Energy Modeling Research Articles

Topology‐preserving phase‐field modeling of elastic bending energy

AbstractIn vesicle membranes, the minimum of the elastic bending energy determines the equilibrium shape. We can reformulate the energy using a phase‐field function and optimize it using the standard gradient flow approach. The method, in its typical formulation, allows topological changes in the membrane; however, in certain events, like in the simulation of blood cells, maintaining the initial topology of the membrane is crucial. In this study, we add a constraint on the phase‐field method to preserve the topology. We note that even if the phase‐field method is formulated using a hyperbolic tangent function, during a change in topology, the membrane's profile deviates from the tanh function. Owing to this idea, the constraint imposes an additional requirement on the profile to maintain the tanh shape, thus preserving the topology. We perform extensive experiments in two‐dimensional and three‐dimensional scenarios to demonstrate that our method preserves the topology during the simulation.

Going beyond Climate Neutral Cities: Evidence from a Positive Energy District in the Stuttgart Region

Abstract To successfully develops Positive Energy Districts (PEDs), it is essential to increase citizen awareness and encourage their engagement. Without acceptance and active participation from the citizens, they might not benefit from the technologies and inclusive business models that PEDs offer to foster energy efficiency, productivity, and flexibility. This contribution provides new evidence about the importance of prioritising citizens’ needs when developing PEDs, describing key findings from a case study recently conducted by the authors, in Nürtingen, in the Stuttgart Metropolitan Region, Germany. The research used urban building energy modeling tools, analyzed on-going projects of the city administration, and directly engaged with citizens by different research methods such as d go-along interviewing, SWOT analysis, and the promotion of best practices. The analysis and conclusions on working on the municipality project “Bahnstadt” were checked against the understanding of a “human-centred” PED. Five action areas were considered: energy efficiency, production and flexibility, carbon footprint, water management, and capital expenditures. This action research, part of the EU-funded project TELOS (2020-2024), aimed to provide recommendations to improve the quality of life of the community and enhancing citizen participation in planning and implementation processes, and towards the ambitious European climate neutrality goals.

Towards decarbonizing large-scale industries: A decision support framework for optimizing grid-connected PV-battery energy systems planning – Case study of an OCP mining site, Morocco, North Africa

Incorporating renewable energy into forthcoming grid-connected or decentralized energy systems assumes an escalating significance and can potentially enhance endeavors toward accomplishing Sustainable Development Goal 7 (SDG7). Nevertheless, deploying sustainable renewable energy-intensive systems may present challenges related to their intermittency and cost instability. This paper proposes the development of a decision support model aimed at planning an optimal on-grid PV battery system. The model builds upon the Open Energy Modeling Framework (OEMOF) and defines asset capacities, energy dispatch, operational strategies, and optimal costs for the designated system. Key factors considered include energy demand profile, photovoltaic potential, electricity tariffs, and the availability of the National Grid. Furthermore, the model evaluation incorporates the computation of pertinent performance, feasibility, and viability indicators, notably the Levelized Cost of Energy (LCOE). To validate the framework, the article conducts a case study to determine the optimal sizing and planning of a grid-connected PV battery energy system. The objective is to cater to the electricity needs of an OCP (Office Chérifien des Phosphates) mining site in Morocco. The study considers the characteristics of the national power grid, incorporating time-varying electricity tariffs based on a Demand-Response program taking into account various rates according to the time of use (ToU). The case study includes a sensitivity analysis that examines different factors, namely the proportion of renewable energy, the investment costs of photovoltaic systems and batteries, and the financial parameter known as the weighted average cost of capital (WACC). Through this analysis, the study assesses the impact of these variables on the calculated cost of energy. Experimental results revealed a range of LCOE values from $0.07111/kWh to $0.11847/kWh for high renewable energies integration.

Energy Transition Analysis and Climate Action Strategy in the Power Sector: A Case Study of the Java-Bali System

Abstract The transformation of societies over time has led to a marked shift in energy consumption patterns. In the face of rapid industrial growth and a burgeoning population, Indonesia confronts the multifaceted challenge of achieving a balance among energy trilemma: energy security, economic affordability, and environmental sustainability. This study assesses the feasibility of low-carbon energy systems in Indonesia, evaluating current policies and proposing a roadmap for net-zero emissions. Utilizing the Low Emissions Analysis Platform (LEAP) and the Next Energy Modeling System for Optimization (NEMO), the study explores multiple scenarios—namely, Business-as-Usual (BAU), Carbon Capture and Storage Mitigation (CCSM), and New and Renewable Energy Mitigation (NREM). Of these, the NREM scenario, emphasizing the integration of innovative and renewable energy sources, achieves the most substantial reduction in greenhouse gas emissions while maintaining economic viability. This strategy advocates for the comprehensive adoption of new and renewable energy, aiming to approximate zero emissions by 2060. The peak direct cost of production for this scenario is estimated at $270.8 billion. However, each scenario presents its own set of technological, economic, and policy-related challenges, necessitating strategic solutions for a successful transition to sustainable energy systems.

Building stock energy modeling to assess annual progress in stock energy efficiency and carbon emission reduction of commercial buildings

Short-term progress assessments of carbon dioxide emission reductions are essential for nations to meet their medium/long-term targets. Bottom-up building stock energy modeling (BSEM) methods can contribute to this issue but have not been applied because of the need to represent short-term building stock dynamics in building systems and energy conservation measures (ECMs). To fill this gap, this study developed a BSEM framework that integrates top-down building stock decomposition, including building systems and ECMs, and bottom-up physics-based energy demand quantification using reference building models representing building stock segments. The framework was verified through a case study of a Japanese commercial building stock. The results indicate that the developed model effectively captures the short-term dynamics and contributions of the technologies. Although there are significant errors in estimating some building subsectors and end uses, the model predicts the changes in the aggregated energy consumption with acceptable accuracy. The Japanese 2030 emission reduction target cannot be achieved with the current technology deployment trends; however, the shortfall can be addressed by applying additional measures. Owing to its applicability to diverse building stocks, the framework promotes the use of BSEM for policy assessment and evidence-based policymaking for climate change mitigation.

Hybrid building energy modeling method with parameterized prototype models and rapid calibration

Building energy models were widely used in building energy performance analysis and efficiency improvement, such as building demand response assessment and energy system optimization. However, building energy modeling was a time-consuming and complex process. Therefore, this paper proposed a hybrid building energy modeling method based on parameterized prototype models and rapid calibration. The main feature of the method was the rapid generation of target building energy models using the prototype building energy performance database. The parameterized prototype building energy model considered 13 uncertainty parameters. Based on the range of 13 uncertainty parameters, such as wall U-value and solar heat gain coefficient, 1000 simulations were performed by Monte Carlo sampling to generate the prototype building energy performance database. The target building energy model was quickly calibrated by learning from this database. It filtered the models that met the requirements based on the actual building measurement data. Based on this hybrid modeling method, the rapid modeling tool AutoBPS-Hybrid was developed in a ruby environment. Shopping mall buildings located in three different climate zones were selected as case studies for this study. The results showed that if only one building energy model meeting the percentage errors was needed, only 3–4 simulations were required. If it was necessary to match the real uncertainty parameter distributions, an average of about 53 simulations was needed. The building energy models were applied to plug load and lighting control in buildings. The shopping mall buildings in Harbin, Beijing and Chengdu could reduce energy consumption by 10.18–13.33 kWh/m2, 14.43–18.15 kWh/m2 and 11.54–14.69 kWh/m2 per year, respectively.

Predictive Artificial Intelligence Models for Energy Efficiency in Hybrid and Electric Vehicles: Analysis for Enna, Sicily

Developments in artificial intelligence techniques allow for an improvement in sustainable mobility strategies with particular reference to energy consumption estimates of electric vehicles (EVs). This research proposes a vehicle energy model developed on the basis of deep neural network (DNN) technology. This study also explores the potential application of the model developed for the movement data of new vehicles in the province of Enna, Sicily, Italy, which are characterized by numerous attractors and the increasing number of hybrid and electric cars circulating. The energy model for electric vehicles shows high accuracy and versatility, requiring vehicle velocity and acceleration as input data to predict energy consumption. This research article also provides recommendations for the energy modeling of electric vehicles and outlines additional steps for model development. The implemented methodological approach and its results can be used by transport decision-makers to plan new transport policies in Italian cities aimed at optimizing vehicle charging infrastructure. They can also help vehicle users accurately estimate energy consumption, generate maps, and identify locations with the highest energy consumption.

A numerical study of vortex nucleation in 2D rotating Bose–Einstein condensates

This article implements a numerical method for the minimization under constraints of a discrete energy modelling multicomponents rotating Bose–Einstein condensates in the regime of strong confinement and with rotation. Moreover, this method allows to consider both segregation and coexistence regimes between the components. The method includes a discretization of a continuous energy in space dimension 2 and a gradient algorithm with adaptive time step and projection for the minimization. It is well known that, depending on the regime, the minimizers may display different structures, sometimes with vorticity (from singly quantized vortices, to vortex sheets and giant holes). The goal of this paper is to study numerically the structures of the minimizers. In order to do so, we introduce a numerical algorithm for the computation of the indices of the vortices, as well as an algorithm for the computation of the indices of vortex sheets. Several computations are carried out, to illustrate the efficiency of the method, to cover different physical cases, to validate recent theoretical results as well as to support conjectures. Moreover, we compare this method with an alternative method from the literature.

Mathematical Modeling of Concrete Fracture Energy of Notched Specimens Using Experimental Evidence

Mathematical Modeling of Concrete Fracture Energy of Notched Specimens Using Experimental Evidence

A holistic analysis of environmental impacts and improvement pathways for the Brazilian electric sector based on long-term planning and life cycle assessment

Brazil presents a high share of renewable electricity generation, mainly due to its favorable hydroelectric potential. However, reports estimate that demand will grow substantially in the long term, requiring a significant diversification of the electricity mix. In this context, this paper analyzes how the environmental impacts of electricity generation tend to change over the horizon 2025–2050 in Brazil and identifies improvement opportunities accounting for environmental and economic factors. To do so, the Open Source Energy Modeling System (OSeMOSYS) is applied to estimate the composition of the electricity mix in 2050. Then, life cycle assessment (LCA) is used to evaluate the associated environmental impacts across ten categories. Lastly, hybrid weighted ɛ-constraint multi-objective optimization (MOO) is employed to evaluate which sources can be used to minimize climate change and the levelized cost of electricity while ensuring that other categories are reasonably restrained. The results indicate that the environmental performance of the Brazilian electricity matrix tends to deteriorate substantially, both when analyzing overall impacts and impacts per kWh. Moreover, the proposed MOO approach suggests that some sources, mainly hydropower, onshore wind, and centralized PV present an all-around economic/environmental performance and might be favorable for expanding the power system.

Predicting UK Domestic Electricity and Gas Consumption between Differing Demographic Household Compositions

This paper examines the influence of building characteristics, occupant demographics and behaviour on gas and electricity consumption, differentiating between family groups; homes with children; homes with elderly; and homes without either. Both regression and Lasso regression analyses are used to analyse data from a 2019 UK-based survey of 4358homes (n = 1576 with children, n = 436 with elderly, n = 2330 without either). Three models (building, occupants, behaviour) were tested against electricity and gas consumption for each group. Results indicated that homes without children or elderly consumed the least energy. Property Type emerged as the strongest predictor in the Building Model (except for homes with elderly), while Current Energy Efficiency was less significant, particularly for homes with elderly occupants. Homeownership and number of occupants were the most influential factors in the Occupants Model, though this pattern did not hold for homes with elderly. Many occupant and behaviour variables are often considered ‘unregulated energy’ in calculations such as SAP and are thus typically disregarded. However, this study found these variables to be significant, especially as national standards improve. The findings suggest that incorporating occupant behaviour into energy modelling could help reduce the energy performance gap.

Using an interactive simulation tool of national energy and climate policy planning to explore environmental policy options from the perspectives of different interest groups

AbstractTo reach the Paris Agreement goals, European governments have defined national contributions to the EU binding climate goals and have developed national climate and energy plans for 2021–2030 (NECPs). NECPs are detailed strategies in which governments can flexibly emphasize specific sectors, technologies and national energy policy choices. Every country has built its own energy modelling capacity for forecasting purposes. In most countries, this national modelling relies on optimization tools such as Markal or TIMES. For Latvia's energy sector, a system dynamics (SD) model was built to complement the TIMES model. The SD model deals with an integrated energy system, including the primary energy supply and transformation sector; energy distribution and storage system; and energy demand sectors (residential, tertiary, public, industry and transportation). Sectoral policies are presented in the Stella Architect interface. This study demonstrates how the interactive simulation tool was used in the Environmental Engineering Master level course ‘Environmental Policy and Economics’ as an experiential learning approach. The student assignment involved applying this tool to develop and analyse the energy transition policy package for one of four ideological interest groups: deep greens, bright greens, light greens and greys. Students had to assume a role and adopt its perspective by applying their border‐crossing competence and reflecting on selected policy mixes for that ideology. Various transition pathways, based on different ideologies, illustrate the possibilities for application. Results show that students were successful in fulfilling the assignment.

Non-isothermal crystallization kinetics and activation energy modeling of carbon nanotubes dispersed high-density polyethylene and polypropylene blends

The study presents the effect of the reinforcement of carbon nanotubes (CNT) into high-density polyethylene (HDPE) and polypropylene (PP) blends. The Avrami exponent of CNT blends ranges within the 1.3–1.6 range. It demonstrates the 3D growth of crystals and nucleation activity during crystallization. The change in the Ozawa exponent indicates a two-stage crystallization process. The values of the Mo exponent show that the nucleation activity is affected by changing temperatures during the cooling process and the formation of high-quality crystal growth. The activation energy of the HDPE/PP matrix is 420.5 kJ/mol and adding 1.5% CNT resulted in 369.9 kJ/mol for Run-3. The polymer chains encounter difficulties in transitioning during the cooling process and release a significant amount of energy. The composition of the nanofiller in the blend affects the nucleation activity of the crystals. The permeability of the pure HDPE/PP sample obtained from sorption studies is 1.05 × 10−5 m2/s and adding 1.5% CNT in Run-3 provides a permeability of 5.2 × 10−5 m2/s. The CNT has a homogeneous dispersion in the matrix to form a net-like morphology. The crystallinity index calculated using the Seagal method for HDPE/PP, Run-3, Run-4 and Run-5 is 75%, 75.8%, 77.34% and 78%, respectively.

Status and prospects of energy efficiency in the glass industry: Measuring, assessing and improving energy performance

The significant share of energy-related emissions in the glass industry necessitates robust energy efficiency strategies. This paper evaluates the status and prospects of energy efficiency by integrating the measurement, assessment, and improvement of energy performance through energy data and modelling. Measurement and assessment are crucial for effective energy management in hard-to-abate industrial sectors, providing insight into the existence and extent of potential energy efficiency gains. The use of appropriate performance indicators allows for comparison across production contexts and the monitoring of improvements achieved through interventions. The benchmarking of a representative sample of container glass furnaces reveals a current potential for improvement of around 10% based on alignment with best practices. Energy modelling is used to map energy flows along the various systems involved in production, thereby identifying major energy losses. Energy efficiency options can then be categorised based on their contribution to improving energy performance, facilitating the identification of solutions that address current bottlenecks. The study discusses promising opportunities arising from improved batch compositions and outlines the role of renewable penetration on the viability of hydrogen use and furnace electrification. The proposed approach can be transferred to other hard-to-abate industrial sectors to drive progress in decarbonisation.

Analysis and Modelling for Industrial Energy Efficiency in the Cosmetics Industry: A Real Industrial Case in an Italian Manufacturing Company

Economic and environmental issues translating into energy costs and pollution within the production environment are increasingly attracting attention. Industrial Energy Efficiency (IEE) is gaining ever-higher importance within production environments. Since cosmetic consumers and companies are becoming increasingly sensitive to sustainability, the cosmetic field is working to reduce the environmental and social impact along the whole supply chain. Furthermore, IEE actions in business processes can lead to several medium- and long-term economic and environmental benefits. This paper is the first work in the literature presenting a real-case application of energy analysis and modeling to achieve better energy performances in a cosmetics production process. Thus, in the body of knowledge, it contributes by providing a real case of good practice to be benchmarked for future IEE interventions in cosmetics manufacturing processes. The work has been conducted by analyzing the production process’s energy consumption and developing an energy model of a selected machine (i.e., a turbo-emulsifier). The analysis and modeling performed aimed at assessing the different operational phases of the machine and evaluating the different behaviors of the data. Finally, the results allowed us to propose possible improvements to be applied to the production process to achieve better energy performances.

Predictive cheminformatics modeling of reorganization energy (RE) for p-type organic semiconductors: Integration of quantitative read-across structure-property relationship (q-RASPR) and stacking regression analysis

Organic semiconductors (OSCs), being light in weight, decomposable, cheap, and flexible, can be an excellent replacement for inorganic semiconductors. Reorganization energy (RE) is an essential parameter that can help determine charge carriers' mobility. Understanding and controlling the reorganization energy (RE) of OSCs is necessary for the design and optimization of organic electronic devices such as optoelectronic devices, organic photovoltaics, organic light-emitting diodes (OLEDs), photodetectors, and organic field-effect transistors (OFETs). The quantitative read-across structure-property relationship (q-RASPR) is an emerging computational methodology that can efficiently predict the material's property using structural and physiochemical features, and similarity measures. This method has also shown an enhancement in the predictivity of the models so developed. The stacking regression methodology uses the aggregates/predictions from the individual models and uses them according to their weights to produce an output in the new model. This study elaborates on the sequential steps for the development of a stacking q-RASPR model for the prediction of reorganization energy (RE) of the p-type organic semiconductors (OSCs) comprising 171 diverse organic structures of acenes, thiophenes, thienoacenes, and some pantalenes moiety as well. The predictions from the individual q-RASPR models developed using different similarity measures were used to perform the final stacking regression. Different machine learning (ML) algorithms were also used to enhance the model quality and predictivity. The model so developed through the stacking regression using support vector machine (SVM) method was of statistically good quality (R2 = 0.735), robustness (Q2LOO = 0.668), and effective predictive ability (Q2F1 = 0.801). The error in the model's predictions is also low compared to the other models developed earlier. This q-RASPR model was also used to predict an external set of 10888 compounds, and it shows an error (MAE) of only 87.95 meV, much lower than that reported earlier. The model developed in this study may be used to predict RE during high throughput screening of OSCs.

The impact of energy prices in decarbonizing buildings’ energy use in the EU27

The recent surge in end-use energy prices in the EU has intensified the debate on how price signals influence the decarbonization of the building sector, a key contributor to global energy consumption and emissions. This study investigates the role of energy price signals in driving the decarbonization of the EU-27 building sector using the Invert/EE-Lab model to simulate various scenarios up to 2050. The analysis examines the elasticity of energy demand, particularly how different price scenarios impact investment decisions in energy-related technologies. The findings reveal that a 10 % increase in energy prices can lead to a reduction in energy demand by up to 3 %, depending on the scenario. Furthermore, price elasticity varies across different policy and price settings, with stricter regulations resulting in lower elasticity. The results underscore that while stringent regulatory measures can achieve significant energy savings and reduce greenhouse gas emissions, they also limit the responsiveness to price signals, with heat pump shares reaching 47–86 % in high-efficiency scenarios. Conversely, more moderate policy measures increase price elasticity, leading to 15–55 % heat pump shares. This study highlights the importance of better understanding price elasticity in energy modeling to project better the outcomes of policy interventions aimed at achieving a low-carbon energy system.

A robust decision-making approach in climate policy design for possible net zero futures

The energy modelling community’s analysis of net zero often relies on approaches that hide the extent of uncertainty. Meanwhile the extent of uncertainty involved in the realisation of net zero is proliferating. Conventional consolidative modelling approaches lack of transparency is distorting decision making and policy design around net zero. This contribution uses the UK’s Committee on Climate Changes 6th Carbon Budget as a case study. An exploratory, Robust Decision-Making approach is used to highlight the fragility of conventional UK modelling approaches in shaping national climate policy. A new suite of tools, orientation of analysis and mixed approaches are needed to address the extent of complexity, uncertainty and emergence in possible net zero futures. Only then will robust, inclusive and realisable climate policy be designed.

A Megacities Review: Comparing Indicator-Based Evaluations of Sustainable Development and Urban Resilience

Urbanization is defining global change, and megacities are fast becoming a hallmark of the Anthropocene. Humanity’s pursuit toward sustainability is reliant on the successful management of these massive urban centers and their progression into sustainable and resilient settlements. Indicators and indices are applied assessment and surveillance tools used to measure, monitor, and gauge the sustainable development and urban resilience of megacities. Unknown is how indicator-based evaluations of sustainable development and urban resilience of the world’s largest 43 cities compare. In response, this review paper used the PRISMA reporting protocol, governed by 33 established and 10 emerging megacities, to compare and contrast evaluations of sustainable development and urban resilience. Results reveal that applied assessments of sustainable development of megacities appeared earlier in time and were more abundant than those of urban resilience. Geographically, China dominated other nations in affiliations to scientific research for both sustainable development and urban resilience of megacities. Among the 100 most recurrent terms, three distinct key term clusters formed for sustainable development; seven budding key term clusters formed for urban resilience suggesting breadth in contrast to sustainable development depth. The most cited assessments of sustainable development emphasize topics of energy, methodological approaches, and statistical modeling. The most cited assessments of urban resilience emphasize topics of flooding, transit networks, and disaster risk resilience. Megacities research is dominated by few countries, suggesting a need for inclusion and international partnerships. Lastly, as the world’s people become increasingly urbanized, sustainable development and urban resilience of megacities will serve as a key barometer for humanity’s progress toward sustainability.

Advancing building energy modeling with large language models: Exploration and case studies

The rapid progression in artificial intelligence has facilitated the emergence of large language models like ChatGPT, offering potential applications extending into specialized engineering modeling, especially physics-based building energy modeling. This paper investigates the innovative integration of large language models with building energy modeling software, focusing specifically on the fusion of ChatGPT with EnergyPlus. A literature review is first conducted to reveal a growing trend of incorporating large language models in engineering modeling, albeit limited research on their application in building energy modeling. We underscore the potential of large language models in addressing building energy modeling challenges and outline potential applications including simulation input generation, simulation output analysis and visualization, conducting error analysis, co-simulation, simulation knowledge extraction and training, and simulation optimization. Three case studies reveal the transformative potential of large language models in automating and optimizing building energy modeling tasks, underscoring the pivotal role of artificial intelligence in advancing sustainable building practices and energy efficiency. The case studies demonstrate that selecting the right large language model techniques is essential to enhance performance and reduce engineering efforts. The findings advocate a multidisciplinary approach in future artificial intelligence research, with implications extending beyond building energy modeling to other specialized engineering modeling.