JOURNAL OF ENERGY PLANNING AND POLICY RESEARCH
Year:2018 | Volume:4 | Issue:11|Papers Count:7

Since the formation of the Iranian electricity market in 1382 (2003), power plants have been competing with each other on a daily basis in the ISO by registering their bid prices. In this competition, the winners are those power plants whose bid prices are lower than the market clearing price for each hour in the next day, so the forecasting the next day market prices is vital for energy producers. In this study, using a combination of K-means algorithm and support vector machine, a new model for predicting the next day market settlement prices is proposed and the model has been used the hourly electricity market prices for 1395-1396 (2016-2017). According to the results, seven competitive clusters were identified for the Iranian electricity market. The average forecasting accuracy of the proposed model for each of these clusters for the years 1395 (2016) and 1396 (2017) was 96% and 94%, repectively.

Oil contracts are supposed to facilitate knowledge transfer from foreign investors to the host country staff improving local knowledge and managerial skills. This paper aims at examining knowledge management in Iran's oil buy-back contracts using structural equations modeling. According to Probst et al. (1998) knowledge management is a multi-dimensional phenomenon including knowledge targets, identification, acquisition, development, allocation, deployment, preservation and measurement of knowledge. Based on the two questionnaires relating to KM in Probst model and practice of KM in Iran oil industry, data gathered from experts of National Iranian Oil Company have been used to evaluate the knowledge management implications of buy-back contracts through structural equations modelling approach. According to the results buy-back contracts have no effect on the existing knowledge management situation in the oil industry. All of KM dimensions should be taken into account in buy-back contracts to improve the KM status in the oil industry.

Oil exporting countries are continuously subject to oil price shocks. They have to deploy effective policy instruments and fiscal rules to manage the negative effects of these shocks. Sovereign wealth funds have been developed as stabilizing instruments to smooth the government expenditures and provide financial resources for optimal domestic investments. This paper develops and calibrates a dynamic general equilibrium model under uncertainty conditions to develop a fiscal rule for the optimal allocation of oil revenues between saving and current expenditures in a 65 year horizon. According to the results, a conservative fiscal policy in the booming periods is highly advised, since permanent oil shocks are more frequent than transitory shocks. During oil booms, excess oil revenues should be allocated to the National Development Fund (NDF) or the Foreign Exchange Reserve Account. The results also show that optimal investment rate increases is higher in the long run since in a longer term horizon the social planner allocates more funds to NDF to compensate for the negative oil shock.

Technology Intelligence System for Energy Supply and Power Plant Industries: Case of MAPNA Power Plant Construction and Development Company

This paper uses the Mork approach to decompose the price shocks and analyzes the welfare effects of gasoline and gasoil price shocks for the period of 2007-2014 in Esfahan, Tehran, Khorasan, Fars, Mazandaran, and Khuzestan provinces using a dynamic panel data approach. According to the results gasoline demand is less responsive than gasoil demand to price shocks. For these provinces, the long run price elasticity of gasoline demand in the transportation sector has been found to be more than its short run price elasticity. This is not the case as regards gasoil demand. Price elasticity of gasoline is very small both in the long run and short run although it is more than gasoil price elasticity. Using an Almost Ideal Demand system, it is concluded that welfare effects of gasoil price shocks, as calculated by Equivalent Variation welfare change index (EV), are less than gasoline price shocks.

In recent decades, supply chain uncertainties have significantly increased mainly due to rapidly changing business environments. This has put the resiliency at the top of priority issues of service provider firms. This paper aims at designing an interpretive structural model (ISM) to analyze the supply chain resiliency of the Power Distribution Company of Bushehr Province based on the data collected through a questionnaire filled by a sample of experts from both industry and academia. After literature review and using content analysis approach, factors affecting the supply chain resiliency were identified. A multi-level interpretive structural modeling was proposed and then validated using structural equation modeling approach. According to the results agility, flexibility, adaptability, and visibility are the most significant factors affecting the supply chain resiliency of the Power Distribution Company of Bushehr Province.

Environment protection and sustainable development have recently grabbed the attention of policy makers towards renewable energies. Agriculture has been a major energy consuming sector in Iran in recent years as pressurized irrigation systems have changed into a new policy priority. This paper explains how photovoltaic (PV) agricultural water pumps can be adopted under different conditions. A life cycle cost analysis has been done for solar, diesel and electric pumping systems for the case of grape cultivation in Kaashmar region. According to the results, under subsidized fuel supply for electricity generation, solar pumps can only compete with diesel pumps for demands of less than 4. 5 kilowatts. Moreover, the feasibility of solar irrigations highly depend on the area under cultivation, the type of cultivated crop, access to other energies and their costs, solar system technologies, and the type of irrigation system. Thus, feasibility studies for solar irrigation should be done on a case by case basis.