An Economic Risk Analysis in Wind and Pumped Hydro Energy Storage Integrated Power System Using Meta-Heuristic Algorithm

Due to the restructuring of the power system, customers always try to obtain low-cost power efficiently and reliably. As a result, there is a chance to violate the system security limit, or the system may run in risk conditions. In this paper, an economic risk analysis of a power system considering wind and pumped hydroelectric storage (WPHS) hybrid system is presented with the help of meta-heuristic algorithms. The value-at-risk (VaR) and conditional value-at-risk (CVaR) are used as the economic risk analysis tool with two different confidence levels (i.e., 95% and 99%). The VaR and CVaR with higher negative values represent the system in a higher-risk condition. The value of VaR and CVaR on the lower negative side or towards a positive value side indicates a less risky system. The main objective of this work is to minimize the system risk as well as minimize the system generation cost by optimal placement of wind farm and pumped hydro storage systems in the power system. Sequential quadratic programming (SQP), artificial bee colony algorithms (ABC), and moth flame optimization algorithms (MFO) are used to solve optimal power flow problems. The novelty of this paper is that the MFO algorithm is used for the first time in this type of power risk curtailment problem. The IEEE 30 bus system is considered to analyze the system risk with the different confidence levels. The MVA flow of all transmission lines is considered here to calculate the value of VaR and CVaR. The hourly VaR and CVaR values of the hybrid system considering the WPHS system are reported here and the numerical case studies of the hybrid WPHS system demonstrate the effectiveness of the proposed approach. To validate the presented approach, the results obtained by using the MFO algorithm are compared with the SQP and ABC algorithms’ results.

System risk assessment based on the probabilistic model “exposure-susceptibility” at the enterprises of storage and processing of vegetable agricultural products

At present, elements of probabilistic safety and risk assessment have been introduced into the design and analysis of complex technical systems, one of the main disadvantages of which is the difficulty due to the selection of initial data in the form of probabilities of initiating events. As a consequence, the use of known methodologies for quantifying risk can lead to either underestimation of threats or unreasonably high security costs. On the example of an enterprise for the storage and processing of vegetable agricultural products, an approach was considered for assessing the risk of a technical system based on the probabilistic model “exposure-susceptibility”.

How Is the Risk of Major Sudden Infectious Epidemic Transmitted? A Grounded Theory Analysis Based on COVID-19 in China

The outbreak of a sudden infectious epidemic often causes serious casualties and property losses to the whole society. The COVID-19 epidemic that broke out in China at the end of December 2019, spread rapidly, resulting in large groups of confirmed diagnoses, and causing severe damage to China's society. This epidemic even now encompasses the globe. This paper takes the COVID-19 epidemic that has occurred in China as an example, the original data of this paper is derived from 20 Chinese media reports on COVID-19, and the grounded theory is used to analyze the original data to find the risk transmission rules of a sudden infectious epidemic. The results show that in the risk transmission of a sudden infectious epidemic, there are six basic elements: the risk source, the risk early warning, the risk transmission path, the risk transmission victims, the risk transmission inflection point, and the end of risk transmission. After a sudden infectious epidemic breaks out, there are three risk transmission paths, namely, a medical system risk transmission path, a social system risk transmission path, and a psychological risk transmission path, and these three paths present a coupling structure. These findings in this paper suggest that people should strengthen the emergency management of a sudden infectious epidemic by controlling of the risk source, establishing an efficient and scientific risk early warning mechanism and blocking of the risk transmission paths. The results of this study can provide corresponding policy implications for the emergency management of sudden public health events.

PENGUKURAN FAKTOR RISIKO KETIDAKPATUHAN PENGOBATAN TUBERCULOSIS KHUSUSNYA HEALTH SYSTEM PADA PASIEN PINDAHAN

During the Covid-19 pandemic, there was a transfer of TB patient referrals from other health facilities to The X Hospital which took effect in April 2020. One of the five factors that caused non-compliance with treatment was the health system. The sub factor included in this is Health facility. The transfer of patient referrals is predicted to be associated with the success rate of TB treatment. This study aims to describe the health system risk factors for non-adherence to treatment of drug-resistant TB (RO) patients. This study uses a quantitative descriptive approach. The population in this study were RO TB patients. The sampling technique is total sampling. The inclusion criteria in this study were RO TB patients whose treatment period was > 6 months and there had been a transfer of health facilities to the Cempaka Putih Islamic Hospital. The total sample obtained is 35 respondents. The results showed that the health system risk factor for non-adherence to treatment of Drug Resistant TB (RO) patients was low because patients did not experience problems in terms of distance to health facilities and competent health workers in providing information about treatment for RO TB patients.

GSS-RiskAsser: A Multi-Modal Deep-Learning Framework for Urban Gas Supply System Risk Assessment on Business Users

Gas supply system risk assessment is a serious and important problem in cities. Existing methods tend to manually build mathematical models to predict risk value from single-modal information, i.e., pipeline parameters. In this paper, we attempt to consider this problem from a deep-learning perspective and define a novel task, Urban Gas Supply System Risk Assessment (GSS-RA). To drive deep-learning techniques into this task, we collect and build a domain-specific dataset GSS-20K containing multi-modal data. Accompanying the dataset, we design a new deep-learning framework named GSS-RiskAsser to learn risk prediction. In our method, we design a parallel-transformers Vision Embedding Transformer (VET) and Score Matrix Transformer (SMT) to process multi-modal information, and then propose a Multi-Modal Fusion (MMF) module to fuse the features with a cross-attention mechanism. Experiments show that GSS-RiskAsser could work well on GSS-RA task and facilitate practical applications. Our data and code will be made publicly available.

Household Waste Management in Cameroon: The Case of Bamenda and Buea Municipalities

Urban sanitation and hazard risk reduction strategies that aim to maintain a clean and healthy society will only be successful and sustainable if it includes educating and training the people on how to handle their household waste. Nations and organisations must innovate in order to sustain the health of population growth and other global demands. It is not easy to obtain a complete assessment of risk involve in any undertaking. The problem is even more in an innovative project like the case of hysacam waste disposal in Buea and Bamenda in Cameroon. What distinguishes standard processes from innovation is their level of uncertainty. Therefore, ways of assessing and addressing the magnitude of risks involve must come high on the list of techniques for managing innovations. The risk surrounding garbage disposal in Cameroon is a course for concern. The objective of this paper is to present and discuss the assumptions which underlie the key frame of reference used to understand waste disposal in order to make garbage disposal using the new system risk free and sustainable. The study focuses on household’s waste disposal in Bamenda and Buea towns in Cameroon. According to prior studies in this area, (Jake Ansell & Frank Wharton 1992; Keith Goffin & Rick Mitchell 2005) risk is an unavoidable feature of human existence. Neither man nor organisations/societies can survive for long without taking risks. Mark and Eve (2006) suggest that people be advised to separate organic waste from solid waste before depositing for collection to a compositing depot. Data was gathered through face to face interview with those disposing garbage and the population concern. In all, 150 people were interviewed, 5 employees holding executive posts, 35 workers involve in the day to day carrying of garbage, 100 service users in the town of Buea and Bamenda where the service is being delivered and 10 people, both service delivers and service users in Douala (Cameroon) where such a service started and has since been operating for long (since the 1960s) were interviewed. Data is analysed both qualitative and quantitative. Result suggests that while the innovative garbage disposal technique is good as it is quick and cover large areas, keeps the city clean for healthy living, it is an innovative action therefore, both the service providers and users need to be trained (educated) to make the service worthwhile and sustainable. Education will empower people with appropriate knowledge on how to handle garbage with care. The implications would be: reduced risk of bacterial spread in streets, reduced ardour, clean air and quality well being greatly improved. The validity of the study lies in its ability to spread information that can improve the operation system of garbage disposal, making the service attain its objective of keeping a city clean and promote healthy living.