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Showing 4 results for Energy
Dynamics of forecasting the development of renewable energy technologies in Ukraine and Chile
Vitalina Babenko, Olena Rayevnyeva, Dmytro Zherlitsyn, Olena Dovgal, Goncharenko Natalia, Miroshnichenko Tetyana,
Volume 31, Issue 4 (11-2020)
Abstract
The processes of transformation of the energy space, namely the impact of alternative energy resources on it, are characterized by changes in the national economy in general and in the energy market in particular. The results of the analysis confirmed the significant dependence of electricity production indicators on renewable sources and such factors as GDP, CO2 emissions, total electricity production, which requires improvement of organizational and economic bases for policy development of state support for renewable energy technologies in countries with exogenous factors. The interdependence between electricity production from renewable sources and economic indicators in Ukrainian-Chilean relations using macroeconomic multifactor analysis based on the correlation method allowed to identify the most influential factors.
Three-Echelon Power Supply Network Design Considering Energy Storage System to Ensure Network Sustainability
Zohre Farmani, Gholamreza Nasiri, Gholamreza Zandesh,
Volume 34, Issue 3 (9-2023)
Abstract
Today, the use of electrical energy storage has a significant role in flatting the load curve, peak shaving, increasing reliability and also increasing the penetration of distributed generation, reducing carbon emissions, and reducing network losses. In this article, a three-echelon power supply chain is investigated considering energy storage as a new level in the power supply chain. The model in this article is an integrated model of locating and capacity planning of distributed energy storage with the aim of maximizing profit and reliability, which is modeled with two different approaches. The first model is modeled from the point of view of the distribution network as the owner of the energy storage and the second model is modeled from the perspective of the electricity subscribers as the owner of the energy storage. Finally, the model is solved by GAMS software and the results of sensitivity analysis are presented. According to the obtained results, the presented model is the most sensitive to the changes in demand and production, and the owners of energy storage should be sensitive to the changes in production and demand in different seasons of the year in order to get the maximum profit.
 
Single machine preemptive scheduling Considering Energy Consumption and Predicting Machine failures with Data Mining Approach
Ali Qorbani, Yousef Rabbani, Reza Kamranrad,
Volume 34, Issue 4 (12-2023)
Abstract
Prediction of unexpected incidents and energy consumption are some industry issues and problems. Single machine scheduling with preemption and considering failures has been pointed out in this study. Its aim is to minimize earliness and tardiness penalties by using job expansion or compression methods. The present study solves this problem in two parts. The first part predicts failures and obtains some rules to correct the process, and the second includes the sequence of single-machine scheduling operations. The failure time is predicted using some machine learning algorithms includes: Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), Naïve Bayes, and k-nearest neighbors. Results of comparing the algorithms, indicate that the decision tree algorithm outperformed other algorithms with a probability of 70% in predicting failure. In the second part, the problem is scheduled considering these failures and machine idleness in a single-machine scheduling manner to achieve an optimal sequence, minimize energy consumption, and reduce failures. The mathematical model for this problem has been presented by considering processing time, machine idleness, release time, rotational speed and torque, failure time, and machine availability after repair and maintenance. The results of the model solving, concluded that the relevant mathematical model could schedule up to 8 jobs within a reasonable time and achieve an optimal sequence, which could reduce costs, energy consumption, and failures. Moreover, it is suggested that further studies use this approach for other types of scheduling, including parallel machine scheduling and flow job shop scheduling. Metaheuristic algorithms can be used for larger dimensions. 

Identifying and Prioritizing the Factors Affecting Renewable Energy Development: A Case Study
Ali Mostafaeipour, Ghasem Ghorbannia Ganji, Hasan Hosseini-Nasab, Ahmad Sadegheih,
Volume 34, Issue 4 (12-2023)
Abstract
Compared to coal and other fossil fuels, renewable energy (RE) sources emit significantly less carbon dioxide (CO2). In this sense, switching to such sources brings many positive effects to the environment through mitigating climate change, so the terms green energy and clean energy, have been derived from these constructive environmental impacts. Given the utmost importance of RE development, the primary objective of this study was to identify and prioritize the effective RE development strategies in Mazandaran Province, Iran, using different methods, including the Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis, along with other decision-making techniques. Recruiting a team of 11 industrial and academic experts, the strategies to implement in this region were developed in line with the RE development plans. For this purpose, the Multi-Criteria Decision-Making (MCDM) methodologies were utilized within the gray fuzzy environment to manage the existing uncertainties. The Gray-Additive Ratio Assessment System (Gray ARAS) was further applied to rank the main factors at each level. According to the SWOT analysis and the Stepwise Weight Assessment Ratio Analysis (SWARA) outcomes, among the major factors shaping RE development in Mazandaran Province, Iran, the economic criterion, with the final weight of 0.24, was ranked first; and then the geographical and environmental criteria, having the final weights of 0.23 and 0.19, were put in the second and third places, respectively. In this regard, appropriate location, with the final weight of 0.226, was ranked first; and subsequently pollution reduction and energy production costs, receiving the final weights of 0.103 and 0.094, were the second and third sub-criteria, respectively. As a final point, the validation results based on the Gray-Weighted Aggregated Sum Product Assessment (Gray-WASPAS) and ranking obtained through the Gray-ARAS were confirmed.
 
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