JOURNAL OF ENERGY PLANNING AND POLICY RESEARCH
Year:2019 | Volume:4 | Issue:13|Papers Count:7

This paper evaluates the structure, topological features and stability of the global trade of natural gas, including both pipeline and Liquefied Natural Gas (LNG), using complex network theory. According to the results, the natural gas trade network can be mostly described as a free scale network with heterogeneous characteristics. The results indicate that for most countries LNG trade network is more flexible than pipeline natural gas trade network. In addition, supply security and resilience of gas importing countries highly depends on diversification of supply sources and enhancing trade relationships with politically stable gas exporting countries. Although natural gas trade networks are mostly resilient to random disturbances, they are vulnerable to deliberate manipulations. Finally, LNG networks are much vulnerable to any trade disturbance compared to pipeline networks.

The concentration of fossil fuel resources in specific geographic regions of the world and the strategic nature of fuel as a tradable commodity has created an intertwined and complex network of trade relationships among importing and exporting countries of fuel. In this research, using Graph Theory, the topology of the fuel trade network in 1995 and 2017 is analyzed and the position and significance of the main actors of the fuel trade were evaluated. The results show that the fuel trade network is a fully connected network with different growth patterns of trade clusters. It was also observed that countries with higher export share do not necessarily have a greater influence on the volume of fuel trade. While increasing the share of fuel trade, countries tend to trade beyond trade clusters. We also found that changes in trade clusters indicate a more regionalized trade in fuel. Finally, the results show a decrease in Iran's influence in fuel trade.

The purpose of this study is to estimate the bio-oil from the pyrolysis process of waste products in terms of moisture, constant carbon, volatile matter and ash. The results of 41 different studies were used to modeling. We use the neural network model as a policy tool in the evaluation and prediction bio-oil from the pyrolysis process of waste products. Statistical method was used to determine the optimal values of the neural network parameters. The results of comparisons between two Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) neural networks showed that the RBF has a high ability to estimate the bio-oil. The value of correlation coefficient between experimental and predicted bio-oil by RBF was equal to 0. 99. The neural network evaluation results showed that it can be used as a tool to estimate the production of bio-oil and it has been used in bio-oil production management decisions.

This study provides a dynamic behavior analysis of roadmap development for enhanced oil recovery (EOR) technologies based on system dynamics in order to reduce domestic R&D costs. The key variables and causal-loop diagram were identified via expert opinion and data. The mathematical relationships amongst variables were identified based on background research and in approximately two years, a mathematical model of the system was developed using computer simulations in VENSIM. The results indicated two types of damped oscillation and goal-seeking dynamic behaviors for model variables. Further, the dynamic hypotheses testing indicated that increased technological maturity and education reduces costs while increased technologic complexity would increase the costs. The best scenario was identified as a combination of highly-mature technology, high complexity, and high education level as it leads to minimum costs.

Municipal waste disposal can lead to many environmental, social and economic damages requiring effective solutions to manage municipal solid waste (MSW). Moreover, biomass energy produced out MSW is considered as an important source of renewable energy. This paper is an attempt to compare and identify the priority ordering of four available technologies to convert urban waste into biofuels for the case of Iran using Analytic Hierarchy Process (AHP) method and Expert Choice software. According to the results Anaerobic Digestion gets the first rank in the priority ordering of urban waste conversion into biofuels.

This paper aims at identification, classification and measurement of conservation barriers of electrical energy in the industries of Semnan Province using a combination of qualitative and quantitative research methods. In the first step a thematic analysis is used as the most common form of qualitative analysis for pinpointing, examining, and recording "themes" of energy conservation barriers based on a sample of 25 experts selected by targeted sampling method. Using Nvivo Software, the barriers are classified into six categories of managerial expertise, awareness and knowledge, financial resources, political and legal environment, socio-cultural incentives, and technological factors. In the second step a quantitative analysis has been done using path analysis and confirmatory factor analysis. According to the results, managerial factor has the highest rank (0. 503), followed by awareness and knowledge (0. 581), financial resources (0. 476), political and legal environment (0. 616), socio-cultural incentives (0. 402), and technical and technological factors (0. 416).

Demand Response (DR) is an effective method for electricity coservation. Time-based demand response is basically based on general categorization of consumers including industrial, agricultural, commercial, and residential subscribers. Due to identicalness of tariffs within each category, time-based (PTB) demand response generally leads toexcessive load transfer. In this paper, with the subcategorization of each category and introduction of different tariffs for each subcategory, a new type of time-based demand response is introduced. It is expected that this proposal will lead to better consumption pattern within subcategories as well as its encompassing category. To validate the proposed scheme we have used real data for the case of glass and cement industries. Genetic algorithms and particle swarm are used to optimize the objective function. Usind data garnered from energy audits in these industries and categorizing load data in accordance to their time of use, it has been concluded that demand response tariffs can result into load shifts and reduced electricity consumption.