نشریه تحقیقات نوین در سیستم های قدرت هوشمند
سال هفتم شماره 2 (پیاپی 16، پاییز و زمستان 1397)

Distribution economic burden in power system is one of the important and essential issues in power plant production planning. This thesis presents the economic burden for generating power plants with smooth and uneven functions and considering the constraints of the power plant (steam valve, forbidden areas, with and without transmission losses) in a multi-generator power system. The goal is to achieve the best production cost and minimize losses. For this purpose, a hybrid particle swarm algorithm based on sine and cosine acceleration coefficients is used to solve the problem. A comprehensive analysis of how to distribute the economic burden to optimize production costs and minimize losses is provided. The proposed method is implemented on a power system of 15and 40 IEEE standard generators. The constructs illustrate the accuracy and efficiency of the proposed method.

Since distribution networks have a large share of the losses in power systems, reducing losses in these networks is one of the key issues in reducing the costs of global networks, including issues Which has always been considered. In this thesis, the reconfiguration of the distribution network in the presence of distributed generation sources (DGs) with respect to two types of bus, P bus and PQV (remote voltage control bus) is presented. The 'P' bus is only shown with active power specifications while the PQV bus is buss, while the voltage magnitude is remotely controlled by the P bus. A simple method for select the "P" bus in a distribution network to control the "PQV" bus voltage magnitude. A sensitivity analysis method has been used to select the bus to placement DGs with a unit power factor. The location of the DGs are in two ways: a) sequential placement b) non-sequential placement of DG in a distribution network. The  exchange Market Optimization (EMA) algorithm is used to determining the optimal size (DGs) of distribution systems following network reconfiguration. The exchange market algorithm is a robust, efficient, and Resistant algorithm for solving optimization problems. This algorithm is inspired by the optimization of human intelligence and the way exchange trading in the exchange market. The EMA algorithm has been simulated on a radial distribution system IEEE 33 using MATLAB software. The objective function for network reconfiguration in this thesis is to reduce power losses.

In this paper, a generalized Lagrangian-based algorithm (TRALM) and gravitational search algorithm (GSA) are used to solve the Optimal Load Distribution Problem (OPF) in the presence of FACTS equipment. Four TCSC, TCPST UPFC and SVC equipment’s have been tested. Modeling of FACTS equipment is based on power injection method. To optimally manage production, losses and voltage profiles, a four-objective function is formulated: Minimum fuel cost, Minimum investment cost of equipment, Minimum loss of transmission and voltage profile improvement and formulated in a single-objective optimization problem. To achieve the optimal point due to the contradiction of the goals, a series of coefficients are used to normalize the objective function. The solution is that the GSA algorithm first randomly determines the FACTS equipment configuration and normalization coefficients, and after installation, the TRALM algorithm solves the problem in the presence of FACTS equipment in the common market. The test results on the BUSIEEE 14 test system indicate that the algorithm has a good effect on the problem objectives.

Load balancing and phase balancing are important complement to reconfiguration of the feeder and the network.In the distribution automation ,these issues must be solved continuously and simultaneously to ensure the optimal performance of a distribution network.Distribution network imbalance has various consequences such as increase in power losses, voltage drop,cost increase,etc.In this paper,the problem of phase balancing of distribution transformers in a radial structure and load balancing during a MV feeder between the special feeder at the surface is formulated as flow equilibrium optimization problems due to different constraints.In fact,the problem of reconfiguration of the power supply and the grid is formulated as a problem of minimizing power losses due to its importance for controlling the opening and closing of sections and switches.The results showed that the ant colony optimization algorithm approach better than other proposed methods and successfully using real data obtained from the local municipal electricity supplier.From a practical point of view ,these methods can be very effective,because these methods usually have a very long runtime.The optimal ant colony method is more suitable and offers a better response than the neural network.

As fuel prices increase and environmental issues become more serious due to the use of fossil fuels, as well as the expiration of fossil fuels, the researchers are looking to find alternative fossil fuels. Using renewable and green energy like solar energy using photovoltaic systems would be a convenient and cost-effective way. It is important to note that the efficiency of these cells changes dramatically as the angle of radiation changes. Therefore, the need for a control method to detect maximum power point (1MPPT) is absolutely essential. In fact, MPPT is an automatic control algorithm for power level adjustment, which achieves the highest harvesting power from moment to moment variations of radiation level, shadow, temperature and photovoltaic module characteristics. In this study, using the Genetic Algorithm to reach the MPPT point by simulating the photovoltaic system intended for the vehicle, and battery charging and discharge methods at different times, given the cost of electricity during daylight hours, a clear conclusion is reached in Get used to electric cars with photovoltaic arrays. The major and serious challenge is the rapid changes in radiation and partial shading. MPPT behavior was also investigated in partial shading conditions in this study.

In recent years due to problems such as population growth and as a result increase in demand for electrical energy, power systems have been faced with new challenges that not existed in the past. One of the most important issues in modern power systems is economic load dispatch, which is a complex optimization problem with a large number of variables and constraints. Due to the complexity and the non-linearity nature of economic load dispatch, a powerful algorithm is needed to find the optimal solution in terms of available constraints and the existence of local minimum points. In economic load dispatch, variables are generators' power output that must be within the minimum/maximum range so that objective function is minimized. The main purpose of this problem is to plan electrical energy units in such a way as to meet the required network requirements (loads) and minimize the economic costs of these units. Considering the importance of problem, in this study a new method based on the Particle Swarm Optimization (PSO) algorithm with adaptive learning strategy is suggested with taking into account valve point effects of generators. To evaluate the performance of proposed method, two IEEE test systems have been used and the results have shown that the proposed method has a very good performance.