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Modern energy systems necessitate accurate peak demand predictions to minimize costs. This study presents an adaptive FastICA-Transformer that improves feature extraction and forecasting precision using entropy-guided component selection and a redesigned transformer architecture. The model modifies transformer depth according to the forecast horizon and initializes attention heads using statistically independent FastICA components, resulting in interpretable and efficient networks. In comparison to PCA-Transformer and sophisticated neural architectures (LSTM, TCN, N-BEATS, TFT), the FastICA-Transformer demonstrates enhanced MAE, RMSE, and MAPE in forecasting for Next-Hour, Next-Day, Next-Week, and Next-Quarter intervals. The results indicate that architectural adaptation, informed by temporal and statistical characteristics, efficiently tackles peak demand forecasts in practical power systems.

The optimal energy consumption is one of sustainable development issues in many countries to the appraisal of the economic and technical indices in the energy sector. The consumption of multi-carrier energy hub systems like electricity, natural gas, and heat has been expanding in recent years. Due to technical signs of progress in heat and electrical energy networks, multi-carrier energies are employed in parallel modes to meet the energy required on the demand side. In this article, the operation of the multi-carrier energy system is studied based on local energy price and penetration of the renewable energies. The optimization of the energy system is performed with attention to maximization of the reliability and minimization of the costs subject to peak demand pricing. The modeling optimization is implemented in GAMS software with numerical simulation and several case studies. The proposed optimization algorithm is implemented to peak demand reshape at a high energy price. The obtained results in case studies show the cost-effectiveness of the multi-carrier energy system with acceptable reliability in peak demand. With implementing the peak demand pricing, costs, reliability index, and penetration of the renewable energies are improved by 16.38%, 100%, and 1.2%, than non-implementing the peak demand pricing.

One of the main goals of any power grid is sustainability. The given study proposes a new method, which aims to reduce users’ anxiety especially at slow charging stations and improve the smart charging model to increase the benefits for the electric vehicles’ owners, which in turn will increase the grid stability. The issue under consideration is modelled as an optimisation problem to minimise the cost of charging. This approach levels the load effectively throughout the day by providing power to charge EVs’ batteries during the off‐peak hours and drawing it from the EVs’ batteries during peak‐demand hours of the day. In order to minimise the costs associated with EVs’ charging in the given optimisation problem, an improved version of an intelligent algorithm is developed. In order to evaluate the effectiveness of the proposed technique, it is implemented on several standard models with various loads, as well as compared with other optimisation methods. The superiority and efficiency of the proposed method are demonstrated, by analysing the obtained results and comparing them with the ones produced by the competitor techniques.

Aged care communities have been under the spotlight since the beginning of 2020. Energy is essential to ensure reliable operation and quality care provision in residential aged care communities (RAC). The aim of this study is to determine how RAC’s yearly energy use and peak demand changed in Australia and what this might mean for RAC design, operation and energy asset investment and ultimately in the healthcare plan for elderly residents. Five years of electricity demand data from four case study RACs in the same climate zone are analyzed. Statistical tools are used to analyze the data, and a clustering algorithm is used to identify typical demand profiles. A number of energy key performance indicators (KPIs) are evaluated, highlighting their respective benefits and limitations. The results show an average 8% reduction for yearly energy use and 7% reduction for yearly peak demands in the COVID-19 year compared with the average of the previous four years. Typical demand profiles for the four communities were mostly lower in the pandemic year. Despite these results, the KPI analysis shows that, for these four communities, outdoor ambient temperature remains a very significant correlation factor for energy use.

This study examined three different approaches to reduce the heating cost while maintaining indoor thermal comfort at acceptable levels in an educational office building, including decentralized (DDRC) and centralized demand response control (CDRR) and limiting peak demand. The results showed that although all these approaches did not affect the indoor air temperature significantly, the DDRC method could adjust the heating set point to between 20–24.5 °C. The DDRC approach reached heating cost savings of up to 5% while controlling space heating temperature without sacrificing the thermal comfort. The CDRC of space heating had limited potential in heating cost savings (1.5%), while the indoor air temperature was in the acceptable range. Both the DDRC and CDRC alternatives can keep the thermal comfort at good levels during the occupied time. Depending on the district heating provider, applying peak demand limiting of 35% can not only achieve 13.6% maximum total annual district heating cost saving but also maintain the thermal comfort level, while applying that of 43% can further save 16.9% of the cost, but with sacrificing a little thermal comfort. This study shows that demand response on heating energy only benefited from the decentralized control alternative, and the district heating-based peak demand limiting has significant potential for saving heating costs.

An unparalleled rise in peak electricity demand across the tropics over recent decades signals the need for conscious planning of investment in power infrastructure and a boosting of demand-side management measures. This paper estimates the extremes in daily peak electricity demand across eight Indian states from 2010 to 2018 by using the extreme value mixture models and the generalised Pareto distribution (GPD) method. A combination of different mixture models is used to obtain the optimum threshold level, and the exceedances above it are declustered and fitted with a non-stationary GPD using the daily maximum temperature and trend terms. To our knowledge, this is the first attempt to apply non-stationary GPD models in Indian context for analysing the extreme peak electricity demand in the country. Results show that the shape parameter of extreme peak demand appears to be time-variant for the different values of maximum temperature and shows a linear trend for Punjab, Madhya Pradesh, Maharashtra, Gujarat, and Haryana. However, the scale parameter is found to be time-variant for all the states. Most states experienced the highest monthly frequency of peak electricity demand during July–October, and the largest yearly frequency during 2015, 2016, and 2018. Additionally, the estimated return values highlight a higher increase in daily peak demand for Rajasthan, Delhi, Madhya Pradesh, and Maharashtra compared to other states during the next 25 years. These findings will be pertinent to the decision-makers in planning for peak reserve capacity in various Indian states.

Dihydrogen (H2), commonly named ‘hydrogen’, is increasingly recognised as a clean and reliable energy vector for decarbonisation and defossilisation by various sectors. The global hydrogen demand is projected to increase from 70 million tonnes in 2019 to 120 million tonnes by 2024. Hydrogen development should also meet the seventh goal of ‘affordable and clean energy’ of the United Nations. Here we review hydrogen production and life cycle analysis, hydrogen geological storage and hydrogen utilisation. Hydrogen is produced by water electrolysis, steam methane reforming, methane pyrolysis and coal gasification. We compare the environmental impact of hydrogen production routes by life cycle analysis. Hydrogen is used in power systems, transportation, hydrocarbon and ammonia production, and metallugical industries. Overall, combining electrolysis-generated hydrogen with hydrogen storage in underground porous media such as geological reservoirs and salt caverns is well suited for shifting excess off-peak energy to meet dispatchable on-peak demand.

It has been suggested that climate change impacts on the electric sector will account for the majority of global economic damages by the end of the current century and beyond [Rose S, et al. (2014) Understanding the Social Cost of Carbon: A Technical Assessment]. The empirical literature has shown significant increases in climate-driven impacts on overall consumption, yet has not focused on the cost implications of the increased intensity and frequency of extreme events driving peak demand, which is the highest load observed in a period. We use comprehensive, high-frequency data at the level of load balancing authorities to parameterize the relationship between average or peak electricity demand and temperature for a major economy. Using statistical models, we analyze multiyear data from 166 load balancing authorities in the United States. We couple the estimated temperature response functions for total daily consumption and daily peak load with 20 downscaled global climate models (GCMs) to simulate climate change-driven impacts on both outcomes. We show moderate and heterogeneous changes in consumption, with an average increase of 2.8% by end of century. The results of our peak load simulations, however, suggest significant increases in the intensity and frequency of peak events throughout the United States, assuming today’s technology and electricity market fundamentals. As the electricity grid is built to endure maximum load, our findings have significant implications for the construction of costly peak generating capacity, suggesting additional peak capacity costs of up to 180 billion dollars by the end of the century under business-as-usual.

Having adopted a dynamic zero-COVID strategy to respond to SARS-CoV-2 variants with higher transmissibility since August 2021, China is now considering whether, and for how long, this policy can remain in place. The debate has thus shifted towards the identification of mitigation strategies for minimizing disruption to the healthcare system in the case of a nationwide epidemic. To this aim, we developed an age-structured stochastic compartmental susceptible-latent-infectious-removed-susceptible model of SARS-CoV-2 transmission calibrated on the initial growth phase for the 2022 Omicron outbreak in Shanghai, to project COVID-19 burden (that is, number of cases, patients requiring hospitalization and intensive care, and deaths) under hypothetical mitigation scenarios. The model also considers age-specific vaccine coverage data, vaccine efficacy against different clinical endpoints, waning of immunity, different antiviral therapies and nonpharmaceutical interventions. We find that the level of immunity induced by the March 2022 vaccination campaign would be insufficient to prevent an Omicron wave that would result in exceeding critical care capacity with a projected intensive care unit peak demand of 15.6 times the existing capacity and causing approximately 1.55 million deaths. However, we also estimate that protecting vulnerable individuals by ensuring accessibility to vaccines and antiviral therapies, and maintaining implementation of nonpharmaceutical interventions could be sufficient to prevent overwhelming the healthcare system, suggesting that these factors should be points of emphasis in future mitigation policies. Estimates from a new modeling study suggest that current levels of vaccine coverage in China are insufficient to prevent overwhelming the healthcare system, and that, if left untreated, a nationwide Omicron wave could result in up to 1.55 million deaths.

There is growing empirical evidence that anthropogenic climate change will substantially affect the electric sector. Impacts will stem both from the supply side—through the mitigation of greenhouse gases—and from the demand side—through adaptive responses to a changing environment. Here we provide evidence of a polarization of both peak load and overall electricity consumption under future warming for the world’s third-largest electricity market—the 35 countries of Europe. We statistically estimate country-level dose–response functions between daily peak/total electricity load and ambient temperature for the period 2006–2012. After removing the impact of nontemperature confounders and normalizing the residual load data for each country, we estimate a common dose–response function, which we use to compute national electricity loads for temperatures that lie outside each country’s currently observed temperature range. To this end, we impose end-of-century climate on today’s European economies following three different greenhouse-gas concentration trajectories, ranging from ambitious climate-change mitigation—in line with the Paris agreement—to unabated climate change. We find significant increases in average daily peak load and overall electricity consumption in southern and western Europe (∼3 to ∼7% for Portugal and Spain) and significant decreases in northern Europe (∼−6 to ∼−2% for Sweden and Norway). While the projected effect on European total consumption is nearly zero, the significant polarization and seasonal shifts in peak demand and consumption have important ramifications for the location of costly peak-generating capacity, transmission infrastructure, and the design of energy-efficiency policy and storage capacity.