In this paper, two new computationally efficient improved stochastic algorithms for solving multi-area DC Optimal Power Flow (DC-OPF) in interconnected power systems have been presented. These algorithms are based on the combined application of Fuzzy Logic strategy incorporated in both Evolutionary Programming (EP) and Tabu Search (TS) algorithms, hence termed as Fuzzy Mutated Evolutionary Programming (FMEP) and Fuzzy Guided Tabu Search (FGTS). Multi-area DC-OPF calculations determine optimum generation schedule, optimal control variables and system quantities of each area with due consideration of generation and transmission system limitations for efficient power system operation. The popularity of EP and TS algorithms are due to their significant property of dealing with the optimization problems without any restrictions on the structure or type of the function to be optimized and due to the ease of computation. The proposed methods are tested on single area, two area and four area IEEE 30-bus interconnected systems. The optimal solutions obtained using SLP (Successive Linear Programming), EP, TS, FMEP and FGTS are compared and analyzed. The analysis reveals that the proposed algorithms are relatively simple, efficient, reliable and suitable for real-time applications. And these algorithms can provide accurate solution with fast convergence and have the potential to be applied to other power engineering problems.

Transmission expansion planning (TEP) is one of the most important parts of expansion planning in power systems. Competition in these systems has resulted in essential changes in TEP models and criteria. While methods introduced so far are essentially based on dc load flow, the proposed method utilizes the ac optimal power flow (OPF) in order to model the real world condition and obtaining optimal plans. Furthermore, in order to model the operation and investment costs, transmission tariffs are used. Investigation on 8-bus system confirms the advantages of the proposed method as compared with previously presented approaches.

The future of development and design is impossible without study of Power Flow(PF), exigency the system outcomes load growth, necessity add generators, transformers and power lines in power system. The urgency for Optimal Power Flow (OPF) studies, in addition to the items listed for the PF and in order to achieve the objective functions. In this paper has been used cost of generator fuel, active power losses network and system loadability index, so artificial neural network used to compare two propagation algorithms of this type of network and define the model OPF analysis is carried out. The performances of the two algorithms are analyzed and compared using the model assessment index and MGN tests. Statistical method of Bootstrap has been used on achieve the best performance to improve OPF estimates. In order to reduce steps with less than 1%, for evaluate and improve the OPF estimation with single-objective optimization functions of the Bayesian and Perceptron neural networks have been studied in IEEE 30 bus test system. The results show the effective role of bootstrapped Bayesian neural network in terms of performance, using MATLAB software.

In this paper, a combinational optimization algorithm is introduced to obtain the best size and location of Static Compensator (STATCOM) in power systems. Its main contribution is considering contingency analysis where lines outages may lead to infeasible solutions especially at peak loads and it commonly can be vanished by load shedding.The objective of the proposed algorithm is firstly to prevent infeasible power flow solutions without undesired load-shedding, which is critical in contingency analysis; and secondly to mitigate overall power losses and costs. Moreover, active and reactive powers generation costs are considered in the proposed objective function. Since there are various constraints such as lines outages number, cost and their duration that must betaken to account, Bacterial Foraging oriented by Particle Swarm Optimization (BF-PSO) algorithm combined with Optimal Power Flow (OPF) is used to solve and overcome the complexity of this combinational nonlinear problem. In order to validate the accuracy of the proposed method, two test systems, including IEEE 30 bus standard system and Azarbaijan regional power system of Iran, are applied in simulation studies. All obtained optimization results show the effectiveness of the suggested combinational method in loss and cost reduction and preventing load-shedding.

By adding renewable energy sources, such as solar and wind, advanced metering infrastructure, and energy storage systems, the traditional power grid is becoming a smart grid. To prevent the uneconomic operation of a smart grid and increase the penetration of renewable resources, the Demand Response (DR) method is crucial for reducing the peak load and passing critical conditions. In this context, this study presents a multi-objective optimization of the AC optimal power flow (AC-OPF) problem with respect to DR. The novelty of the proposed demand-response-based OPF approach consists of decreasing the system cost through the simultaneous participation of active and reactive power in DR, considering the physical constraints of the AC network and various renewable energy sources in the smart grid, and increasing the calculation accuracy by demand prediction based on previous data using deep learning methods. Finally, using the TOPSIS method, the best DR value was determined according to multi-objective optimization. The effectiveness and resiliency of the proposed method were validated using a modified IEEE 24-bus testing system. The results illustrate that the optimal demand response (20%) achieved not only peak reduction and valley filling in active and reactive power but also minimized the total voltage deviation and system cost.

Oxidized Polyacrylonitrile fibers (OPF) are made by thermal stabilization of polyacrylonitrile (PAN) fibers and are used as precursors materials for production of carbon fibers. In this paper, effect of adding these fibers on tensile properties of polymer matrix composites was studied. So thirteen kind weights of epoxy matrix composite reinforced with one of the carbon, Kevlar and glass fabrics including OPF by hand lay up method as four layers with different layering were made. For making of composites epoxy Bisphenol F resin and polyamine hardener were used and the resin to fiber fraction was selected as 60 percent. Then tensile properties and failure cross section of them were studied. Results showed that by increasing OPF to carbon, Kevlar and glass fabric ratio; the tensile and modulus strength decreased but in more than 50 weight percent ratios failure strain increased. Study of the fail cross section showed that composites made with one of the carbon, Kevlar, glass fabrics including OPF have lateral, explosive and edge delamination failure modes respectively and with increasing the OPF to carbon, Kevlar and glass fabric transverse failure mode happens.

This paper presents a new AC optimal power flow (AC OPF) model for sub-transmission networks. This model, which consists of sub-transmission and distribution bus-bar switching actions, can avoid undesirable over-current (OC) status and subsequent actions of OC relays. The proposed AC OPF optimizes the bus-bar switching actions along with optimizing sub-transmission control actions. Also, to consider the impact of OC relays’ actions in the proposed AC OPF, the cost of load shedding caused by these relay actions is included in the objective function and is minimized along with the sub-transmission operation cost. The bus-bar switching actions are modeled using binary decision variables. Therefore, the proposed AC OPF model is formulated as a Mixed Integer Non-linear Programming (MINLP) optimization problem. The effectiveness of the proposed model is illustrated on a real-world sub-transmission network of Iran’ s power system.