Energy Modeling Research Articles

Exploring the impacts of heterogeneity and stochasticity in air-conditioning behavior on urban building energy models

Heterogeneity and stochasticity, two main aspects of uncertainty in occupant behavior (OB), at the urban scale are not featured in most current urban building energy modeling (UBEM) platforms, and their respective impacts on urban-scale building energy consumption remain unclear. We aimed to introduce OB uncertainty into the UBEM workflow and assess the differences in the impacts of heterogeneity and stochasticity on cooling demand. Different OB models were integrated into the cooling demand simulation of residential building stocks, considering the heterogeneity and stochasticity in both occupancy and energy-use behavior. The impacts of heterogeneity and stochasticity on urban-scale cooling demand and its applications for different purposes in UBEM are discussed. We found that stochasticity in occupancy decreased the peak cooling demand by 54%, whereas the uncertainty in air-conditioning (AC) behavior had little effect. Heterogeneity is the main reason for the diversity in cooling demand, whereas stochasticity better reflects the dynamics of cooling demand., which Occupancy and AC behavior models with higher fidelity are required to obtain results with higher spatial or temporal resolution should be selected according to the UBEM applications. The proposed approach will contribute to the development of appropriate urban-scale occupant behavior models and bridge urban mobility with occupant-centric UBEMs.

Ranking building design and operation parameters for residential heating demand forecasting with machine learning

The European Union's Energy Performance in Buildings Directive has made significant strides in enhancing building energy efficiency since its inception in 2002. However, approximately 75% of EU buildings still fall short of energy-efficient standards. Furthermore, there is a growing momentum to extend the concept of nearly zero-energy buildings to entire districts, thereby fostering Net-Zero Energy Districts. This underscores the necessity for large-scale urban building energy modelling to identify and improve underperforming buildings and energy transition planning. Given the increasing interest in black box models for building energy performance, this study aims to identify common input variables in buildings energy demand literature, analyse their influence, and develop a heating demand prediction model using different algorithms: Random Forest, XGBoost, and Extra Trees. Four large datasets generated from white-box simulation in three Spanish cities were used for training and testing the models. Four features consistently stand out as the most important for prediction: shape factor, infiltration rate, south equivalent surface, and internal gains, regardless of the training algorithm or the climatic zone. The multi-location XGBoost algorithm with an optimizer emerged as the best-performing model, with an average Mean Absolute Percentage Error value hovering around 40%. Analysis employing SHapley Additive exPlanation (SHAP) values showcases the model's ability to identify factors that drive higher heating energy demand, alongside its strong predictive performance. This suggests potential integration of the model into programmes to identify key variables to be addressed during building renovation. Additionally, results show the potential of XGBoost-based software's potential for identifying building renovation targets.

Towards a net-zero-energy building with smart control of Trombe walls, underground air ducts, and optimal microgrid composed of renewable energy systems

In recent years, permanent outages and lack of access to electricity have been significant problems in most cities of Iran. The increasing growth of fossil fuel consumption made it necessary to change the design of buildings. Designing a net-zero energy building (with natural ventilation and renewable energy systems) is a solution for reducing fossil fuels. In this paper, dynamic energy modeling of the building for the smart operation of the trapezoidal Trombe wall and underground air ducts is performed. In the first step, the Trombe Zones are considered for heating. Then the combination of the Trombe zones with underground air ducts for cooling is simulated. Then the system combination with a solar hot water system to supply hot water energy is evaluated. Finally, the economic analysis of the optimal hybrid system (with a photovoltaic system, wind turbine, battery, and inverter) for supplying the electricity of the building equipped with both passive energy strategies was evaluated by Homer software. The results show that by integrating the Trombe wall, the heating load is reduced by 44 %, and by integrating a coupled geothermal cooling system with the air duct and the Trombe wall, the cooling load is reduced by 39%. Also, the net present cost over the 25-year life of the optimized renewable system is reduced by about 12.5% due to both passive strategies. The average daily electricity energy demand of the base building is equal to 86.37 kWh and for the passive building, it is equal to 67.49 kWh.

Predicting building energy consumption in urban neighborhoods using machine learning algorithms

Assessing building energy consumption in urban neighborhoods at the early stages of urban planning assists decision-makers in developing detailed urban renewal plans and sustainable development strategies. At the city-level, the use of physical simulation-based urban building energy modeling (UBEM) is too costly, and data-driven approaches often are hampered by a lack of available building energy monitoring data. This paper combines a simulation-based approach with a data-driven approach, using UBEM to provide a dataset for machine learning and deploying the trained model for large-scale urban building energy consumption prediction. Firstly, we collected 18,789 neighborhoods containing 248,938 buildings in the Shanghai central area, of which 2,702 neighborhoods were used for UBEM. Simultaneously, building functions were defined by POI data and land use data. We used 14 impact factors related to land use and building morphology to define each neighborhood. Next, we compared the performance of six ensemble learning methods modeling impact factors with building energy consumption and used SHAP to explain the best model; we also filtered out the features that contributed the most to the model output to reduce the model complexity. Finally, the balanced regressor that had the best prediction accuracy with the minimum number of features was used to predict the remaining urban neighborhoods in the Shanghai central area. The results show that XGBoost achieves the best performance. The balanced regressor, constructed with the 9 most contributing features, predicted the building rooftop photovoltaics potential, total load, cooling load, and heating load with test set accuracies of 0.956, 0.674, 0.608, and 0.762, respectively. Our method offers an 85.5%-time advantage over traditional methods, with only a maximum of 22.75% of error.

Interpretable Data-Driven Methods for Building Energy Modelling—A Review of Critical Connections and Gaps

Technological improvements are crucial for achieving decarbonisation targets and addressing the impacts of climate change in the built environment via mitigation and adaptation measures. Data-driven methods for building performance prediction are particularly important in this regard. Nevertheless, the deployment of these technologies faces challenges, particularly in the domains of artificial intelligence (AI) ethics, interpretability and explainability of machine learning (ML) algorithms. The challenges encountered in applications for the built environment are amplified, particularly when data-driven solutions need to be applied throughout all the stages of the building life cycle and to address problems from a socio-technical perspective, where human behaviour needs to be considered. This requires a consistent use of analytics to assess the performance of a building, ideally by employing a digital twin (DT) approach, which involves the creation of a digital counterpart of the building for continuous analysis and improvement. This paper presents an in-depth review of the critical connections between data-driven methods, AI ethics, interpretability and their implementation in the built environment, acknowledging the complex and interconnected nature of these topics. The review is organised into three distinct analytical levels: The first level explores key issues of the current research on the interpretability of machine learning methods. The second level considers the adoption of interpretable data-driven methods for building energy modelling and the problem of establishing a link with the third level, which examines physics-driven grey-box modelling techniques, in order to provide integrated modelling solutions. The review’s findings highlight how the interpretability concept is relevant in multiple contexts pertaining to energy and the built environment and how some of the current knowledge gaps can be addressed by further research in the broad area of data-driven methods.

Assessing the impacts of urban morphological factors on urban building energy modeling based on spatial proximity analysis and explainable machine learning

The investigation of the relationship between urban morphology and building energy consumption on a broad scale has garnered significant scholarly interest. Particularly in the early phases of urban building design, the optimization of urban morphological factors (UMFs) has demonstrated its efficacy and cost-effectiveness in enhancing the energy efficiency of urban buildings. This paper presents a framework for exploring the relationship between urban morphology and energy consumption in urban buildings. The framework encompasses defining and quantifying UMFs using a spatial proximity analysis approach, constructing an urban building energy model, and employing explainable artificial intelligence (AI) methods to analyze the impact of each factor on energy consumption. The findings identify the potential impact zones surrounding target buildings and identify 26 UMFs related to urban buildings and the road network. The study reveals high-impact UMFs significantly influencing energy consumption and provides corresponding recommendations for urban building planning. Moreover, the impact of these factors on energy consumption is similar across different building types, although there are variations in their contributions. The research contributes to identifying influential UMFs and provides practical implications for early urban building planning. The proposed methodology can be generalized to other cities, enabling broader applications of the framework.

Enhancing BIM-BEM integration: solutions for efficient data exchange and energy performance assessment

ABSTRACT This paper explores the efficiency of exporting data from Building Information Modeling (BIM) to Building Energy Modeling (BEM) and proposes solutions to encountered problems. The study considers both detached and attached buildings, analyzing the impacts of resolution parameters, and evaluating energy performance based on BIM model. A case-study approach is employed using a residential apartment building, with data exported from Autodesk Revit and imported into DesignBuilder. The results demonstrate the significance of adjusting resolution parameters (the primary parameters that influence the energy analytical model's accuracy and processing time) for efficient data export. Exploring different values leads to improvements in resolving unclosed spaces and enhancing the geometrical model of building elements. Challenges persist in allocating material finishes to walls. By improving and integrating energy performance evaluation through enhanced accuracy and efficiency of data exchange, the findings have significant impacts on stakeholders involved in building design, construction, and operation. Additionally, this process can optimize energy consumption in buildings. Further research is needed to address remaining challenges and improve BIM to BEM systems compatibility including the development of smart mechanisms for energy model creation and resolution parameters refinement. Case studies on different building types and long-term performance monitoring are recommended. This research contributes to the efficiency of exporting data from BIM to BEM interfaces, offering insights for practitioners and guiding future research in BIM-BEM tools integration.

The Impact of Simplifications of the Dynamic Model on the Motion of a Six-Jointed Industrial Articulated Robotic Arm Movement

This research investigates the impact of model simplification on the dynamic performance of an ABB IRB-140 six-jointed industrial robotic arm, concentrating on torque prediction and energy consumption. The entire mathematical model of forward, reverse, differential kinematics, and dynamic model proposed based on the technical specifications of the arm, and to obtain the center of the mass and inertia matrices, which are essential components of the dynamic model, Utilizing Solidworks, we developed three CAD/CAM models representing the manipulator with varying detail levels, such as simplified, semi-detailed, and detailed. Our findings indicate minor differences in the model's torque and energy consumption graphs. The semi-detailed model consumed the most energy, except for joint 1, with the detailed model showing a 0.53% reduction and the simplified model a 6.8% reduction in energy consumption. Despite these variations, all models proved effective in predicting the robot's performance during a standard 30-second task, demonstrating their adequacy for various industrial applications. This research highlights the balance between computational efficiency and accuracy in model selection. While the detailed model offers the highest precision, it demands more computational resources, which is suitable for high-precision tasks. In discrepancy, simplified, less precise models offer computational efficiency, making them adequate for specific scenarios. Our study provides critical insights into selecting dynamic models in industrial robotics. It guides the optimization of performance and energy efficiency based on the required task precision and available computational resources. This comprehensive comparison of dynamic models underscores their applicability and effectiveness in diverse industrial settings.

Adaptation to extreme weather events using pre-conditioning: a model-based testing of novel resilience algorithms on a residential case study

Climate change increases the frequency and intensity of extreme weather events that can be a prominent cause of power outages in North America. These events may cause buildings to experience outages for hours to days, endangering occupant well-being. Although typical adaptive strategies can offer assistance, they often demand substantial initial investments. Thus, due to the need for low-cost solutions, this paper evaluates the efficacy of the proposed pre-heating/cooling algorithm using smart thermostats. The ongoing research employs automated energy modelling through Python scripting to streamline the energy model upgrade process and the EnergyPlus Energy Management System (EMS) algorithm to incorporate pre-conditioning features during grid outages. The results indicated an average 18% improvement in peak intensity and 9% in overall performance during extreme events. Also, it offers the potential for future studies to employ this methodology in assessing the effects of other low-cost strategies for adapting to grid disruptions.

Improved Energy Management of Capacitive Energy Storage in Metro Railcar by Simulation

This paper focuses on the use of modeling and simulation of a renewable energy. The capacitive energy storages contribute to the rapid energy recovery associated with regenerative braking in electric vehicles. This power system allows the acceleration and deceleration of the vehicle with a lower loss of energy. Short-distance passenger traffic on electrified lines is a domain where brake energy recuperation might reduce the total energy consumption significantly. In this paper the results of simulation models by Matlab-Simulink for an urban-metro railcar and some methods for reducing the needed value of capacitance by an improved energy management are presented. The aim of the new management of the energy storage system for decreasing the needed value of capacitance and improving the system performance is presented.

Relative comparison of the benefits of wall envelope insulation materials in the South African energy zones, subject to the new national building energy efficiency standards

Introduction: South Africa is ranked among the 20 highest greenhouse gas (GHG) emitters worldwide, mainly due to its heavy reliance on coal for energy. The residential building industry can play a significant role towards the minimization of both GHG emissions and energy consumption. This research assesses the beneficial impacts of utilizing wall envelope insulation measures using five insulation materials, for a detached residential building, within the seven energy zones of South Africa in accordance with the South African National code for Building energy efficiency (SANS10400-XA).Methods: Lifecycle analysis (for 50 years), regression methods and sequential search techniques of building energy modelling were used to determine both the energy saving implications of adopting the insulation materials (at various levels of thickness), and their corresponding energy payback periods. The study area consisted of eight selected locations representing all the seven energy zones in South Africa (Welkom, Witbank, Thohoyandou, Cape Town, Pretoria, Ixopo, Sutherland, and Fraserburg).Results: Sutherland (zone 6), Cape Town (zone 4) and Fraserburg (zone 7) benefitted most from energy savings due to application of insulation measures. Witbank (Zone 2), Thohoyandou (zone 3) and Pretoria (zone 5) never benefitted from application of wall envelope insulation (in terms of energy savings). Cellulose and straw yielded the lowest payback periods. Generally, polyurethane yielded the highest net energy savings at lower insulation thickness levels, while cellulose was preferable at higher insulation thickness levels in Welkom (zone1) Cape Town (zone 4), Ixopo (zone 5H), Sutherland (zone 6) and Fraserburg (zone 7).Discussion: Lower optimal wall envelope insulation thicknesses (that maximized energy savings) appeared to correspond to locations with higher annual temperature ranges. Higher temperature ranges (hence, higher degree days) would ensure that more energy is saved in order to keep the indoor temperatures within the acceptable comfort limits, due to application of insulation. However, the selection of insulation material is also influenced by other factors such as resistance to insect and mould attack (which easily affect bio-insulation materials), fire retardancy, durability (which affects cellulose), sound proofing, structural strength, and resistance to water vapour.

Improving robustness in strategic energy planning: A novel decision support method to deal with epistemic uncertainties

This paper addresses the challenge of dealing with epistemic, i.e. non-probabilistic, uncertainties in strategic energy planning modelling. Current models have limited consideration of this type of uncertainty compared to probabilistic uncertainty, and also typically lead to overly conservative results. To address this issue, the contribution of the paper is to propose a novel decision support method which combines two decision-making methodologies into a single, internally consistent algorithm, and to show its applicability to real-size energy planning studies. Robust optimization is applied to address constraint uncertainties, while the minimax regret criterion is utilized for uncertainties in the objective function. This approach facilitates energy modelling exercises that can be more closely aligned with decision-makers' preferences for both feasibility and optimality. To demonstrate its effectiveness, the method is applied to a real-size strategic energy planning model, and the algorithm is shown to be able to provide detailed solutions in reasonable times. Ex-post evaluations confirm that this approach maintains robust optimization performance by effectively reducing the occurrence and magnitude of infeasibilities, while satisfying the minimax regret criterion across the entire range of uncertainties. Therefore, this integration preserves the distinct advantages of each methodology without any adverse effects when used together.

Delay and Energy Efficient Offloading Strategies for an IoT Integrated Water Distribution System in Smart Cities

In the fast-moving world of information and communications technologies, one significant issue in metropolitan cities is water scarcity and the need for an intelligent water distribution system for sustainable water management. An IoT-based monitoring system can improve water distribution system management and mitigate challenges in the distribution network networks such as leakage, breakage, theft, overflow, dry running of pumps and so on. However, the increase in the number of communication and sensing devices within smart cities has evoked challenges to existing communication networks due to the increase in delay and energy consumption within the network. The work presents different strategies for efficient delay and energy offloading in IoT-integrated water distribution systems in smart cities. Different IoT-enabled communication network topology diagrams are proposed, considering the different water network design parameters, land cover patterns and wireless channels for communication. From these topologies and by considering all the relevant communication parameters, the optimum communication network architecture to continuously monitor a water distribution network in a metropolitan city in India is identified. As a case study, an IoT design and analysis model is studied for a secondary metropolitan city in India. The selected study area is in Kochi, India. Based on the site-specific model and land use and land cover pattern, delay and energy modeling of the IoT-based water distribution system is discussed. Algorithms for node categorisation and edge-to-fog allocation are discussed, and numerical analyses of delay and energy models are included. An approximation of the delay and energy of the network is calculated using these models. On the basis of these study results, and state transition diagrams, the optimum placement of fog nodes linked with edge nodes and a cloud server could be carried out. Also, by considering different scenarios, up to a 40% improvement in energy efficiency can be achieved by incorporating a greater number of states in the state transition diagram. These strategies could be utilized in implementing delay and energy-efficient IoT-enabled communication networks for site-specific applications.

Optimizing thermal properties and heat transfer in 3D biochar-embedded organic phase change materials for thermal energy storage

Enhancing the thermal properties and light-absorbing capabilities of phase change materials (PCMs) through the utilization of environmentally friendly, economically viable biochar materials is pivotal for optimizing solar energy capture and utilization. Herewith, initially, a green, three-dimensional, eco-friendly carbon nano inclusion is synthesized from Prosopis juliflora through vacuum oven carbonization at 130 °C, followed by size reduction via ball milling, promising high-impact contributions. Subsequently, green-synthesized nano-inclusions are dispersed in PEG-1000, creating advanced nano-enhanced phase change materials with improved thermo-physical properties using a two-step ultrasonication technique for enhanced thermal conductivity. This innovative study comprehensively explores the morphological behaviour, chemical stability, optical absorptivity, thermal properties, and reliability of the PEG-PJ composite. Remarkably, present research revealed that the composite achieved its highest thermal conductivity, an impressive 0.49 W/m⋅K, at 0.7 wt% of 3-D (PJ) biochar. Notably, the melting temperatures of the PEG-PJ composites consistently ranged from 40.1 °C to 40.5 °C. At the same time, their latent heat capacities displayed a notable increase, ranging from 145 J/g to 152.7 J/g, marking a substantial enhancement of 3.968% and 1.758%, respectively. Furthermore, to confirm the reliability and consistency of experimental findings, 500 thermal cycles were performed. Additionally, a numerical analysis study is conducted by utilizing 2-D energy modelling software to simulate the heat transfer rate owing to the improved thermal conductivity of the developed PEG-PJ composite PCM compared to PEG-1000. In conclusion, developed composites optimize solar storage, improve building thermal control, and enhance industrial heat exchangers for sustainable innovation in energy.

Modeling of thermal energy saving in commercial buildings of Australia by balanced tree growth optimizer

Low-energy cases can be designed more efficiently by using analytical optimization. The non-linear thermal performance of buildings has led to the development of optimization techniques based on simulation. In building optimization, it is important to achieve superior solutions while minimizing calculation expenses. This study aims to optimize an Australian office building using the Balanced Tree Growth Optimizer (BTGO). It has resulted that more than 11.7 % of energy can be saved by the optimization process and also some energy-saving measures. A comparison of the utilized algorithm with benchmark algorithms including the Nelder-Mead method, hybrid Particle Swarm Optimization, Hooke-Jeeves, and Ant Colony Optimization for continuous domain showed that the BTGO can achieve better solutions and needs less computational time.

Integrated energy demand-supply modeling for low-carbon neighborhood planning

As the building stock is projected to double before the end of the half-century and the power grid is transitions to low-carbon resources, planning new construction hand in hand with the grid and its capacity is essential. This paper presents a method that combines urban building energy modeling and local planning of renewable energy sources (RES) using an optimization framework. The objective of this model is to minimize the investment and operational cost of meeting the energy needs of a group of buildings. The framework considers two urban-scale RES technologies, photovoltaic (PV) panels and small-scale wind turbines, alongside energy storage system (ESS) units that complement building demand in case of RES unavailability. The urban buildings are modeled abstractly as “shoeboxes” using the Urban Modeling Interface (umi) software. We tested the proposed framework on a real case study in a neighborhood in Chicago, Illinois, USA. The results include estimated building energy consumption, optimal capacity of the installed power supply resources, hourly operations, and corresponding energy costs for 2030. We also imposed different levels of CO2 emissions cuts. The results demonstrate that solar PV has the most prominent role in supplying local renewables to the neighborhood, with wind power making only a small contribution. Moreover, as we imposed different CO2 emissions caps, we found that ESS plays an increasingly important role at lower CO2 emissions levels. We can achieve a significant reduction in CO2 emissions with a limited increase in cost (75% emissions reduction at a 15% increase in overall energy costs). Overall, the results highlight the importance of modeling the interactions between building energy use and electricity system capacity expansion planning.

Urban Building Energy Modeling to Support Climate-Sensitive Planning in the Suburban Areas of Santiago de Chile

Greenhouse gas emissions depend on natural and anthropic phenomena; however, to reduce emissions, we can only intervene in terms of anthropic causes. Human activity is very different in various countries and cities. This is mainly due to differences in the type of urban environment, climatic conditions, socioeconomic context, government stability, and other aspects. Urban building energy modeling (UBEM), with a GIS-based approach, allows the evaluation of all the specific characteristics of buildings, population, and urban context that can describe energy use and its spatial distribution within a city. In this paper, a UBEM is developed using the characteristics and consumption of eight typical buildings (archetypes) in the climate zone of Santiago de Chile. The archetype-based UBEM is then applied to the commune of Renca, a critical suburb of Santiago, with the use of QGIS to analyze the energy demand for space heating and the potential for energy saving after four retrofitting interventions. Knowing the costs of the retrofitting interventions and the energy price, the simple payback time was evaluated with the reduction in GHG emissions. Starting from the actual building stock, the results show that the most effective retrofitting intervention for the commune of Renca is the thermal insulation of walls and roofs; due to the type of dwellings, this particular intervention could be more convenient if associated with the installation of solar technologies. This methodology can be replicated with the data used by urban planners and public administrations available for many Chilean cities and in other countries.

Effect of the grain boundary character distribution on the sulfur corrosion behaviour and mechanisms of copper windings under different high temperatures

AbstractAs a typical failure phenomenon in transformers, sulfur corrosion has garnered significant attention in the field of high‐voltage engineering. Grain boundary character distribution (GBCD) copper windings have been introduced to enhance sulfur corrosion resistance by slowing down intergranular corrosion. In this study, the sulfur corrosion behaviour and mechanisms of the GBCD copper windings under various temperatures were experimentally and theoretically studied. Results show that GBCD can enhance the corrosion resistance of copper in liquid environments. With the increase in temperatures, the insulating properties of oil and papers in traditional copper windings experience notable degradation, while GBCD copper windings show more stable insulating behaviours. In addition, modelling of grain boundary energy indicates that the grain boundary structure of GBCD copper windings has a lower average interface energy of 0.170 eV/Å2. Calculations of reaction thermodynamics show that GBCD copper windings possess a higher failure temperature (135.2°C) and inhibition degree (activation energy) of the sulfur corrosion (32,557.62 J/mol), revealing the stability and enhanced sulfur corrosion resistance at elevated temperatures.

Modelling Policy Pathways to Maximise Renewable Energy Growth and Investment in the Democratic Republic of the Congo Using OSeMOSYS (Open Source Energy Modelling System)

This study sought to generate, evaluate, and recommend possible national policies for the government of the Democratic Republic of the Congo (DRC) to implement to most effectively boost growth and investment in renewable energy technologies (RETs) through 2065 using Open Source Energy Modelling System (OSeMOSYS). The novelty of this study stems in-part from the scarcity of RET modelling completed for specific West African countries rather than for broader regions. Market-based instruments were identified as the policy type most practical for DRC. From modelling the resulting energy systems for policy pathways involving a 16% RET subsidy, a 70% fossil fuel tax, and both in combination relative to no-policy baseline scenarios, the scenarios including the tax had the lowest net costs (USD304–306 B) and the highest proportion of RETs (above 90%). Additionally, despite the current reliance on hydropower to fulfil 98% of its energy needs, hydropower played a very minor role in all of a modelled scenarios (no future investment beyond residual capacity). Finally, a post-modelling market potential assessment was performed on the technology that dominated off-grid supply across policy pathways: a 0.3 kW small solar home system (SHS). Based on learning rates for solar photovoltaics (PV), demand for a small SHS in DRC (>160 million units in total) was found to be sufficient to substantially reduce the unit cost as deployment scales. Ultimately, this study yielded four recommendations for the DRC government: (1) Pursue financial incentives to catalyse DRC’s renewable energy supply. (2) Tax fossil fuel energy production. (3) Re-evaluate focus on hydropower. (4) Promote DRC as a healthy market for solar home systems.

Modelling a Decarbonization Agenda for Bembibre’s Industrial Park

The design of the Sustainable Strategy Agenda for the Bembibre industrial zone in the Bierzo region employing the Open-Source Energy Modeling System (OSeMOSYS) is presented. This municipality, which has been affected by the collapse of the coal mining industry, is at the epicentre of the reindustrialization and Just Transition (none left behind) initiatives of the Spanish government. Among the challenges to be tackled are the zone's entire decarbonization by 2030, the incorporation of circular economy initiatives, and the development of quality jobs that attract and retain new residents. To facilitate decision-making, different roadmaps have been evaluated, including the repurposing of an old coal mine galley as a reverse hydro storage system, the deployment of enterprises dedicated to the recycling of solar panels, and the incorporation of second-life batteries from electric vehicles as energy storage. Among the scenarios evaluated, the most complex turns out to be the most interesting. With an initial outlay of 190 M€, it incorporates a 210 MW photovoltaic park linked to a mixed storage system comprised of 0.25 GWh of reverse hydraulic systems and 68MWh of second life reconditioned batteries. About 850 jobs in the implementation stage and 70 more permanent in the exploitation phase are envisaged.