International Journal of Smart Electrical Engineering
Year:2015 | Volume:4 | Issue:2|Papers Count:8

Reliability evaluation of a large-scale composite power system faces to numerous events/outage and consequently imposes an extensive burden of calculations. In order to simplify the problem, determination of an equivalent system for large-scale power system is inevitable. This paper proposes a framework as reduction technique to separate a composite power system to three areas: external area, optimization area and equipment outage area. This separation enables policy makers of power systems to evaluate reliability of large-scale power systems with less time of calculation and extraordinary precision. The reduction technique is applied to composite power system of Iran with more than 4600 buses to determine an equivalent network for reliability evaluation of Semnan Province network. Comparative discussions and simulations for case study are presented at the end.

Electric and hybrid electric vehicles are attractive candidates for sustainable transportation due to its higher efficiency and low emission. The critical choice on the electric motors is its capability of motoring and regenerative braking characteristics. Switched reluctance machines are viable candidate as with proper control and extended constant power range operation replacing the multi-gear transmission. Recently developed dual magnetic circuit reluctance machine configuration make it added advantage with its feasibility on the optimizing the magnetic flux flow. The dual magnetic circuit reluctance machines through double rotor is presented and compared with conventional machine for its power density. Further to evaluate the dynamic performance the proposed machine is compared with that of the conventional machine. It is found that the torque density of the proposed machine is 65% higher than that of the conventional machine and show promising candidate for the EV and HEV applications in the near future.

In most industrial zones, electric energy is one of the most important energy sources. Since electrical motors are the main energy consumers of industrial factories, consumption optimization in these motors can be considered as a main option related to energy saving. One very effective way to reduce the consumption of these equipment is to use a motor speed controllers or drives. Since the loss of inductive motor has a direct relationship with motor flux, in this paper, the rotor flux vector control has been used. Due to the strength of fuzzy controllers in load failure and noise generation states, this controller has been used to adjust the drive speed. Two fuzzy logic inputs including speed error and speed variation derivative, and a fuzzy output, motor reference torque (Te*) are estimated. The genetic optimization algorithm has been used in order to improve the Efficiency and reduce the losses. As such, the drive performance in GA and Fuzzy-Genetic (FG) states is reviewed and the simulation results are presented. Finally, the obtained results in this paper have been compared to the results of FOC inductive motor with PI controller and without optimization. It can be seen that when FG method is employed, the results show a higher performance and losses are reduced up to almost 40 to 50% in different loads, and the amount of input power is also reduced up to approximately 30%.

Maximum power point tracking controller is essential to obtain the maximum power from a solar array in the photovoltaic systems as the PV power module varies with the temperature and solar irradiation. In this paper, several methods for the MPPT of the PV systems are discussed and it can to be used as a Helpful reference for the upcoming MPPT user in the PV applications.

This paper proposes a stochastic framework for demand response (DR) aggregator to procure DR from customers and sell it to purchasers in the wholesale electricity market. The aggregator assigns fixed DR contracts with customers based on three different load reduction strategies. In the presented problem the uncertainty of market price is considered and the risk of aggregator participation is managed in stochastic optimization problem with CVaR. The feasibility of this problem is studied on a case of Alberta electricity market.

With the development of restructured power systems and increase of prices in some hours of day and increase fuel price, demand response programs were noticed more by customers. demand response consists of a series of activities that governments or utilities design to change the amount or time of electric energy consumption, to achieve better social welfare or some times for maximizing the benefits of utilities or consumers. In this paper the effect of emergency demand response program on composite system reliability of a deregulated power system is evaluated using an economic load model, AC power-flow-based load curtailment cost function and reliability evaluation techniques. In this paper for calculation the reliability indexes, the Emergency Demand Response Program (EDRP) cost is considered and in each contingency state, the EDRP cost with the customer load curtailment cost is compared and the load appropriate value is selected for load shedding or participating in EDRP. In the next stage, the system and nodal reliability indexes are calculated. To investigate the impact of EDRP activity on composite reliability of restructured power systems the IEEE 6 bus Roy Billinton Test System is utilized. According to obtained results, EDRP using lead to increasing nodal and system reliability. It can be said that solving problems such as congestion in transmission lines, power system reliability decrease at load network peak hours, is impossible without customer interfering in power market. In other hand Consumer participation, makes the power markets more competition and enhance its performance.

In this paper a new algorithm based on phasor measurements for estimating the Thevenin Equivalent (TE) and system inertia constant is proposed. The method estimates the network TE seen from a generator terminal and equivalent system inertia constant by means of phasor data that is available at Phasor Data Concentrator (PDC) center. It is shown that the parameters of the TE could be estimated at any system condition, but the value of system inertia constant is available only after some disturbances. The algorithm is tested on a standard Nine-Bus test system in different conditions. Results show that this method can efficiently estimate the TE both in steady-state and transient conditions as well as system inertia constant.

The renewable energy sources and plug-in electric vehicles (PEVs) are becoming very popular because of the combination of high fuel costs and concerns about emission issues. This paper presents modelling and control of a Building Integrated Fuel Cell and Plug-in Electric Vehicles (BIFC-PEV) in smart distribution systems. In BIFC-PEV system, conventional building elements could be replaced by special fuel cell and PEV for delivering power to the house. The interest in battery electric vehicles (BEVs) and plug-in electric vehicles (PEVs) has increased due to their impact on redistribution of the pollution from tail pipe to smog stuck, low-cost charging, and reduced petroleum usages. First, the overall configuration of the BIFC-PEV including dynamic models of fuel cell, battery and its power electronic interfacing are briefly described. Then, to distribute the power between power sources, the fuzzy power controller has been developed to stabilize the DC-link power. Simulation results are illustrated to demonstrate the effectiveness and capability of proposed control strategy during different operating conditions in utility grid.