The increasing share of renewable energy sources for electricity, driven by variable output technologies such as wind and solar photovoltaics, is expected to have an impact on the operational reserve requirements of power systems. This study applies a probabilistic approach to estimate reserve requirements and establishes a methodology that makes it possible to distinguish between different categories of reserves based on the imbalance drivers of wind power. The methodology is based on sizing fast-response reserves based on the distribution of output fluctuations inside the settlement period, and sizing slow-response reserves based on the distribution of the average prediction error over the settlement period. The main advantage of this methodology is a reduction of the fast-response reserves, which are generally assessed as expensive compared to slow-response reserves. This approach is applied in a case study and compared with alternative strategies. The results for 500 MW of wind power installed in a North Sea country confirm these reductions and show that with the suggested approach the required fast-response and slow-response reserves, respectively, amount to 7 and 23–26% of the installed wind power capacity.

An electronic load interface (ELI) for improving the operational margin of a photovoltaic (PV) dual-converter system under dynamic conditions is presented. The ELI – based on a modified buck–boost converter – interfaces the output of the converters and the load system. It improves the operational margin of the PV dual-converter system by extending the conditions under which the dual-converter system operates at the maximum power point. The ELI is activated as and when needed, so as minimise system losses. By employing the ELI, utilisation and efficiency of a PV dual-converter system increases. In general, the concept of the ELI can be applied to multi-converter PV systems – such as multi-converter inverters, and multi-converter DC–DC converter systems – for performance and efficiency improvement.

This study proposes a power angle control strategy of grid-connected doubly fed induction generators (DFIGs) to improve the transient performance during grid fault ride-through (FRT). The proposed power angle control can efficiently restrict the rotor-side current under a limited converter rating, support the terminal voltage of DFIGs and hence improve system stability during the fault period. In this study, the power angle of DFIGs in a multi-machine power system is defined firstly and then the relation between the defined power angle and rotor-side current surge and terminal voltage dip of DFIGs are analysed, respectively. Based on the analysis, control of DFIG power angle is implemented expediently on the existing flux magnitude and angle control (FMAC) scheme of DFIGs as an improved FMAC scheme. In this study, two test examples are presented to demonstrate and validate the effectiveness of improved FMAC with power angle control and to compare it with the conventional PQdq control and FMAC. Simulation results show that it can significantly enhance the FRT capability of grid-connected DFIGs.

This study presents an improved sliding mode model reference adaptive system (SM-MRAS) speed observer for the fuzzy control of direct-drive wind power generation system with a permanent magnet synchronous generator (PMSG). The SM-MRAS speed sensorless observer is described and the corresponding algorithm is derived. The designed fuzzy controller is compared with the conventional PI controller by simulations and experiments. A dc motor is controlled to simulate the wind turbine and an active machine-side converter with space vector pulse width modulation control is adopted to realise the maximum power extraction. A 250-W PMSG experimental platform is built and the experiment results verify the validity of the proposed SM-MRAS speed observer.

The authors present a four-season model representing the aggregate output of a possible British wind fleet circa 2030, suitable for providing synthetic wind time series or a statistical characterisation of the transitional behaviour at timescales of 1 h and above. The model is fitted to an aggregated power output time series derived from historic onshore anemometry data and shown to provide a good fit to both long-term and transitional statistics. The authors show that the use of a constant factor to extrapolate anemometer-height wind speeds to hub height leads to an excessive diurnal variation in the implied wind power output. They adjust the model parameters to compensate for this and to account for the offshore component that is not present in the raw data. The complete parameter set is presented.

Increasing penetration of intermittent energy sources in power systems has been requiring higher operational flexibility and cross-border transmission capacity. Intermittent generation, especially wind and solar power, has grown significantly in the Iberian Peninsula during the last decade and it will keep growing in the next year. Owing to limited interconnection capacity with the French and the Moroccan power systems, generation intermittency has been dealt with mostly within Spain and Portugal, helped by a flexible generation mix. Despite the relatively high flexibility of the Iberian system, in some situations, renewable generation output had to be curtailed. In general, renewable energy curtailment is an emergency action taken in case of network constraints or in case of generation surplus at single node. This emergency action is expected to be more common in the near future if no additional operating measures are taken. This study focuses on the expansion of interconnection capacity that would be required in order to integrate intermittent generation surplus in the Iberian Peninsula in the period 2020–2050. For this purpose, the trade-off between transmission investment costs and the costs of renewable energy curtailment is analysed. Results for different intermittent generation levels and price scenarios are presented and discussed.

Grid synchronisation is crucial for integration of renewable energy sources and distributed generation systems into the grid. Accurate and fast estimation of the fundamental positive sequence component is a necessity for synchronisation of three-phase distributed generation systems with the grid. In this study, a simple synchronisation technique based on the real-complex-coefficient filters is proposed. It has a very simple structure, and can directly estimate the fundamental positive and negative sequences under the distorted, unbalanced and offset conditions with no need of a phase-locked loop. Furthermore, the dc offset can also be extracted precisely. For optimising the steady-state and dynamic performance, a parameter tuning guidance is provided from the eigenvalue analysis point of view. In addition, a simple auxiliary unit is presented to remain its frequency-adaptive feature. Finally, experimental results based on a 32-bit fixed-point TMS320F2812 DSP verify the effectiveness of the proposed synchronisation method.

High-performance solar cells and photovoltaic modules exhibit high internal capacitance, limiting the speed of their transient responses including the current–voltage characteristics scans. This study proffers a model-based method to obtain optimal scan time during the current–voltage performance characterisation of a solar cell or module while preserving a pre-set accuracy. Static model parameters are extracted from the quasi-static current–voltage characteristic, whereas the capacitive character, modelled by two bias voltage dependent capacitances, is determined from the open-circuit voltage decay measurement. The obtained model is used to calculate the optimal current–voltage curve scan time. Efficacy of the proposed method is demonstrated through test results obtained on three wafer-based solar cells. I–V curve errors determined by the proposed method at different scan times are in good agreement with the measurements. Results show that in order to achieve < 0.5% error in curve fitting, determined scan times of tested crystalline silicon solar cells lie within the range of 3.6–45 ms for constant angle step semiconductor curve tracer. Use of a capacitive-based curve tracer, however, requires approximately twice that time to retain a comparable error.

For the grid-connected doubly-fed induction generator (DFIG)-based wind energy conversion system (WECS), many improved control algorithms have been developed for the rotor-side converter (RSC) to suppress the overcurrents in the rotor-side under voltage dips. However, such objective can hardly be achieved under severe grid fault conditions because of the limitation of RSCs output voltage. An analysis tool is proposed to estimate the the theoretical control limit of the RSC in suppressing the short-circuit rotor currents during grid faults in this study. The tool is based on the optimisation theory and takes the practical constraints of the RSC into account. To execute the analysis, a simplified DFIG model with decoupled stator and rotor fluxes is presented, and the low-voltage ride through (LVRT) problem can be formulated as an optimisation problem, which intends to suppress the rotor winding currents with voltage constraints. The Pontryagin's minimum principle is employed to solve the optimisation problem and the results can identify the control limit of the RSC. A case study based on a typical 1.5 MW DFIG-based WECS under various grid voltage dips is carried out to validate the analytical method. The proposed method is also further verified by experimental tests on a scaled 3 KW DFIG system. The results are expected to help the manufacturers to assess and improve their RSC controllers or LVRT measures.

Single-sensor maximum power point tracking algorithms for photovoltaic systems are presented. The algorithms have the features, characteristics and advantages of the widely used incremental conductance (INC) algorithm. However; unlike the INC algorithm which requires two sensors (the voltage sensor and the current sensor), the single-sensor algorithms are more desirable because they require only one sensor: the voltage sensor. The algorithms operate by maximising power at the DC–DC converter output, instead of the input.