A phase locked loop (PLL) tracks the phase of the terminal voltage of a grid-connected permanent magnetic synchronous generator (PMSG) for wind power generation. Phase tracking by the PLL is used by the vector control for the grid connection of the PMSG. This study investigates the impact of phase-tracking performance of the PLL on power system small-signal angular stability. Damping torque analysis conducted in this study explains why the impact of the PLL is normally small. The analysis indicates that under the special condition of open-loop modal resonance, however, the effect of the PLL may become significant. It is very likely that the open-loop modal resonance may reduce the power system small-signal angular stability. Hence, in tuning the parameters of the PLL, the open-loop modal resonance should be avoided. In this study, the procedure about the parameter tuning of the PLL to consider the effect of open-loop modal resonance is proposed and demonstrated in an example multi-machine power system integrated with a PMSG. Analysis and conclusions made in this study are evaluated using the example power system.

In this study, optimum values of unknown seven parameters of proton exchange membrane fuel cells (PEMFCs) stack are generated for the sake of appropriate modelling. An objective function is adopted to minimise the sum of square errors (SSE) between the experimental data and the corresponding estimated results. A novel application of grasshopper optimisation algorithm (GOA) is engaged to minimise the SSE subjects to set of inequality constraints. Three study cases of typical commercial PEMFCs stacks are demonstrated and verified under various steady-state operating scenarios. Necessary subsequent comparisons to new results by others found in updated state-of-the-art are made. Sensitivity analysis of defined parameters is carried out. It is found that the PEMFC model is susceptible to the deviations of optimised parameters as the errors are substantially disturbed which signifies the value of the GOA-based method. In addition, performance measures to indicate the robustness of the GOA-based methodology are pointed out. At this moment, dynamic model of the stack is addressed and incorporated to demonstrate its dynamic response. Detailed MATLAB/SIMULINK simulation model is implemented to study the PEMFC dynamic performance. The simulated test cases emphasise the viability and effectivity of the GOA-based procedure in steady-state and dynamic simulations.

A decentralised control scheme is proposed to manipulate the speed and the pitch angle of a 1.5 MW wind turbine. In region 2, the goal is to capture maximum power by tracking a reference turbine's speed. For region 3, the goal is to regulate the turbine's speed at its rated value and harvest the rated power. L ₁ adaptive controllers are proposed to achieve the previous goals. A new L ₁ adaptive algorithm, based on fuzzy modelling and variable structure adaptation, is introduced. Performance bounds on the proposed controllers are derived. Simulation results illustrate the superiority of the proposed algorithm compared with both proportional–integral and basic L ₁ adaptive controllers.

This study presents a flexible unbalance compensation strategy for doubly fed induction generator (DFIG)-based wind power generation system connected to an unbalanced weak grid, which can achieve flexible trade-off between the two common control targets, i.e. the balanced output current of DFIG and the balanced voltage at the point of common coupling (PCC), thus the operation performance of DFIG and the power quality at PCC can be considered comprehensively. In order to achieve this target, a novel indirect virtual impedance (IVI) method is proposed, which can control the negative sequence output impedance (NSOI) of the DFIG from zero to infinity. The advantage of the proposed IVI method is to solve the drawback of the traditional virtual impedance method, i.e. the open-loop gains for the system disturbances are highly related with the required virtual impedance, which may introduce oscillation during dynamic periods or even risk the system stability when the required NSOI is large. Meanwhile, the sequential decomposition of the voltage or the current can be avoided in the proposed strategy. The compensation performance and the system stability are theoretically investigated. The availability and advantages of the proposed flexible unbalance compensation strategy are verified by experimental results.

This study proposes a comprehensive decision support framework to optimally select the solid medium and heat transfer tubes material composing the thermal energy storage (TES). The proposed decision model aims to maximise the net present value (NPV) associated with the TES investment over a given time horizon. Compared with related works in the literature, the authors’ design accounts for various practical investment costs and design parameters that are the number of heat transfer tubes, storage module length, and the storage unit diameter. The decision problem, which maximises the NPV is formulated as an optimisation problem to find out the optimal combination among a set of solid media and heat transfer tube materials considering their thermo-physical characteristics. The proposed design is evaluated through case studies to test its concrete practices and to evaluate the impacts of the design parameters on the TES investment costs.

The development and application of photovoltaic (PV) systems are becoming increasingly more important as the global need for energy resources expands and environmental protection becomes more highly valued. Parameters of PV models can be identified by measuring their current–voltage (I–V) characteristic curves. Identifying these parameters quickly, accurately and reliably is critical in determining the operating status of in situ PV arrays and, in turn, optimising solar energy conversions. To achieve both fast and accurate parameter identification with high reliability, a new algorithm called algorithm based on SSO and Nelder–Mead simplex (NMS) (SSSO) based on the simplified swarm optimisation (SSO) and the NMS is proposed in this study. To demonstrate the performance of SSSO in identifying solar cell system parameters, its performance on the single diode model and the double diode model was compared with existing algorithms in terms of both fitness value and run time. The experiment results indicate that SSSO outperformed the compared algorithms in both run time and standard deviation of fitness value.

With the increase in wind power integration in power systems, wind power uncertainty can no longer be neglected in the determination of the day-ahead power generation schedules. Aiming to achieve the best economy of power generation of the system, this study proposes a unit commitment (UC) optimal model for thermal plants, considering the fuel costs required for compensating for the wind power below schedule. The AC power flow equations are included as constraints based on the second-order cone programming (SOCP) method. SOCP is further improved here by considering line loss as constraint to guarantee the validity of its solution. Research shows that generators with lower minimum-output and fuel-cost rates are preferred to be online and that reasonable wind curtailment is beneficial for reducing the generation cost. Finally, AC power flow verification is carried out and the results suggest that the improved SOCP method could model the AC power flow equations correctly. The proposed methodology can be effective in making UC decisions for power grids with high wind power penetration.

To maximise functional efficacy and reliability of distributed generations (DGs), this study leads to modelling, control, stability analysis, and hardware validation of a new adaptive current controller in the application of an on-grid voltage source converter (VSC) system. For effective mitigation of power system hindrances without affecting the power quality (PQ), self-tuning of weights associated with the proposed variable leaky adaptive step-size-least mean square (VLAS-LMS) control algorithm based on artificial neural network (ANN) is updated in natural frame and felicitous shaping of VSC outputs are witnessed. The selection of a constant step-size associated with proposed controller usually yields updating of same weights by all the sampling periods and hardly takes care of rate of convergence factor, which decides the stability of the controller. It can no longer avoid wavering of weights during grid disturbances, resulting in high-filtering gains. Again, a constant leaky factor may lead to over- or under-parameterisation of regularisation component. These disputes can be overcome in the proposed algorithm by the introduction of an adaptive step size along with a variable leaky factor. Furthermore, PQ is maintained as trade-off by the inclusion of detuned LC filter. Experimental outcome ensures the validation and effectiveness of the proposed controller.

Higher cost and stochastic nature of intermittent renewable energy (RE) resources complicate their planning, integration and operation of electric power system. Therefore, it is critical to determine the appropriate sizes of RE sources and associated energy storage for efficient, economic and reliable operation of electric power system. In this study, two constraint-based iterative search algorithms are proposed for optimal sizing of the wind turbine (WT), solar photovoltaic (PV) and the battery energy storage system (BESS) in the grid-connected configuration of a microgrid. The first algorithm, named as sources sizing algorithm, determines the optimal sizes of RE sources while the second algorithm, called as battery sizing algorithm, determines the optimal capacity of BESS. These algorithms are mainly based upon two key essentials, i.e. maximum reliability and minimum cost. The proposed methodology aims to avoid over- and under-sizing by searching every possible solution in the given search space. Moreover, it considers the forced outage rates of PV, WT and utilisation factor of BESS which makes it more realistic. Simulation results depict the effectiveness of the proposed approach.

The planning of active network management (ANM) schemes for distribution systems with distributed generation (DG) does not consider the random participation of customer-owned DGs. This study analyses and addresses the impact of the integration of customer-owned DG on the planning of ANM schemes and maximum DG penetration limits. The random customer DG installations are incorporated by considering sets of events where in each event the number, size and location of DG units are randomly generated. The events, in a set, are chronologically ordered and used to represent a possible scenario of customer-owned DG installations. A two-phase planning approach is proposed where the problem is formulated as a mixed-integer non-linear programming problem with an objective of determining the optimal ANM scheme for maximising the utility DG penetration considering customer DG installations. Several case studies are conducted on a generic 33 kV UK distribution network, including a comparison with the one-time planning approach. The results show that the optimal ANM scheme will vary with the number, locations and sizes of customer DGs and thus for utilities to achieve maximum DG penetration, it is recommended to adaptively control and equip DG units with the capability of switching between various ANM schemes.

Existing reactive power systems do not readily provide support or anti-disturbance capabilities. This study was conducted to explore the predisposing factor and suppression measures of low-frequency oscillation in large-scale wind power cluster systems by establishing a wind farm cluster mode with wind power fluctuation in a DIgSILENT/power factory. Considering the multiple time scale and the operating characteristics of cluster system, a novel modified fruit fly optimisation algorithm (nMFOA) combined with probabilistic sensitivity indices is proposed to coordinate and optimise static VAR compensator (SVC) damping controller parameters to enhance the power system stability of the wind farm cluster. Adverse effects in the SVC damping controller are eliminated via the nMFOA with probabilistic eigenvalue, which can be used to effectively coordinate and optimise SVC parameters. The proposed scheme was tested on a certain wind farm cluster in Hami, Xinjiang Province.

In this study, a multi-objective predictive control strategy is presented for the stability improvement of a power system in the presence of wind farms and STATCOM. The main contribution of this study is in the multi-objective consideration for controlling the active and reactive powers of the rotor-side converter in each of the induction generators, controlling the voltage of the synchronous generators’ excitation system, and designing the damping controller of STATCOM using the predictive strategy. To reduce the computational burden, and to accurately choose the input paths into the predictive control, the Laguerre functions are used. Also, for reducing the sampling time in the selection of large prediction horizons, the exponential data weighting has been employed. The simulation results were evaluated using MATLAB software in the field of time and frequency under different scenarios. Moreover, the obtained results of each domain are compared using the two techniques of the predictive strategy, i.e. the classic model, Laguerre functions, and also the conventional proportional integral controller. The comparison of these three methods reveals that the functional predictive control outfits the two other controllers in damping of the oscillations.

Switch-model power electronic inverters are heavily deployed as the main technology in enabling flow of variable DC power into the AC grid. Various distributed energy system (DES) architectures have been designed depending on many attributes including size and application of the installed system. The reliability of the power electronic interfaces (PEI), i.e. inverters is critical in all these architectures. Recent studies demonstrate enhanced operation of a PEI can be reached by optimum adjustment of its controller parameters. While conventional tuning methods are mostly based on trial and error, their optimum performance can be achieved for primarily first-order systems. For systems with increasing number of PEIs or more complexity, application of these tuning methods becomes challenging and expensive. Thus, considering the operational performance of DES, a controller self-tuning methodology has been presented for PEIs with particle swarm optimisation capability. The optimal parameters for both kW-scale and multi-megawatt PV systems’ inverters are determined via a time-domain performance objective function. Typical PV system performances are presented for step changes and dynamic weather conditions. Effectiveness of the controller self-tuning methodology has been demonstrated via the reduction of transient energy, when the system is subjected to dynamic changes and disturbances.

This study presents a comprehensive decision support model formulated as a finite-horizon-constrained optimisation problem to optimally design the geometry variables that maximise the net present value (NPV) associated to the thermal energy storage (TES) investment over a given time horizon. This study faces one of the main problems in a TES, which is to react to the unpredictable production/demand processes, by determining a high-level optimal size of the TES maximising the NPV that captures the storage benefits as well as detailed fixed and variable costs over a chosen time horizon. The storage benefits are defined, so that they model the costs of the expected discharged thermal energy over a year. Moreover, the authors account for various costs model regarding the total costs of the heat transfer tube material, the storage material, and the insulation material. The proposed decision model can be considered as practical framework that can support engineers and decision makers in the process of design and planning of future. They investigate performance and efficiency of the proposed decision support system framework through representative case studies. Numerical studies demonstrate the usefulness and efficacy of the proposed decision model.