الفهرس | Only 14 pages are availabe for public view |
Abstract Smart grid (SG) is a type of electrical grid that attempts to predict and intelligently respond to the behavior and actions of all electric power users such as suppliers, consumers and those that do both in order to efficiently deliver reliable, economic and sustainable electricity services. At the heart of the future SG lie two related challenging optimization problems: monitoring of a power system and enhancement of distribution system performance. SG technologies combine power generation and delivery systems with advanced communication systems to help save energy, reduce energy costs and improve reliability. A few benefits are connected with the consumption of renewable energy technologies, including quite low or no greenhouse-gas emissions, making them a key segment in any environmental change moderation methodology. In this thesis, a multi-stage method is proposed to make the power system complete observability by the optimal placement of phasor measurement units (PMUs) taking into account the minimum availability of PMUs measuring channels. In order to solve the optimization problem, a two-stage optimal method is introduced with and without considering zero injection buses (ZIBs). In stage- 1, the ant colony optimization (ACO) algorithm is used to find the optimal number and locations of PMUs considering measuring channels and maximize the measurement redundancy (MR) at normal operating condition as well as emergency conditions such as any single line or PMU outage. In Stage-2, the reduction strategy (RS) is proposed to reduce the number of PMUs measuring channels with keeping the complete observability. To prove the robustness and capability of the proposed method, the results are compared with other optimization techniques. Simulation results show the capability of the proposed method to find the optimal PMU placement for significant saving in the total cost with more accuracy and efficiency, especially with increasing in the power system sizing. This thesis presents a two-stage procedure to determine the optimal locations and sizes of capacitors with an objective of power loss reduction for iv improvement the voltage profile in radial distribution systems. In first stage, the loss sensitivity analysis using two loss sensitivity indices (LSIs) is employed to select the candidate locations for the capacitors to reduce the search space in the optimization procedure. The suggested LSIs are based on the following physical quantities; the variation of the active power losses with respect to the load bus voltage at variant nodes, the variation of the active power losses with respect to the level of reactive power at variant nodes. In second stage, the ACO algorithm is used to find the optimal locations and sizes of capacitors considering the minimization of total energy loss and total costs of capacitors as objective functions, while the security and operational constraints are fully achieved. The fixed and practical switched capacitors are considered to find the optimal solution. The backward/forward sweep (BFS) algorithm is introduced for the load flow calculations. The numerical results are compared with other methods to show the capability of the proposed procedure to find the optimal locations and sizes of capacitors for significant saving in the total cost with more accuracy and efficiency, especially with increasing in the distribution system sizing. In this thesis, a proposed procedure which consists of two stage methodologies is proposed to determine the optimal combination of distributed generations (DGs) and capacitor banks with different single and multi-objective functions in radial distribution systems. In first stage, two LSIs are used to select the candidate locations for the DGs and capacitor banks. The suggested LSIs are based on the following physical quantities; the variation of the active power losses with respect to the level of active power at variant nodes, the variation of the active power losses with respect to the level of reactive power at variant nodes. In second stage, the ACO algorithm is introduced to find the optimal locations and sizes of DGs and capacitor banks according to single and multiobjective functions. The different single objective functions are: power loss reduction, minimize the voltage deviation (VD) and maximize the voltage stability index (VSI), while the multi-objective function is simultaneously optimizing all the objectives. The obtained results are compared with other v methods. Simulation results show the capability of the proposed procedure to find the optimal solution for significant minimization in the objective functions with more accuracy and efficiency. |