Household Energy System Research Articles

Energy consumption, structural transformation and related carbon dioxide emissions of rural households on the Tibetan plateau

The use of solid fuels presents a dual challenge for rural household energy systems: transitioning to cleaner energy sources while reducing carbon dioxide (CO2) emissions. Particularly in the Tibetan Plateau (TP), which is highly sensitive to global climate change, addressing rural household energy consumption, structural changes, and related CO2 emissions is crucial for implementing targeted measures. Based on household-level survey data, this study analyzes the quantitative and structural changes in energy consumption patterns among rural households on the TP from 2001 to 2021, as well as their related CO2 emissions. The results indicate that: (1) Yak dung is the primary fuel for rural households on the TP and a major contributor to direct household emissions. (2) While cooking has shifted considerably towards a modern clean energy structure, heating continues to rely on solid fuels such as firewood, yak dung, and coal. (3) Commercial energy used by high-income groups for agriculture, transportation, and other purposes has resulted in an overall increase in total household energy consumption and related CO2 emissions. Policymakers must implement more effective measures to accelerate the transition to cleaner, modern energy sources for heating and cooking purposes.

Uncertainty analysis of the electric vehicle potential for a household to enhance robustness in decision on the EV/V2H technologies

Electric vehicles (EVs) and vehicle-to-home (V2H) technologies are regarded as promising technologies for optimizing household energy management, particularly as valuable resources for optimizing the management of distributed photovoltaic (PV) generation. However, the potential of EVs and V2H for households is closely related to their usage behavior. As EVs and V2H technologies are big investments for a family, especially for families in rural area, their benefits for households need to be analyzed in an uncertain environment to obtain robust results and mitigate investment risks for households. To achieve this, this study builds a 7-parameter stochastic model to simulate random driving patterns and employs a mixed-integer linear programming model to derive the optimal solution for the household energy system. Subsequently, the potential value of EVs and V2H technologies for a household is assessed based on three key performance indicators: economic viability, carbon emissions reduction, and energy and power autonomy. The findings of this study demonstrate that the EV is a viable technology for significantly reducing household energy expenditures, lowering household carbon emissions, and enhancing household energy and power autonomy. Furthermore, the V2H technology can provide additional benefits by further reducing carbon emissions as well as increasing the autonomy of the household energy system. However, the impact of V2H technologies on household energy expenditures is limited. The above conclusions still hold when the EV travel behavior stochasticity is taken into account, but an unexpected finding is that EV travel behavior stochasticity has almost no effect on the economy and carbon emission indicators, while significantly affecting the autonomy of the household energy system. Further investigation uncovers that the external charging price plays a critical role in determining the potential of EV + V2H technology as both stationary and mobile energy storage for households. Additionally, it is observed that the impact of EV travel behavior stochasticity on household energy expenditures becomes pronounced as the external charging price decreases, which can assist families in making reasonable decisions on configuring emerging technologies, like EV and V2H.

Household energy systems based on biomass: Tracing material flows from source to service in rural Ethiopia

Biomass remains the most important energy carrier of rural households in low- and middle-income countries, but its indoor combustion has grave impacts on human health and its extraction is associated with negative effects on ecosystems. Currently, robust and comprehensive data are lacking to trace biomass flows from ecosystems to consumption in households and quantify the related services. This impedes analyses of the social and environmental impacts of biomass use.By developing a source-to-service approach, this paper analyses the provision of domestic energy services in three villages in rural Ethiopia using a socio-metabolic perspective. We combine quantitative and qualitative methods to study the dynamics of domestic biomass use for energy and examine social and environmental implications. We find that the average household consumes 84 GJ/year of biomass (15 GJ per capita/year). Space heating, food and drinking water preparation combined require 86% of domestic energy. Improved cookstoves can reduce domestic energy use by 12%.Our results open new avenues for advancing the scientific understanding of rural energy systems dependent on biomass. These insights are essential to enhance research on sustainable energy systems in rural, bioenergy-dependent areas and may also prove useful in designing policies and innovations to improve provisioning of energy services.

An optimization capacity design method of household integrated energy system based on multi‐objective egret swarm optimization

AbstractThe construction of a household integrated energy system will reduce greenhouse gas emissions and promote sustainable development. Firstly, a household energy system is proposed, which consists of a photovoltaic, wind turbine, electrolysis cell, hydrogen storage tank, and hydrogen‐fired gas turbine. The proposed system is modelled as a bi‐objective optimization problem in which the minimum daily system economic cost, and the minimum loss of energy supply probability. Secondly, a novel multi‐objective egret swarm optimization algorithm with strong search capability and fast convergence is proposed. Thirdly, a household load corresponding to a typical day in spring is chosen as the study case. The optimization results show that the daily system economic cost with the optimal number of devices is 97.48 RMB, and the loss of energy supply probability is 8.33% at the lowest. Finally, to validate the efficiency of the proposed method, the proposed method is compared with NSGA‐II (a widely used multi‐objective evolutionary algorithm). The comparison indicates that the proposed method has a better diversity due to the random searchability. As a consequence, the proposed method can be used in the optimization capacity design of the integrated energy system.

A Kernel-Based Real-Time Adaptive Dynamic Programming Method for Economic Household Energy Systems

Modern home energy management systems (HEMSs) have great flexibility of energy consumption for customers, but at the same time, bear a range of problems, such as the high system complexity, uncertainty and time-varying nature of load consumptions, and renewable sources generation. This has brought great challenges for the real-time control. To solve these problems, we propose an HEMS that integrates a kernel-based real-time adaptive dynamic programming (K-RT-ADP) with a new preprocessing short-term prediction technique. For the preprocessing short-term prediction, we propose a gated recurrent unit-bidirectional encoder representations from the transformer (GRU-BERT) model to improve the forecasting accuracy of electrical loads and renewable energy generation. In particular, we classify household appliances into the temperature-sensitive loads, human activity sensitive loads, and insensitive/constant loads. The GRU-BERT model can incorporate weather and human activity information to predict load consumption and solar generation. For real-time control, we propose and employ the K-RT-ADP HEMS based on the GRU-BERT prediction algorithm. The objective of the K-RT-ADP HEMS is to minimize the electricity cost and maximize the solar energy utilization. To enhance the nonlinear approximation ability and generalization ability of the adaptive dynamic programming (ADP) algorithm, the K-RT-ADP algorithm leverages kernel mapping instead of neural networks. Hardware-in-the-loop experiments demonstrate the superiority of the proposed K-RT-ADP HEMS over the traditional ADP control through comparison.

Fusing domain knowledge and reinforcement learning for home integrated demand response online optimization

Electricity–gas integrated household energy systems (HESs) expose obvious system uncertainties, requiring their integrated demand response (IDR) programs to be able to adapt automatically and quickly to system changes. Deep Reinforcement Learning (DRL) methods, though having been proven promising to tackle such problems, are typically not efficient in learning from random explorations. This paper proposes a method based on DRL with HESIDR knowledge penetration. By interpreting the domain IDR knowledge as a set of control rules, the DRL agent gains learning samples from knowledge-based exploration in addition to the traditional exploration–exploitation tradeoff. Correspondingly, we develop a cooperation scheme for action selection and replay sampling, which is based on exponential probability functions to balance the penetration of knowledge-based exploration, random exploration and policy exploitation. We conduct case studies in a typical home multi-energy system environment. After determining parameters in the exponential probability functions, the learning and cost reduction performance of the proposed algorithm was tested. The results show that the method proposed in the present study spends 48.78% less training time than the standard DQN, which enables few-minute optimization on a lightweight PC. Also, the proposed method can further reduce energy bills by 26.17% compared to the uncontrolled scenario and by 9.88% to the integrated rule-based controller.

Energy flexible heat pumps in industrial energy systems: A review

This paper aims for a review of the operational and design approaches of industrial heat pump systems within industrial smart grids. This work identifies the strengths and potentials of energy flexible heat pumps integrated into the industrial energy system. Several review papers have already worked on the topic of energy flexible heat pumps in smart grids, usually addressing household energy systems. Therefore, a review of the academic literature is carried out, aiming to identify technical operation and integration approaches of energy flexible heat pumps in industrial energy systems. Furthermore, the differences between energy flexible heat pumps in small-scale systems and in large-scale energy systems are pointed out.

Dimensioning and Lifetime Prediction Model for a Hybrid, Hydrogen-Based Household PV Energy System Using Matlab/Simulink

This paper presents a model of an energy system for a private household extended by a lifetime prognosis. The energy system was designed for fully covering the year-round energy demand of a private household on the basis of electricity generated by a photovoltaic (PV) system, using a hybrid energy storage system consisting of a hydrogen unit and a lithium-ion battery. Hydrogen is produced with a Proton Exchange Membrane (PEM) electrolyser by PV surplus during the summer months and then stored in a hydrogen tank. Mainly during winter, in terms of lack of PV energy, the hydrogen is converted back into electricity and heat by a fuel cell. The model was created in Matlab/Simulink and is based on real input data. Heat demand was also taken into account and is covered by a heat pump. The simulation period is a full year to account for the seasonality of energy production and demand. Due to high initial costs, the longevity of such an energy system is of vital interest. Therefore, this model was extended by a lifetime prediction in order to optimize the dimensioning with the aim of lifetime extension of a hydrogen-based energy system. Lifetime influencing factors were identified on the basis of a literature review and were integrated in the model. An extensive parameter study was performed to evaluate different dimensionings regarding the energy balance and the lifetime of the three components, electrolyser, fuel cell and lithium-ion battery. The results demonstrate the benefits of a holistic modelling approach and enable a design optimization regarding the use of resources, lifetime and self-sufficiency of the system.

青藏高原牧区家庭能源消费对生态环境和人体健康的影响研究现状与展望

PDF HTML阅读 XML下载 导出引用 引用提醒 青藏高原牧区家庭能源消费对生态环境和人体健康的影响研究现状与展望 DOI: 10.5846/stxb202202190395 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目（72174197，41901241，71904189）；第二次青藏高原综合科学考察研究项目（2019QZKK0304） Research status and prospect of household energy consumption impact on eco-environment and human health in the pastoral of Qinghai-Tibet Plateau Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:探究家庭能源消费对生态环境和人体健康的研究是系统推进家庭能源绿色低碳转型和实现联合国2030可持续发展目标的关键。重点综述了目前家庭能源消费、影响因素及其对生态环境和居民健康影响的国际研究现状。目前研究不足主要聚焦在以下两个方面：一方面，影响家庭能源消费因素的贡献率和影响消费行为的驱动机制尚不清晰；另一方面，目前主要针对家庭能源消费或者生态系统或者环境或者居民健康其中的某一方面或者某两个方面开展研究，鲜有研究系统的考虑家庭能源消费对生态环境和人体健康影响的综合研究。基于上述研究现状，结合青藏高原的实际情况，提出未来需要在宏观层面和微观层面加强研究。宏观层面：（1）亟需厘清青藏高原家庭能源与生态环境和人体健康之间的内在联系与驱动机制，形成青藏高原牧区"家庭能源-生态效应-环境效应-健康效应"的理论分析框架；（2）构建模块化的牧区"能源-生态-环境-健康-经济"等多学科交叉的综合评估模型，形成完整的、系统的评估方法。微观层面：（1）迫切开展青藏高原家庭能源消费特征与变化规律的研究；（2）深入开展家庭能源消费特征和变化规律与生态系统的反馈机制与量化研究；（3）加强牧区室内空气污染物浓度测定，丰富高寒地区室内污染物浓度数据以及人群暴露特征及其健康风险的研究。 Abstract:Exploring the household energy consumption and its impacts on eco-environment and human health is essential to promote the green and low-carbon transition of household energy system and achieve the 2030 United Nations Sustainable Development Goals. This paper presents a review on the state of the art for the domestic and international research on the factors that influencing the household energy consumptions and the impacts on eco-environment and residents' health. At present, particular attention should be given to the following two research deficiencies. On one hand, the contribution rate of factors that influencing the household energy consumption and the mechanism that driving the consumption behavior are not well understood; on the other hand, the comprehensive studies that systematically consider the correlations among household energy consumption, ecological environment and human health are still lacking. Based on the research status and the actual situation of Qinghai-Tibet Plateau, this paper strengthens the study of household energy consumption and its impact on eco-environment and human health through macro level and micro level. On the macro level, we need to (1) clarify the correlation and driving mechanism between household energy, grassland ecology and human health and form the theoretical framework for analysis in the pastoral region of Qinghai-Tibet Plateau; (2) build a comprehensive modular assessment method around the energy-ecology-environment-health-economy nexus. On the micro -level, we should (1) carry out the research on the characteristics and dynamics changes of household energy consumption on the Qinghai-Tibet Plateau; (2) establish and quantify the feedback mechanism between household energy consumption and ecosystem; (3) strengthen the measurement of indoor air pollutant concentration in the pastoral areas and complete the data collection on the exposure characteristics and the associated health risks for the residents living in the alpine areas. 参考文献 相似文献 引证文献

Hydrogen energy of mining waste waters: Extraction and analysis of solving issues

Billions liters of wastewater are created every day from industrial and domestic locations, and while wastewater is frequently regarded as a concern, it includes the potential to be regarded as a rich supply of energy and materials. The creation of hydrogen gas by electrolysis utilizing a solar system is an alternate method for treating this residual water. Wastewater has four to five times the energy necessary to its treatment and a good source of bio-hydrogen as a feedstock chemical, clean energy vector, and a fuel generally acknowledged to play a part in the energy system's decarburization. The goal of this research was to see how well wastewater might be used to produce anaerobic hydrogen. Synthetic wastewater with high- and ordinary strength organic loadings as real-time residential wastewater with(out) a combination of food waste was examined. Hydrogen generation during sewage sludge and mining residue suspensions coupled was measured using electro dialytic methods at 50 and 100 mA. Hydrogen purity has been reached at 33 % along the electro dialytic treat of sewage sludge. Hydrogen purity reached 71 percent and 34 percent, respectively, while adding sewage sludge or effluent as enhancers in waste solutions. The maximum extraction ratios of phosphorus (71 %) and tungsten (62 %), respectively, were obtained while the technique has been performed to waste suspensions mixed with sewage sludge. This study's findings could be used to develop onsite household energy and wastewater recovery systems.

A methodology for multi-criteria assessment of renewable integrated energy supply options and alternative HVAC systems in a household

This study presents a systematic approach for a comprehensive multi-criteria assessment of household energy systems (HES). For this purpose, fourteen energy systems with different combinations of energy mix and HVAC systems are evaluated in four climates. Firstly, energy systems are analyzed for meeting the building demand for electricity, heating, cooling, and hot water by conducting annual simulations in TRNSYS. The performance criteria of HES are non-renewable energy savings, load cover factor, life cycle emissions, life cycle costs, thermal comfort, levelized cost of electricity, and levelized cost of thermal energy. Then the multi-criteria decision making (MCDM) is employed using CRITIC-TOPSIS framework to decide the best HVAC system and renewable integrated energy supply in each location. Finally, a sensitive analysis tests the stability of MCDM results by altering weights of performance indicators (PIs). The multi-criteria assessment shows that the choice of HES is strongly influenced by the climate conditions and local energy tariffs and GHG emissions. The ground source heat pump (GSHP) is a favorable HVAC system regarding energy consumption and operating GHG emissions in cold climates. On the other hand, the absorption chiller with flat plate collectors (FPCs) is suitable for energy efficiency and life cycle emissions in warm or hot climates. Overall, the energy systems with air-to-water heat pump + PV + radiator (Stockholm), brine-to-water heat pump + PV + radiator (Saarbrücken), air-to-air heat pump + PV + FPC (Quetta), and absorption chiller + PV + FPCs (Jakarta) are the best options in different locations. Furthermore, altering the weights of PIs has little influence on the choice of HES except for energy efficiency-prioritized scenario. The results indicate that while applying the MCDM concept to a building energy system, the proposed PIs provide better insight than the traditionally employed evaluation criteria.

A semantic web approach to uplift decentralized household energy data

In a decentralized household energy system comprised of various devices such as home appliances, electric vehicles, and solar panels, end-users are able to dig deeper into the system’s details and further achieve energy sustainability if they are presented with data on the electric energy consumption and production at the granularity of the device. However, many databases in this field are siloed from other domains, including solely information pertaining to energy. This may result in the loss of information (e.g. weather) on each device’s energy use. Meanwhile, a large number of these datasets have been extensively used in computational modeling techniques such as machine learning models. While such computational approaches achieve great accuracy and performance by concentrating only on a local view of datasets, model reliability cannot be guaranteed since such models are very vulnerable to data input fluctuations when information omission is taken into account. This article tackles the data isolation issue in the field of smart energy systems by examining Semantic Web methods on top of a household energy system. We offer an ontology-based approach for managing decentralized data at the device-level resolution in a system. As a consequence, the scope of the data associated with each device may easily be expanded in an interoperable manner throughout the Web, and additional information, such as weather, can be obtained from the Web, provided that the data is organized according to W3C standards.

Techno-economic analysis of a hybrid photovoltaic-thermal solar-assisted heat pump system for domestic hot water and power generation

This work investigates the techno-economic performance of a hybrid photovoltaic-thermal (PVT) solar-assisted heat-pump system for covering the electrical and hot-water demands of a three-bedroom terraced house in Belfast, United Kingdom with four occupants. This system combines a water-to-water heat pump with PVT panels to deliver both electricity and hot water for domestic consumption. The PVT array provides a source of low-temperature heat for the water-to-water heat pump, while cooling the PVT array and thus preventing the electrical efficiency degradation that occurs at higher operating temperatures. Analyses have been performed for PVT arrays of different size, including 12-panel, 20-panel and 24-panel systems. Results show that, thanks to its lower initial investment cost, the most economically viable system configuration for the household considered in this work is based on a 12-panel PVT array covering a total area of 16.3 m2. This system has the potential to produce 2.4 MWh of (gross) electricity and 2.0 MWh of hot water per year, which is equivalent to just over 30% of the electrical and 80% of the hot-water demands of the household under consideration, with the PVT array acting to reduce the electricity consumption of the heat pump in the heating system by a little over 60%. The system has lower recurring yearly costs relative to current household energy systems that use electricity from the grid and natural gas, despite having higher investment costs. It is also found that the system can reduce the investigated household's annual CO2 emissions by 910 kg per year (about 18 tonnes over a lifetime of 20 years) and that, with an electricity generation incentive rate of 5 p/kWh and a heat generation incentive rate of 21 p/kWh, the aforementioned system has a discounted payback period of 14 years.

Off-grid hybrid photovoltaic – micro wind turbine renewable energy system with hydrogen and battery storage: Effects of sun tracking technologies

The residential application of renewable energy is on the rise in sub-Saharan Africa with many of these systems using battery storage systems as back-ups; however, the adoption of hydrogen storage systems in household energy system applications has attracted few research attentions. Since the environmental impact attributed to hydrogen storage devices is small, it can serve as a complementary or alternative storage device. Using the hybrid optimisation model for electric renewables software, this study presents a techno-economic and sensitivity modelling of a solar photovoltaic (PV)/micro wind turbine/ fuel cell (FC) energy system backed up with both battery and hydrogen storage devices, under seven solar photovoltaic tracking orientations. Because of their strategic status in sub-Saharan Africa, one location each in South Africa and Nigeria were selected for the implementation of the study. The results show that the optimal energy system for the Nigerian scenario is a PV/FC/electrolyzer/battery/hydrogen storage system operated in the daily adjusted horizontal axis mode; the total net present cost and the cost of energy for this system is $9421 and $0.754/kWh respectively. As for the South African scenario, the optimal system is also a PV/FC/electrolyzer/battery/hydrogen storage system operated in the dual axis mode; its total net present cost and the cost of energy for the system is $8771 and $0.701/kWh respectively. Overall, the results show that the addition of a hydrogen storage system is technically feasible for most of the sun tracking configurations in both locations of study. Finally, the economic viability of hydrogen storage systems will be increased if the capital costs associated with the hydrogen sub-system is reduced.

Design and HIL Realization of an Online Adaptive Dynamic Programming Approach for Real-Time Economic Operations of Household Energy Systems

Stochastic, nonlinear and time-varying factors bring great challenges to accurate modeling and design of real-time (RT) energy management systems (EMSs) for modern energy systems with intermittent renewable energy sources. In this paper, we propose a novel model-free RT-EMS based on the predictive adaptive dynamic programming (ADPredictive) algorithm for energy scheduling problems in a home EMS environment. The proposed RT-ADPreditive EMS can minimize the total cost of electricity, reduce battery life loss, and maximize the utilization of renewable energy generation. In the proposed RT-ADPredictive EMS, the gated recurrent unit (GRU) neural network (NN) is employed to perform online prediction of renewable energy generation and load consumption of the household based on RT data. The RT-ADPreditive algorithm can approximate the performance index function and the optimal control law based on the RT data and predicted data for the next time step. Convergence of the proposed method is mathematically proven, and a hardware-in-the-loop (HIL) experimental platform comprising dSPACE and RT-lab is built to verify the effectiveness of the proposed RT-ADPreditive home EMS.

Model-Based Approach on Multi-Agent Deep Reinforcement Learning With Multiple Clusters for Peer-To-Peer Energy Trading

Peer-to-peer (P2P) energy trading system has the ability to completely revolutionize the current household energy system by sharing energy among residents. As the number of customers employing distributed energy resources (DERs) such as solar rooftops increase, innovation in the double auction market (DA) system is becoming more significant. In this paper, a novel model-based asynchronous advantage actor-centralized-critic with communication (MB-A3C3) approach is carried out. The model is conducted on a large scale real-world hourly 2012–2013 dataset of 300 households in Sydney having rooftop solar systems installed in New South Wales (NSW), Australia. Results reveal that the MB-A3C3 approach outperforms other reinforcement learning methods (MADDPG and A3C3), producing lower community energy bills for 300 households. In closing the gap between the real-world and theoretical problems, the algorithms herein aid in reducing customers’ electricity bills.

분산전력 제어를 위한 가정용 연료전지 시스템 설계

Energy used at home accounts for about 20% of the total energy used in the country, so efficient use of electricity used at home is very important for national energy conservation and reduction of pollution factors. Therefore, among the distributed fuel cells, the household fuel cell (RPG: Residential Power Generator) that is receiving the most attention is expected to be able to solve heating and cooling in the house and use electricity in 10 years without external help. In general, electric energy production always accompanies the production of heat, but the transport of heat for heating is very difficult. In the case of conventional power generation, electricity is generated from large-scale power generation and hot water is received from a boiler at home, whereas the fuel cell system has the advantage of being able to use heat because it produces electricity and heat directly at home. Therefore, the home is the best place to produce and consume heat and electricity at the same time. It is the most ideal place to use the supplied city gas to produce and use heat and electricity using a locally installed small fuel cell power generation system with low exhaust gas and no noise. It could be a household energy system. In this paper, the operation characteristics of a home fuel cell system for distributed power control are verified by designing and manufacturing a fuel cell simulator, a DC/DC step-up converter, and an inverter suitable for a home cogeneration system.

The challenges of rising income on urban household carbon emission: do savings matter?

As an essential household economic factor, household savings with increasing income affect household energy access. However, there is limited understanding of the household savings accumulation differs the impact of income on household energy and related CO2. Based on the panel data of 284 cities in China, a threshold regression model is constructed to investigate the non-linear effects of income on urban household carbon emissions at different saving levels. We found that the heterogeneity of residential savings substantially mattered the income effect on urban household carbon emission. Especially, low-saving households struggled to obtain clean energy, needed more government intervention to replace the fossil fuel system with a clean household energy system. By comparison, middle- and high-saving households could easily transit towards a clean energy system, including natural gas and electricity. Moreover, residential income strongly drives the rising household electricity and related CO2 at the upper-middle saving level. High-saving households showed high sensitivity to heating energy in northern China. In addition, an in-depth analysis of challenges faced by the energy consumption of Chinese urban households in coping with climate change is carried out, and policy recommendations in most undeveloped regions of China and other counties with high-carbon energy structures are put forward accordingly.

Towards zero energy solar households – A model-based simulation and optimization analysis for a humid subtropical climate

Energy conservation and efficiency in the residential building sector is an important field to control carbon emissions worldwide. Solar energy integration in domestic buildings decreases the grid dependency and carbon emissions. This paper investigates techno-economic feasibility, energy, exergy, and life cycle emissions analysis of solar energy integration to achieve an annual net-zero energy balance for a household. A local survey is carried out in the regions of Pakistan to define the representative building structure, services, and schedules. A representative household is then analyzed. TRNSYS is used for modelling and annual transient simulation of household thermal load and energy systems. TRNSYS-GenOpt co-simulations determine the optimal insulation thickness of the building envelope to minimize the thermal load. The on-site renewable energy supply system consists of solar PV, solar space heating, solar domestic hot water generation, and solar absorption cooling. The envelope insulation results in a 37.2% reduction in annual thermal load. Solar PV system supplies 93.4% of annual electricity demand. Solar collectors provide hot water during winter and thermal comfort during 92.4% of air conditioning hours. Energy and exergy analyses are used to evaluate the thermal performance of solar PV, thermal collectors, and absorption system. The life cycle cost analysis performed for a conventional and the proposed building shows that the net present value of the zero-energy solar household (ZESH) becomes lower than that of the conventional household in 8 years. Also, the life cycle emissions are calculated for conventional and ZESH. The CO2 emissions of ZESH are only 8.7% of that of a conventional household. This study reveals that a ZESH is technically as well as economically viable in humid subtropical climate regions.

Economic Analysis of Hydrogen Household Energy Systems Including Incentives on Energy Communities and Externalities: A Case Study in Italy

The building sector is one of the key energy consumers worldwide. Fuel cell micro-Cogeneration Heat and Power systems for residential and small commercial applications are proposed as one of the most promising innovations contributing to the transition towards a sustainable energy infrastructure. For the application and the diffusion of these systems, in addition to their environmental performance, it is necessary, however, to evaluate their economic feasibility. In this paper a life cycle assessment of a fuel cell/photovoltaic hybrid micro-cogeneration heat and power system for a residential building is integrated with a detailed economic analysis. Financial indicators (net present cost and payback time are used for studying two different investments: reversible-Solid Oxide Fuel Cell and natural gas SOFC in comparison to a base scenario, using a homeowner perspective approach. Moreover, two alternative incentives scenarios are analysed and applied: net metering and self-consumers’ groups (or energy communities). Results show that both systems obtain annual savings, but their high capital costs still would make the investments not profitable. However, the natural gas Solide Oxide Fuel Cell with the net metering incentive is the best scenario among all. On the contrary, the reversible-Solid Oxide Fuel Cell maximizes its economic performance only when the self-consumers’ groups incentive is applied. For a complete life cycle cost analysis, environmental impacts are monetized using three different monetization methods with the aim to internalize (considering them into direct cost) the externalities (environmental costs). If externalities are considered as an effective cost, the natural gas Solide Oxide Fuel Cell system increases its saving because its environmental impact is lower than in the base case one, while the reversible-Solid Oxide Fuel Cell system reduces it.