In this paper, we propose a frequency and voltage control strategy for a standalone microgrid with high penetration of intermittent renewable generation systems, which might cause large frequency and voltage deviation in the system due to unpredictable output power fluctuations. To this end, a battery energy storage system (BESS) is suggested for generating the nominal system frequency instead of a synchronous generator, from a frequency control perspective. This makes the system frequency independent of the mechanical inertia of the synchronous generator. However, a BESS has a capacity limitation; a synchronous generator is used to maintain the state of charge (SOC) of the BESS at a certain value. For voltage control, we proposed that a reactive power/active power (Q/P) droop control be added to the conventional reactive power controller. By adding a Q/P droop control, renewable generation acquires a voltage-damping effect, which dramatically alleviates the voltage fluctuation induced by its own output power fluctuation. Simulation results prove the effectiveness of the proposed control strategy from both frequency and voltage control perspectives.Index Terms-Battery energy storage system (BESS), frequency control, Q/P droop, renewable generation, standalone microgrid, voltage control, voltage-damping effect.

No abstract

Energy storage systems (ESSs) are essential in future power systems because they can improve power usage efficiency. In this paper, a novel coordinated control algorithm is proposed for distributed battery ESSs (BESSs). The neighboring BESSs of a simulation system are grouped and controlled by a main control center. The main control center sends charging or discharging operation signals to each BESS. The primary objective of the proposed coordinated control scheme is to mitigate voltage and frequency deviations. In order to verify the proposed algorithm, the BESSs are connected to a distribution system of the Korea Electric Power Corporation. The results are compared with those obtained using uncoordinated control scheme with onload tap changer considering aspects of power quality (voltage and frequency variation). The simulation results show that the voltage and frequency deviations are reduced with the proposed coordinated control algorithm.

This paper presents dynamic modeling and simulation of a grid connected variable speed wind turbine (VSWT) using PSCAD/EMTDC, a widely used power system transient analysis tool. The variable speed wind system with a direct-drive generator and power electronics interface is modeled for dynamic analysis. Component models and equations are addressed and their implementations into PSCAD/EMTDC are described. Controllable power inverter strategy is intended for capturing the maximum energy from varying wind speed and maintaining reactive power generation at a pre-determined level for constant power factor or voltage regulation. The component models and entire control scheme are constructed by user-defined function provided in the program. Simulation studies provide control performance and dynamic behavior of a gearless VSWT under varying wind speeds. In addition, the system responses to network fault conditions have been simulated. This modeling study can be employed to evaluate the control scheme, output performance and impacts of a VSWT on power grid at planning or designing stage. Index Terms-Gearless wind generator, grid connection, maximum power capture, power electronics interface, reactive power control, variable speed wind turbine (VSWT). LIST OF SYMBOLSλ Tip speed ratio ω M Mechanical speed of wind turbine [rad/s] R Blade radius [m] V WIND wind speed [m/s] P M Mechanical power from wind turbine [kW] ρ Air density [kg/m 3 ] C P Power coefficient T M Mechanical torque from wind turbine [N · m] f B Electrical base frequency of generator [Hz] N P Number of poles RPM TUR Rated speed of wind turbine [rpm] H i Inertia constant of ith mass [s] K Shaft spring constant [Nm/rad] T E Electrical torque of generator [N · m] θ i Mass angle of ith mass (reference on generator) ω M Mechanical speed of generator [rad/s] ω E Electrical speed of generator [rad/s] E f Field voltage of exciter [V] I f Field current of exciter [A] V dc DC link voltage [V] α IGBT switching signal P inv , Q inv Measured real and reactive power [kW], [kVar] V inv , I inv Measured voltage and current of inverter [V], [A] V d , V q d-and q-axis voltage at VSWT terminal [V]

No abstract

No abstract

Standalone microgrids, which are mainly constructed on island areas have low system inertia, may result large frequency deviations even for small load change. Moreover, increasing penetration level of renewable energy sources (RESs) into standalone microgrids makes the frequency stability problem even worse. To overcome this problem, this paper proposes an active power sharing method with zero frequency deviations. To this end, a battery energy storage system (BESS) is operated as constant frequency (CF) control mode, whereas the other distributed generations (DGs) are operated as an active and reactive power (PQ) control mode. As a result, a state of charge (SOC) of the BESS is changed as the system load varies. Based on the SOC deviation, DGs share the load change. The SOC data is assumed to be sent via communication system, hence the communication time delay is considered. To enhance reliability, controllers of DGs are designed to take account of the failure of communication system. The simulation results show that active power can be shared among DGs according to desired ratio without frequency deviations even for large variation of output power of RESs.

-As environmentally friendly energy takes center stage, interests for Electric Vehicles/Plug in Hybrid Electric Vehicles (EVs/PHEVs) are getting increase. With this trend, there is no doubt EVs will take large portion to penetrations of total cars. Therefore, accurate EV modeling is required. Battery is one of the main components with the power system view of aspect. Hence, in this paper, reviews and discussions of some types of batteries for EV are contained by considering energy density and weight of the batteries. In addition, simulations of Li-Ion battery are accomplished with various variables such as temperature, capacity fading and charge/discharge current. It is confirmed that temperature is the main factor of capacity fading. Validation of the modeled battery is also conducted by comparing it with commercialized battery.