mohammadreza miveh

Considering the significant role of electrical energy in economic and social development, as well as the substantial investments in the power industry, particularly in distribution networks, practical strategies to reduce losses and enhance the efficiency of existing facilities are essential. This paper investigates and compares the impact of all available methods on loss reduction and voltage profile improvement from both technical and economic perspectives on a real network. Additionally, to achieve more accurate results, the uncertainties in distributed generation resources and network loads are accounted for using scenario generation methods. The study evaluates all possible methods to determine the optimal location and capacity of equipment as the problem-solving algorithm. Load forecasting is incorporated to assess the effectiveness of the proposed methods for future years. Furthermore, a novel idea of reactive power injection by a solar power plant is considered and compared with the case of a solar power plant without reactive power injection. The proposed methods are simulated on five real high-loss distribution feeders in Iran using DIgSILENT software and Python programming. The simulation results indicate that the optimal placement of a solar power plant with reactive power injection provides the best technical performance, while capacitor placement offers the highest economic efficiency.

Renewable energy sources are particularly important in clean energy transitions and must be considered in Generation Expansion Planning (GEP) problems due to low cost, ease of installation, and ability to implement Demand Response (DR) programs. However, challenges such as the stochastic nature of renewable energy sources, consumer unawareness regarding participation in DR programs, and difficulties in integrating some resources have posed challenges to the use of these resources in the GEP problem. This paper addresses these challenges by using the Weibull distribution function to model wind power plants' uncertainty and rewards and penalties to motivate consumer participation in the GEP problem. To achieve these objectives, initially, the adequacy assessment of the generation system is performed analytically using the reliability index, which includes Expected Energy Not Supplied (EENS), considering the forced outage rate of generators in the DIgSILENT power factory through Python programming. Subsequently, an optimized GEP model is presented to enhance the generation system's adequacy against short-term demand for the next year. In this model, wind farms along with the DR program are integrated and optimized using the genetic algorithm, employing Python programming. The genetic algorithm selects the number of existing turbines in the wind power plant and the level of consumer participation needed to reduce the EENS to the desired value at the minimum cost. Validation of the proposed model is conducted on a 9-bus network. The strength of the presented method lies in its applicability to real-world networks modeled in the DIgSILENT power factory.

In this paper, an improved online method for simultaneous estimation of parameters and fault location in transmission lines based on Phasor Measurement Units (PMUs) installed on both sides of the transmission line is proposed. Unlike offline methods, the proposed method does not depend on the geometric characteristics of the lines, the fault resistance and the impedance of the equivalent Thevenin sources. The most important application of line modeling is to estimate the thermal rating of transmission lines, which limits the power transmission capacity. Offline methods cause part of the power transmission capacity to be unused and create unrealistic congestion in the transmission system. Therefore, the estimation must be determined in a way that is online and has the appropriate accuracy and speed. Calculating the thermal rating is a complex task that requires estimating the parameters of the lines, calculating the temperature of the lines and determining the climatic conditions of the environment around the studied lines. Therefore, a new method based on a combination of direct conductor temperature monitoring method and method based on climatic conditions is proposed in this paper. In this method, first, the parameters of the transmission line are estimated and then using these parameters and the weather conditions of the desired line, the thermal rating is calculated online. In this paper, the Nonlinear Least Squares (NLS) method is used to perform the process of simultaneously estimating the fault location and line parameters. The simulations were performed in the MATLAB software environment on a standard series capacitor-compensated transmission lines. The simulation results show that the proposed method has high accuracy and speed and can play a significant role in increasing the speed of line repairs and system reliability and reducing social and economic losses.

Today, with the depletion of fossil fuels, the trend towards using renewable energies has increased. Among the various types of renewable energy resources, solar systems received more attention due to technical, environmental and economic reasons. However, the efficiency of solar panels is not high due to several reasons. In addition to using high-efficiency control equipment such as maximum power point tracking (MPPT) methods, cleaning the surface of solar panels is one of the best and easiest ways to improve their efficiency and performance. In this paper, to increase the efficiency of solar panels, a robot for cleaning solar panels with the ability to adjust the height with the help of tank wheel technology has been developed. Moreover, due to water shortages in the country, the design of this robot has been done without the use of water. Considering that the designed robot has a telecommunication system with remote control capability, the speed of cleaning is very high and there is no need for the operator. The used software in the robot provides proper scheduling for the cleaning of solar panels. In addition to the aforementioned advantages, the proposed robot has a much lower price than foreign products. The performance of this robot on the five kW solar power plant of Tafresh University has been evaluated. Increasing the efficiency of the power plant, more revenue from the sale of energy to the grid and significant savings in water consumption are the main advantages of this intelligent robot.