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The flexible distribution network presents a promising architecture to accommodate highly integrated distributed generators and increasing loads in an efficient and cost-effective way. The distribution network is characterised by flexible interconnections and expansions based on soft open points, which enables it to dispatch power flow over the entire system with enhanced controllability and compatibility. Herein, we propose a multi-resource dynamic coordinated planning method of flexible distribution network that allows allocation strategies to be determined over a long-term planning period. Additionally, we establish a probabilistic framework to address source-load uncertainties, which mitigates the security risks of voltage violations and line overloads. A practical distribution network is adopted for flexible upgrading based on soft open points, and its cost benefits are evaluated and compared with that of traditional planning approaches. By adjusting the acceptable violation probability in chance constraints, a trade-off between investment efficiency and operational security can be realised. Flexible distribution networks with soft open points present a promising way to accommodate distributed generators and increasing loads. Here, authors present a multi-resource dynamic coordinated planning method, allowing allocation strategies to be determined over long-term planning periods.

In typical underground mine planning models, development activities and the downstream flow of materials are treated as distinct operations from ore extraction sequencing. Sublevel caving (SLC) includes development, stockpiling, and production, each occurring independently on different levels. To improve planning efficiency, it is crucial to integrate these simultaneous activities into a holistic model. However, most studies on SLC scheduling primarily focus on ore extraction sequencing, often neglecting the interplay between different activities. This study addresses the gap by introducing an integrated mixed-integer linear programming (MILP) framework, implemented in Python using PuLP and CPLEX, to optimize production, development, and control material flow between the mine, plant, and stockpile. The model aims to maximize net present value (NPV) while accounting for key operational constraints, including development activities, mining and processing capacities, continuous mining, limits on active and newly added mining units (MUs), grade control, sequencing precedences, and stockpile management. It schedules MUs across production areas and levels and optimizes material flow by prioritizing direct transfer from the mine to the plant to avoid rehandling costs while ensuring the plant’s average head grade remains within limits. The proposed framework was verified through an optimization process and applied to a real-world SLC iron mine with 367 MUs over 23 periods, demonstrating the practicality of the integrated model. By accounting for mining direction and all SLC constraints, including operational limitations, the model achieved an NPV of 1.94 B$.

Climate change has emerged as one of the most important environmental issues worldwide. As the world’s biggest developing country, China is participating in combating climate change by promoting a low carbon economy within the context of global warming. This paper summarizes the pathways of China’s low carbon economy including the aspects of energy, industry, low carbon cities, circular economy and low carbon technology, afforestation and carbon sink, the carbon emission trading market and carbon emission reduction targets. There are many achievements in the implementation of low carbon policies. For example, carbon emission intensity has been reduced drastically along with the optimizing of energy and industry structure and a nationwide carbon trading market for electricity industry has been established. However, some problems remain, such as the weakness of public participation, the ineffectiveness of unified policies for certain regions and the absence of long-term planning for low carbon cities development. Therefore, we propose some policy recommendations for the future low carbon economy development in China. Firstly, comprehensive and long-term planning should be involved in all the low carbon economy pathways. Secondly, to coordinate the relationship between central and local governments and narrow the gap between poor and rich regions, different strategies of carbon emission performance assessment should be applied for different regions. Thirdly, enterprises should cooperate with scientific research institutions to explored low carbon technologies. Finally, relevant institutions should be regulated to realize comprehensive low carbon transition through reasonable and feasible low carbon pathways in China. These policy recommendations will provide new perspectives for China’s future low carbon economy development and guide practices for combating climate change.

Generation expansion planning (GEP) is a complex, highly constrained, non-linear, discrete and dynamic optimization task aimed at determining the optimum generation technology mix of the best expansion alternative for long-term planning horizon. This paper presents a new framework to study the GEP in a multi-stage horizon with reliability constrained. GEP problem is presented to minimize the capital investment costs, salvage value cost, operation and maintenance, and outage cost under several constraints over planning horizon. Added to that, the spinning reserve, fuel mix ratio and reliability in terms of Loss of Load Probability are maintained. Moreover, to decrease the GEP problem search space and reduce the computational time, some modifications are proposed such as the Virtual mapping procedure, penalty factor approach, and the modified of intelligent initial population generation. For solving the proposed reliability constrained GEP problem, a novel honey badger algorithm (HBA) is developed. It is a meta-heuristic search algorithm inspired from the intelligent foraging behavior of honey badger to reach its prey. In HBA, the dynamic search behavior of honey badger with digging and honey finding approaches is formulated into exploration and exploitation phases. Added to that, several modern meta-heuristic optimization algorithms are employed which are crow search algorithm, aquila optimizer, bald eagle search and particle swarm optimization. These algorithms are applied, in a comparative manner, for three test case studies for 6-year, 12-year, and 24-year of short- and long-term planning horizon having five types of candidate units. The obtained results by all these proposed algorithms are compared and validated the effectiveness and superiority of the HBA over the other applied algorithms.

Extreme heat events have adverse effects on population health, causing heat-related illnesses, such as heat exhaustion and heat stroke, but also exacerbating underlying medical conditions, such as cardiac and respiratory diseases, through various mechanisms.1 In the United States, from 2000 to 2010 there were approximately 28 000 recorded heat-related hospitalizations, and between 2004 and 2018, an average of about 700 people died because of heat-related illnesses, making heat the deadliest weather-related hazard in the United States.2,3 These figures do not represent heat morbidity and mortality that were not attributable by International Classification of Diseases (Geneva, Switzerland: World Health Organization) Ninth Revision (1980) or 10th Revision (1992) code to a confirmed diagnosis of heat-related illnesses, which likely results in underreporting.4 Additionally, the health consequences of extreme heat are amplified by sociodemographic vulnerabilities and our built environment. As extreme heat events continue to increase in frequency and intensity, individuals, communities, and the municipalities in which they live will need to prepare and adapt.Health impacts from high ambient temperatures have led many municipalities to develop plans to respond to extreme heat events. These plans are sometimes referred to as excessive heat emergency plans, heat-health response plans, or heat action plans (HAPs). Many European countries implemented HAPs following the 2003 European heat wave.5 In the United States, a number of cities have developed HAPs,6,7 although the vast majority of US cities and regions rely only on local National Weather Service offices to issue heat advisories based on heat index forecasts that may not be linked to local HAPs.8In 2020, the US Centers for Disease Control and Prevention (CDC) released a technical report on the summary and strategies for HAPs and ascribed their focus to emergency response planning or long-term planning for extreme heat. The report identifies that plans can stand alone or be an annex to an all-hazards plan and specifically identifies emergency preparedness and management activities when coordinating plans.9 Although the CDC report is not a step-by-step guide or an all-inclusive approach to how to specifically prepare or coordinate a HAP, the reference to emergency operations plans and the location of HAPs in all-hazards mitigation plans suggest that extreme heat is an event that consistently requires an emergency response and is best understood in that context. However, climate change will increase the likelihood and frequency of extreme weather events, such as extreme heat, and these events have increased substantially over the past decades and will continue to affect regions of the globe regularly.10 We argue that the increasing frequency and regularity of these events move them from emergencies to an issue to be planned for with preventive health plans.

Hydrogen energy leads us in an important direction in the development of clean energy, and the comprehensive utilization of hydrogen energy is crucial for the low-carbon transformation of the power sector. In this paper, the demand for hydrogen energy in various fields is predicted based on the support vector regression algorithm, which can be converted into an equivalent electrical load when it is all produced from water electrolysis. Then, the investment costs of power generators and hydrogen energy equipment are forecast considering uncertainty. Furthermore, a planning model is established with the forecast data, initial installed capacity and targets for carbon emission reduction as inputs, and the installed capacity as well as share of various power supply and annual carbon emissions as outputs. Taking Gansu Province of China as an example, the changes of power supply structure and carbon emissions under different scenarios are analysed. It can be found that hydrogen production through water electrolysis powered by renewable energy can reduce carbon emissions but will increase the demand for renewable energy generators. Appropriate planning of hydrogen storage can reduce the overall investment cost and promote a low carbon transition of the power system.

Summary The article shows that for many problems appearing in power system planning and operation the short‐circuit power becomes an important and useful factor. Checking up the short‐circuit power can limit a number of time consuming detailed analyses. The paper concerns mainly the power system angle and voltage stability. Detailed stability analyses of power systems are performed at short‐term and mid‐term planning of transmission network development. Increased load demands and market economics are pushing transmission networks closer and closer to their operational limits. Therefore it is necessary to perform even a simplified power system stability assessment also at long‐term planning. The article shows that very simple criteria of angle and voltage stability based on short‐circuit power can be used as screening filters for fast contingency selection. The suggested criteria have been verified for a real large‐scale system. Copyright © 2014 John Wiley & Sons, Ltd.

Background Vaccination is a critical intervention in the fight against the coronavirus disease 2019 (COVID-19) pandemic. Various levels of COVID-19 vaccination acceptance have been observed around the world. However, a high percentage of the general population and healthcare professionals (HCPs), refuse the COVID-19 vaccination. This study aims to examine the factors influencing COVID-19 vaccine uptake among HCPs and the general population in Cyprus. Methods An online cross-sectional study was conducted, using a self-administered questionnaire to collect information covering various potential determinants including sociodemographic and health-related characteristics, trust in the healthcare system, satisfaction with it, utilization of preventive healthcare services, COVID-19 vaccination information, and general vaccination knowledge. Results A total of 2582 participants completed the survey, with 53.5% of individuals in the general population, and 70.0% of the HCPs received the COVID-19 vaccination. We found that as the age increases by one year among the general population, the odds of being vaccinated against COVID-19 increase by 1.02 units (95% 1.00-1.03, p-value=0.035), whilst those with increased trust in national healthcare authorities’ guidelines (OR = 3.96, 95% CI: 3.41-4.61) and increased vaccination knowledge scores (OR = 1.11, 95% CI: 1.05-1.18) were significantly more likely to be vaccinated. Furthermore, male HCPs (OR = 1.91, 95% CI: 1.01-3.59), and those who reported increased trust in national healthcare authorities’ guidelines (OR = 5.38, 95% CI: 3.65-7.95) were significantly more likely to be vaccinated. Conclusions Public health policymakers can use national campaigns and long-term planning to build public trust in national healthcare authorities and educate and raise awareness about the benefits of vaccination. Such strategies could pave the way for adequate vaccine uptake and prepare the public for unfavorable scenarios, such as future pandemics. Key messages • Our results revealed the importance of vaccination knowledge and trust in healthcare system in respect to COVID-19 vaccination uptake. • The urgent need for national campaigns and long-term planning to build public trust in national healthcare authorities.

Most optimization models developed to solve the long-term planning of municipal solid waste management system (LPMSWMS) problems have some disadvantages, such as the inability to model the problem realistically and the failure of the final decisions to provide global optima due to various mathematical limitations. This study presents an optimization approach for realistically modeling and solving the LPMSWMS problems. This new approach takes into account a non-linear mathematical formulation for the LPMSWMS and solves the model into two main stages. In the first stage, the linear relaxation of the model is used, while the non-linear model formulation is used in the second stage to obtain a more realistic solution for the LPMSWMS. According to the findings, the new optimization approach obtained results for small model volumes in longer times than the reference non-linear model structure. On the other hand, the new approach makes it possible to obtain global optima by producing exact solutions at large model volumes, while the reference non-linear model structure cannot produce any solution. This advantage of the new approach for large model volumes can significantly benefit decision-makers in solving big-scale problems that are likely to be encountered in the real world by giving global optima.