نشریه انرژی ایران
سال بیست و دوم شماره 4 (پیاپی 85، زمستان 1398)

With the adoption of the General Policy Implementation Law, Article 44 of the Constitution, the power generation sector (state power plants) has been recognized as a transferable activity to the private sector.Previously, the government exercised its sovereignty over the property With the start of the process of transferring ownership of power plants to the private sector, the issue of how to apply the Ministry of Energy's sovereignty over the private sector to generate electricity industry in order to ensure the continuation of reliable and sustainable electricity production became important.Because, in accordance with Article 44 of the Constitution, the government's responsibility to ensure a secure and sustainable electricity supply as one of the important infrastructures of public services remains strong, and the development of privatization does not interfere with this. Therefore, the implementation of privatization policies should be done by amending the relevant legal regulations in such a way that the exercise of sovereignty over the electricity generation sector after the implementation of privatization policies will not face any problems.Using the descriptive and analytical method, the present study first deals with the necessity of applying governance in the production sector of electricity industry and then by studying the current situation and examining and pathology of governance practices, the strategies of governance in the private sector of electricity industry production are examined. Provides corrective data and suggestions.

This study aims to forecast Iran's electricity demand by using meta-heuristic algorithms, and based on economic and social indexes. To approach the goal, two strategies are considered. In the first strategy, genetic algorithm (GA), particle swarm optimization (PSO), and imperialist competitive algorithm (ICA) are used to determine equations of electricity demand based on economic and social indexes consisted of population, gross domestic product (GDP), electricity price, and electricity consumption during the years 1968 to 2015. In this regard, linear and nonlinear models are developed. In the second strategy, artificial neural networks (ANNs) trained by meta-heuristic algorithms (GA, PSO, and ICA) are used to forecast electricity demand. The results show that nonlinear PSO with %2.85 mean absolute percentage error (MAPE) is a suitable model to forecast Iran's electrical energy demand. Iran's electricity demand would reach 324 terawatt-hours (TWh) up to the year 2025.

Paying attention to the increasing concerns of global warming, environmental crisis and energy, International research and Development has extended to sustainable energy systems.In this research,Applied method for optimizing the installation space of photovoltaic system Due to the limited space in the buildings of the country's cities, As the effect of the shadow on the efficiency of the panel, The intensity and angle of radiation, In order to produce the maximum calculated energy, That using Meteorological data including sunny hours, cloudy and humid, cities in central Iran classification and Regression model of radiation intensity and sunny hours of each class were identified in SPSS simulation software.Five cities of Tehran, Ahvaz, Yazd,Gorgan and Ardabil were selected and their average radiation intensity was obtained.in order to Generating more photovoltaic energy and increasing the capacity of the distribution network,to minimize the space where the photovoltaic system is installed,Panel angle relative to horizontal surface, Reduce the shadow of each panel compared to the other panel,From the Bee Colony Metabolization Algorithm used in Matlab software and compared to its normal state,also simulation results In the optimal state, It is analyzed and presented with the installed system of electricity subscribers in Tehran. The results show with monthly recorded data of the designated photovoltaic system in Tehran, optimal panel installation space, service and maintenance, reducing shade increases energy production and capacity release and the stability of the distribution network becomes electricity.

The importance of energy in the sustainable development of societies and its role in the economic, social and political relations of today's world is indisputable. The availability and proper use of energy resources ensures the full development of any country. Because of the importance of this issue, the topic of energy literacy was introduced in science education in the recent years. Textbooks play an important role in conveying the concepts of energy resources, production of energy and consuming it. The purpose of this research is to analyze the content of the first-grade students' textbooks in terms of the amount of their attention to the topic of energy. The indicators were used to analyze the content of the textbooks are based on the Powers and De Waters definition of energy literacy. The data from the content analysis were analyzed using descriptive statistics, frequency and percentage of frequency. The results of data analysis show that the environmental issues and the importance of energy in the individual and social life of the people have not been paid much attention. Given the importance of the energy issue in the whole world and the limitation of non-renewable energy sources, such as oil and gas, their issues and problems, it appears this result is not desirable. The most focus of first secondary science textbooks is on the energy resource in the home, which is created a superfical view about main resource energy and does not create a general picture about issues and problems related to high energy consumption in the country. In the discussion of energy consumption, the hidden energy has not addressed, which is one of the main weaknesses of the first secondary science textbooks in energy education. Another noteworthy point is that the content of the first secondary science textbooks has paid little attention to the amount of inputs and exports, and the need for energy conservation.

In this study, supply electrical, heating and cooling loads in the building by a separate production system and a cogeneration system, particle swarm optimization algorithm in five different scenarios were studied and analyzed (For example, Data and Information a high-rise building base 72, in the city of Kerman is used). The results show that, cold and heat power system of micro gas turbine with a capacity of 195kW as the primary driver, of absorption chiller and electric chiller respectively with a capacity of 281kW and 439kW to provide cooling load, boiler with a capacity of 187kW to compensate for the lack of heat load and from photovoltaic/thermal system with a capacity of 52.8kW to generation electric load and supply hot water used, optimal system is to supply the base building loads. In this optimized system, the annual electricity sales revenue of  2,814,420,000R, The annual cost of buying the power of 211,560,000R, The cost of buying fuel annual consumption of 475,560,000R, and annual production carbon dioxide emissions in the building are 230.42tons. The initial payback period of the project is estimated at 5.167 years.

The design of Building Intelligence Energy Management Systems (BIEMS) in the air conditioning sector is a complicated multi-objective problem for large buildings with big engine rooms and scattered inputs that it must provide simultaneously maintaining system stability, adapting to time-varying conditions, reducing energy dissipation and maintaining proper air quality. To achieve these at times conflicting objectives, using a distributed control approach is appropriate for building energy management of these buildings. In this paper, a Cooperative Multi Agent-Building Management System (CMA-BMS) equipped with fuzzy controllers and virtual sensors is proposed, in which each of the agents controls one of the main components of the cooling engine room in the HVACchr('39')s primary circuit and contrary to many previous works, the topology of them is horizontal, and each agent operates independently and in parallel, which leads to faster performance, easier error detection, and more extensibility. For modeling, the virtual sensors implemented with Radial Base Function (RBF) neural networks are utilized, which also have the role of actual sensor backup. To evaluate the project, two BEMSs are designed and tested with a conventional centralized and the proposed strategies, where simulation results indicate that the proposed design is less processor and memory intensive, and the fuzzy controllers show a better performance in terms of energy consumption versus the non-fuzzy controllers.

In Iran, the electricity industry is considered as one of the underlying industries in the development of the country with the supply of electrical energy needed by the industry and the people consuming it. In line with this, a large number of national large scale projects are implemented annually to transfer electricity across the country. Therefore, identifying and prioritizing the factors affecting electricity transmission projects in the country is mainly a serious and multi-criteria issue and is of strategic importance for the Iranian electricity industry. Therefore, the main purpose of this paper is to identify and prioritize the influential factors on EPC contracts of multi-criteria decision making and hierarchical analytical process modeling of the countrychr('39')s electricity transmission projects. Using the results of the present study, we propose practical solutions.