results are wide, due to intrinsic limitationso of the model detailed in the paper, but it is shown that there is a substantial potential for building-integrated solar energy generation in all world regions, and that the Deep Efficiency Scenario allows significantly more building types to meet net zero energy levels by 2050 in contrast to a scenario when only moderate energy efficiency improvements are implemented.

Spatially-explicit models should consider real-world diffusion of renewable electricity: Solar PV example in Switzerland

Spatially-explicit bottom-up energy models with detailed renewable energy representation are increasingly developed. In order to inform such models, we investigate spatial diffusion patterns of solar PV projects in 2′222 Swiss municipalities. Using a dataset of feed-in tariff and one-time subsidy recipients in 2016, we show that PV diffusion was spatially uneven throughout Switzerland in terms of four indicators: the number of PV projects per municipality, per 1′000 inhabitants, per unit of municipal electricity demand, and per unit of municipal land area. Urban-rural divide and exploitable solar PV potential are the key, but not the only predictors of the spatial heterogeneity in PV diffusion. The structure of the municipal economy, socio-demographic characteristics, regional spillover effects, and additional differences in local contexts, such as local policies, matter as well. Spatial diffusion patterns to some extent structurally differ across sub-national regions too, indicating that such empirical investigations are valuable in order to understand what can be generalized. We conclude with recommendations for developing and validating spatially-resolved energy models so that they capture realistic patterns of solar PV diffusion: gather, maintain and analyze spatial data on PV projects and develop robust modelling functions that do not only rely on PV potential.