High penetration level of wind energy affects the generation profile of the power systems, which impose more stringent connection requirements of wind farms. The ability of wind power plant to remain connected during grid faults is very important to avoid cascaded outages because of power deficit. FACT devices are considered to be a key technology to accomplish these requirements. The implementation of three-level inverter based on a static synchronous compensator (STATCOM) for the improvement of the ride-through and stability of fixed speed wind farms is analysed here. This brings the challenges for system planner to determine the appropriate STATCOM rating. This study provides a simple approach to evaluate the STATCOM rating for voltage-level regulation at the point of common coupling around its pre-specified value. Vector current control is used as robust control to inject the required reactive power from STATCOM. This study emphasizes also on the analysis of torque speed curve to evaluate the induction generator speed limit and for defining the critical clearing time of the fault according to the selected STATCOM rating. Simulation results under fault conditions are performed to validate the enhancement of wind farm low-voltage ride-through capability and increasing the critical clearing time by installing STATCOM.

As wind power penetrations increase in isolated power systems, it is very important to understand how variations in wind plant outputs affect the operation of the multi-area isolated system on a day-to-day basis and what the associated costs are. Production cost models need to be further advanced to adequately simulate utility operations and unit commitment (UC) of generation in response to higher wind penetrations. In this study, the dynamic programming (DP) algorithm is extended to facilitate economic sharing of generation and reserve across areas and to coordinate wind and thermal generation scheduling in isolated power systems with large integration of wind capacity. Five heuristic strategies are incorporated in the DP algorithm to improve solution quality and performance. Four important issues of wind capacity integration in congested areas of the Taiwan power system are also investigated and discussed by using the developed UC software. Numerical experiments are included to understand wind generator capacity in production cost analysis and to illustrate the effect of transmission capacity limits on wind power penetration level in each area.

Integrating a battery energy storage system (BESS) with a wind farm can smooth power fluctuations from the wind farm. Battery storage capacity (C), maximum charge/discharge power of battery (P) and smoothing time constant (T) for the control system are three most important parameters that influence the level of smoothing (LOS) of output power transmitted to the grid. The economic cost (EC) of a BESS should also be taken into consideration when determining the characteristic parameters of BESS (C, P). In this study, an artificial neural network-based long-term model of evaluated BESS technical performance and EC is established to reflect the relationship between the three parameters (C, P, T) and LOS of output power transmitted to the grid, the EC of BESS. After that, genetic algorithm is used to find optimal parameter combination of C, P and T by optimising the objective function derived from the mathematical model constructed. The simulation results of the example indicate that the parameter combination of C, P and T obtained by the proposed method can better not only meet the technical demand but also achieve maximum economic profit.

The proliferation of photovoltaic generation in distribution networks requires assessing the control schemes that achieve minimum loss operation without requiring excessive switching of conventional equipment, for instance capacitor banks and network reconfiguration devices. Inverters associated with photovoltaic generation are traditionally operated at unity power factor; however, there is a potential to make use of their capacity to supply or absorb reactive power. The control of capacitor banks and line switches which is done on a slow time scale can be coordinated with the fast control of inverters through a multi-period optimal power flow formulation. This study shows that such a formulation can be cast as a mixed-integer linear program, whose minimum energy loss solution is guaranteed to be globally optimal. Numerical results on a distribution network with different variation patterns of load and photovoltaic generation demonstrate that the method is effective in establishing the trade-off between different combinations of minimum energy loss control schemes.

This paper proposes a control strategy for the stable operation of the micro-grid dluring different operating modes while providing the DC voltage control and well quality DC-Ioads supply. The proposed method adapts the battery energy storage system (BESS) to employ the same control architecture for grid-connected mode as well as the islanded operation with no need for knowing the micro-grid operating mode or switching between the corresponding control architectures. Furthermore, the control system presents effective charging of the battery in the micro-grid. When the system is grid connected and during normal operation, AC grid converter balances active power to ensure a constant DC voltage while the battery has the option to store energy for necessary usage. In order to achieve the system operation under islanding conditions, a coordinated strategy for the BESS, RES and load management including load shedding and considering battery state-of-charge (SoC) and battery power limitation is proposed. Seamless transition of the battery converter between charging and discharging, and that of grid side converter between rectification and inversion are ensured for different grid operating modes by the proposed control method. MATLAB/SIMULINK simulations and experimental results are provided to validate the effectiveness of the proposed battery control system.

The optimal control of state-of-charge (SOC) for superconducting magnetic energy storage (SMES), which is used to smooth power fluctuations from wind turbine, is essential to improve its technical and economical performance. Without an efficient control strategy, the SMES may go to the state of over-charge or deep-discharge, which will pose a significant effect on its service life and its technical performance. In this study, combined with wind forecasting technology and real-time monitoring of SOC for the SMES, a double fuzzy logic control strategy is proposed that is applied to regulate SOC of SMES with the purpose of not only effectively smoothing the power fluctuations of wind turbine, but also preventing the SMES from occurring of the state of over-charge/deep-discharge and adjusting it to the appropriate SOC. The effectiveness of the proposed control strategy is verified by the simulation results.

This study proposes an optimal sizing methodology for a solar photovoltaic (SPV) system considering lifetime cost requirements. The aim of the design is optimal sizing of SPV system, which is obtained by calculating SPV system output power at certain location, taking into account the calculated optimal number of SPV modules, optimal number of inverters, optimal tilt angle, for a given dimension of land. This design is aimed for minimising the annual cost of grid-integrated SPV system over its life or years of operation. The cost function takes into account the capital cost of installation, operation and maintenance, for each component of the system and the cost of selling energy to the grid. The sizing optimisation has been formulated as a non-linear, multi-variable problem and the particle swarm optimisation algorithm has been tested using MATLAB platform for a particular location to swot up the feasibility of integrated system. The monthly averaged daily and hourly solar radiation data for a given location is calculated using empirical relations on MATLAB platform. Other inputs are specifications of commercially available devices and meteorological details of location.

This study proposes a parameter estimation method, together with a grid synchronisation algorithm, for wind turbine-driven doubly fed induction generators (DFIGs). Aiming at achieving an automated control procedure, their integration with a previously published power control strategy is also addressed. During a normal operation mode, the DFIG is grid-connected and generated power is commanded by combining a sliding-mode control (SMC) scheme, which provides high dynamic performance and robust behaviour, with a model reference adaptive system observer, estimating both rotor position and speed without the use of mechanical sensors. In order to preserve performance during start-up and grid connection without the additional requirement of an encoder, the proposed parameter estimation and grid synchronisation schemes are both sensorless. Moreover, the same SMC structure of the power control strategy is also adopted for the grid synchronisation algorithm, which facilitates transfer between synchronisation and power controllers at the instant of grid connection. Thereby, a global sensorless SMC configuration result, which is self-matched by the parameter estimation process. The resulting scheme has been applied to a hardware-in-the-loop-based DFIG virtual prototype under realistic wind conditions, obtaining satisfactory results.