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This paper presents a multi-objective optimization model for a long-term generation mix in Indonesia. The objective of this work is to assess the economic, environment, and adequacy of local energy sources. The model includes two competing objective functions to seek the lowest cost of generation and the lowest CO2 emissions while considering technology diffusion. The scenarios include the use of fossil reserves with or without the constraints of the reserve to production ratio and exports. The results indicate that Indonesia should develop all renewable energy and requires imported coal and natural gas. If all fossil resources were upgraded to reserves, electricity demand in 2050 could be met by domestic energy sources. The maximum share of renewable energy that can be achieved in 2050 is 33% with and 80% without technology diffusion. The least cost optimization produces lower generation costs than the least CO2 emissions, as well as the combined scenario. Total CO2 emissions in 2050 are five to six times as large as current emissions. The least CO2 emissions scenario can reduce almost half of the CO2 emissions of the least cost scenario by 2050. The proposed multi-objective optimization model leads some optimal solutions for a more sustainable electricity system.

Abstract Light wells in the centers of high-rise apartment buildings in Japan are called ‘Voids’. Gas water-heaters built into Voids discharge exhaust gas so a large enough opening has to be designed at the bottom of a Void to keep the indoor air quality (IAQ) acceptable. In order to secure the IAQ in the Void from contamination, a simple calculation method of the ventilation rate induced by wind force and thermal buoyancy through openings at the bottom, along with heat sources such as water-heaters, is presented. The accuracy of this calculation method was examined by wind tunnel testing. As a result, it turned out that the simple calculation methods introduced in this study were valid for predicting the vertical temperature distribution and ventilation rates in Voids.

Biogas is a viable alternative for supplying clean and sustainable energy. Despite all manner of policy measures introduced by the Government of India, biogas is not widely used in India. This article tries to identify factors that influence the decision to adopt biogas at household level. We examine a conceptual framework empirically in which a household wants to maximize utility from biogas by using the India Human Development Survey (IHDS) I, which is a nationally representative, multi-topic survey. By applying both maximum likelihood and penalized likelihood methods (Firthlogit) of logistic regression on a sample size of almost 10,384 households, it has been found that wealthy people are more likely to adopt biogas compared to the marginalized section of the society. We recommend more inclusive policy measures for the weaker section of the society to create an enabling environment to make it a self-promoting technology.

Abstract Globally, three-quarter of a billion people live without electricity. Besides, hundreds of million use a solar lantern for less than 4 h a day. Most of the access-deprived are in the Global South, predominantly in Sub-Saharan Africa and South Asia. The United Nations framed the seventh Sustainable Development Goal to improve access. Solar photovoltaic powered mini-grids are increasingly extending better service to deprived regions. However, poor load-factor and expensive storage adversely affect viability. Also, these mini-grids do not support infrequent large loads to avoid further loss of load-factor. Electric cooking is efficient and non-polluting; water treatment facilities can save millions from contaminant and pathogen by providing clean water. Besides, both electric cooking and water treatment are less expensive than alternatives. But mini-grids frequently do not support these. Indeed, the presence of sustained productive loads favourably influences the mini-grid economy. This study investigates the role of critical household loads to deliver similar bearing on the mini-grid economy. Results underscore realisation of desirable impact with household collaboration under a demand-response program. Collaborative consumption can lower initial investment by 62% and reduce the unit energy cost to $0.23. Also, cooperation improves the mini-grid load factor and promotes viability. Additionally, fast deployment needs during and after Covid-19 remains inherently supported while mitigating the pandemic induced financial stress of both consumer and mini-grid operator. This study of 88 nation-states underscores that demand response in a mini-grid can not only improve affordability for all consumers, but it can also bring 186 million people within affordable access.

Abstract Natural resources exist independent of human intervention. Although these interventions can and do affect the balance between ecological and biological diversity conditions these resources support, and their use to promote economic development. Currently, the unsustainable use of these resources threatens this balance, calling for more sustainable patterns of natural resource use and conservation. The primary responsibility for ensuring the proper balance lies with governments, leading to various policies and programs to preserve natural resources. The ultimate goal is to make the masses aware of natural assets’ importance and encourage their sustainable use. To successfully implement, however, these government practices require public communication and participation, and the full consideration of public opinion at various levels of governance. A predictive analytics framework is proposed for understanding public opinion on government policies to improve sustainable water governance. An integrated policy initiative to balance water resources use and conservation launched by the Indian government served as a test case for applying the framework in an attempt to accurately classify the opinion polarity related to the policy. The conventional feature extraction is applied to pre-processed datasets to extract the relevant features. Subsequently, swarm-based feature selection is applied to filter out optimal features. Lastly, opinion mining and textual analysis are performed to determine the most relevant water management factors that need immediate attention. The proposed framework serves as a policy evaluation strategy in the water management domain. The paper closes with a discussion of the general applicability of the proposed framework.

Residential buildings constitute roughly one-fourth of the total energy use across the globe. Numerous studies have shown that providing an energy breakdown increases residents' awareness of energy use and can help save up to 15% energy. A significant amount of prior work has looked into source-separation techniques collectively called non-intrusive load monitoring (NILM), and most prior NILM research has leveraged high-frequency household aggregate data for energy breakdown. However, in practice most smart meters only sample hourly or once every 15 minutes, and existing NILM techniques show poor performance at such a low sampling rate. In this paper, we propose a TreeCNN model for energy breakdown on low frequency data. There are three key insights behind the design of our model: i) households consume energy with regular temporal patterns, which can be well captured by filters learned in CNNs; ii) tree structure isolates the pattern learning of each appliance that helps avoid magnitude variance problem, while preserves relationship among appliances; iii) tree structure enables the separation of known appliance from unknown ones, which de-noises the input time series for better appliance-level reconstruction. Our TreeCNN model outperformed seven existing baselines on a public benchmark dataset with lower estimation error and higher accuracy on detecting the active states of appliances.

With the spotlight on smart grid development around the world, it is critical to recognize the key factors contributing to changing power system characteristics. This is more apparent in distribution systems with the integration of renewable energy sources, energy storage, and microgrid development. Utilities are also focusing on the reliability and resiliency of the grid. These activities require distribution automation (DA) strategies that take advantage of available technologies while promoting newer solutions. It is necessary to create a roadmap for holistic DA strategies in a smarter grid. Sustainable and resilient grid development is a paradigm shift requiring a new line of thinking in the engineering, operation, and maintenance of the power system. International perspectives on DA are also addressed, with the understanding that one solution will not fit all. Integrating technical, business, and policy decisions into the challenges will generate the development of technologies, standards, and implementation of the overall solution. The challenges in the development of industry standards are also discussed. This paper explores the challenges and opportunities in the changing landscape of the distribution systems. Evolution of technologies and the business case for infrastructure investment in distribution systems are covered in another paper by the same authors.

Light‐emitting solar cells (LESCs) have tremendous potential in the next‐generation optoelectronics industry due to their excellent photophysical properties and general configurational advantages. The important feature of these LESC devices is that it can reversibly transduce optical energy to electrical energy and vice versa in a single platform. Consequently, they have significant potential in the development of solar light‐emitting diode (LED) street lights in rural, semiurban, and urban areas. However, their commercial development has been limited so far due to the complex device geometry, unfavorable alignment of energy levels, poor quality of transport layers, incomplete coverage of the solar spectrum, and more notably, intrinsic instability of perovskites. Herein, the recent developments in the field are discussed, followed by a critical analysis of major challenges and possible solutions to overcome these challenges. Finally, a roadmap for the successful development of efficient and stable dual‐functioning perovskite‐based LESCs is provided, which can be useful for the energy industry.

Abstract Energy demand for space cooling in residential buildings is projected to witness rapid growth, primarily fueled by increasing household incomes in developing countries. To manage this ever-increasing cooling demand, integration of phase change materials (PCMs) in building walls is a potential solution that can reduce the buildings’ cooling energy consumption and peak cooling loads. However, to attain the proposed benefits from PCM integration, it is crucial to appropriately select PCM parameters such as its phase-change temperature and positioning in the wall. Thus, this investigation studied the energy performance of PCM integrated brick masonry walls for cooling load management in residential buildings under periodic steady-state conditions to identify the parameters that govern its performance and develop simple design guidelines. The research found that regardless of the amount of latent heat stored by the PCM, the daily heat gains and cooling loads were equal for wall configurations having equal thermal resistances under identical boundary conditions. Furthermore, even with the application of night ventilation, adding a PCM layer to a well-insulated wall did not reduce its cooling load; thus, PCM integration was ineffective in reducing the cooling load. However, the latent heat stored by the PCM reduced the fluctuations in the hourly heat gains and cooling loads; thus, PCM integration was found suitable for peak load management. For the PCM's proper utilization, its recommended position is on the inner side of the wall with sufficient insulation shielding it from outdoor conditions, and its melting temperature should be close to the indoor set-point temperature.