International Journal of Smart Electrical Engineering
Volume:5 Issue: 3, Summer 2016

With the aim of reducing cost of electricity consumption and peak load reduction, tools requirement for better managing electricity consumption have become inevitable in recent years. Smart home has some equipment which are controllable and this ability is used for increasing comfort and minimizing electricity cost for residence. As a key component of smart home , Electric Vehicle(EV) ,increase the ability of consumers in participating in Demand Response (DR) programs. One of the key issues raised in this aim is to present a way for entry and exit of this equipment to the network in order to reduce electricity cost for customer. So in this article 5 set of appliances and V2H capability of EV together with Time of Use (TOU)-pricing signal based DR strategy are all combined in a single Home Energy Management (HEM) system for the first time. A Mixed-Integer Linear Programming (MILP) is used for this purpose.

In this article using comparative multi-purpose algorithm of PSO, optimum capacity of new resources and their spinning storage is defined. Spinning reserve requirements and optimized to maximize profit per unit is determined according to uncertainty. In fact by increasing the level of cost reservation, power supply increases but penalty of blackout decreases. In this paper in has been tried to accomplish the optimal amount of reservation by creating a conciliation between penalty of blackout and power supply cost of photovoltaic cells. In this research each part of the algorithm has been produced in a way that they do not have maximum potency. Time of problem solving is considered to minutes for every hour which causes planning every hour to improve before reaching the intended time so that there is accurate anticipation of function of the intended call would be in disposal.Utilization of a micro grader cell consists of reproducible sources in need of estimation of spinning storage. The spinning storage is the difference between producible power and produced power of the units in peak time .

In this paper, an intelligent control strategy based on combination of the “flatness based control technique” and the “perturbation and observation (P&O) MPPT algorithm” is developed and investigated to control a hybrid electric energy source (HEPS). This EHPS is composed of a fuel cell system (FC) and a solar panel (SP), as the main source and a supercapacitor bank (SC), as the auxiliary sources. The main property of this strategy is that the system power flow is managed in different operating modes with the same control algorithm (without algorithm commutation). The power flows between the fuel cell, the SC is controlled by the flatness based control technique while a P&O MPPT technique is developed to control power of the SP. To validate performance of the proposed control of the HEPS, the simulation results are presented.The output components follow perfectly their own references which prove favorite operation of the EHPS to reply the high dynamic load power and to help the FC in overload mode. In this strategy, the power delivered by the FC is limited and its dynamics is controlled. The simulation results confirm the validity of the proposed control strategy in the studied topology. The output voltage stays always constant even when a high step load power is imposed to the system.

In the most described maximum power point tracking (MPPT) methods in the literatures, the optimal operation point of the photovoltaic (PV) systems is estimated by linear approximations. However, these approximations can lead to less optimal operating conditions and significantly reduce the performances of the PV systems. This paper proposes a new approach to determine the maximum power point (MPP) in order to increasing the system efficiently as much as possible. The proposed algorithm is a combination of two loops, set point calculation and fine tuning loops. In first stage, the maximum power is approximated based on the nonlinear modeling of the PV panels by using the set point loop. In second stage, the exact amount of the maximum power will be tracked by the fine tuning loop, which is based on the Perturbation and Observation (P&O) method. The proposed method is simulated in MATLAB/SIMULINK software environment. The simulation results demonstrate that the approach clearly improves the tracking efficiency of the maximum power available at the output of the PV panels. The new method reduces the oscillations around the MPP as well as increases the average efficiency of the obtained MPPT. The new MPPT method will deliver more power to any generic load or energy storage media

In this paper some algebraic structures for linguistic fuzzy models are defined for the first time. By definition linguistic fuzzy norm, stability of these systems can be considered. Two methods (normed-based & graphical-based) for stability analysis of linguist fuzzy systems will be presented. At the follow a new simple method for linguistic fuzzy numbers calculations is defined. At the end two simple (stable and unstable) systems are modeled by linguistic fuzzy logic then stability of them by both methods are checked. In this paper some algebraic structures for linguistic fuzzy models are defined for the first time. By definition linguistic fuzzy norm, stability of these systems can be considered. Two methods (normed-based & graphical-based) for stability analysis of linguist fuzzy systems will be presented. At the follow a new simple method for linguistic fuzzy numbers calculations is defined. At the end two simple (stable and unstable) systems are modeled by linguistic fuzzy logic then stability of them by both methods are checked.

In emerging electric power systems, increased transactions often lead to the situations where the system no longer remains in secure operating region. The flexible Ac transmission system (FACTS) controllers can play an important role in the power system security enhancement. However, due to high capital investment, it is necessary to locate these controllers optimally in the power system. FACTS devices such as UPFC can regulate the active and reactive power control as well as being adaptive to voltage-magnitude control simultaneously because of their flexibility and fast control characteristics. Placement and sizing these devices in suitable location can lead to control in line flow and maintain bus voltages in desired level and so improve voltage stability margins. Moreover, this adjustment can improve voltage profile system along with reducing the power system losses. This paper proposes a systematic method by which optimal location and sizing of Unified Power Flow Controller (UPFC) to be installed using two different evolutionary algorithm. FACTS DEVICES model is incorporated into a Newton-Raphson algorithm to perform load flow analysis. Optimizing its location becomes a concern when coming to the practical implementation stage. Proposed algorithm is tested on IEEE 24 bus power system for optimal allocation as well as sizing of UPFC device and results are presented.

A novel method to compute the stability region in power system transient stability analysis is presented. This method is based on the set analysis. The key to this method is to construct the Hamilton-Jacobi-Isaacs (HJI) partial differential equation (PDE) of a nonlinear system, using which we can compute the backward reachable set by applying the level set methods. The backward reachable set of a stable equilibrium yields the stability region of the equilibrium point in power system transient stability assessment. The proposed method is applied to a single machine infinite bus system and a classical two-machine system yielding satisfactory results.Power system transient stability is related to the ability to maintain synchronism when subject to a severe disturbance, such as a short circuit on the bus. The resulting system response involves large excursions of generator rotor angles and is influenced by the nonlinear power-angle relationshipTransient stability assessment essentially determines whether the post-fault operating state can reach an acceptable steady-state operating point or not