Large-scale integration of wind power is one of the main challenges in today's power systems. Therefore, there is an increased importance to generate more accurate yet simplified wind farm (WF) models. The current modelling techniques of WFs depend on aggregationto reduce the degree of model complexity and computational time. The two techniques of aggregation are adopted in the literature: fully aggregated model (FAM) and multi-machine model where the WF is divided into a number of aggregated models for different sections of the WF. A small-signal model based comparison between the two techniques of aggregation is introduced in this study. The aim is to investigate the impacts of the full aggregation of the wind turbines on the overall system critical modes under weak grid conditions as compared to the multi-machine modelling approach. The comparison is done for different operating conditions (output active and reactive power) of the WF and also under different short-circuit ratios to reveal to what extent the FAM can be relied on to provide an accurate response of large WFs.

In this study, the exergy analysis of a weir-type cascade solar still connected to photovoltaic thermal (PV/T) collectors is provided. By formulating the energy balance for different components of solar still system, the governing equations were obtained as a set of non-linear ordinary differential equations. A perfect exergy analysis is used to obtain irreversibility rates, exergy fractions and exergy efficiency. To find various temperatures and freshwater productivity, the governing equations are solved numerically. Moreover, through experimental data of previous studies, the numerical results were validated. In this study, the effect of the mass flow rate of brackish water and the number of PV/T collectors on the hourly variation of exergy efficiency, irreversibility rates and exergy fractions were investigated. Results showed that by connecting two PV/T collectors to the cascade solar still, the exergy efficiency can be improved twice. Moreover, the connection of two PV/T collectors and use of their output electrical power for heating brackish water can increment the fresh water production rate by 56.8%. The absorber plate and PV/T collectors include maximum 55.8 and 36.8% of system total irreversibility, respectively. The maximum exergy fraction is obtained in evaporating with a value of 0.85.

The smart distribution system architecture provides value-based control techniques that facilitate bi-directional power flows and energy transactions. Although consensus and understanding continue to develop around peer-to-peer transactions, a distribution system operator aims to promote and enable interoperability among entities, particularly those who own distributed energy resources such as energy storage system (ESS) and distributed generation (DG). In this study, the authors address the optimal allocation of ESS and DG in the smart distribution system architecture, in order to help the integration of wind energy. The formulated objective is to minimise the sum of the annualised investment cost, the expected profit and the imbalance cost in the two-stage of power scheduling. The proposed model is verified on the modified IEEE 15-bus distribution radial system. The simulation results have verified the proposed planning approach. Also, results show that a more risk-seeking operation strategy is recommended if wind power penetration increases.

Optimal power flow (OPF) as an important operation function of wind power-integrated power systems encounters the uncertainties of load demand and wind power. To cope with these uncertainty sources, various OPF models including deterministic OPF, probabilistic OPF, scenario-based OPF, stochastic OPF, robust OPF and recently information gap decision theory (IGDT)-based OPF have been presented in the literature. A multi-objective IGDT-based AC OPF model is presented which can simultaneously optimise various uncertainty horizons pertaining to load demands and wind powers considering the specified robustness level. Another main contribution of this study is presenting an effective directed search domain (DSD)-based multi-objective solution method to solve the proposed multi-objective IGDT-based AC OPF problem. The proposed OPF model and solution approach are tested on the IEEE 118-bus test system and the obtained results are compared with the results of other OPF models and solution methods. These comparisons illustrate the effectiveness of the proposed multi-objective IGDT-based AC OPF model as well as the proposed DSD-based multi-objective solution method.

The utilisation of conventional industrial converters for development of doubly-fed induction generator (DFIG) test facilities poses an attractive prospect as it would provide proprietary commercial protection and functionality. However, standard commercial converters present significant challenges in attainable DFIG operational capability. This is due to the fact that they are designed for execution of a limited set of pre-programmed common control modes. They typically do not cater for execution of complicated stator flux-oriented vector control (SFOC) schemes required for DFIG drive control. The research work presented in this study reports a methodology that enables effective implementation of SFOC on industrial converters through a dedicated external real-time platform and a velocity/position communication module. The reported scheme is validated in laboratory experiments on an experimental DFIG test-rig facility. The presented principles are general and are therefore applicable to conventional DFIG drive architectures utilising standard industrial converters.

In this research, the effects of sonication time and volume concentration of cupric oxide (CuO)/water nanofluids on the thermal conductivity, viscosity and performance of photovoltaic–thermal (PVT) solar collector are investigated experimentally. Pure water, 0.05, 0.1 and 0.2% volume concentrations of CuO/water nanofluids prepared with 1, 2, 3 and 4 h sonication time are used as the heat transfer fluids in this study. The experimental results show that the thermal conductivity and viscosity of nanofluids are the functions of sonication time and volume concentration and using a 0.2 vol.% nanofluid prepared with 4 h sonication time, the thermal conductivity and viscosity were enhanced up to 3.5 and 7.5%, respectively, with respect to the base fluid, whereas for the same volume concentration and sonication time, the observed thermal and electrical efficiencies of the PVT solar collector are 80.7 and 15.1%, respectively, at 12 noon. It was observed that the highest total efficiency of the PVT solar collector of about 95.8% was obtained for 0.2% CuO/water nanofluid and 4 h sonication time at 12 noon.

This study presents a multi-objective planning approach to optimally place open unified power quality conditioner (UPQC-O) by simultaneously optimising the photovoltaic (PV) hosting capacity (PVHC) and energy loss of distribution networks. The modelling of UPQC-O consisting of a series and a shunt inverter is carried out so as to place in distribution networks. The Pareto-dominance-based approach is used to simultaneously optimise the objective functions to determine the optimal PV generation capacity in each bus and the locations of the inverters of UPQC-O. The limits on bus voltage magnitude, line current flow, maximum PV generation capacity in each bus, and the percentage of voltage sag mitigated load are considered to be the operational constraints in this planning problem. The solution strategy used is the multi-objective particle swarm optimisation. The approach provides the Pareto-approximation set consisting of a number of trade-off solutions in view of PVHC and energy loss. A utility can choose a solution for implementation depending on its desired PVHC and/or energy loss level. The results show that the PV deployment in distribution networks reduces the energy loss. However, the amount of energy loss reduction diminishes with higher and higher values of PV capacity integration.

The variability of solar irradiance with a high ramp rate may lead to fluctuation in the output of photovoltaic (PV) plants and burdens the power system regulations. A novel control method coordinating the solar PV plants and the battery energy storages (BES) is proposed, aiming at minimising the gap between multi-time-scale ramp of solar PV station and the grid code requirement. The proposed control method combines the computationally efficient feedback control and the mathematical optimisation. Both the short-term forecast and the historical samples are utilised to regulate the output of the PV plants and BES. The state-of-charge reference is adapted by the proposed control in real-time operation for the better performances. The necessary solar power curtailment is investigated with limited BES capacity. The effectiveness of the proposed control method is comprehensively verified by simulations based on real-world measurements of solar PV power under various BES capacity and ramp range requirements.

This study deals with estimation of five unknown parameters in single-diode equivalent model of photovoltaic (PV) modules. First, to simplify the problem, the unknown parameters are reduced to series resistance and diode thermal voltage. These two parameters have significant role for PV model identification. On the other hand, PV model has the least sensitivity to the choice of parallel resistance. Hence, an approximation is utilised for parallel resistance and large value is assigned to this electrical parameter. Thanks to the proposed approximation, a novel cost function is designed for the reduced model such that all of its optimum solutions remain in a small interval of the reduced model. A set of inequality constraints are defined to generate an almost convex optimisation problem with all solutions located in a very small set. The gradient update laws are developed to find the solutions in the tiny set forced by the constructed optimisation problem. The proposed estimation technique generates an accurate model for PV modules, especially at voltage values lower and equal to maximum voltage value.

The authors study the modal condition, under which the sub-synchronous oscillations (SSOs) may be caused by the grid integration of a permanent magnet synchronous generator (PMSG) in a power system. The study is conducted by using a closed-loop interconnected model of the power system with the PMSG, which consists of a PMSG subsystem and a subsystem of the remainder of power system (ROPS). The modal condition is the nearness of an SSO mode of open-loop PMSG subsystem to an SSO mode of open-loop ROPS subsystem on the complex plane. It is named as the open-loop modal coupling (OLMOC). The study shows that when the OLMOC takes place, strong sub-synchronous interactions (SSIs) are induced by the PMSG and may very likely decrease the damping of SSOs in the power system. A formula to approximately evaluate the degree of damping decrease caused by the OLMOC is derived in this study. Hence, the mechanism of why the PMSG may jeopardize power system stability is revealed from the viewpoint of open-loop modal condition. Study cases are displayed to confirm the analytical investigation. They indicate that the growing torsional SSOs and the SSIs between the PMSGs take place when the OLMOC occurs.

This study proposes a two-stage stochastic optimisation model for jointly wind turbine (WT) allocation and network reconfiguration (NR) so as to increase the resiliency of distribution system in face of natural disasters. In this regard, in the first level, a possibilistic-scenario method is proposed to select the line outage scenarios. The proposed model is capable with distribution systems and considers different failure probabilities for system components subject to the intensity of natural disaster in its associated zone. After selecting the line outage scenarios, in the second level, a multi-stage optimisation framework is proposed for jointly NR and WT allocation in a multi-zone and multi-fault system, considering the uncertainty of system load and wind power generation. This strategy makes an interconnection between NR and islanded WTs to increase the resiliency of system and decreases the load shedding. Different economic objectives including, costs of load shedding and power generation are considered in the model. In addition, hardening budget is taken into consideration for the transmission lines, which is minimised during the optimisation process. The simulation results demonstrate the capability and necessity of proposed resiliency-oriented method and prove the importance of hardening budgets.

With the integration of wind energy into electricity grids, it is becoming increasingly important to obtain accurate wind speed forecasts, and accurate wind speed forecasts are necessary to schedule power system. In this study, an artificial neural networks (NNs) model with a variational mode decomposition (VMD) for a short-term wind speed forecasting was presented. To reduce the non-stationary of wind speed time series, the historical wind speed was decomposed into different intrinsic mode functions (IMFs) by a VMD. The back-propagation NN with Levenberg–Marquardt was adopted to build sub-models according to the different characteristic of each IMF. The sub-models corresponding to different IMFs were superposed to obtain wind speed-forecasting models. In the experiment, the proposed forecasting model was compared with an NN with wavelet decomposition and empirical mode decomposition. The performance was evaluated based on three metrics, namely maximum absolute error, root mean square error and the correlation coefficient. The comparison results indicate that significant improvements in forecasting accuracy were obtained with the proposed forecasting models compared with other forecasting methods.