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Power system utilities need to replace the conventional measuring and monitoring devices to the phasor measurement units (PMUs)-based wide area monitoring system (WAMS) to be able to move from conventional to smart grids. The challenge of this action is the cost of WAMS. Many researchers have tried to deal with this challenge by optimal allocation of PMUs to find the minimum number that fulfils the full observability of the system. The WAMS is not only PMUs but also it comprises communication infrastructures (CI) such as communication links, switches and phasor data concentrators. This paper proposes an optimization algorithm to solve the problem based on two strategies. In the first strategy, the objective function is to find the minimum number of PMUs, i.e. conventional objective function. While in the second strategy, the objective function is the minimum cost of the WAMS, i.e. cost of PMUs and their CI. The objective function in the two strategies is solved under normal operating condition and under contingencies such as failure of PMU and outage of network lines. The proposed artificial electric field algorithm (AEFA) is applied using two strategies on IEEE standard networks: 14-bus, 30-bus, 57-bus and 118-bus. The results show that the number of PMUs using two strategies is not the same and this means that the second strategy should be chosen to solve this problem. The results are also compared with other optimization algorithms, and they depict that the AEFA has the efficiency and superiority to find minimum cost of WAMS and lesser distance path of establishing communication links with higher observability. [ABSTRACT FROM AUTHOR]

This article introduces a problem solution of optimal allocation and hourly scheduling of capacitor banks (C-Bs) using the sine cosine optimization algorithm (SCA) to maximize the cost saving and enhance the system reliability. The impact of reliability enhancement reveals in the objective function as a reduction of energy not supplied cost. The optimal location and size of C-Bs are determined based on two strategies; strategy 1, loss sensitivity factor is used to select the candidate buses after that optimal size of C-Bs is determined by SCA while in strategy 2, the optimal location and size of C-Bs are determined by SCA. The proposed algorithm is tested on IEEE 33 and 69-bus radial distribution systems with different scenarios of single and multi-C-Bs installation. Also the daily load variations have been considered to optimally schedule switched C-Bs steps. The simulation results using Matlab programing environment have been verified using the non-parametric Wilcoxon signed rank statistical test. The superiority of proposed algorithm is also ensured by comparing its results with other optimization methods. [ABSTRACT FROM AUTHOR]

Distributed generations (DGs) have been utilized in some electric power networks. Power loss reductions, voltage improvement, increasing reliability, postponement of system upgrading and environmental friendliness are some advantages of DG-unit applications. This paper presents a new optimization approach that employs a flower pollination algorithm (FPA) to determine the optimal DG-unit’s size and location in order to minimize the total system real power loss and improve the system buses voltage. The FPA is a new metaheuristic optimization technique and it is inspired by the reproduction strategy of the flow pollination process of flowering plants. To reveal the validity of the FPA algorithm, IEEE 33-bus, 69-bus and 136-bus radial distribution test systems are examined with different test cases of the objective function using the MATLAB. Furthermore, the results obtained by the proposed FPA algorithm are compared with other metaheuristic optimization techniques such as backtracking search optimization algorithm, artificial bee colony, and selection algorithm. The outcomes verify that the FPA algorithm is efficient, robust, and capable of handling mixed integer nonlinear optimization problems. [ABSTRACT FROM AUTHOR]