IRANIAN ELECTRIC INDUSTRY JOURNAL OF QUALITY AND PRODUCTIVITY (IEIJQP)
Year:2021 | Volume:9 | Issue:4 (21)|Papers Count:7

The main goal of this paper is to analytically analyze of the effect of the electric vehicle aggregators on the statistical behaviors of Locational Marginal Prices (LMPs) of busses, considering network congestion. In order to achieve this aim, at the first step, by extending the LMP decomposition into 6 sections in Lemma1, the sensitivities of LMPs to the power generation of aggregators in each bus are analyzed analytically. With the help of the coefficients of LMP decomposition, it can be possible to rank the busses to the presence of electric vehicle aggregators, based on the impact on the random behavior of electricity prices. It must be noted that this ranking does not same with the cap of electric vehicle aggregators. Finally, in order to confirm the analytical results, at second step, the random behaviors of power generations of aggregators in each bus are simulated in the IEEE-24-bus system, and the STandard Deviations (STD) of LMPs are calculated for all basses. The correlation of STD with LMP decomposition coefficients indicates the accuracy of LMP decomposition in the prediction of the effect of the presence of electric vehicle aggregators on the random behavior of LMP.

There are several methods to cover the costs of a transmission system and distribution networks. These methods are divided into either incremental or marginal approaches, which can be either long-term or short-term. The main difference between the incremental and marginal approach is how to calculate the cost of using the network. In the incremental approach, simulation and in the marginal approach, cost sensitivity analysis methods are used to determine the cost. If the tariff is based on the optimal nodal or regional prices, that is a short-term approach, then it only considers the existing equipment. Unless cost optimization is combined with network design optimization. Since the nodal pricing principle is to generate the economic signal, the tariff does not cover the entire network cost. Long-run incremental or marginal approaches can be used to create incentives for future investment. In this paper, a long-run marginal approach has been modified to create the economic signal for the future investment, which can show the effect of power injections on the buses over the long-term costs of the network reinforcement. At the same time, since the marginal approach does not cover the full cost of the network, extended benefit factor method have also been used to compensate for the lack of income. A practical distribution network is used to illustrate the theoretical and practical principles of the proposed solution. Results are compared for different system conditions. The results show that the price of the proposed solution involves not only the future development costs of the network but also the distance between production and consumption and shows the factors that affect network tariffs.

Due to incremental use of renewable energies, some challenges have been arised in control of wind power systems. One of the important ones is the uncertainty of wind power that affects directly on the cost of utilization. In this paper some algorithms based on the fuzzy logic and neural networks have been implemented to analyze the uncertainty of the wind power using historical data collected from a sample wind field. The algoriths are BLS, RLS, MLFE and ANN. Simulation results show that all of the methods results in acceptable outputs but the accuracy of the cobined RLS-MLFE is much better than the others.

Events with low probability and high impact, which annually cause high damages, seriously threaten the health of the distribution networks. Hence, more attention to the issue of enhancing network resilience and continuity of power supply, feels more than ever, all over the world. In modern distribution networks, because of the increasing presence of distributed generation resources, an alternative way for load supply and enhance network resilience, is use of distributed generation resources during failure occurrence on the main network. In this paper, first, the resilience concept and modeling the natural disasters including flood and storm in the presence of distributed generation resources is presented. Then in order to study the impact of distributed generation resources on resilience of the distribution network, a new index based on resilience for the load supply and resilience of distributed generation sources, including solar cells and conventional gas-fired sources, is formulated, and the resiliency index of the network is achieved in the presence of these resources. Ultimately, using genetic algorithm as a tool for optimization, with the aim of enhancing the network resiliency, we attempt to solve the optimal distributed generation planning problem that means determination the optimal type, site of available capacity of these sources, and the performance of the proposed approach is illustrated by numerical studies on a typical distribution network.

In order to solve the problem of voltage drop and voltage imbalance in the distribution systems, the injection of reactive power by multiple static compensators is used. The distributed generation such as photovoltaic systems could play a role of the static compensators by producing reactive power. In this paper, the integral to droop line algorithm is used to control the reactive power in busbars. The droop coefficient and integral gain are important parameters in this algorithm. The determination process of these coefficients is modeled as a nonlinear and multi-objective optimization problem. The objective function in this problem is defined to establish a tradeoff between the voltage deviation and voltage unbalanced factor considering stability condition, in which the salp swarm optimization algorithm is used to solve the problem. The above problem is solved by three other optimization algorithms, which the salp algorithm has a better performance than the three algorithms. Applying the obtained optimal gain coefficients to the compensators, not only the voltage profile is improved but also the voltage unbalanced factor is reduced. The proposed method is simulated on the IEEE-34 Node test feeder. To evaluate the stability of the system, the eigenvalue analysis is used. The results show the validity of the proposed method on the system stability.

Energy supply is one of the main challenges of the present century. Population growth, rising energy demand, fossil fuel scarcity, and environmental concerns have made energy security as major issue for all countries in the world. Traditional power system, including low-efficiency generation, long-distance transmission and then a complex distribution system, face various challenges that make it inappropriate as a future secure energy system. Today, the energy hub as a framework of generation, conversion, storage and consumption of different energy carriers, is considered by many researchers as the prospect of a future secure energy system. This research analyzes and classifies the latest innovative research achievements in this field. A review of research in the field of long-term energy hub planning, optimal operation of this multi carrier energy infrastructure, and the concepts of micro and macro energy hubs is presented in this paper. A bibliographic study is also discussed with a historical review of the energy hub researches. In addition, one of the main goals of this research is to explain the concept of smart city consisting of smart energy hubs using a review of recent research by researchers in this field.

The purpose of the research is to identify, prioritize and measure the impact and impressible of factors affecting the productivity of organizational human resources and practices, their review in Tabriz Electrical Distribution Company by the descriptive-analytical method and with the questionnaire (with a sample of 50 employees) and the validity is 0. 84. At first, factors affecting human resource productivity were identified and then by Multi-criteria decision making methods, DEMATEL and Fuzzy Neural Network Process the interactive inter-communication between factors and importance of their weights was determined. In the factors, there are five main factors and 21 sub-factors affecting the productivity of the workforce Accordingly, the priorities of the main factors include job satisfaction, quality of work-life and work incentives and organizational commitment and prioritized sub-factors of satisfaction with the conditions and facilities, the satisfaction of performance appraisal, occupational safety and mental well-being, job satisfaction, and identification and Staff appreciation, satisfaction with education and having a well-deserved guardian (51% weight)and of course, the job satisfaction factor has the most impact and the least impressible and organizational commitment of employees, contrary to other factors, and there is a need for the company to focus on priority factors, respectively.