مجله کیفیت و بهره وری صنعت برق ایران
سال دوازدهم شماره 3 (پیاپی 32، پاییز 1402)

In this paper a bi-level optimization approach is introduced to increase the participation of household investors to install battery storage systems in the distribution level. The incentive prices are used here to encourage the private investor. In the upper level the viewpoint of the government is modeled in which, the peak shaving is the goal while minimizing the cost due to the allocated subsidies. In the lower level of the model the investors’ point of view is modeled and their profit is maximized. The bi-level model is converted into its equivalent single level optimization using KKT optimality condition. The proposed single level nonlinear model has been linearized using big-M method and the strong duality theorem (SDT). A dynamic iterative model based on grid-search is also implemented to model the behavior of the participants. The performance of the presented model has been analyzed using real data of Yazd network.

Optical fiber day lighting systems help the efficiency of using solar energy in the lighting system in domestic, industrial and agricultural sectors. A lot of work has been done on these systems, but the lack of a reliable simulation method to reduce the tolerance, time and cost is evident. In this regard, in this paper, a novel method based on two powerful Optifiber and Zemax software has been proposed for simulating and optimizing an optical fiber daylighting system to increase the efficiency of output power from the fiber. A comparison has been made between popular types of polymer fibers with OptiFiber software, which shows the maximum propagation of modes in PMMA polystyrene fiber. Extracted detailed characteristics from Optifiber related to the selected fiber with 1mm diameter have been implemented in Zemax software and a daylighting system has been simulated. The coupling of light into the optical fiber has been optimized with the Global Optimization Method based on the genetic algorithm. By selecting a 100 watts resource as the sun according to the AM 1.5 standard, the simulation indicates a transfer of 22 watts of power. After optimization, this power increases to 30 watts, which shows the role of optimization in increasing the efficiency to 36%. Practical results by testing on a system with intensity measurement confirm the simulation results. As a result, by choosing the right fiber and accurately determining the characteristics, and optimizing the coupling into the fiber, it is possible to help increasing the efficiency of optical fiber daylighting systems.

Improving reliability indices, increasing the grid production capacity, reducing power loss during peak periods and fuel cost, and economic and environmental considerations on the one hand, and the progresses made in the field of semiconductor devices on the other hand, are some of the factors that increase the penetration of the renewable energy sources to the distributed network. When the photovoltaic panels are present at the level of the distribution network, there are periods of time when the voltage exceeds its allowable area. Regarding this issue, it is necessary to consider solutions to overcome this challenge. This paper suggests an online dynamic voltage restorer whose control algorithm is designed according to the genetic algorithm. The simulations are carried out according to the voltage profile of a part of Mashhad. They have been investigated by DIgSILENT software. For this purpose, the real data of energy consumption in the residential sector with a time step of one hour has been used and the simulations have been implemented in the form of three scenarios. The first scenario is when there is no solar cell. The second is when the solar cell is online. And the third state is when both the cell and the dynamic voltage recovery in the line help the stability of the system and improve its power quality. According to the obtained results, by applying the mentioned control method, not only more than 7% of the range of voltage variations is reduced, but by reducing the level of voltage imbalance, the level of efficiency and reliability of the network is also increased.

Given the significant power generation and consumption of variable-speed pumped storage power plants (VSPSPs), it is crucial to enhance drive methods and decrease drive losses, thereby increasing productivity. This paper proposes the topology for VSPSP drivers with two level voltage source converters (2LVSC) with 6+1 converters. Hydraulic and electrical VSPSP models are presented, following which VSPSP driver is introduced. Likewise, drive switching and control method is described and 2LVSC (6+1) is introduced. Subsequently, VSPSP drivers with 6+1 modules are proposed as a novel topology. The thermal loss model of the driver is subsequently introduced and the thermal losses of one driver are simulated and evaluated. The simulation results demonstrate that the driver with 2LVSC (6+1) based on a direct torque and flux control (DTFC) scheme as a VSPSP driver results in decreased Mean Time to Between Failure (MTBF) and enhanced the speed of the repairing and maintenance in driver and VSP. Presented thermal model is shows the temperature of the driver stabilizes during the entire duty cycle. Eventually, the outputs of this project is simulated in MATLAB 2021/Plexim software and are validated and verified using the sample of variable-speed wind power plant (VSWP) with 2.2KW.

Climate change and environmental instability have made the supply chain of the electricity industry face a serious challenge. Calculating the productivity of the three-stage sustainable supply chain of this industry during consecutive years is the main problem of this research. The method of this research is of an applied type with the aim of developing data coverage analysis models, which is quantitative in terms of variables and retrospective in terms of cross-sectional time. In this article, a new model based on data envelopment analysis is presented, which is able to combine with Malmquist's productivity index, in addition to determining the efficiency score, the productivity level in the three-stage supply chain of electricity production areas in measure the country of Iran during the years 1398 to 1400 and show the amount of progress or regression, according to which, out of 16 districts under study, 5 districts have progressed consecutively.

Deep learning method is used to predict the future value of load demand. Based on obtained results, a new model based on the forward-backward load shifting and unnecessary load shedding is presented. As well, to increase energy efficiency, excess renewable energy has been used to produce green hydrogen. For this purpose, GAMS optimization software has been used for optimal operation of the microgrid in the presence of renewable energy sources, battery, diesel generator, aqua electrolyzer, and fuel cell considering demand side management (DSM) restrictions. The obtained results from the proposed model of the considered microgrid show that the huge amount of excess electricity can be saved to enhance energy efficiency. This issue increases green hydrogen production that can be used for fuel cell consumption. As well, the proposed model provides lower cost of operation cost.  In addition, the diesel generator consumes lower diesel fuel.

In this paper, a new two-stage control algorithm to reach the maximum power point in photovoltaic (PV) systems under partially shaded conditions is presented. This algorithm tracks the maximum power point without any need to measure the open circuit voltage, short circuit current and making use of any extra switches. To achieve maximum power performance, the method firstly selects the relevant region of maximum power point based on received radiations from solar panels. After finding the correct power region, the accurate point of maximum power is reached by applying P&O method. Using this algorithm decreases the costs and also finds the MPP with more speed and accuracy at the first cycle. Performance validation of the proposed algorithm, is performed by running simulation on a typical system. The simulation results show the superior performance of the proposed approach in comparison with the conventional methods.

In this paper, a two-level optimization model of mixed quadratic integer programming (MIQP) is presented in order to optimally operate microgrids under worst-case output conditions of renewable energy sources. This two-level model is divided into two high-level and low-level problems. In the high-level problem, the goal is to reduce energy loss and load shedding in the demand response program, optimal charging and discharging of electric vehicles and energy storage systems. In the low-level problem, the objective is to maximize the power outage of renewable energy sources. In this model, a new method for solving the optimization problem is proposed, which is based on the reformulation of the problem to Karush-Cohen-Tucker (KKT) optimality conditions. For the analysis, a 33 bus microgrid is considered. The simulation results show that the proposed model maintains the flexibility of the network in the worst output conditions of renewable energy sources and no load interruption occurs in the network.