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As a relatively mature technology, biomass molded fuel (BMF) is widely used in distributed and centralized heating in China and has received considerable government attention. Although many BFM incentive policies have been developed, decreased domestic traditional fuel prices in China have caused BMF to lose its economic viability and new policy recommendations are needed to stimulate this industry. The present study built a regionalized net present value (NPV) model based on real production process simulation to test the impacts of each policy factor. The calculations showed that BMF production costs vary remarkably between regions, with the cost of agricultural briquette fuel (ABF) ranging from 86 US dollar per metric ton (USD/t) to 110 (USD/t), while that of woody pellet fuel (WPF) varies from 122 USD/t to 154 USD/t. The largest part of BMF’s cost composition is feedstock, which accounts for up 50%–60% of the total; accordingly a feedstock subsidy is the most effective policy factor, but in consideration of policy implementation, it would be better to use a production subsidy. For ABF, the optimal product subsidy varies from 26 USD/t to 57 USD/t among different regions of China, while for WPF, the range is 36 USD/t to 75 USD/t. Based on the data, a regional BMF development strategy is also proposed in this study.

The decarbonisation of heating in the United Kingdom is likely to entail both the mass adoption of heat pumps and widespread development of district heating infrastructure. Estimation of the spatially disaggregated heat demand is needed for both electrical distribution network with electrified heating and for the development of district heating. The temporal variation of heat demand is important when considering the operation of district heating, thermal energy storage and electrical grid storage. The difference between the national and urban heat demands profiles will vary due to the type and occupancy of buildings leading to temporal variations which have not been widely surveyed. This paper develops a high-resolution spatiotemporal heat load model for Great Britain (GB: England, Scotland a Wales) by identifying the appropriate datasets, archetype segmentation and characterisation for the domestic and nondomestic building stock. This is applied to a thermal model and calibrated on the local scale using gas consumption statistics. The annual GB heat demand was in close agreement with other estimates and the peak demand was 219 GWth. The urban heat demand was found to have a lower peak to trough ratio than the average national demand profile. This will have important implications for the uptake of heating technologies and design of district heating.

Deployment of photovoltaic (PV) systems has recently been encouraged for large-scale and small-scale businesses in order to meet the global green energy targets. However, one of the most significant hurdles that limits the spread of PV applications is the dust accumulated on the PV panels’ surfaces, especially in desert regions. Numerous studies sought the use of cameras, sensors, power datasets, and other detection elements to detect the dust on PV panels; however, these methods pose more maintenance, accuracy, and economic challenges. Therefore, this paper proposes an intelligent system to detect the dust level on the PV panels to optimally operate the attached dust cleaning units (DCUs). Unlike previous strategies, this study utilizes the expanded knowledge and collected data for solar irradiation and PV-generated power, along with the forecasted ambient temperature. An expert artificial intelligence (AI) computational system, adopted with the MATLAB platform, is utilized for a high level of data prediction and processing. The AI was used in this study in order to estimate the unprovided information, emulate the provided measurements, and accommodate more input/output data. The feasibility of the proposed system is investigated using actual field data during all possible weather conditions.

One of the repeating themes around the provision of the knowledge and skills needed for delivering sustainable communities is the idea of a “common language” for all built environment professionals. This suggestion has been repeated regularly with each new political and professional review within and between different sectors responsible for the delivery of sustainable communities. There have been multiple efforts to address academic limitations, industry fragmentation and promote more interdisciplinary working and sector collaboration. This research explored the role of skills for sustainable communities, particularly within the higher education (HE) sector, and the responses to support the development of a “common language of sustainability” that can be shared between different sectors, professional disciplines and stakeholders. As an interdisciplinary group of academics and practitioners working with the HE sector in the North East of England, we evaluate the progression of sector collaboration to develop a quintuple helix model for HE. We use this as a suitable framework for systematically “mapping” out the mixed sector (academic, public, business, community and environmental organisations) inputs and influences into a representative sample of HE degree modules that are delivered from foundation and undergraduate to postgraduate levels, including examples of part-time and distance-learning modules. We developed a cascade of models which demonstrate increasing levels of collaboration and their potential positive impact on the effectiveness of education on sustainable communities. The methodological assessments of modules were followed by semi-structured group reflective analysis undertaken through a series of online workshops (recorded during the Covid19 lockdown) to set out a collective understanding of the generic skills needed for the delivery of sustainable communities. These generic skills for sustainable communities are presented as a pedagogical progression model of teaching activities and learning outcomes applied to the levels within HE. We propose sustainability education principles and progressions with the hope that they can have an impact on the design or review of current degree modules and programmes. The paper informs future sustainability research to be grounded in holism and systems thinking; better understanding of values, ethics, influencing and political impact; and procedural authenticity.

This paper proposes the computation and assessment of optimal tilt and azimuth angles for a receiving surface, using a mathematical model developed at the University of Tomsk, Russia. The model was validated and analyzed for the Nuevo León State, Northeast Mexico, utilizing a set of metrics, comparing against satellite data from NASA. A point of interest in the city of Monterrey was analyzed to identify orientation patterns throughout the year for an optimal solar energy gathering. The aim is providing the best orientation tilt angles for photovoltaic or solar thermal panels without tracking systems. In addition, this analysis is proposed as a tool to achieve optimal performance in sustainable urban development in the region. Based on the findings, a set of optimal tilt and azimuth surface angles are proposed for the analyzed coordinates. The aim is to identify the optimal performance to obtain the maximum solar irradiation possible over the year for solar projects in the region. The results show that the model can be used as a tool to accelerate decision making in the design of solar harvesting surfaces and allows the design of discrete tracking systems with an increase in solar energy harvesting above 5% annually.

PV-based mini-grids are identified as a feasible and, often, only economically viable option for the electrification of Kenyan remote and scattered rural areas, where connection to the national grid is challenging, and the related costs are high, if not prohibitive. This paper presents the analysis of typical Kenyan PV mini-grids by using some results of the work in the project “Reliable, Efficient and Sustainable Mini-Grids for Rural Infrastructure Development in Kenya (RESILIENT)”. After presenting average annual and seasonal daily load profiles of residential and small commercial mini-grid customers identified from the measured demands, the paper introduces the main mini-grid components and their models, including a simplified, but reasonably accurate, model of a mini-grid battery storage system based on the manufacturer’s charge–discharge curves. All mini-grid components are assembled in a scalable and easily reconfigurable simulation model of an actual Kenyan PV mini-grid, and they are implemented for the evaluation of PV mini-grid performance and the potential for expansion and connection of additional residential and small commercial customers. During the validation of the developed simulation model using available measurement data, an empirical approach for adjusting the PV system output power is specified for a more accurate match with the measurements. The presented results indicate the importance of the information on the actual control algorithms and control settings of the mini-grid energy management systems, on the thermal dependencies and characteristics of both PV generation system and battery storage system, and on the availability of on-site measurements of temperature and input solar irradiance. The developed PV mini-grid model can be used for further analyses, such as to study the techno-economic performance of different mini-grid configurations, to identify the optimal sizing of mini-grid components, and to specify efficient control and operation schemes based on the locally available resources.

Located in East Africa, Uganda is one of the most economically deprived countries that is likely to be dramatically affected by climate change. Over 50% of Ugandan families live in single-roomed overcrowded properties and over 60% of the country’s urban population live in slums. Moreover, the gradual shift towards relatively modern and low thermal resistance building materials, in addition to imminent thermal discomfort due to global warming, may considerably affect the health and wellbeing of low-income people, the majority of whom live in low quality homes with very little or no access to basic amenities. This paper evaluates the effects of various construction methods as well as refurbishment strategies on thermal comfort in low-income houses in Uganda. It is aimed at helping low-income populations adapt to climate changes by developing simple, effective and affordable refurbishment strategies that could easily be applied to existing buildings. Dynamic thermal simulations are conducted in EnergyPlus. The adaptive model defined in BS EN 15251 and CIBSE TM52 is used to evaluate the risk and extent of thermal discomfort. Roofing methods/materials are found to be the key factor in reducing/increasing the risk of overheating. According to the results, roof insulation, painting the roof with low solar absorptance materials and inclusion of false ceilings are, respectively, the most effective and practical refurbishment strategies in terms of improving thermal comfort in low-income houses in Uganda. All refurbishment strategies helped to pass Criterion 3 of CIBSE TM52, as an indicator of “future climate scenarios”, making low-income houses/populations more climate resilient.