International Journal of Smart Electrical Engineering
Volume:4 Issue: 4, Autumn 2015

This paper proposes an optimal transmission expansion planning (TEP) which is based on determining share of each line in merchandizing surplus (MS) of system, determining by Independent System Operator (ISO). The more share of a line in MS of system denotes the priority of a line for expansion. The procedure of determining MS of each line in a power system is based on determining the MS share of each energy exchange between certain generator and certain demand bus in the power system. By analyzing all energy exchange the optimal planning of transmission line is obtained by ISO.  The variable revenue of a Transco is related to performing the optimal planning of transmission lines which is obtained through ISO. The proposed method determines the TEP economical resources and the procedure of receiving these resources (MS of system) from generators and customers. By performing proposed method Transco capacity withholding and misusing is prevented spontaneously caused by relating the revenue of Transco to its optimal performance.

In this paper, photovoltaic (PV) grid-connected inverter which is the core device in PV grid-connected system has been in depth research.An improved predictive current controller has been developed by the Authors for single-phase grid-connected voltage source inverters (VSI).Based on DSP TMS320LF2407A, a 10 kW single-phase grid-connected inverter has been built in this paper.Thus is aimed at use in distributed generation.Because of the powerful real time processing ability of the DSP, the output power factor of the PV inverter connected with grid can be controlled to unity. The composition of main circuit and control loop is described in detail and the operating principle of each functional block is analyzed deeply.The experiment results show that the improved predictive controller has a superior performance. The single-phase grid-connected VSI implemented with the proposed predictive controller has shown very low current THD in laboratory test

This paper proposes a new method for parameter estimation of distorted single phase signals, through an improved demodulation-based phase tracking incorporated with a frequency adaptation mechanism. The simulation results demonstrate the superiority of the proposed method compared to the conventional SOGI (Second-Order Generalized Integrator)-based approach, in spite of the dc-offset and harmonic distortions.

In recent years, significant research efforts have been devoted to the optimal planning of power systems. Substation Expansion Planning (SEP) as a sub-system of power system planning consists of finding the most economical solution with the optimal location and size of future substations and/or feeders to meet the future demand. The large number of design variables, and combination of discrete and continuous variables makes the substation expansion planning a very challenging problem. So far, various methods have been presented to solve such a complicated problem. Since the Bacterial Foraging Optimization Algorithm (BFOA) has been proper results in studies of power systems, and has not been applied to SEP problem yet, this paper develops a new BFO-based method to solve the SEP problem. The technique discussed in this paper uses BFOA to simultaneously optimize the sizes and locations of both the existing and new installed substation and feeders by considering reliability constraints. To clarify the capabilities of the presented method a typical network is considered and the results of applying GA and BFOA on the network are compared. The simulation results demonstrate that the BFOA has the potential to find more optimal results than the other algorithm under the same conditions. Also, the fast convergence, consideration of real-world networks limitations as problem constraints and simplicity in applying to large scale networks are the main features of the proposed method.

This paper, presents a suitable control system to manage energy in distributed power generation system with a Battery Energy Storage Station and fuel cell. First, proper Dynamic Shape Modeling is prepared. Second, control system is proposed which is based on Classic Controller. This model is educated with Genetic Algorithm and particle swarm optimization. The proposed strategy is compared with Classic Controller. The robustness of suggested strategy in the front of nature of wind energy and Environmental conditions led to Parameter changes and load changes. It was investigated and simulated in MATLAB and the result was observed. As a result of simulation, control system has reflected a better behavior rather than load changes. The proposed methods are applied on various situations with actual climate data.

Electricity price predictions have become a major discussion on competitive market under deregulated power system. But, the exclusive characteristics of electricity price such as non-linearity, non-stationary and time-varying volatility structure present several challenges for this task. In this paper, a new forecast strategy based on the iterative neural network is proposed for Day-ahead price forecasting. For improved accuracy of prediction an intelligent two-stage feature selection is proposed here to remove the irrelevant and redundant inputs. In order to have a fast training the neural network normalization is vital, so in this paper the above technique is used.  The proposed approach is examined in the Ontario electricity market and compared with some of the most recently published price forecast methods.

In this paper particle swarm optimization with smart inertia factor (PSO-SIF) algorithm is proposed to solve combined heat and power economic dispatch (CHPED) problem. The CHPED problem is one of the most important problems in power systems and is a challenging non-convex and non-linear optimization problem. The aim of solving CHPED problem is to determine optimal heat and power of generating units with the minimized cost of total system and satisfied constraints of problem. In proposed algorithm inertia coefficients are controlled with respect to cost function in each population. So, each population has unique inertia coefficient and as a result unique velocity in convergent direction for the best group solution. In order to examine the proposed algorithm's capabilities and find optimum solution for CHPED problem, two test systems considering valve-point effect, system power loss and system constraints are optimized. The obtained results demonstrate the superiority of the proposed method in solving non-convex CHPED problem over other new and efficient algorithms.