Heat Demand Research Articles

Fuel constrained sustainable water-gas-heat-power generation expansion planning of isolated microgrid

On account of slowly reducing of fossil-fuel, profitable utilization of available fossil-fuel to produce electric power is a major concern for electric power producers. Here, short-period fuel constrained power, heat, natural gas and clean water augmentation planning (FCPHGWAP) of isolated microgrid considering carbon capture is presented. FCPHGWAP finds out the most economical and consistent augmentation plan to meet the prophesied power, heat, natural gas and clean water demand over a short-term time horizon whilst satisfying several technical as well as societal constraints. An archetypical system having extant diesel generators (DGs), PVT panel, wind turbine generator (WG), micro-cogeneration unit (MCU), battery energy storage system (BESS), plug-in electric vehicles (PEVs), thermal energy storage system, natural gas storage unit, carbon capture unit (CCU), electrolyzer, hydrogen storage unit and aspirant PVT panel, WGs, MCUs and PEVs is taken into consideration. Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients and grey wolf optimization are used to solve this FCPHGWAP problem. It is observed that net present value having fuel constraints is less than that of without fuel constraints.

Assessing the energy performance certification effectiveness for the Spanish building stock in response to recent climate change data

This comprehensive study analyses the energy demand for heating and cooling in the building stock across different climatic zones in Spain, incorporating the effects of weather evolution over the past 30 years (from 1992 to 2022). In evaluating 7200 cases across four climatic years and six climatic zones which include six Spanish cities, the analysis examines the construction characteristics of the building stock. The focus is on the thermal envelop properties under two scenarios: the current state and a renovated state that complies with the new Spanish building code (in agreement with the EPBD EU-Directive EU/2024/1275).The study’s findings reveal potentially significant changes in energy demand due to climate. The heating demand decreases significantly due to increasing temperatures ranging from 17% in Madrid up to 37% in Almería. This effect is contrasted by a substantial increase in cooling demand, which has surged 223% in Barcelona, a surge closely linked to higher summer temperatures.The paper concludes that the current energy certification scheme fails to provide a real overview of the energy performance and needs of buildings in Spain. In particular, it underestimates actual sensible cooling demand by 56% in Barcelona and 41% in Almería. Additionally, the failure of existing building regulations to address the issue of indoor overheating is proven.

Electrification of Heat Demand: An Estimation of the Impact on the Future Italian Energy System

Electrification of Heat Demand: An Estimation of the Impact on the Future Italian Energy System

Optimisation of Integrated Heat Pump and Thermal Energy Storage Systems in Active Buildings for Community Heat Decarbonisation

The electrification of residential heating systems, crucial for achieving net-zero emissions, poses significant challenges for low-voltage distribution networks. This study develops a simulation model to explore the integration of heat pumps within active building systems for community heating decarbonisation. The model optimises heat pump operations in conjunction with thermal energy storage units to reduce peak demand on low-voltage networks by using real-time measured electricity demand data and modelled heat demand data for 76 houses. The study employs an algorithm that adjusts thermal storage charging and discharging cycles to align with off-peak periods. Three scenarios were simulated: a baseline with unoptimised heat pumps, a fixed threshold model, and an active building model with daily optimised thresholds. The results demonstrate that the active building model achieves a 21% reduction in peak demand on the low-voltage substation compared to the baseline scenario; it also reduces the total electrical energy consumption by 12% and carbon emissions by 17%. The fixed threshold scenario shows a 16% improvement in peak demand reduction, but it also shows an increase in energy consumption and emissions. These findings highlight the potential of active buildings to enhance the efficiency and sustainability of residential energy systems, marking a significant step toward decarbonising residential heating while maintaining grid stability.

Data-driven stochastic dynamic economic dispatch for combined heat and power systems using particle swarm optimization

In modern power systems, the uncertain behavior of renewable energy sources (RESs) may result in deviations from the optimal operating dispatches for the various power sources. The electric power generation from Combined Heat and Power (CHP) units is characterized by high efficiency with less pollution. To use CHP units more efficiently, the dynamic economic dispatch problem is applied to obtain the optimal power and heat sources’ outputs to satisfy heat and power demands while meeting the different operational constraints. In this paper, data-driven stochastic optimization is used to model these uncertainties utilizing the Generative Adversarial Networks in scenario generation that are accurate and do not require fitting models or modeling probability distributions. Then, the Fast Forward Selection technique is used to decrease the number of scenarios to improve system tractability. The results obtained indicate that the variability in electrical load, photovoltaic (PV), and wind turbine (WT) output significantly impacts the overall operational costs of the power system. Therefore, it is essential to incorporate the uncertainties associated with electrical load, PV, and WT when aiming for accurate results in economic dispatch. the study found that the overall operation cost of the dynamic economic dispatch of the electrical system for 24 hours after integrating RES achieved a daily savings of 2.5 % in the first scenario. Furthermore, the findings demonstrate that RESs positively contribute to reducing the total operational costs of the power system. The economic dispatch of CHP systems is influenced by the integration of RESs and the uncertainties associated with electrical parameters.

Research on Energy-Saving Transformation of Rural Residential Building Envelope Structures and Heating Modes in Northeast China

Rural areas in Northeast China present a large demand for heating energy in winter, but there are problems in such areas with poor thermal performance of building envelopes and poor indoor thermal comfort. In addition, coal-fired boilers are still widely used. China’s “Dual-Carbon Goals” and “Clean Heating” policy call for the creation of a green and comfortable living environment for rural residential buildings. This paper considers the impact of the improvement of the thermal performance of envelope structures on the initial investment of the transformation program and the rated power of the ASHP and proposes an energy-saving transformation method to replace traditional coal-fired boilers with the ASHP on the basis of the improvement of the thermal performance of envelope structures. By establishing a typical rural residential building model in Northeast China, this energy-saving method is simulated based on TRNSYS. The results show that the payback period of investment of the transformation method of “envelope structure + heating system” is not superior to that of the transformation method of only improving the thermal performance of the envelope structure, but it has advantages in the comprehensive life-cycle benefits and it has great advantages in improving the satisfaction of rural residents in the use of heating systems.

Techno-economic optimization and feasibility of PCM-based seasonal thermal energy storage systems for district heating and cooling

Phase change materials (PCM) are an attractive seasonal thermal energy storage solution for load shifting due to relatively high energy density. Nevertheless, the choice of the right design parameters, such as storage size and phase-change temperature, is nontrivial, as these are tightly linked to the expected seasonal operation of the storage. This paper presents a combined design and operation optimization framework for sizing PCM-based seasonal thermal storage, which can capture dynamic behaviour of the storage in sensible and latent phases, and its impact on the efficiency of connected heat pumps and chillers. The proposed method, formulated as a mixed integer quadratically-constrained programming problem, was applied to a case study of heating-dominated district. It was found that the optimal PCM melting temperature equals to the heating demand supply temperature, thus removing the need of a heat pump to discharge the storage. The optimal operation of storage requires a full charging and discharging cycle of both latent and liquid phase. Higher CO2 emission price does not lead to a significant reduction of CO2 emissions, but the storage size does, leading to a higher heating coverage ratio of the storage, and consequently CO2 emissions and cost savings, which can be further enhanced with PV integration. The economic analysis indicates that, unless other benefits such as storage volume reduction are accounted for, PCM cost should drop by a factor 4 to make this solution economically viable for seasonal storage.

Assessing the viability of using ground-air heat exchangers during winter months in different climatic conditions of Iran

ABSTRACT Ground combined with subsurface tubes as heat exchangers, known as Ground-Air Heat Exchangers (GAHEs), have recently gained attention for heating and cooling. However, several simplifications were used to capture complex airflow characteristics in previous studies which potentiate inaccuracy in their results. This gap is addressed in this study by developing a 3D transient model incorporating realistic heat dynamics and turbulent airflow simulation using the k-epsilon model over a complete temperature cycle. The experimental result serves to corroborate the model’s accuracy. Further, the model is utilised to assess energy-conversion potential of GAHEs for heating a generic building during January in four Iranian cities with different climates. Findings show distinct differences between the GAHE’s energy-conservation capacity in these cities. The GAHE can conserve up to 714.6 kWh of energy monthly in the coldest climate and reduce peak heating loads by up to 36.0% in the hottest climate, with these averages observed across all soil types. GAHE energy saving is increased up to 27.7% when the soil around it is silt with a higher conductivity. Additionally, an adverse impact on building’s heating is created by the GAHE in some cases, highlighting the significance of hourly management of the system. Highlights A 3D transient numerical scheme was created based on fluid mechanics principles to simulate a multitube GAHE, and the solution was verified using empirical data. The effectiveness of the GAHE and its ability to reduce the maximum heating demand of a particular building for different climates within Iran in January were examined. The impact of soil characteristics on the energy-saving capability of the GAHE was found to be significant in the four cities under study, and a distinct difference in heat flux to the working fluid was discovered in different climates.

Long-term climate-based sizing and economic assessment of air-water heat pumps for residential heating

This study presents a novel methodology for sizing air–water heat pumps (ASHP) for space heating and hot water supply in buildings. Utilizing long-term local climatic data, the developed tool determines the coefficient of performance (COP) and feasibility of ASHP. The sizing algorithm incorporates peak heat demand, unitary final energy demand, and seasonal heat demand across various detached house sizes. Annual local heating system operation time and key economic indicators are derived for each case across 3 European countries. A new approach is precise investigation of 10-year outdoor temperatures and apply it to heat pump (HP) modeling, which enabled a distinction between so-called warm and cold years. The results underscore substantial variations in net present value (NPV) between warm and cold years, illustrating the critical influence of precise climate data on the performance of HPs. The study revealed that for a 160 m2 house in Poland, annual electricity consumption by HPs ranged from 3736 kWh during warm years to 12,908 kWh during cold years, showing a significant variation in performance based on climate conditions. The comprehensive tool allows for precise adjustments of heating systems to actual local climate conditions, improving both economic and environmental outcomes. This comparative assessment aids in the optimal selection of HPs, ensuring energy efficiency and sustainability in residential heating systems. Building on studies from various climatic regions, this work addresses the knowledge gap in performance and explores the feasibility of HP in a changing climate using precise modeling techniques.

Impact of space utilization and work time flexibility on energy performance of office buildings

This study investigates the impact of different space utilization on energy use intensity, heating load, cooling load, and thermal comfort of occupants using a combination of empirical data gathering and simulation-based studies. The increasing worldwide preoccupation with energy consumption and environmental sustainability has led to increased attention on optimizing interior spaces to mitigate total energy demand. The considered case study for conducting this research was the Norwegian living lab namely Zero Emission Building (ZEB) Flexible Lab in Trondheim, Norway. In this study, 10 different scenarios of occupancy schedules based on flexible arrangements from standard workweeks to extensive remote work configurations were designed and analyzed using IDA ICE 5.0. The findings demonstrate significant reductions in energy use across scenarios with increased teleworking and compressed work weeks. The remote scenario achieved the most significant decrease in Energy Use Intensity (EUI), with a reduction of 46 % compared to the base case. Similarly, the implementation of flexible hours and remote working in scenarios resulted in a reduction of electric heating demand by up to 23 %, underscoring the potential of occupancy-based strategies in enhancing building energy efficiency. However, uncomfortable hours increased by 59 % in the 2-day remote working scenario compared to the base case, demonstrating the need to consider climate conditions when implementing remote work. The research offers valuable insights into the complex connections between flexible arrangements and energy efficiency, considering many elements such as occupancy schedule and use dynamics. This article offers a comprehensive analysis that may give architects, building managers, and policymakers valuable insights. This study contributes to the developing sustainable architecture by emphasizing the impact of dynamic occupancy on the energy performance of office buildings.

Thermal performance of a novel composite phase change material for solar-assisted air source heat pump packed bed thermal energy storage application

The thermal properties of the heat storage medium in the solar-assisted air source heat pump (SAASHP) systems must be aligned with the system’s heat generation temperature while satisfying the heat demand. In this study, a novel composite phase change material (CPCM) for efficient heat storage in SAASHP systems was developed. Our investigation revealed that the incorporation of 8 wt% KNO3, 1.5 wt% thickener, and 1.0 wt% Al2O3 nanoparticles into sodium acetate trihydrate resulted in the manifestation of desirable properties suitable for SAASHP systems. The compound exhibited a suitable melting point of 48.66 °C, high latent heat of 235.97 kJ/kg, low supercooling degree of 2.25 °C, and good thermal conductivity of 0.905 W/(m·K). Furthermore, the novel CPCM demonstrated exceptional thermal stability, with only an 8.64 % loss of latent heat after undergoing 200 cycles, while maintaining a supercooling degree within a range of 6 °C for each cycle. Moreover, molecular dynamics simulation results further indicated that the presence of KNO3 weakened the interaction between CH3COONa molecules and water molecules, leading to a reduction of the melting point. Additionally, numerical analysis revealed that the total heat storage capacity and exergy storage capacity of the packed bed thermal energy storage (PBTES) system filled with the novel CPCM are 358.8 MJ and 25.1 MJ, respectively. With an increase in the heating power of the SAASHP system from 10 kW to 30 kW, the cycle heat storage time of the PBTES system can decrease by 38.8 %, while an increase in the flow rate from 2 m3/h to 6 m3/h can reduce the time by 26.3 %. These findings have significant implications for the selection and composition design of thermal storage materials in SAASHP systems.

Climate Change and Building Renovation: The Impact of Historical, Current, and Future Climatic Files on a School in Central Italy

Climate change significantly affects the operating environment of buildings. These changes impact both energy efficiency and occupants’ comfort and remain crucial even in building restoration, where design decisions typically rely on historical data, yet performance depends on anticipated future scenarios. The present work evaluates the impact of different climate datasets on dynamic energy simulations for an educational building in Central Italy, focusing on estimating heating demands across historical, current, and future climatic scenarios. The assessment considers both the building’s current state and potential energy-efficient retrofits. Initially, various meteorological datasets, including measured and model-generated data, are selected to predict key weather parameters. The analysis reveals the potential and limitations of regional climate models (RCMs) in estimating these variables, with the MM5 dataset emerging as the most reliable. Subsequently, the energy performance of the reference building and its vulnerability to climate change are assessed. Our results show significant differences in energy demand based on construction periods, with the oldest section consuming 29% to 54% more energy monthly than the newer sections. Moreover, using non-representative climatic files can lead to prediction errors of up to 199%. Finally, the building’s energy behaviour is analysed under future climate conditions by generating typical meteorological years (TMYs) for 2030, 2050, and 2070. This analysis evaluates the energy requirements for both existing and retrofitted building configurations. The findings confirm that retrofit interventions with high-performance insulation and upgraded windows significantly enhance the building’s energy efficiency and resilience to future climate conditions, leading to annual energy savings of 50% to 57%.

Investigation of a Novel Thermochemical Reactor for Medium- and Low-Temperature Heating Applications in Buildings

This research paper investigates a novel triangular honeycomb thermochemical energy storage reactor for low- and medium-temperature applications in buildings, emphasizing its potential to enhance sustainable heating. Using a validated 3D numerical model, the reactor’s performance is analyzed in depth across various configurations, focusing on key parameters such as energy storage density, pressure drop, internal air flow distribution, and round-trip efficiency. Results show that the reactor achieved an energy storage density of 872 kJ/kg and a round-trip thermal efficiency of 41.51% under optimal conditions. Additionally, the triangular honeycomb reactor (30°, 60, and 90°) configuration achieved the highest temperature lift of 48.7 °C. In a feasibility analysis for residential heating in northern China, the reactor with 30°, 60°, and 90° angles required 24.91% less volume to meet daily heating demands compared to other configurations. This study contributes valuable insights for the development of efficient, low-carbon heating solutions for low- and medium-temperature applications in buildings, offering interesting advancements in the field of thermochemical energy storage technology.

Decarbonizing European Industry: A Novel Technology to Heat Supply Using Waste and Renewable Energy

This study examines the potential for the smart integration of waste and renewable energy sources to supply industrial heat at temperatures between 150 °C and 250 °C, aiming to decarbonize heat demand in European industry. This work is part of a European project (SUSHEAT) which focuses on developing a novel technology that integrates several innovative components: a Stirling cycle high-temperature heat pump (HTHP), a bio-inspired phase change material (PCM) thermal energy storage (TES) system, and a control and integration twin (CIT) system based on smart decision-making algorithms. The objective is to develop highly efficient industrial heat upgrading systems for industrial applications using renewable energy sources and waste heat recovery. To achieve this, the specific heat requirements of different European industries were analyzed. The findings indicate that industrial sectors such as food and beverages, plastics, desalination, textiles, ceramics, pulp and paper, wood products, canned food, agricultural products, mining, and chemicals, typically require process heat at temperatures below 250 °C under conditions well within the range of the SUSHEAT system. Moreover, two case studies, namely the Pelagia and Mandrekas companies, were conducted to validate the effectiveness of the system. An analysis of the annual European heat demand by sector and temperature demonstrated that the theoretical potential heat demand that could be met by the SUSHEAT system is 134.92 TWh annually. Furthermore, an environmental impact assessment estimated an annual significant reduction of 19.40 million tonnes of CO2 emissions. These findings underscore the significant potential of the SUSHEAT system to contribute to the decarbonization of European industry by efficiently meeting heat demand and substantially reducing carbon emissions.

Thermal energy and thermo-economic analysis of PCM-TES for space heating based on low-temperature waste heat: An experimental and numerical study

Great potential for using low-temperature waste heat for energy savings has been revealed in recent years. In this study, a novel PCM-TES unit integrated into a fresh air system was proposed to effectively utilize low-temperature waste heat to address shortcomings of preheating outdoor air for space heating. An experimental setup was built to investigate the heat storage and release performance of proposed unit. Well-validated CFD simulations further parametrically studied PCM thermal characteristics to HTF’s velocity and temperature changes. Energy and thermo-economic analyses were employed to evaluate unit potentials across three Chinese cities with different climate zones. The results showed that charging process was completed in 142–253 min at water temperatures of 55–60 °C; discharging process efficiency increased by 36.10% as the air velocity increased from 2 to 4 m/s. EPI revealed that, theoretically, the system could handle up to 28.2% of the heating demand. Energy and economic analysis indicated the system’s significant potential in colder climates, with Harbin achieving average energy savings of 7.93% and 13.48% over Qingdao and Chongqing, respectively, and cost savings up to 67.65% and 229.61% compared to those cities. This study is expected to shed light on the utilization of low-temperature waste sources for indoor space heating.

Parametric and optimization analyses of a dynamic trombe wall incorporating PCM to save heating energy under cold climate zones

The Trombe wall system incorporating a dynamic PCM layer (DTWPS) has been recognized as a viable envelope to reduce the energy demand for heating effectively. However, properly selecting PCMs in the DTWPS is complicated and much less studied. Therefore, parametric and optimization analyses of the PCM thermal properties of a DTWPS are carried out for heating energy saving under cold climate zones using a validated CFD model, multiple linear and nonlinear regression models, and the particle swarm optimization (PSO) algorithm. The results show nonlinear relationships between the four critical PCM thermal properties and the energy saving, time lag, and thermal comfort. Moreover, the optimal values of the melting point, latent heat, density, and thermal conductivity of the PCM are 26.4 °C, 200 kJ/kg, 2080 kg/m3, and 1.34 W/(m·K), respectively. Meanwhile, the energy saving for the test room equipped with the DTWPS with optimal thermal properties could reach 100 % compared to that for the traditional reference Trombe wall. With available PCMs, heating energy consumption can be saved by up to 90 % or more. Further, the DTWPS demonstrates a reduced PCM mass compared to other passive walls.

Analysis of the Impact of Funding Policies for the Energy Refurbishment of Buildings Using Dynamic Simulations

Dynamic simulations can accurately estimate the thermal demands for space heating and cooling in buildings, as well as the energy and economic performance of specific energy refurbishment actions. This study aims to evaluate the energy and economic savings resulting from the adoption of particular energy measures applied to a cluster of residential condominium buildings, also considering some possible Italian funding policies. To this scope, dynamic simulation models of several buildings with different features in terms of geometry, shape, and thermo-physical properties are considered. The selected buildings are representative of the most common buildings in the city of Naples, Southern Italy. Two scenarios regarding the possible penetration of the refurbishment actions are considered: the “25% scenario”, where 25% of buildings in the Naples municipality adopt the selected measures, and the “100% scenario”, where all buildings adopt such energy refurbishment actions. The results of the simulations, reported over different time periods, compare the economic, energy, and environmental benefits of the specific energy measures. This study evaluates the replacement of conventional natural gas-fired boilers with natural gas-fired condensing boilers, as well as the use of thermal insulation on the external walls of the buildings. The primary energy demand for space heating decreased by 28% when the proposed energy measures were implemented in all buildings of the Naples municipality.

Fabric Retrofit of a Hard-to-Treat, Pre-1919 House in Preparation for Heat Pump Use

The uptake of low-carbon domestic heating systems is a significant strategy towards global targets of reducing greenhouse emissions and mitigating climate change. Pre-1900 hard-to-treat houses will still be existing in the next 25 years, and they have the greatest potential for improved energy-efficiency. This study investigates the potential of fabric retrofit to prepare an older, hard-to-treat house type for heat pump use. The house type was modelled in DesignBuilder and validated using the Ulster University test house. The wall, loft and floor insulation, as well as glazing upgrades can yield up to 50% reduction in heating demand for a hard-to-treat house type, thereby preparing it for heat pump installation. Additionally, upgrading insulation and glazing in line with the current building standards was cost-effective, with a net present value of approximately GBP 12,000.

Environmental and energy analysis of the renovation of social housing buildings under various climate change scenarios and user profiles

The renovation of social housing buildings, a priority for the European Union, must be assessed under a Climate Change (CC) context. This work, centred on the user and the environmental impact, analyses the climate resilience of the renovation of the social housing building stock of the Basque Country, located in Northern Spain. In this region, characterised by cold winters and mild summers, CC projections indicate a moderate warming even in the worst-case scenario. The assessment of passive renovation actions indicates that reducing heating demand is key no matter the CC scenario. The reduction achieved in heating demand with deep passive renovations is up to 82.2 % when the worst CC scenario is considered. Indeed, since low-income tenants occupy these buildings, it has been found that global warming would help users achieve indoor standard conditions in a more effective way than with passive renovation actions. Consequently, to improve indoor conditions independently of the CC scenario, active renovation measures must also be considered. Furthermore, the thermal comfort analysis proved that the risk of overheating in this region is negligible, with less than 4 % of the yearly hours above 26 °C. Finally, the Life Cycle Impact Assessment shows that the environmental impact of the renovation is short when compared to the impact of the operational stage. The use of conventional or ecological materials during renovation would save up to 6.5 % of Non-Renewable Primary Energy Consumption and 3.7 % of CO2 emissions during the life-cycle of the building.

Accurate evaluation on peak shaving capacity of combined-heat-and-power thermal power units based on physical information neural network

The high proportion of new energy requires the power system to have sufficient flexibility and peak shaving capacity. The combined-heat-and-power thermal power unit is one of the main flexibility resources. Accurately evaluating the peak shaving capability from thermal power units is of great significance for power system scheduling. However, there are some problems in the previous evaluation models. Fully mechanistic models may exhibit poor accuracy due to component degradation and other reasons, while fully data-driven models may exhibit poor generalization performance due to limited training conditions. To address these issues, this paper first uses physical information neural network to fuse thermal power mechanisms and operational data, and constructs a high-precision and strong-generalization power output evaluation model. Then, error indicators and generalization indicators are used to validate the effectiveness and superiority of the model. In the test set, the mean absolute error, root mean square error and Pearson correlation coefficient of the model are1.633 MW, 1.971 MW and 0.9643, respectively. Based on this model, the peak shaving capacity range of the target thermal power plant under different heating demands is calculated. Finally, the experiment analyzed the real-time peak shaving capability and peak shaving margin.