International Journal of Smart Electrical Engineering
Volume:4 Issue: 1, Winter 2015

This paper proposes a novel approach for coherent generators online clustering in a large power system following a wide area disturbance. An interconnected power system may become unstable due to severe contingency when it is operated close to the stability boundaries. Hence, the bulk power system controlled islanding is the last resort to prevent catastrophic cascading outages and wide area blackout. Meanwhile, the aggregation of the coherent generators is the most important step in large power grids intentional defensive splitting to guarantee the dynamic stability of the created islands and reduce the computational burden of the huge initial search space. The proposed method of this paper determines the coherent machines based on the electrical parameters of the synchronous generators instead of the dynamical parameters such as rotor angle or speed curves, speed participation factors, etc. The stator and excitation windings flux, excitation voltage and current have been proposed as coherency indices. The proposed coherency based aggregation has been carried out on New England 39-bus test system. The time-domain simulation results demonstrate the effectiveness and capability of the proposed method to identify the coherent machines following a severe contingency.

Concerns related to climate change and security of energy supply is pushing various countries to make strategic energy planning decisions. In this regard, energy system modelling is an appropriate method to find out the utilization of current declining non-renewable energy resources and other possible scenarios. Consequently, it is available to consider various aspects of energy system decisions and some probable alternatives. This paper investigates different scenarios for Iran's electrical energy system applied in LEAP. Two different demand forecasting methods are used and the effects of applying four supply- side scenarios on CO2 emissions from power sector are finally compared.

Due to uncertain nature of wind and photovoltaic power units, the participation of this units in electricity markets is subjected to significant deviation penalties. This issue leads to despondency or even admission of these units in the competitive environment. With regard to this fact that the low deviations are available when predictions are performed in a short-term horizon and also distributed generation (DG) units have several potential benefits to provide ancillary services, in this article the participation of DG units in intra-market ancillary services is investigated. The intra-day market consists of 3-8 hours scheduled horizon time and will lead to reduction in deviations. Here, three kinds of uncertainties, consist of renewable DG unit’s output, load and price of electricity markets will be predicted by using an adaptive neuro-fuzzy inference system (ANFIS). The proposed method is optimized by Genetic Algorithm (GA) and is tested on a test system. The results supported the efficiency of proposed method.

This paper is concerned with the problem of synchronization for complex dynamic networks with state and coupling time-delays. Therefore, larger class and more complicated complex dynamic networks can be considered for the synchronization problem. Based on the Lyapunov-Krasovskii functional, a delay-independent criterion is obtained and formulated in the form of linear matrix inequalities (LMIs) to ascertain the synchronization between each nodes of the complex dynamic network. The effectiveness of the proposed method is illustrated using a numerical simulation.

The power supply of remote sites and applications at minimal cost and with low emissions is an important issue when discussing future energy concepts. This paper presents modeling and optimization of a photovoltaic (PV)/wind/diesel system with batteries storage for electrification to an off-grid remote area located in Rafsanjan, Iran. For this location, different hybrid systems are studied and compared in terms of cost. For cost analysis, a mathematical model is introduced for each system's component and then, in order to satisfy the load demand in the most cost-effective way, particle swarm optimization algorithm are developed to optimally size the systems components. As an efficient search method, IPSO has simple concept, is easy to implement, can escape local optima, by use of probabilistic mechanisms, and only needs one initial solution to start its search. Simulation results indicate that, the role of the diesel generator decreases in hybrid (PV/wind/diesel/battery) energy systems

In this paper, an attempt has been made to introduce a new control strategy including Plug-in Hybrid Electric Vehicle (PHEV) and Diesel engine generator to control the voltage and frequency of autonomous microgrids. The proposed control strategy has multiple advantages over the recent control methods in microgrids. The proposed method applies the primary and secondary frequency control strategy, simultaneously. However the secondary voltage control scheme is obligatory. In present study, Artificial Neural Networks (ANN) has been implemented for training and validating the advanced droop control (ADC). After ADC unit training, the inverter based DG can be installed in complex microgrids consist of several DGs and loads. Simulation results indicate the effectiveness and applicability of proposed method in controlling the voltage and frequency in autonomous microgrids.