With a large number of photovoltaic (PV) generation systems connected to power grid, accurate forecasting becomes important, the results can be used to alleviate their impacts on the grid effectively. However, most of existing methods strongly rely on the numerical weather prediction (NWP), accuracy of them under highly volatile weather conditions is poor. In this study, without using meteorological data, an improved prediction method based on optimised chaotic phase space reconstruction is presented. Firstly, chaos theory is introduced to analyse the evolution law of PV output power. Then, in order to decrease the delay effect of original chaotic attractor and the negative effect of each phase space power sequence's fluctuation on forecasting accuracy, the algorithm of ensemble empirical mode decomposition (EEMD) is introduced to perform further analysis, so as to raise the regularity of chaotic attractors and extract the partial fluctuation features. Finally, based on optimized chaotic attractor and genetic algorithm-back propagation (GA-BP) neural network, the authors build a combined prediction model and apply it into actual measurement data to verify its validation. Numerical results show that by carrying out the optimised chaotic phase space reconstruction, proposed prediction approach achieves better accuracy than the chaos-GA-BP, EEMD-GA-BP and NWP-GA-BP methods.

The power coefficient parameter represents the aerodynamic wind turbine efficiency. Since the 1980s, several equations have been used in the literature to study the power coefficient as a function of the tip speed ratio and the pitch angle. In this study, these equations are reviewed and compared. A corrected blade element momentum algorithm is used to generate three sets of data representing different ranges of wind turbines, going from 2 to 10 MW. With this information, two power coefficient models are proposed and shared. One model is based on a polynomial fitting, whereas the other is based on neural network techniques. Both were trained with the blade element momentum model output data and showed good behaviour for all operating ranges. In the results, compared to all the algorithms found in the literature, the proposed models reduced the power coefficient error by at least 55% compared to the best numerical approximation from the literature. An error reduction in the power coefficient parameter may have a large impact on many wind energy conversion system studies, such as those treating dynamic and transient behaviours.

In this study, an optimisation method, based on bacteria foraging, is investigated to tune the parameters of the proportional–integral (PI) controllers in a doubly-fed induction generator (DFIG) wind energy system connected to the grid. The generator is connected to the grid directly at the stator and through the back-to-back converter at the rotor. The control system includes PI controllers, at the rotor side, to regulate the rotor currents and PI controller to regulate the dc-link voltage for efficient power transfer. The control parameters, of three PI controllers, are optimised offline using the bacteria foraging optimisation algorithm and a modelled DFIG wind energy system. Various performance criteria, based on the tracking errors, are used to assess the efficiency of the optimisation method. Furthermore, the conventional tuning method and genetic algorithm optimisation method are conducted and compared to the bacteria foraging optimisation method to demonstrate its advantages. The optimised control parameters are evaluated on a DFIG wind energy experimental setup. Experimental and simulation results are provided to validate the effectiveness of each optimisation method.

Development of control technologies facilitates the integration of renewable energy sources to the grid through power electronic converters. In this study, the inverter acts as shunt active power filter along with the power conversion operation. Various sliding mode control (SMC) techniques namely relay and signum method, hysteresis method and equivalent control method are implemented to control the real, and reactive power flow between Renewable Energy System (RES), load and grid using an inverter. To reduce the complexity, the control law (u _eq) for equivalent control method is derived using a simple single-phase equivalent circuit of a three-phase voltage source inverter. In this study, the robustness is enhanced by choosing the integral sliding surface. These controllers are used to reduce the total harmonic distortion (THD) and to ensure unity power factor (PF) in the grid during balanced and unbalanced grid voltage conditions even in the existence of nonlinear loads. The SMC using equivalent control function outperforms the other methods in terms of chattering, THD and PF. To illustrate the effectiveness, three different modes are considered: (i) no RES power, (ii) insufficient RES power and (iii) excess RES power. The performance of controllers is validated by the simulation and experimental results.

Incorporation of transformer in grid-photovoltaic (PV) interfaces makes the systems bulky and expensive, and reduces the system efficiency. Consequently, in recent years, researchers have proposed many transformers-less inverter topologies for grid-PV interface applications. Among them, the H5 topology is one with the simplest structure, least switches, and higher efficiency. However, its ground-leakage current repression property is unsatisfactory due to its asymmetry structure and switch-junction capacitance effects. From the aspect of single-phase transformer-less grid-PV interface applications, this study proposes an improved H5 topology, namely 2D-H5 topology, by incorporating a capacitor divider with a clamp branch consisting of two blocking diodes in the basic H5 structure in order to maintain constant common-mode (CM) voltage. The constant CM voltage will cause no CM (i.e. ground-leakage) current through the stray capacitance between the PV array and the ground. Besides, the proposed topology provides the same differential-mode characteristic as good as in a unipolar modulation full-bridge (H4) inverter for ensuring quality of grid injected power. The proposed topology has been analysed in detail, and verified with satisfactory simulation and experimental results in comparison to the existing transformer-less H5 topology. The proposed improvement enables a reduction in the ground-leakage current in conformity with PV interface standards.

To enhance the power grid's stability and safety, pumped-storage power stations (PSPSs) have to keep themselves stable and safe strictly. However, instabilities happen frequently in some PSPSs due to the S-shaped characteristics of pump-turbines. When the operating point goes through the S-shaped region, intensive radial forces on the runner could cause shaft swing of the turbine-generator unit. In this study, the runaway transient scenarios of a pump-turbine in a model PSPS were simulated by using a one-dimensional and three-dimensional coupled computational fluid dynamics model, and the influence of the unit rotational inertia on the runner radial forces was investigated. The results show that when the pump-turbine runs in the S-shaped region, large rotational inertia is easier than a small one to induce abrupt increases in runner radial forces. The reason is that large rotational inertia gives slower changes in rotational speed and discharge, providing enough time to generate unstable and uneven flow patterns in the pump-turbine. This indicates that apart from the running away region in the characteristics plane, the running away duration is also essential for the transient instability of a pump-turbine. The finding is different from the traditional understandings and should be considered when selects the rotational inertia of a pump-turbine unit.

The interaction between wind farm and power grid may cause the stability issue in the wind farm integration system. Practically, the integration system contains many collector feeders, and one feeder connects a large number of wind turbine generators. Moreover, frequency coupling effect is introduced in all parts of the wind farm system due to the asymmetrical controller of the wind turbine converter. So, it is complicated to analyse the stability of the whole integration system. This study proposes the modular admittance model for wind farm, which is easy to expand for modelling and analysis of the whole integration system. In addition, the operation condition of wind turbine generators in wind farm varies with the prevailing wind speed. The system stability may alter constantly according to the wind speed. How to effectively evaluate the system stability in the entire wind speed operation space is also a challenge. Thus, for wind farm system, this study develops an approach with two-variable admittance model and criterion based on magnitude and phase contours plot to overcome this problem. The proposed admittance model and stability analysis approach are verified by simulation.

Renewable energy generation exhibits a distributed development trend, and has changed the way distribution networks operated. Due to the integration of large-scale distributed photovoltaic (PV) generation cluster and the access of electronic devices, distribution networks are suffering from more and more serious voltage risks. By using appropriate methods, PV inverters can autonomously regulate reactive power output in a distributed manner to improve voltage profile in networks. In this paper, a distributed Newton-based voltage control method for large-scale PV generation cluster in distribution networks is presented to realize distributed coordination of PV inverters, which is based on matrix splitting and approximate Newton iteration, and can fast respond to reactive power mismatch and realize voltage profiles optimization. Exhibiting a more efficient, reliable and flexible performance than existing decentralized methods in IEEE test systems with different PV penetration, the proposed method is demonstrated to be effectively to organize and control PV cluster to realize fast voltage optimization in distribution networks.

Extending current deterministic tools to incorporate significant stochastic wind power is becoming an important as well as challenging task for present-day power system decision-making. This study proposes a novel probabilistic assessment method to assess the available transfer capability (ATC). Usually, repeated ATC evaluations with an exhaustive set of samples are needed to obtain converged results by the Monte Carlo simulation. To alleviate the computation burden, a statistically-equivalent surrogate model for the ATC solution is constructed based on the canonical low-rank approximation (LRA). By implementing LRA for the base case and a set of enumerated contingencies, the uncertainties of wind power generation and load, as well as transmission equipment outages, are addressed efficiently. With the proposed method, the probabilistic ATC is characterised, and the most influential uncertain factors are identified, which helps to determine a suitable ATC level. The effectiveness of the proposed method is validated via case studies with a modified IEEE 118-bus system.

This study proposes a two-stage robust optimisation framework to model uncertainties in construction cost, load and renewable power generations (RPGs) in the coordinated transmission and reactive power infrastructure expansion planning problem. The proposed planning model, based on the network AC model and its voltage constraints, is represented as a mixed-integer linear programing problem with the aim of minimising: (i) the construction costs of lines, (ii) installation cost of reactive power resources and (iii) active load shedding as RPGs curtailment. Using the suggested robust optimisation approach, the best expansion plan is obtained even in the worst case of the defined uncertainty sets and the plan robustness level is controlled via parameter definition for the individual uncertainty sets. The Benders decomposition algorithm is used to solve the two-stage problem where the plan security level is also taken into consideration using the N−1 contingency criterion. The standard Garver 6-bus system and the IEEE 24-bus reliability test system are used to validate the proposed robust framework.

This study focuses on future distribution networks featuring the integration of extra-high distributed energy resources (DERs). In these distribution networks, the extra-high DER penetration and the randomness and volatility of DER outputs increase the likelihood of voltage problems. Hence, it is necessary to design efficient solutions for DER output setpoints to regulate the voltages of distribution networks. However, the existing online distributed algorithms were designed based on different linearised technologies for power flow equations, which will cause imprecise results under extra-high DER integration. In this study, the authors propose an increment-correction method to improve the linearised power flow model. To obtain an efficient distributed manner, they introduced an indirect function into the end-customer (the DER owner)'s objective function, stimulating them to provide voltage-regulating service. In particular, two price incentive signals contained in the indirect function were transmitted from the distribution system operator to drive the DER owner to regulate output increments, which is suitable for cases where the DER belongs to different end-customers. The proposed method can not only protect the private information of end-customers but also enhance the robustness of the distribution network for the DER integration level. Numerical test results demonstrate the merits of the proposed method.

Studies on transient stability constrained optimal power flow (TSCOPF) have become crucial for power systems to guarantee their dynamic securities against credible contingencies, while their optimum operations are to be continuously projected under changing conditions. However, the current approach to the TSCOPF problem is not sufficient to meet the expectations of a modern power system because it suffers from uncertainties mainly due to the rapid and large integration of distributed energy sources. This study proposes a novel method using the information-gap decision theory (IGDT) technique to solve the TSCOPF problem in the presence of uncertainties due to the penetration of wind farms. The IGDT is a non-probabilistic decision-making method that can be easily implemented to handle uncertainty in optimisation problems. While presenting applicable strategies, it does not require any information about the historical data, probability density function or membership function of the uncertain parameters. The proposed method offers an analysis for the economic dispatch in a power system with wind energy resources while providing robustness against transient instabilities and uncertainties in power generation. To demonstrate the effectiveness of the proposed method, it is implemented on New England 39-bus and IEEE 118-bus test systems.

This study presents a hybrid resonant three-level (TL) zero-current-switching (ZCS) converter for distributed photovoltaic power applications accessing medium-voltage DC (MVDC) distribution network. The main constituents of the converter is a neutral point clamped TL circuit, an auxiliary circuit along with two transformers and six switches. The ZCS condition of the TL circuit is reached by operating the auxiliary circuit utilising pulse width modulation technique. It delivers the majority of the power and the switching loss is remarkably reduced. A section on the influence of the turn's ratio of the auxiliary transformer on the current is also presented. The piecewise linear electrical circuit simulation simulation results validate the designed parameter principles. For the sake of comparison, an experimental prototype is developed of 300–1500 V/1500 W to validate the performance of the proposed converter.

Equivalent modelling for active distribution networks (ADNs) is essential for improving the efficiency of analysing transmission networks. Current equivalent modelling methods for ADNs neglect the probabilistic characteristics of renewable energy sources (RESs) and loads. To address this issue, this study proposes a probabilistic equivalent modelling method (PEMM) for ADNs considering the uncertainty of RESs and loads. The uncertainty of the RESs and loads is transferred to the equivalent boundary bus injection using the properties of cumulant and power transfer matrices. The PEMM is extended to incorporate the correlations of RESs through an orthogonal transformation. A sampling method using the Gaussian copula function is employed to generate the correlated samples and the joint cumulants, providing the input data for the PEMM. The comparative results of the case studies on two different test systems demonstrate the effectiveness of the PEMM. The equivalent model developed in this study is a practical solution for analysing the transmission network efficiently and taking the uncertainty of RESs and loads in the ADNs into account simultaneously.

As the scale of distributed generations connecting to the power system increases continuously, communication system considering the requirements of real-time control is becoming much more important in active distribution system. Therefore, in order to improve the responsiveness of the distribution network during the communication interruption, an offline backup strategy is proposed considering the combined output of distributed generations and energy storage systems involving the characteristics of the active distribution system. Based on a typical set of clusters which describe the features of wind turbines and photovoltaics, the strategy analyses operational vulnerability combining structural characteristics of the active distribution system and solves the output level of each distributed generation according to the improved algorithm. A numerical case study is presented to demonstrate the effectiveness of this model.

A study has been performed to extract the unknown parameters of a photovoltaic (PV) module using mathematical model-driven analysis. Neglecting the less significant terms, the number of non-linear exponential terms are reduced to develop a simplified algorithm to estimate five unknown parameters such as diode ideality factor, series resistance, shunt resistance, diode saturation current and photogenerated current of a single diode model PV module accurately with less computational complexities from the manufacturer's datasheet values. Substituting the estimated values of the unknown parameters, the PV module output power is obtained at different irradiance and temperature, which shows close co-ordination with the datasheet values. To improve the accuracy in the estimation of the PV module output using the proposed model, the nominal operating cell temperature (NOCT) is considered. The results of the proposed model are verified with the experimental output of two (one 315 Wp and another 3 Wp) PV modules, which shows better correlation with the proposed model considering NOCT. The measurement uncertainty to obtain the output power experimentally is not more than 6.88%.

Increasing the world-wide efforts towards low-carbon economy, highlights the role of renewable energy resources (RESs) in power systems. The volatility of RESs, however, is a major challenge for system operators, and there is need to deal with their uncertainty. Regarding this, microgrids (MGs), as the clusters of different RESs and other distributed energy resources, should take the responsibility of dealing with uncertainty. To do so, a risk-averse energy management strategy (RA-EMS) is introduced for distribution systems under the penetration of renewable energy-based MGs. The status of the connecting switches between clusters and their interaction with the distribution system is examined in several cases. In addition to wind turbine and photovoltaic as RESs, the MGs are equipped with electric vehicles, as another environmentally friendly forms of technology, demand response program, and micro turbines. The uncertainty of renewable generation is modelled via information gap decision theory (IGDT) technique. To validate the effectiveness of the proposed model, it is tested on 94 bus actual Portuguese distribution test system. The results show the importance of MG clustering in improving the techno-economic characteristics of the distribution system. Also, it is evident from the results that uncertainty of RESs is a crucial factor in MG clustering.

Traditional way to enable frequency support provided by MTDC system for the connected weak AC systems is to modify the power reference of each converter under standard power-sharing droop control based on the weak grid's frequency deviations. However, this scheme artificially couples all asynchronous AC systems to tackle with the frequency disturbances of any connected AC systems, which might result in broader frequency disturbance propagations. To overcome this, this study proposes a decoupled frequency control (DFC) scheme to improve weak AC systems frequency stabilities. First, the relationship between the active power and power set points of each converter is derived by the linearization of MTDC power flow equations. Then, the frequency coupling properties of each AC system with traditional frequency control are derived, and it shows that the frequency disturbance will be propagated to all the connected AC systems with traditional control. Furthermore, the control law of each converter in the proposed DFC is developed through the defined decoupled matrix, and the independent frequency regulation characteristic of each converter can be obtained. Finally, numerical simulations have demonstrated the effectiveness of the proposed DFC scheme in events of load changes and converter loss in the main and weak AC grid.

Photovoltaic (PV) emulator (PVE) is a non-linear power supply that produces a similar output to that of the PV module. It requires a closed-loop controller such as the proportional–integral (PI) and fuzzy logic controllers to operate properly. The PI controller has a low-computational burden and is not robust since the transient response varies under different load conditions. Although the fuzzy logic controller is robust, it requires high-computation capability. This study proposed an alternative approach called the shift controller for the PVE to easily compute and maintain a fast transient response under various load conditions. The performance of the proposed shift controller for the PVE is compared with the performance of the PVEs using the PI and fuzzy logic controllers, respectively. The controllers are simulated using MATLAB/Simulink and implemented using dSPACE ds1104 for experimental validation. The results show that the proposed shift controller computes up to 18 times faster than the fuzzy logic controller. The proposed controller also responds faster to load change than the PI controller.

As the structural limits for the conventional horizontal-axis wind turbine are being reached, innovative concepts are needed to ensure increased reliability and reduced costs. One option is limiting the fatigue to which a rotor is subjected. This study, therefore, intends to experimentally investigate the tower shadow effect for a deeper understanding of the aerodynamic fluctuations, and to study the capability of a lower surface microtab to mitigate such fluctuations. For this, aerodynamic load variations caused by the presence of the tower on the rotor blades were reproduced and studied in the wind tunnel using a setup that mimics the interaction between the tower and the blade. Anemometers and flow visualisation techniques were used to explore the influence of the tower on the surrounding flow field. Real-time measurements show that the alterations in the lift force are mainly caused by the alterations in the flow angle and have proven that a lower surface microtab can partially mitigate aerodynamic load variations in the case of an upwind rotor blade. Experimental results are validated using a tower shadow model that is based on potential flow theory. The tower shadow model shows a reduction in 3p power oscillations by a factor of 2.