الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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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
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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
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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.