A new optimized demand management system for smart grid-based residential buildings adopting renewable and storage energies

Demand Side Management (DSM) implies intelligently managing load appliances in a Smart Grid (SG). DSM programs help customers save money by reducing their electricity bills, minimizing the utility’s peak demand, and improving load factor. To achieve these goals, this paper proposes a new load shifting-based optimal DSM model for scheduling residential users’ appliances. The proposed system effectively handles the challenges raised in the literature regarding the absence of using recent, easy, and more robust optimization techniques, a comparison procedure with well-established ones, using Renewable Energy Resources (RERs), Renewable Energy Storage (RES), and adopting consumer comfort. This system uses recent algorithms called Virulence Optimization Algorithm (VOA) and Earth Worm Optimization Algorithm (EWOA) for optimally shifting the time slots of shiftable appliances. The system adopts RERs, RES, as well as utility grid energy for supplying load appliances. This system takes into account user preferences, timing factors for each appliance, and a pricing signal for relocating shiftable appliances to flatten the energy demand profile. In order to figure out how much electricity users will have to pay, a Time Of Use (TOU) dynamic pricing scheme has been used. Using MATLAB simulation environment, we have made effectiveness-based comparisons of the adopted optimization algorithms with the well-established meta-heuristics and evolutionary algorithms (Genetic Algorithm (GA), Cuckoo Search Optimization (CSO), and Binary Particle Swarm Optimization (BPSO) in order to determine the most efficient one. Without adopting RES, the results indicate that VOA outperforms the other algorithms. The VOA enables 59% minimization in Peak-to-Average Ratio (PAR) of consumption energy and is more robust than other competitors. By incorporating RES, the EWOA, alongside the VOA, provides less deviation and a lower PAR. The VOA saves 76.19% of PAR, and the EWOA saves 73.8%, followed by the BPSO, GA, and CSO, respectively. The electricity consumption using VOA and EWOA-based DSM cost 217 and 210 USD cents, respectively, whereas non-scheduled consumption costs 273 USD cents and scheduling based on BPSO, GA, and CSO costs 219, 220, and 222 USD cents.publishedVersio

Analysis of the power supply restoration time after failures in power transmission lines

This paper presents the analysis of power supply restoration time after failures occurring in power lines. It found that the power supply restoration time depends on several constituents, such as the time for obtaining information on failures, the time for information recognition, the time to repair failures, and the time for connection harmonization. All these constituents have been considered more specifically. The main constituents' results values of the power supply restoration time were analyzed for the electrical networks of regional power supply company "Oreolenergo", a branch of Interregional Distribution Grid Company (IDGC) of Center. The Delphi method was used for determining the time for obtaining information on failures as well as the time for information recognition. The method of mathematical statistics was used to determine the repair time. The determined power supply restoration time (5.28 h) is similar to statistical values of the examined power supply company (the deviation was equal to 9.9%). The technical means of electrical network automation capable of the reduction of the power supply restoration time have also been found. These means were classified according to the time intervals they shorten.Web of Science1311art. no. 273

Biomass Gasification and Applied Intelligent Retrieval in Modeling

Gasification technology often requires the use of modeling approaches to incorporate several intermediate reactions in a complex nature. These traditional models are occasionally impractical and often challenging to bring reliable relations between performing parameters. Hence, this study outlined the solutions to overcome the challenges in modeling approaches. The use of machine learning (ML) methods is essential and a promising integration to add intelligent retrieval to traditional modeling approaches of gasification technology. Regarding this, this study charted applied ML-based artificial intelligence in the field of gasification research. This study includes a summary of applied ML algorithms, including neural network, support vector, decision tree, random forest, and gradient boosting, and their performance evaluations for gasification technologies

A hybrid supervised machine learning classifier system for breast cancer prognosis using feature selection and data imbalance handling approaches

Nowadays, breast cancer is the most frequent cancer among women. Early detection is a critical issue that can be effectively achieved by machine learning (ML) techniques. Thus in this article, the methods to improve the accuracy of ML classification models for the prognosis of breast cancer are investigated. Wrapper-based feature selection approach along with nature-inspired algorithms such as Particle Swarm Optimization, Genetic Search, and Greedy Stepwise has been used to identify the important features. On these selected features popular machine learning classifiers Support Vector Machine, J48 (C4.5 Decision Tree Algorithm), Multilayer-Perceptron (a feed-forward ANN) were used in the system. The methodology of the proposed system is structured into five stages which include (1) Data Pre-processing; (2) Data imbalance handling; (3) Feature Selection; (4) Machine Learning Classifiers; (5) classifier's performance evaluation. The dataset under this research experimentation is referred from the UCI Machine Learning Repository, named Breast Cancer Wisconsin (Diagnostic) Data Set. This article indicated that the J48 decision tree classifier is the appropriate machine learning-based classifier for optimum breast cancer prognosis. Support Vector Machine with Particle Swarm Optimization algorithm for feature selection achieves the accuracy of 98.24%, MCC = 0.961, Sensitivity = 99.11%, Specificity = 96.54%, and Kappa statistics of 0.9606. It is also observed that the J48 Decision Tree classifier with the Genetic Search algorithm for feature selection achieves the accuracy of 98.83%, MCC = 0.974, Sensitivity = 98.95%, Specificity = 98.58%, and Kappa statistics of 0.9735. Furthermore, Multilayer Perceptron ANN classifier with Genetic Search algorithm for feature selection achieves the accuracy of 98.59%, MCC = 0.968, Sensitivity = 98.6%, Specificity = 98.57%, and Kappa statistics of 0.9682.Web of Science106art. no. 69

Electric Heating System with Thermal Storage Units and Ceiling Fans for Cattle-Breeding Farms

A combined energy–saving heat supply system was proposed that included a combined ETS unit and a ceiling fan, and provided the normative air parameters in a livestock room, with an air temperature of −17 °C and air relative humidity (ARH) of −75%. A heat supply system of a preventive maintenance premises for calves was chosen as the subject of the study. Comparative analysis of the temperature and ARH distribution with height in the preventive maintenance premises, was carried out, with and without a ceiling fan. The study showed that, during the heating period, application of the ceiling fans helped to raise the air temperature and to reduce ARH, in the areas where young stock is located, in accordance with the normative indicators. The energy-saving effect was achieved by supplying warmer ventilation air, which accumulated in the upper zone of the premises from the ceiling fan to the locations of the animals. At the same time, there was a decrease in the consumption of electric energy for the heat supply system of up to 14%

Monitoring the number and duration of power outages and voltage deviations at both sides of switching devices

The need for monitoring the electrical network parameters is identified to use methods and means to improve power supply reliability and power quality. The article lists the exiting sensors for monitoring electrical parameters and substantiates the necessity of monitoring the parameters at both sides of switching devices. In the paper, there is basic information on the structure, operation and capabilities of the monitoring system for power supply reliability and power quality. A functional electrical circuit of the device for monitoring the number and duration of power outages and voltage deviations is proposed for monitoring the parameters at both sides of switching devices. An algorithm for the device operation has also been developed, which allows detecting the main emergency modes in the consumer's internal network. The article also describes laboratory tests of a prototype of the device for monitoring the number and duration of power outages and voltage deviations, which is based on the Arduino NANO V3 ATmega 328 microprocessor.Web of Science813718413717

Allocation of 0.4 kV PTL Sectionalizing Units under Criteria of Sensitivity Limits and Power Supply Reliability

Sectionalizing 0.4 kV power transmission lines (PTL) improves power supply reliability and reduces electricity undersupply through the prevention of energy disconnection of consumers in the event of a short circuit in the power line behind the sectionalizing unit (SU). This research examines the impact of sectionalizing on power supply reliability and reviews the literature on sectionalizing unit allocation strategies in electrical networks. This paper describes the experience of the use of sectionalizing units with listing strengths and weaknesses of adopted technical solutions and describes the new structure of sectionalizing units. A new methodology is proposed, whereby there are two criteria for allocating SU in 0.4 kV power transmission lines. The first criterion is the sensitivity limits against single-phase short circuits used for calculating the maximum distance at which SU can be installed. The second criterion is power supply reliability improvement, evaluating the cost-effectiveness of installing sectionalizing equipment by reducing power supply outage time. The established methodology was put to the test on an actual electrical system (Mezenka village, Orel area, Russia), which demonstrated that the installation of a sectionalizing unit paid off

Challenges and methods of monitoring the occurrence of unsanctioned voltage in the power grid

The review of sources dedicated to the issues of monitoring in electric networks made in the article showed that the works of many scientists are aimed at developing methods, technical means, systems for monitoring current and voltage in various operation modes of power grids. The main objectives of monitoring are identified, it is shown that monitoring of parameters in the network operation modes provides observability of the network, which, in turn, allows to make timely decisions about switching in the network, regulating the parameters of the network operation modes. The relevance of monitoring for detecting cases of unauthorized voltage in the 0.4 kV power networks is shown. Similar cases lead to the risk of electric shock to people, increasing the risk of operating electrical networks. Identification of the occurrence of unauthorized voltage in the 0.4 kV network provides ways to prevent its transformation at substations of 10/0. 4 kV to a voltage of 10 kV. Therefore, it is relevant to develop methods for detecting unauthorized voltage in the 0.4 kV electric system. The methodological principles and one of the developed methods for monitoring the occurrence of unauthorized voltage in power transmission lines of 0.4 kV and blocking the reverse transformation on substations 10/0.4 kV, as well as the device for its implementation, are shown

Blockchain–Cloud Integration: A Survey

Over the last couple of years, Blockchain technology has emerged as a game-changer for various industry domains, ranging from FinTech and the supply chain to healthcare and education, thereby enabling them to meet the competitive market demands and end-user requirements. Blockchain technology gained its popularity after the massive success of Bitcoin, of which it constitutes the backbone technology. While blockchain is still emerging and finding its foothold across domains, Cloud computing is comparatively well defined and established. Organizations such as Amazon, IBM, Google, and Microsoft have extensively invested in Cloud and continue to provide a plethora of related services to a wide range of customers. The pay-per-use policy and easy access to resources are some of the biggest advantages of Cloud, but it continues to face challenges like data security, compliance, interoperability, and data management. In this article, we present the advantages of integrating Cloud and blockchain technology along with applications of Blockchain-as-a-Service. The article presents itself with a detailed survey illustrating recent works combining the amalgamation of both technologies. The survey also talks about blockchain–cloud services being offered by existing Cloud Service providers