Building Energy Demand Research Articles

Data-driven key performance indicators and datasets for building energy flexibility: A review and perspectives

Energy flexibility, through short-term demand-side management (DSM) and energy storage technologies, is now seen as a major key to balancing the fluctuating supply in different energy grids with the energy demand of buildings. This is especially important when considering the intermittent nature of ever-growing renewable energy production, as well as the increasing dynamics of electricity demand in buildings. This paper provides a holistic review of (1) data-driven energy flexibility key performance indicators (KPIs) for buildings in the operational phase and (2) open datasets that can be used for testing energy flexibility KPIs. The review identifies a total of 48 data-driven energy flexibility KPIs from 87 recent and relevant publications. These KPIs were categorized and analyzed according to their type, complexity, scope, key stakeholders, data requirement, baseline requirement, resolution, and popularity. Moreover, 330 building datasets were collected and evaluated. Of those, 16 were deemed adequate to feature building performing demand response or building-to-grid (B2G) services. The DSM strategy, building scope, grid type, control strategy, needed data features, and usability of these selected 16 datasets were analyzed. This review reveals future opportunities to address limitations in the existing literature: (1) developing new data-driven methodologies to specifically evaluate different energy flexibility strategies and B2G services of existing buildings; (2) developing baseline-free KPIs that could be calculated from easily accessible building sensors and meter data; (3) devoting non-engineering efforts to promote building energy flexibility, standardizing data-driven energy flexibility quantification and verification processes; and (4) curating and analyzing datasets with proper description for energy flexibility assessm.

Enhances of nearly zero-energy public buildings in regions with hot summers and cold winter

Nearly Zero-energy residential buildings have attracted widespread attention due to their superior energy-saving performance. In order to achieve a balance between energy production and demand in residential buildings, the application of renewable energy is essential. This paper optimizes typical zero-energy public buildings in hot summer and cold winter locales using the concepts of passive priority, active optimization, and 100% renewable energy supply guarantee. The importance of passive energy-saving techniques like heating, shading, and heat recovery; in accordance with local resource conditions; analysis of building renewable energy utilization systems like solar energy and air energy; and recommendation of the most technologically efficient building energy system. According to the simulation results, if the criteria for zero-energy buildings are met, the annual energy consumption index per unit area is 37.6 kWh/m2, and the 8.9 kW building photovoltaic system can generate 36.4 kWh/m2 of power annually.

Unravelling the impact of courtyard geometry on cooling energy consumption in buildings

At present, the energy used for air conditioning in buildings in urban areas accounts for over 36% of total global energy consumption. Energy efficiency has become a critical factor in the urban planning of cities worldwide. Courtyard buildings in hot cities are a prime example of the approach used in traditional vernacular architecture to mitigate the effects of extreme weather. However, given the challenge of guaranteeing accurate modelling of microclimates within these courtyards, their impact on energy demand in buildings has been routinely overlooked by energy certification tools.This work examines three empirical case studies selected in Seville city (Spain), where temperatures during the summer months are extreme. The case studies selected display distinct geometric variations, and the primary objective of the research is to assess the influence of this geometric factor on the cooling energy demand of the building indoors. To achieve this, a validated methodology combining experimental and numerical data was implemented to evaluate the energy performance of buildings with courtyards. Different simulations were conducted to detect the impact of individual courtyard features. The results show a reduction in cooling demand of 8–18% depending on the geometry of the courtyard. Analysis was also carried out on the influence of the floor level and the orientation of adjacent rooms, revealing differences of 15% and 22%, respectively. The main conclusion of the research is that the use of courtyards as functional devices, paying particular attention to their geometry, is a key factor in the cooling energy demand of buildings.

Electricity peak shaving for commercial buildings using machine learning and vehicle to building (V2B) system

Reducing electricity peak demand is essential to maintain the balance between supply and demand side in electricity power markets, as well as reduce utility costs and environmental impacts. With growth in adoption of Electric Vehicles (EVs), there is an emerging opportunity to balance electrical power demand of buildings by storing electricity in EVs during low demand periods and discharging electricity into buildings during peak demand periods. Due to uncertainty in time and magnitude of peak demand, decision makers are always faced with a challenging task to identify optimal schedules for charging and discharging EVs to minimize peak electricity demand. This paper presents the development of a novel system that is capable of predicting day-ahead building electricity demand profile and identifying optimum schedule of charging and discharging EVs to minimize electricity peak demand. The system is designed to comply with planned EV trip schedules and minimum state of charge (SOC). The system consists of (1) machine Learning (ML) model to predict electrical power demand, and (2) demand management optimization model to identify optimal schedule for charging and discharging EVs. Four methods are explored to develop the ML model, including histogram-based gradient boosting, random forest, deep artificial neural network (DNN), and long short-term memory (LSTM). A case study of multi-tenant commercial building is analyzed to evaluate the performance of the system and demonstrate its new capabilities. The results of the case study shows that LSTM has the best performance in terms of mean absolute error, root mean square error, and mean absolute percentage error with average values of 7.44, 17.78, and 20.08 %, respectively. Five scenarios for shaving peak electricity demand, including combinations of two electric vehicles, a stationary battery, and a PV system are investigated. Scenarios including the stationary battery and the PV system are considered to evaluate the full potential of peak demand reduction in the case study building. The results of the demand management optimization model show up to 36 % reduction in peak demand using two EVs, one stationary battery, and PV system of 40 kW capacity. The key contributions that this study adds to existing knowledge are: (1) developing machine learning models to predict day-ahead electricity demand in 15-minute intervals, (2) integrating machine learning and optimization algorithms in identifying EV charging and discharging schedules to minimize utility cost by shaving peak energy demand, and (3) considering planned EV trips and minimum SOC requirements in identifying optimal charging and discharging of EVS to shave peak energy demand in buildings. The implementation of this system provides practical solutions for managing electricity demand in commercial buildings using EVs. By reducing energy consumption and promoting the innovative use of EVs, this system offers a sustainable approach to managing electricity demand in commercial buildings.

Impact of a Weather Predictive Control Strategy for Inert Building Technology on Thermal Comfort and Energy Demand

The sun’s total radiation alone exceeds the world population’s entire energy consumption by 7.500 times and ignites secondary renewable energy sources. The end energy consumption buildings use for heating amounts to 28% of Germany’s total energy consumption. With the ongoing trend of digitalization and the transition of the German energy supply away from fossil fuels and the consequent political dependency, electric heat pumps and photovoltaic (PV) systems have become increasingly important to the discussion. This has led to an increasing demand for smart control strategies, especially for inert systems such as thermally activated building systems (TABS). This paper presents and analyses a weather predictive control (WPC) strategy using a validated thermodynamic simulation model. The literature review of this paper outlines that the current common control strategies are data intense and complex in their implementation into the built environment. The simple approach of the WPC uses future ambient temperature and solar radiation to optimize the control of the heating, cooling, ventilation, and sun protection system. The thermal comfort and energy demand evaluate the concept. We show that with a WPC for TABS, thermal comfort can improve without increasing the energy demand for the office building in the moderate climate of Munich. Furthermore, this paper concludes that the WPC works more effectively with more thermal mass. This simplified building control strategy promotes the European roadmap goal of climate neutrality in 2050, as it bridges the phenomenon of the performance gap.

The Effect of Degradation on Cold Climate Building Energy Performance: A Comparison with Hot Climate Buildings

The issues of reducing energy consumption in buildings and their decarbonisation are currently among the most pressing. However, such an important aspect of the problem under discussion as the impact of unavoidable degradation processes on energy demand in buildings remains poorly understood. In addition, there are only a limited number of practical guidelines that can be used to take this factor into account at the design stage and during the further operation of buildings. The aim of this work was to assess the potential impact of component degradation and ageing on heating energy consumption in buildings, including insulated glass units, thermal insulation, airtightness, heat recovery of mechanical ventilation systems, and photovoltaic modules. The detached and apartment buildings were considered to be in a cold climate in the context of the Republic of Belarus. The study was based on simulation research using EnergyPlus. As a result, it was found that a possible increase in heating energy consumption might reach 17.6–61.2% over 25 years in detached houses and up to 23.6–89.8% in apartment buildings. These indicators turned out to be higher than the previously identified values for cooling energy consumption in a hot–humid climate. Based on the findings, recommendations for considering the degradation factor in cold climates in practice were developed, which were compared and integrated into the author’s existing guidelines.

Crystallization-free and low-cost deep eutectic solvents for absorption thermal battery utilizing ultra-low-grade energy

Absorption thermal batteries (ATBs) play a significant role in balancing the mismatch between ultra-low-grade renewable/waste energy supply and building energy demand. Working fluids are critical to the high performance and reliability of ATBs, but conventional salt-based mixtures suffer from crystallization risks, while the popular ionic liquid-based mixtures are quite expensive. Therefore, crystallization-free and low-cost deep eutectic solvents (DESs) are proposed to achieve reliable and affordable ATBs. The property estimation based on the NRTL model and the cycle model based on mass and energy conservation are established and verified with high accuracies, which are used to investigate the energy storage performance of five H2O/DESs and four NH3/DESs. A comprehensive comparison between the emerging DES-based and traditional working fluids from multifaceted viewpoints (efficiency, compactness, exergy, response rate, applicability, heat transfer, reliability, and economics) is conducted. For a basic ATB with inferior operating conditions (generation temperature < 72 °C, evaporation temperature < 1 °C, or absorption temperature > 40 °C), NH3/Glyceline yields the highest energy storage efficiency (ESE) and exergy efficiency (EXE), outperforming the traditional refrigerant/salts. A compression-assisted ATB is further adopted, enabling NH3/DESs to utilize ultra-low-grade heat as low as 40 °C. NH3/Glyceline yields the highest ESE of 0.752 and EXE of 0.599 at a generation temperature of 50 °C. It also exhibits a rapid response rate with the highest maximum vapor mass flow rate of 5.4 g/s and the shortest discharging time (73.22 min). NH3/Glyceline exhibits the widest applicability, with an applicable operating range ratio of 87.72%, much higher than that of H2O/LiBr (23.72%). The compression-assisted ATB using NH3/Glyceline is the most promising for large-scale applications owing to the lowest levelized cooling capacity cost of 0.239 $/kWh, which is 41.89% lower than that of H2O/LiBr and 96.58% lower than that of H2O/[EMIM][OAc].

Performance study and heating simulation on novel latent heat thermal energy storage device suit for air source heat pump

Latent heat thermal energy storage (LHTES) technology can solve the problem of limited performance improvement of air source heat pump (ASHP) systems due to the time mismatch between building energy demand and operational efficiency in low temperatures. Therefore, this paper proposes a LHTES device suitable for the ASHP system. Firstly, the internal structure is obtained by ANSYS numerical simulation. Furthermore, experiments are carried out to verify the heat storage and release performance of the proposed device under different inlet water temperatures and flow rates. Finally, the operational effects of the ASHP-LHTES combined heating system are analyzed by TRNSYS. The design results show that a liquid phase ratio of 97.24 % is achieved with a pipe diameter of 16 mm, tube pitch of 70 mm, and 1/2 horizontal tube fins. Experiment results exhibit that the proposed device performs excellently under optimal conditions, with 89.7 % effective heat storage efficiency and 92.5 % available heat release efficiency. TRNSYS results indicate that the annual operating cost of the combined heating system saves 32.9 % compared with the single ASHP heating system, although the power consumption increases by 24.4 %. This study can provide theoretical support for the application of low-temperature LHTES devices in the ASHP system.

Cool roofs: A climate change mitigation and adaptation strategy for residential buildings

Cool roofs offer a passive solution to reduce cooling demand in buildings. However, they increase heating demand in cold seasons. The annual energy-saving potential of cool roofs greatly depends on climate characteristics and may change over time due to global warming. This is particularly important for cool and mild temperate climates, in which the cool roofs' cooling benefits may exceed their heating penalties in the future. To understand the impact of cool roofs on building energy demand for each climate and to explore their future energy-saving potential, these roofs should be evaluated under both present and future climates. Accordingly, this study aims to: 1) quantify the energy savings (or energy penalties) of cool roofs under a wide range of present climate conditions (from cool to hot climates); 2) evaluate cool roofs' energy-savings potential under future climates; and 3) predict when (and under which climate change scenarios) cool roofs will improve buildings' thermal performance in the future for cool or mild temperate climates. Results showed that cool roofs reduce annual energy loads by up to 14% and 22% in tropical and subtropical climates under present and future climates, respectively. For cool and mild temperate climates, while cool roofs increase the building's total energy demand under present climates, it is predicted that they offer energy-savings in the 2050s and 2070s for Canberra and Melbourne, respectively. Understanding the present and future impacts of cool roofs is essential for the implementation of appropriate climate change mitigation and adaptation strategies for residential buildings in each climate.

Evaluating the Impact of Urban Microclimate on Buildings’ Heating and Cooling Energy Demand Using a Co-Simulation Approach

The current research proposes an integrated computational method to consider the effect of the urban microclimate and the higher urban air temperatures on the assessment of urban building energy demands on an annual basis. A one-way coupling procedure is established to generate datasets on typical weather years that can capture the particularities of the urban microclimate as a function of their morphological and geometrical characteristics, thus providing a global perspective of the annual building energy performance at a reasonable computational cost. The proposed simulation method, here applied for an energy performance analysis of generic, non-insulated building units located in four different urban sites of Thessaloniki, Greece, is based on the three tools: (a) the ENVI-met v.4 microclimate model, (b) the Meteonorm weather generator and (c) the dynamic BEPS tool EnergyPlus. The obtained simulation results indicate a decrease in the annual heating energy needs of the examined building units of 8.2–11.5% when the effect of urban warming was accounted for, along with a rise in the annual cooling energy needs of between 13.4 and 28.2%, depending on the case study area.

Impact of coupled heat and moisture transfer on indoor comfort and energy demand for residential buildings in hot-humid regions

Moisture significantly affects indoor thermal comfort and energy demand. However, the coupled heat and moisture transfer (HAMT) effect inside the wall assemblies is often ignored in building performance calculations. To quantify its impact on building performance under hot and humid climates, the thermal environment and energy demand of an apartment in Guangzhou (China) were evaluated with and without HAMT finite element algorithm, respectively. The HAMT effects are quantified and analyzed in different periods. Different boundary conditions regarding climate uncertainty, wind-driven rain effect, and wall solar absorptance are considered. The results show that under naturally ventilated conditions, ignoring the effect of HAMT in walls can lead to an overestimation of indoor operative temperature by 1.2 °C in summer and an underestimation of 1.7 °C in winter. The deviation in indoor operative temperature calculated by the two models is noticeable from noon to midnight. On the other hand, cooling and dehumidification demand energy demand can be underestimated by 8.4% and 12.4%, respectively, without consideration of HAMT. The HAMT also increases the probability of high hourly energy demand for cooling and dehumidification under the HAMT effect. These findings emphasize the importance of considering the HAMT effect when evaluating building performance in hot and humid regions.

Dual-objective building retrofit optimization under competing priorities using Artificial Neural Network

Building retrofit has received renewed interests in recent years, driven by energy-savings and indoor environmental quality goals. Digital technologies such as building performance simulation and optimization algorithms have been used to identify optimal retrofit schemes, yet the existing approaches are limited by the slow running speed of physics-based models and sub-optimal results. This study describes a novel framework, the Building Performance Optimization using Artificial Neural Network (BPO-ANN), which can automatically identify optimal building retrofit schemes. A robust Artificial Neural Network model was developed and validated as a surrogate to rapidly assess building performances, which was then connected to a genetic algorithm in search of Pareto optimal. The impact of key design attributes on building performances have been assessed using sensitivity analysis. The BPO-ANN framework has been tested in a high-performing campus building in Northern China under two competing objectives: building energy demand and occupant thermal comfort. It can automatically identify optimal design schemes, which were expected to achieve an energy-savings of 4% and reduce the annual thermal discomfort percentage by 4%. Sensitivity analysis suggested that window-to-wall ratio and HVAC setpoint have contributed the most to the performances of the campus building, followed by the roof U-value and wall U-value. The study has contributed methodologically to simulation-based optimization method, with novelties in the use of neural network algorithms to accelerate the otherwise time-consuming physics-based simulation models. It has also contributed a robust procedure in the tuning of hyperparameters in neural network models, with marked improvements in model prediction and computational efficiency.

Forecasting energy demand of PCM integrated residential buildings: A machine learning approach

Forecasting energy demand has become an essential element for energy stakeholders in planning and reducing the energy consumption of buildings. Machine learning techniques have become popular for forecasting building energy demand owing to their reliability and cost efficiency. This research aims to propose a model for predicting the energy consumption of PCM-integrated residential buildings in the Mediterranean climate region. For the model development, Multiple Regression (MR), Support Vector Machines (SVM), and Artificial Neural Networks (ANN) were used. For the first time, the PCM melting point, building, and environment parameters were considered simultaneously as input parameters to predict the energy consumption of PCM-integrated buildings. The energy simulation of nine different building types located in seven different cities of the Mediterranean climate region were performed to generate the database. After the model development, the most influential design parameters were established by performing sensitivity and parametric analysis. The results showed that the optimum PCM for annual energy savings varied from PCM-25 to PCM-27. The shape factor significantly influenced the specific heating and cooling demand of buildings. Moreover, the statistically evaluated prediction models showed that SVM and ANN methods are more reliable, with R2 value of over 0.99. The externally validated prediction models demonstrated that the ANN model can estimate the energy consumption of PCM-integrated buildings with more accuracy. From sensitivity analysis, it was found that cooling degree days, heating degree days, volume, shape factor, and PCM melting point are the key influencing parameters affecting the energy demand of buildings.

Evaluation of building energy demand forecast models using multi-attribute decision making approach

With the existence of several conventional and advanced building thermal energy demand forecast models to improve the energy efficiency of buildings, it is hard to find an appropriate, convenient, and efficient model. Evaluations based on statistical indexes (MAE, RMSE, MAPE, etc.) that characterize the accuracy of the forecasts do not help in the identification of the efficient building thermal energy demand forecast tool since they do not reflect the efforts entailed in implementation of the forecast model, i.e., data collection to production/use phase. Hence, this work presents a Gini Index based Measurement of Alternatives and Ranking according to COmpromise Solution (GI-MARCOS), a hybrid Multi Attribute Decision Making (MADM) approach for the identification of the most efficient building energy demand forecast tool. GI-MARCOS employs (i) GI based objective weight method: assigns meaningful objective weights to the attributes in four phases (1: pre-processing, 2: implementation, 3: post-processing, and 4: use phase) thereby avoiding unnecessary biases in the expert's opinion on weights and applicable to domains where there is a lack of domain expertise, and (ii) MARCOS: provides a robust and reliable ranking of alternatives in a dynamic environment. A case study with three alternatives evaluated over three to six attributes in four phases of implementation (pre-processing, implementation, post-processing and use) reveals that the use of GI-MARCOS improved the accuracy of alternatives MLR and BM by 6% and 13%, respectively. Moreover, additional validations state that (i) MLR performs best in Phase 1 and 2, while ANN performs best in Phase 3 and 4 with BM providing a mediocre performance in all four phases, (ii) sensitivity analysis: provides robust ranking with interchange of weights across phases and attributes, and (iii) rank correlation: ranks produce by GI-MARCOS has a high correlation with GRA (0.999), COPRAS (0.9786), and ARAS (0.9775).

PLATFORM BASED ON DATA ANALYTICS AND STATISTICS TO PREDICT AND MONITOR THE INTEGRATED AND INTELLIGENT ENERGY MANAGEMENT OF ENERGY-INTENSIVE BUILDINGS

ABSTRACT: Currently, large buildings are often equipped with building management systems (BMS), but either because of the type of control or the difficulty of interacting with them, they do not allow optimization policies to be implemented. As far as energy efficiency is concerned, optimization strategies often result from the analysis of monitored data by a specialist, which slows down both decision making and implementation. Aware of this problem, this project arises, in which a complementary platform to the BMS systems is developed to provide them with greater intelligence. For its design, the Puerta del Hierro Hospital in Madrid has been taken as a pilot case, from the data captured by its own BMS and using the 6 SIGMA methodology, algorithms have been developed capable of optimizing the production systems themselves, thus helping to make up for the weaknesses in the operation of the systems and reducing energy consumption, minimizing the environmental impact and allowing to obtain the maximum performance of its assets. The platform uses the information gathered by the BMS and weather forecasts to predict a building's energy demand and, based on this, optimize energy production at any given moment. Keywords: Commissioning, Energy efficiency, Smart building, Degree-days, BMS, 6 SIGMA.

The potential of lake-source district heating and cooling for European buildings

Lake-source thermal district networks can efficiently supply heating and cooling to buildings and thus save energy and CO2 emissions. However, it remains unclear to which degree they are a sustainable alternative at a larger geographical scale. An evaluation of the potential of developing technically and economically feasible lake-source district systems in Europe was performed in this study, with an integrated spatial explicit techno-economic assessment that accounts for different boundary conditions, such as electricity price, CO2 price and climate change. The feasibility of covering building energy demand near lakes was found to be particularly sensitive to the relationship between capital costs from network design and operational costs from heat pumps, associated electricity consumption and CO2 emissions. Results suggest a European techno-economic potential of 1.9 TWh/y considering only direct cooling and 11.3 TWh/y if thermal networks supply both direct heating and cooling by heat pumps. Respective electricity savings are 0.36 TWh/y and 0.78 TWh/y. An estimated 17% of the cooling demand near European lakes can thus be covered by viable cooling-only lake-source systems. For combined systems, the viable potential is estimated to be 7% of the total combined heating and cooling demand. Lake-source district systems are found to be particularly promising for Italy, Germany, Turkey and Switzerland. The integration of lake-source thermal networks should rarely lead to severe lake water temperature alteration and therefore not limit the techno-economic potential. The introduced methodology allows for a combined evaluation of technological, ecological and economic boundary conditions for using lakes as a source for district heating and cooling. Thereby, a more realistic estimation of their potential implementation becomes possible, enabling informed energy planning for central or decentral system configurations.

Experimental validation of a solar system based on hybrid photovoltaic-thermal collectors and a reversible heat pump for the energy provision in non-residential buildings

This work aims to validate a transient model of a solar hybrid pilot plant based on photovoltaic-thermal (PV-T) collectors integrated via thermal storage tanks with an air-to-water reversible heat pump (rev-HP). The pilot plant is in operation and provides space heating, cooling, domestic hot water (DHW) and electricity to an industrial building located in Zaragoza (Spain). The plant consists of eight uncovered PV-T collectors (2.6 kWe, 13.6 m2), two water tanks and a rev-HP with a nominal thermal power of 16 kW for heating and 10.5 kW for cooling. The validation results show that the transient model fits the experimental performance of the PV-T collectors, with an average error of -16% and 3%, for the thermal and electrical generation respectively. The accuracy of the estimated rev-HP performance depends on the operation mode. The estimated COP in cooling mode has an average error of 14%, while in heating mode has an average error of -10%. The results show that the integration of the thermal and electrical generation of the PV-T collectors with a high-performance rev-HP allows the solar PV-T system to be self-sufficient to satisfy the building energy demand.

Energy impact of heat pipe-assisted microencapsulated phase change material heat sink for photovoltaic and thermoelectric generator hybrid panel

As the importance of energy saving continues to increase owing to climate change, various Internet of Things sensors, related to building temperatures, humidity, and energy usage, are being employed in buildings, such as in building energy management systems. Aiming to satisfy the rapidly increasing electric energy demands of buildings, studies have combined various building-integrated photovoltaic (BIPV) and thermoelectric generators (TEGs) to recover sunlight and heat wasted from building envelopes. However, in most previous studies, active methods for heat rejection were applied to increase the temperature difference between both ends of the TEG and cooling of the solar panel. The actual constructability problems, such as leakage problems, were insufficiently addressed. Therefore, this study proposed a BIPV-TEG system with increased constructability and heat dissipation efficiency using a heat pipe and microencapsulated phase change material (mPCM). In addition, this study analyzed the thermal behaviors and improvements in power generation efficiency through field tests by manufacturing a prototype. The results of the outdoor experiment indicated that the BIPV-TEG-PCM prototype improved power generation efficiency by approximately 2% in the intermediate season and 2.5% in the summer relative to the efficiency of a general PV panel. Moreover, using a single TEG, approximately 3.06 Wh of the heat wasted in the building envelope can be recovered as a form of electricity annually.

Techno-Economic Analysis and Multi-Objective Optimization of Cross-Flow Wind Turbines for Smart Building Energy Systems.

This work reports a technical, economic, and environmental investigation of the possibility of using a recently developed smallscale crossflow wind turbine (CFWT) to supply the energy demand of buildings for different integration scenarios. For this purpose, three CFWT-assisted building energy system configurations with heat pumps, with and without batteries, and two-way interaction with the local grid in two residential building models in Iran and Germany are investigated. Triobjective optimization with a Nondominated Sorting Genetic Algorithm (NSGA-II) is performed for finding the optimal configuration of the energy system in different configurations. For economic assessment, the Capital Budgeting Analysis method is used with four indicators, namely, payback period (PP), net present value (NPV), internal rate of return (IRR), and profitability index (PI). The results show that due to different energy market regulations and prices, different integration scenarios and system configurations can outperform others in Germany and Iran. Overall, due to the exchange rate instability and low energy tariff in Iran, in order for the project to be feasible, either the CFWT cost must fall to below 30% of its current cost or the local electricity price should increase significantly to get a Levelized cost of energy of as low as 0.6 $ kWh-1.

Air quality and health co-benefits of climate change mitigation and adaptation actions by 2030: an interdisciplinary modeling study in Ahmedabad, India

Climate change-driven temperature increases worsen air quality in places where coal combustion powers electricity for air conditioning. Climate solutions that substitute clean and renewable energy in place of polluting coal and promote adaptation to warming through reflective cool roofs can reduce cooling energy demand in buildings, lower power sector carbon emissions, and improve air quality and health. We investigate the air quality and health co-benefits of climate solutions in Ahmedabad, India—a city where air pollution levels exceed national health-based standards—through an interdisciplinary modeling approach. Using a 2018 baseline, we quantify changes in fine particulate matter (PM2.5) air pollution and all-cause mortality in 2030 from increasing renewable energy use (mitigation) and expanding Ahmedabad’s cool roofs heat resilience program (adaptation). We apply local demographic and health data and compare a 2030 mitigation and adaptation (M&A) scenario to a 2030 business-as-usual (BAU) scenario (without climate change response actions), each relative to 2018 pollution levels. We estimate that the 2030 BAU scenario results in an increase of PM2.5 air pollution of 4.13 µg m−3 from 2018 compared to a 0.11 µg m−3 decline from 2018 under the 2030 M&A scenario. Reduced PM2.5 air pollution under 2030 M&A results in 1216–1414 fewer premature all-cause deaths annually compared to 2030 BAU. Achievement of National Clean Air Programme, National Ambient Air Quality Standards, or World Health Organization annual PM2.5 Air Quality Guideline targets in 2030 results in up to 6510, 9047, or 17 369 fewer annual deaths, respectively, relative to 2030 BAU. This comprehensive modeling method is adaptable to estimate local air quality and health co-benefits in other settings by integrating climate, energy, cooling, land cover, air pollution, and health data. Our findings demonstrate that city-level climate change response policies can achieve substantial air quality and health co-benefits. Such work can inform public discourse on the near-term health benefits of mitigation and adaptation.