Forecasting the needs for electricity and timely implementation of electricity projects and the prevention of power outages, is from the tasks of electricity companies. The purpose of this research is to identify the causes of delay and design of the factors affecting t More
Forecasting the needs for electricity and timely implementation of electricity projects and the prevention of power outages, is from the tasks of electricity companies. The purpose of this research is to identify the causes of delay and design of the factors affecting the implementation of electrical industry projects. This research is in terms of applied and qualitative method. In this research, to identify the effective factors, an unscheduled interview was conducted with managers and experts in the electricity industry in the regional power company of Khorasan. After identifying the effective factors and grouping them using the Structural Interpretation Modeling (ISM), the classification of the criteria and the relationship between them and the presentation of the model was performed. In this regard, 162 criteria were identified as factors affecting the implementation of projects that, after sorting and categorizing these criteria, were divided into 11 groups, of which 7 criteria, in turn, were divided into several other criteria. They were After identifying the above factors and classifying the factors affecting the implementation of the electricity industry projects, in particular the projects of the dispatching centers and providing the model, preventive measures are proposed.
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As a promising vision toward obtaining high reliability and better energy management, nowadays power grid is transferring to the smart grid (SG). This process is changing continuously and needs advanced methods to process big data produced by different segments. Artific More
As a promising vision toward obtaining high reliability and better energy management, nowadays power grid is transferring to the smart grid (SG). This process is changing continuously and needs advanced methods to process big data produced by different segments. Artificial intelligence methods can offer data-driven services by extracting valuable information which is produced by meter devices and sensors in smart grids. To this end, machine learning (ML), deep learning (DL), reinforcement learning (RL), and deep reinforcement learning (DRL) can be applied. These methods are able to process huge amounts of data and propose an appropriate solution to solve power industry complex problems. In this paper, the state-of-the-art approaches based on artificial intelligence used by smart power grids for applications and data sources are investigated. Also, the role of big data in smart power grids, and its features such life cycle, and efficient services such as forecast, predictive maintenance, and fault detection are discussed.
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The mismatch between production and consumption and concern for providing the energy needed in the power grid has always been one of the problems of grid operators. Also, large-scale electricity production has always been a costly and polluting industry. Therefore, engi More
The mismatch between production and consumption and concern for providing the energy needed in the power grid has always been one of the problems of grid operators. Also, large-scale electricity production has always been a costly and polluting industry. Therefore, engineers and power generation companies have always been looking for a cheap and clean way to generate power so that they can overcome the power imbalance and ensure stability and the allowed level of pollutants in the network. The most common sources used in the power grid to generate power are gas and thermal units, these units enter the circuit faster and, of course, have a higher production cost. When to use thermal units and when to use gas units in the network is a complex issue, which depends on various factors such as peak time, fuel price and gas supply network conditions. None of these things are certain in their true state. Therefore, in this article, the uncertainty of gas sources and the variability of gas prices in the problem of the participation of power plant units in the power system have been modeled and analyzed by GAMS software. Also, the mixed integer non-linear programming solution method has been used to solve the problem. The results show that if there is uncertainty in the gas sources, there is a greater tendency to use other power plants in the network in order to maintain the security and stability of the network.
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