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Abstract

This paper presents a novel approach for reactive power planning of a connected power network. Reactive power planning is nothing but the optimal usage of all reactive power sources i.e., transformer tap setting arrangements, reactive generations of generators and shunt VAR compensators installed at weak nodes. Shunt VAR compensator placement positions are determined by a FVSI (Fast Voltage Stability Index) method. Optimal setting of all reactive power reserves are determined by a GA (genetic algorithm) based optimization method. The effectiveness of the detection of the weak nodes by the FVSI method is validated by comparing the result with two other wellknown methods of weak node detection like Modal analysis and the L-index method. Finally, FVSI based allocation of VAR sources emerges as the most suitable method for reactive power planning.

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Authors and Affiliations

Biplab Bhattacharyya
Shweta Rani
Ram Ishwar Vais
Indradeo Pratap Bharti
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Abstract

In the event of occupational accidents in mining, investors can calculate approximately how much loss will be incurred at the time of the accident. However, in halting mining as a result of occupational accidents or legislation, investors, will perhaps not care about how much of a loss to profits will arise due to the resulting downtime of mining operations. The reason for this is that there is no such halting in mining operation as yet and mining activity is continued. Avoiding halting mines due to occupational accidents and legislation would enable the prevention of unexpected costs resulting from these time losses. The aim of this study was to find out how much the loss of profits resulting from the downtime of mining enterprises due to the aforementioned reasons are in total, and how much the ratio of loss of profits to annual operating costs is on average on an annual basis. To determine the loss of profits and to minimize the accidents in enterprises, permanent supervisors, who are assigned in the enterprises where they are working, were given a survey through the SurveyMonkey program. Of the 235 permanent supervisors who filled out the survey on behalf of the mining enterprises, 58 answered all of the multiple-choice questions examined in the study. These questions were analyzed together according to different mineral groups and differences in mining operation methods. As a result of the analysis, it was determined that the annual loss of profits of mining enterprises resulting from the aforementioned periods of downtime, and the ratio of these values to the annual operating costs constitute a rather significant share. The aim of the article was to raise awareness to have mining companies appropriate more funds for occupational health and safety.
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Authors and Affiliations

Taşkın Deniz Yıldız
1
ORCID: ORCID

  1. Adana Alparslan Türkeş Science And Technology University, Department of Mining Engineering, Turkey
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Abstract

Several conjunctive use approaches can be distinguished. Drought cycling of groundwater (GW) usage and storage relies on more surface water (SW) during wetter years and delivers more water from GW during drought years. This method has the benefit of temporal changes in water availability. Additionally, it is usually desirable in areas with internal variability of SW where surface storage of wet-year surpluses is uneconomical, suffer excessive evaporative losses, or cause unacceptable environmental disruption. In previous studies, the purpose of operating the drought cycling was to reduce operating costs. In these studies, the objective function of the proposed model was to minimise the present value cost derived from the system design and operation to satisfy a predefined demand during a finite planning and operation horizon. However, it is important to consider other objectives in operating water resources systems, including minimising water shortages accurately. Hence, in this study, two scenarios were focused on: 1) mi-nimising water shortagages, 2) minimising operational costs. Pareto solutions are then presented with the objectives of minimising costs and water deficit. In this study, the weighting method has been used to extract Pareto options. The results show that reducing costs from 234 to 100 mln USD will increase water shortage from 9.3 to 11.3 mln m3.
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Authors and Affiliations

Tzu-Chia Chen
1
ORCID: ORCID
Tsung-Shun Hsieh
2
Rustem A. Shichiyakh
3
ORCID: ORCID

  1. Dhurakij Pundit University, Bangkok, Thailand
  2. Krirk University, Thanon Ram Intra, Khwaeng Anusawari, Khet Bang Khen, Krung Thep Maha Nakhon 10220, Thailand
  3. Kuban State Agrarian University named after I.T. Trubilin, Department of Management, Krasnodar, Russian Federation
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Abstract

Today’s electricity management mainly focuses on smart grid implementation for better power utilization. Supply-demand balancing, and high operating costs are still considered the most challenging factors in the smart grid. To overcome this drawback, a Markov fuzzy real-time demand-side manager (MARKOV FRDSM) is proposed to reduce the operating cost of the smart grid system and maintain a supply-demand balance in an uncertain environment. In addition, a non-linear model predictive controller (NMPC) is designed to give a global solution to the non-linear optimization problem with real-time requirements based on the uncertainties over the forecasted load demands and current load status. The proposed MARKOV FRDSM provides a faster scale power allocation concerning fuzzy optimization and deals with uncertainties and imprecision. The implemented results show the proposed MARKOV FRDSM model reduces the cost of operation of the microgrid by 1.95%, 1.16%, and 1.09% than the existing method such as differential evolution and real coded genetic algorithm and maintains the supply-demand balance in the microgrid.
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Authors and Affiliations

G. K. Jabash Samuel
1
ORCID: ORCID
M. S. Sivagama Sundari
2
R. Bhavani
3
A. Jasmine Gnanamalar
4

  1. Department of Electrical and Electronics Engineering, Rohini College of Engineering and Technology, Kanyakumari, India
  2. Department of Electrical and Electronics Engineering, Amrita College of Engineering and Technology, Nagercoil, India
  3. Department of Electrical and Electronics Engineering, Mepco Schlenk Engineering College, Sivakasi-626004, India
  4. Department of Electrical and Electronics Engineering, PSN College of Engineering and Technology, Anna University, India

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