نشریه انرژی ایران
سال بیست و ششم شماره 4 (پیاپی 101، زمستان 1402)

Any extensive change at the level of society requires a systemic approach and understanding the relationship of its effective components for better decision-making and understanding. One of these systemic changes is the occurrence of energy transitions in different countries, which originates from political-social and technological developments. In Iran, based on the upcoming changes, the transition in the field of energy is inevitable in the near future. Therefore, it is necessary to try to manage it properly and adjust the path of transition in accordance with the country's overall goals, rather than showing its adverse effects on the society. This research, focusing on the direction of the macro movement of the oil and gas industry (as the most important sub-sectors of the energy transition), towards the expansion of the value chain, first identified the network of actors in this industry, then using the attitude of the activists of this network, the obstacles to development has introduced the value chain of oil and gas in the country. Finally, by using the rules of transition management theory, a decision model has been presented in line with the transition of this industry towards the expansion of the oil and gas value chain.

Predicting daily peak electricity consumption and identifying its determining factors are helpful in balancing electricity supply and demand. In this study, the maximum daily electricity consumption of Iran is modeled and predicted using different approaches in the period from March 20, 2017 to june 6, 2022. 90% of the observations are used to build the model and the rest are used to evaluate the predictive model. Based on the minimum prediction error criterion, the optimal neurons of the hidden layers of the neural network (NN) model were estimated to be 11 in the first layer and 8 in the second layer. The results show that the frequency of the first consumption peak at 11 a.m. and the frequency of the second consumption peak at 9 p.m. are higher than in the other hours. The variables holidays, hours of energy consumption, number of new corona patients, temperature and relative humidity of the air and population have a significant influence on peak electricity consumption in the regression model. The influence of holidays and air temperature is also stronger than other variables. Comparing the peak power consumption prediction results shows that the prediction accuracy of different models and approaches is not the same. The average prediction error percentage of the GLM, NN, and ARIMA models during 187 days (02/12/2021 - 06/06/2022) are 0.0799, 0.0754, and 0.0714, respectively. Therefore, the ARIMA model with the minimum average forecast error is a suitable model for peak consumption forecasting.

In this paper, a novel interleaved high step-up converter is proposed through coupled inductors (CI) and built-in transformer (BIT) voltage multiplier cells, which is suitable for renewable energy systems. The combination of these techniques gives a greater degree of freedom to increase voltage gain and reduce voltage stress in power switches without operating the converter in high duty cycles compared to other converters that only include BIT or CI. Therefore, semiconductors with low nominal voltage can be used to reduce conduction losses and cost. In addition, an active clamp scheme is used to ensure ZVS turn-on of power MOSFETs, and ZCS performance is provided by leakage inductances. In such a case, the switching losses are minimized and the reverse current recovery problem is reduced. Due to the proposed interleaved structure of the converter, not only the current stress is minimized, but also the input current ripple is limited, which leads to an increase in the lifetime of the input source. All these factors improve the efficiency and performance of the proposed converter. The performance principles and specifications of the proposed converter are analyzed in detail and the simulation results are presented in PSIM software.

This research will deal with the design and technical and economic evaluation of a solar preheating system to be installed in the engine room of a four-story building with the use of a student dormitory with a substructure of 900 square meters and in this research, instead of using the solar heating system as a solar water heater along with the auxiliary heating system with a double tank to install solar and auxiliary system converters, from a separate system as a solar preheater, which has an independent tank as a preheat tank to install the solar system converter inside that tank and a separate tank to install the auxiliary heating system converter in the engine room of the mentioned building with the aim of preventing possible waste of heat energy of the auxiliary system. Energy from fossil fuel) is used for various reasons. In this regard, Carrier, AutoCAD and especially the powerful T*SOL analysis and simulation software are used for project design and calculations. The results show that the use of solar panels as a preheater has an optimal performance in addition to preventing the possibility of energy loss.

One of the influencing factors on energy consumption behavior is having knowledge and awareness in the field of its issues, including electricity generation methods, renewable energies, optimal consumption methods and its environmental consequences. The purpose of this research is to study the level of awareness of Iranian citizens and its effect on electricity consumption behaviors. For this purpose, with a quantitative approach, survey method and online questionnaire technique, we collected the data of household electricity sector subscribers from all over Iran, and finally 1081 individuals completed questionnaires were obtained. Data analysis and interpretation was done with SPSS software. The variable of knowledge and awareness was measured by knowing the comfortable temperature, how to calculate the cost of electricity, peak consumption hours, high consumption devices, the main sources of electricity production and citizens' self-declaration of their level of awareness in energy-related issues. The results showed that the citizens' awareness score is slightly less than 50 (out of 100 points). The behavior variable was measured with twenty items in cooling, lighting, cleaning, washing, etc. It was also asked about the time and amount of consumption of electrical appliances at home. The answers are combined and then the scores are added and standardized in the range of 0 to 100. For the average behavior, a number of 56.48 was obtained. In the correlation analysis between these two variables, Pearson's coefficient was used and the obtained number "0.078" indicates a positive but not very strong relationship between knowledge and awareness and electricity consumption behavior. In the analysis of this finding, by returning to the theories of this field, including theory practice, we find that knowledge and awareness is only one of the elements that shape energy consumption behavior. Other elements such as laws, technologies, infrastructures, norms, skills and meanings also influence the practice variable in combination. Therefore, an important finding of this research is the need to adopt a comprehensive approach to analyze the network of human and non-human actors involved in shaping energy consumption behavior, which in addition to humans, their awareness, beliefs and activities, the role of non-human factors such as technology, infrastructure, tools and facilities is considered as well.

One of the most important technologies that has attracted the attention of researchers and industries in recent years is the Internet of Things (IoT). The energy industry is considered one of the main and important industries of every country, and innovation in this sector is necessary due to its numerous benefits. Despite the importance of IoT technology and the energy industry, studies indicate the weakness of this industry in implementing the technology, and therefore it is necessary to identify and model the challenges and barriers. This research was conducted in two phases with the aim of identifying and modeling the challenges and barriers of implementing the Internet of Things in the supply chain of the energy industry in Iran. In the first phase, the challenges were identified by reviewing the literature and were reviewed and completed by 14 Iranian energy industry experts using the Delphi method. In the second phase, after reviewing the views of 10 experts, the challenge's model was drawn using the fuzzy total interpretive structural modeling method. The data collection tool was a questionnaire, and Purposive and snowball sampling methods were used. Finally, 67 challenges of the implementation of Internet of Things in the supply chain of Iran's energy industry were identified, and these challenges were grouped into 17 categories. . The results of challenges modeling show that the challenges of governance conditions, government standards and regulations, lack of strategic and perceptive management, are the most important challenges of the Internet of Things in the supply chain of Iran's energy industry and should be considered for the implementation of the technology.