Real-time pricing algorithms with uncertainty consideration for smart grid

In today modern life smart electrical devices are used to make the human lives more comfortable. Actually, this is the combination of electronics and communications that provides the opportunity for real time communication while the measured electricity by smart meters is sent to the energy provider. In this way smart meters in residential areas play an important role for two way interaction between several users and energy provider. Solving an optimization problem with regard to consideration of satisfaction of both sides of users and energy providers tends to achieve the optimum price that is sent to the users to optimize their consumption in peak demand periods that is the main goal of demand response management programs. As nowadays the renewable energy plays an important role in providing the request of the users specially in residential areas consideration of the concept of uncertainty is an important issue that is considered in this thesis. Therefore, solving the optimization problem in presence of load uncertainty is important topic that is investigated. Another interesting issue is consideration of users' number variation in presence of load uncertainty in dynamic pricing demand response programs which gives the advantage of having good estimation of optimum consumption level of users according to the optimum announced price. In this thesis these issues are considered for solving an Income Based and Utility Base optimization problems that are further explained in upcoming chapters. In chapter III ,which provides the first contribution of the thesis a novel algorithm called Income Based Optimization (IBO) is defined and compared with previously proposed Utility Based Optimization problem (UBO). The price, users' consumption versus provided energy capacity by energy provider in 24 hours period are simulated and analyzed. The effect of variation in other parameters dependent to the cost imposed to the energy provider and the parameters that affect the users level of satisfaction is also evaluated. In Chapter IV, existence of load uncertainty is considered in proposed UBO algorithm when it is assumed that number of users in each time slot is varying based on different distributions such as Uniform or Poison. The results for the average gap between energy provider's generating capacity and consumption of the users are compared with when number of users kept constant in presence of load uncertainty in 24 hours period. Moreover, the effect of different distributions on the gap between generating capacity and the users consumption is evaluated assuming the number of users are increasing and following the distributions. The results for the announced price in 24 hours period is also evaluated and further is extended to the average announced price with respect to increase in number of users when it is assumed that user entry and departure type is varying based on different distributions and the load uncertainty also is existed. In chapter V, the proposed IBO algorithm in chapter three is further extended to the Uncertain IBO and is called UIBO. Therefore, it is assumed that bounded uncertainty is added to the users consumption. This algorithm is further extended in a way that variation in number of users is considered based on different distributions. The results are evaluated for the average gap between generating capacity and users consumption in 24 hours period and is further extended with respect to consideration of the increasing pattern for the number of users in presence of load uncertainty and different types of distributions for the users number variation. With respect to consideration of UIBO algorithm the price in 24 hours period is evaluated and the results are further extended to evaluate the average price with respect to increasing pattern for number of users that are varying based on different distributions when the bounded uncertainty is added to the users consumption. Moreover, the achieved gain of the proposed algorithm based on the ratio of the variation of the announced price to the varying number of users is evaluated. Finally chapter VI provides the conclusion and suggestion for future work

The study of the effect of united Nations Technology Indexes on Economical Development of Iran (from 2000 to 2012)

The United Nations (U.N) has accepted technology as a series of necessary information, skills, methods and tools for making required Products and their application (use) or providing required and useful services; in a way that it has suggested one of the international methods in technology measurement through the criterion of Technology Achievement Index (TAI)[1]. This index which is comprehensively  expressed in annual report of united Nations Human Development; is a multi- dimensional figure which is defined given the achievements of a country in the creation and application of technology at large scale and providing experience infrastructure and human skills in the innovations of technology[2]. Ignoring the point that which country is the first in the world development of technology, above index focuses on the degree of success of that country in producing and applying technology .In this investigation, the effect of each of the technology achievement indexes suggested by United Nations on economic development of Iran between the years 2000 and 2012 is studied

Review on biomedical sensors, technologies, and algorithms for diagnosis of sleep-disordered breathing: Comprehensive survey

This paper provides a comprehensive review of available technologies for measurements of vital physiology related parameters that cause sleep disordered breathing (SDB). SDB is a chronic disease that may lead to several health problems and increase the risk of high blood pressure and even heart attack. Therefore, the diagnosis of SDB at an early stage is very important. The essential primary step before diagnosis is measurement. Vital health parameters related to SBD might be measured through invasive or non-invasive methods. Nowadays, with respect to increase in aging population, improvement in home health management systems is needed more than even a decade ago. Moreover, traditional health parameter measurement techniques such as polysomnography are not comfortable and introduce additional costs to the consumers. Therefore, in modern advanced self-health management devices, electronics and communication science are combined to provide appliances that can be used for SDB diagnosis, by monitoring a patient's physiological parameters with more comfort and accuracy. Additionally, development in machine learning algorithms provides accurate methods of analysing measured signals. This paper provides a comprehensive review of measurement approaches, data transmission, and communication networks, alongside machine learning algorithms for sleep stage classification, to diagnose SDB

A review on communication aspects of demand response management for future 5G IoT- based smart grids

In recent power grids, the need for having a two-way flow of information and electricity is crucial. This provides the opportunity for suppliers and customers to better communicate with each other by shifting traditional power grids to smart grids (SGs). In this paper, demand response management (DRM) is investigated as it plays an important role in SGs to prevent blackouts and provide economic and environmental benefits for both end-users and energy providers. In modern power grids, the development of communication networks has enhanced DRM programmes and made the grid smarter. In particular, with progresses in the 5G Internet of Things (IoT), the infrastructure for DRM programmes is improved with fast data transfer, higher reliability, increased security, lower power consumption, and a massive number of connections. Therefore, this paper provides a comprehensive review of potential applications of 5G IoT technologies as well as the computational and analytical algorithms applied for DRM programmes in SGs. The review holistically brings together sensing, communication, and computing (optimization, prediction), areas usually studied in a scattered way. A broad discussion on various DRM programmes in different layers of enhanced 5G IoT based SGs is given, paying particular attention to advances in machine learning (ML) and deep learning (DL) algorithms alongside challenges in security, reliability, and other factors that have a role in SGs’ performance

Stevens-Johnson Syndrome From Combined Allopurinol and Angiotensin-Converting Enzyme Inhibitors: A Narrative Review

Stevens-Johnson syndrome (SJS) is a severe and potentially debilitating skin reaction frequently related to medication use. Allopurinol and angiotensin-converting enzyme (ACE) inhibitors are commonly prescribed medications for prevalent health conditions worldwide, and their interaction associated with SJS warrants further investigation. A comprehensive literature search was performed to investigate cases as studies related to SJS occurring in patients with concomitant use of allopurinol and ACE inhibitors. We identified case reports and studies detailing hypersensitivity reactions, including SJS, attributed to a combination of allopurinol and ACE inhibitors. Despite the drug-drug interactions or lack thereof seen in patient populations, there is no definitive evidence of a pharmacokinetic interaction between allopurinol and ACE inhibitors. We were only able to find one case report specifically detailing SJS in a patient on combined ACE inhibitors and allopurinol. While the exact mechanism of the interaction is unclear, those reported cases of severe hypersensitivity reactions suggest a previous history of impaired renal function as a predisposing factor in the development of SJS. The potential risk of SJS with coadministration of ACE inhibitors and allopurinol is a drug-drug interaction that physicians should be aware of. This topic requires additional attention to determine if this drug combination should be avoided entirely in certain patients

Interventional procedures for refractory neuropathic pain

Neuropathic pain is an increasingly common disease affecting millions of individuals worldwide. Refractory pain poses a significant impact on patients’ quality of life, financial and economic stability, and social interaction. Numerous effective modalities for treatment of refractory neuropathic pain are presently available. Currently, many options provide symptomatic treatment but are associated with an unfavorable side effect profile and increased risk of addiction. The present investigation reviews current medical management for refractory neuropathic pain including the use of antidepressants, anticonvulsants, gabapentinoids and opioid therapy, as well as interventional pain procedures such as spinal cord stimulation (SCS) and intrathecal targeted drug delivery. While multidisciplinary management with lifestyle modification and pharmacologic regimens remains at the forefront of treating many of these patients, interventional modalities are growing in popularity and have been demonstrated to be highly efficacious. In this regard, continued understanding of the pathophysiology surrounding refractory neuropathic pain has led to the development of interventional procedures and better outcomes for patients suffering from refractory neuropathic pain. When and if patients fail conservative therapy, interventional techniques are desirable alternatives for pain management. SCS and intrathecal targeted drug delivery are important tools for the treatment of refractory neuropathic pain. In summary, treatment modalities for refractory neuropathic pain are evolving with demonstrated efficacy. This review aims to outline the efficacy of various interventional procedures for refractory neuropathic pain in comparison to traditional drug therapies

Bupropion Mediated Effects on Depression, Attention Deficit Hyperactivity Disorder, and Smoking Cessation

Bupropion had been in use since the late 1980s as an unconventional treatment for depression. Unlike other antidepressants, bupropion has no serotonergic activity and inhibits the reuptake of norepinephrine and dopamine. The drug has been used to treat depression, Attention Deficit Hyperactivity Disorder (ADHD), and smoking cessation. This investigation reviews the pharmacokinetic and pharmacodynamic effects of bupropion and its mechanisms of action and interactions with other drugs. We evaluated the efficacy of major on and off-label uses of bupropion, focusing on the indications, benefits, and adverse effects. Our review demonstrates that bupropion is superior to placebo and non-inferior to SSRIs such as escitalopram in treating major depressive disorder. More research is needed to determine positive patient-centered outcomes such as increases in quality of life. In the case of ADHD, the evidence for efficacy is mixed with poorly conducted randomized clinical trials, small sample sizes, and a lack of long-term assessments. The same is true in the case of bipolar disorder in which there is still limited and controversial data available on bupropion's safety and efficacy. In the case of smoking cessation, bupropion is found to be an effective anti-smoking drug with synergistic benefits when used as a combination therapy. We conclude that bupropion has the potential to provide benefit for a subset of patients who do not tolerate other typical antidepressants or anti-smoking therapies or for those whose treatment goals align with bupropion's unique side effect profile, such as smokers who wish to quit and lose weight. Additional research is needed to determine the drug's full clinical potential, particularly in the areas of adolescent depression and combination therapy with varenicline or dextromethorphan. Clinicians should use this review to understand the varied uses of the drug and identify the situations and patient populations in which bupropion can lend its greatest benefit

A Comprehensive Review on Food Waste Reduction Based on IoT and Big Data Technologies

Food waste reduction, as a major application area of the Internet of Things (IoT) and big data technologies, has become one of the most pressing issues. In recent years, there has been an unprecedented increase in food waste, which has had a negative impact on economic growth in many countries. Food waste has also caused serious environmental problems. Agricultural production, post-harvest handling, and storage, as well as food processing, distribution, and consumption, can all lead to food wastage. This wastage is primarily caused by inefficiencies in the food supply chain and a lack of information at each stage of the food cycle. In order to minimize such effects, the Internet of Things, big data-based systems, and various management models are used to reduce food waste in food supply chains. This paper provides a comprehensive review of IoT and big data-based food waste management models, algorithms, and technologies with the aim of improving resource efficiency and highlights the key challenges and opportunities for future research

A Comprehensive Review on Food Waste Reduction Based on IoT and Big Data Technologies

Food waste reduction, as a major application area of the Internet of Things (IoT) and big data technologies, has become one of the most pressing issues. In recent years, there has been an unprecedented increase in food waste, which has had a negative impact on economic growth in many countries. Food waste has also caused serious environmental problems. Agricultural production, post-harvest handling, and storage, as well as food processing, distribution, and consumption, can all lead to food wastage. This wastage is primarily caused by inefficiencies in the food supply chain and a lack of information at each stage of the food cycle. In order to minimize such effects, the Internet of Things, big data-based systems, and various management models are used to reduce food waste in food supply chains. This paper provides a comprehensive review of IoT and big data-based food waste management models, algorithms, and technologies with the aim of improving resource efficiency and highlights the key challenges and opportunities for future research