This study presents a new approach for optimal allocation of distributed generation (DG) and energy storage system (ESS) in microgrids (MGs). The practical optimal allocation problems have non-smooth cost functions with equality and inequality constraints that make the problem of finding the global optimum difficult using any mathematical approaches. A dynamic capacity adjustment algorithm is incorporated in the matrix real-coded genetic algorithm (MRCGA) framework to deal with the non-smooth cost functions. The proposed cost function takes into consideration operation cost minimisation as well as investment cost minimisation at the same time for the MG. Moreover, an energy storage equality constraint is applied to manage the state of charge of EES in MGs. The MRCGA is used to minimise the cost function of the system while constraining it to meet the customer demand and security of the system. For each studied case, sets of optimal capacities and economic operation strategies of ESS and DG sources are determined. The computational simulation results are presented to verify the effectiveness of the proposed method.

This study proposes a maximum power tracking (MPT) controller based on chaos synchronisation (CS) for a wind-energy-conversion system. The output power conversion of a wind generator depends on the wind speed, and therefore the optimal conversion of wind energy can be obtained by a variable-speed variable-frequency model. Based on a sensorless controller, CS can express dynamic behaviours by using an incremental conductance to adjust the terminal voltage to the maximum power point. A voltage detector based on the Sprott system is used to track the desired voltage and to control the duty cycle of a boost converter. For a permanent-magnet synchronous generator, the simulation results demonstrate the effectiveness of the proposed MPT controller.

Several research efforts are conducted towards the impact of wind power variability on the operating reserve requirements of power systems. In most systems, reserve capacity is determined by means of a heuristic or statistical analysis of the different drivers for system imbalances. This contribution puts forward a dynamic reserve approach in which the operating reserve capacity is modified on hourly basis, based on expected wind power conditions. This would improve the current methodologies where the capacity is fixed for longer periods, avoiding expensive overestimations. By means of a decentralised approach, the suggested methodology conforms with an unbundled and liberalised market framework. Results show how the average upward and downward capacity reductions are reduced with at least 36% and 16%, compared to the corresponding static reserve strategy. System simulations integrating this dynamic reserve strategy reveal substantial reductions in operational costs. The average cost of withholding reserve capacity is observed to decrease from 4.5, 5.1, 17.1 and 51.2 to 3.1, 4.8, 7.9 and 14.6 €/MWh wind energy injected, respectively, for an installed capacity representing 6, 12, 18 and 24% of the annual electricity demand. In conclusion, the results of this contribution encourage transmission system operators to evolve towards dynamic reserve strategies.

To maximise the economic benefit, photovoltaic (PV) systems in general operate in the so-called maximum power point tracking (MPPT) mode. However, in certain occasions (e.g. in a microgrid or in a weak system), it is beneficial for a PV system not to always operate in the MPPT mode, but occasionally in the power dispatch mode, because of the top priority of maintaining system stability. To this end, a Newton quadratic interpolation-based power control strategy for PV system is proposed to iteratively obtain the required terminal voltage of PV system by approximating the power–voltage characteristic curve with a quadratic curve. With this control strategy, PV systems can operate in the power dispatch mode to flexibly adjust the active power output in a wide range, or adaptively switch to the MPPT mode if necessary. Details on the convergence rate and the way to achieve the fault ride-through capability are also discussed. Simulation is performed based on a detailed PV dynamical model, illustrating that the proposed method has fast convergence rate and robust performance compared with a revised perturb and observe method which can attain the same function.

Conventional photovoltaic (PV) I–V-curve tracer uses a lot of resistors and switches to produce a smooth curve. We propose a binary I–V-curve tracer with an adjustable-resistance load, which means fewer switches and resistors to achieve the same smooth curve. The prototype was built in an ATMega 8535 microcontroller and then tested in various shade conditions of the PV array.

In the outdoor environment and under real operational conditions, photovoltaic (PV) systems are affected by several parameters which cannot be considered constant, principally PV cell temperature and incident radiation, and neither of these will in practice be uniform across a PV array or even an individual module. This last aspect results in non-standard IV characteristics that can exhibit more than one local maximum. The challenge of the maximum power point tracker is finding the true maximum power point (MPP) in the face of all the variation and complexity the real environment imposes on the PV behaviour. It must do this quickly and effectively so the algorithm which controls the DC/DC converter that controls the PV system terminal voltage and thus its operating power point has to be fast and precise. This study proposes a new algorithm that is demonstrated to be highly effective in tracking the MPP under real operating conditions.

This study presents a transformerless topology for a grid-tied single-phase inverter capable of performing the simultaneous maximum power point tracking of two independent and series connected photovoltaic sources. This topology is derived from the neutral point clamped multilevel inverter in half-bridge configuration. The use of a half-bridge topology reduces the leakage current to very low values, whereas the multilevel topology presents an output voltage quality similar to that of a full-bridge inverter. To simultaneously track the maximum power of both photovoltaic sources, a generation control circuit is used. With this topology, it is possible to improve the performance of the converter under partial shadowing conditions, very common in photovoltaic facilities operating in residential areas. A 5 kW prototype of this topology has been implemented and tested in the laboratory.

As a matter of course, the unprecedented ascending penetration of distributed energy resources, mainly harvesting renewable energies such as wind and solar, is concomitant with environmentally friendly concerns. This type of energy resources are innately uncertain and bring about more uncertainties in the power system context, consequently, necessitates probabilistic analysis of the system performance. Moreover, the uncertain parameters may have a considerable level of correlation to each other, in addition to their uncertainties. The two point estimation method (2PEM) is recognised as an appropriate probabilistic method. This study proposes a new methodology for probabilistic power flow studies for such a problem by modifying the 2PEM. The original 2PEM cannot handle correlated uncertain variables, but the proposed method has been equipped with this ability. To justify the impressiveness of the method, two case studies namely the Wood & Woollenberg 6-bus and the IEEE118-bus test systems are examined using the proposed method, then the obtained results are compared against the Monte Carlo simulation results. Comparison of the results justifies the effectiveness of the method in the respected area with regards to both accuracy and execution time criteria.

Most wind turbine generators installed in large windfarms are of variable speed types operating at the maximum power point tracking mode to generate the maximum amount of power. Owing to this fact and regarding the random nature of the windspeed, the output power of the windfarm fluctuates. Fluctuating power is a serious problem for high capacity power plants and should be smoothed. As an effective factor on the required battery energy storage system (BESS) capacity value, tracking is the most important part performed by a coordinated control system in the power smoothing process. An ADAptive LInear NEuron (ADALINE)-based power tracking method with a flexible learning rate is proposed in this study. Furthermore, a particle swarm optimisation-based calculation of the learning rate is presented for optimising the proposed tracking method which reduces the required BESS capacity and the investment cost. Moreover, a charging/discharging algorithm for the BESS units is proposed which decreases the number of required BESS units and increases their useful life by reducing the switching activity as well. To evaluate the performance of the proposed coordinated control approach, the real output data of a 99 MW windfarm are tested. The simulation results verify the effectiveness of the proposed approach.

The power–voltage (P–V) curve of a photovoltaic (PV) power generation system under partially shaded conditions (PSCs) is largely non-linear and multimodal, and hence, global optimisation techniques are required for maximum power point tracking. A traditional optimisation algorithm is proposed here, namely random search method (RSM) for tracking the global maximum power point in a solar power system under PSC. The RSM is based on the use of random numbers in finding the global optima and is a gradient independent method. The major advantage of RSM is its very simple computational steps, which requires very less memory. The performance of RSM in tracking the peak power is studied for a variety of shading patterns and the tracking performance is compared with two-stage perturb and observe (P&O) and population-based particle swarm optimisation (PSO) methods. The simulation results strongly suggest that the RSM is far superior to two-stage P&O method and better than PSO method. Experimental results obtained from a 120-watt prototype PV system validate the effectiveness of the proposed scheme.

The modelling of photovoltaic (PV) solar cells using a hybrid adaptive neuro-fuzzy inference system (ANFIS) algorithm is presented. It is based on the decomposition of the cell output current into photocurrent and junction current. The photocurrent is linearly dependent on solar irradiance and cell temperature; consequently, its analytical computation is done easily. However, the junction current is highly non-linear and depends on cell voltage and temperature. Therefore, its analytical computation is complicated and the manufacturers do not supply any information about this parameter. Moreover, there is no way to measure it physically. Therefore, it is proposed to use the ANFIS algorithm as a powerful technique in order to estimate this current and reconstruct the output PV cell current using the photocurrent. The model validation is based on the gradient descent and chain rule applied to a set of data different than the one used for training process. The advantage of the proposed model is that only one climatic parameter is used as the input to the ANFIS algorithm, which makes it less sensitive to climatic variations.

Unbalanced grid voltage conditions in wind farms degrade the performance of the wind turbines and inject severely distorted current into the power system. This study proposes a new model-predictive direct power control strategy for a doubly-fed induction generator (DFIG) under unbalanced grid voltage conditions. An optimised cost function is derived in order to select an appropriate voltage vector. This directly regulates the instantaneous active and reactive powers in the stationary stator reference frame without the requirement for coordinate transformation, proportional–integral regulators, switching table or pulse-width modulation modulators. After that, the behaviour of the DFIGs under unbalanced grid voltage is investigated. A power compensation scheme is developed, which does not require the extraction of the negative stator current sequence. This can achieve several different control objectives; that is, obtaining sinusoidal and symmetrical stator currents, and the cancellation of electromagnetic torque oscillations. The effectiveness of the proposed control strategy is validated experimentally on a 20 kW laboratory DFIG prototype.

This study deals with the control of a low-power off-grid wind energy conversion system. As rotor blades are not pitchable, control is ensured by variable-speed operation in order to limit harvested power in high winds and to maximise it at low winds. Turbine power regulation is achieved through active speed stall control, which supposes operation at low rotational speed values. In this way, operation within admissible limits of rotational speed and torque is also ensured. A unique power controller is designed, with classical proportional–integral (PI) structure and switched parameters and references. An original solution for switching controller parameters so as to avoid local instability and preserve smooth transition between partial-load and full-load operating regimes is proposed. Switching is performed in an operating point where power controller output is sufficiently deep in saturation and the antiwindup structure is active. Guidelines for choosing this switching point are provided. This control structure does not require estimation of either wind speed or wind torque, or use of gain scheduling structures. The proposed approach is validated through experiments on a dedicated real-time simulator.

This study presents the design, development and comprehensive analysis of voltage and frequency controllers (VFCs) for standalone wind energy conversion systems (SWECSs). An isolated asynchronous generator, a synchronous generator (SG) and a permanent magnet SG are used with these SWECSs. These VFCs are developed with three-phase generators driven through a wind turbine to feed three-phase and single-phase loads. A battery energy storage system is used invariably with each system configuration to facilitate load leveling under change in wind speeds and consumer loads. The performance of VFCs are demonstrated to validate its operation as a load leveler, load balancer, phase balancer, neutral current compensator and an active filter along with a VFC.

The shuffled frog leaping algorithm (SFLA) is presented, to optimally design multiple proportional–integral (PI) controllers of the static synchronous compensator (STATCOM) system with the purpose of improving the transient stability of a grid-connected wind farm. The control strategy of the STATCOM system depends on a sinusoidal pulse width modulation voltage source converter, which is controlled by the cascaded PI control scheme. The response surface methodology is used to establish a second-order fitted model of the transient voltage responses at the point of common coupling in terms of PI controllers’ parameters. For realistic responses, a three-mass drive train model is used for the wind turbine generator system because of its great influence on the dynamic analyses. The SFLA code is built using MATLAB software. The effectiveness of optimised PI-controlled STATCOM by the SFLA is then compared to that optimised by genetic algorithms technique taking into consideration symmetrical and unsymmetrical fault conditions. The proposed control scheme is applied to a real grid-connected wind farm system at Hokkaido Island, Japan. The validity of the proposed system is verified by the simulation results, which are performed using PSCAD/EMTDC software environment. With the proposed SFLA-based STATCOM, the transient stability of a grid-connected wind farm can be enhanced.