With the development of large wind farms, the voltage problem caused by the power changes of wind farms occurs continuously because of the wind speed variation. The problem cannot be resolved by extra reactive power compensation because of the limitations in cooperation and response speed. A doubly fed induction generator (DFIG) possesses the power decoupling control capability, which can be used to prevent the voltage change rapidly. However, the dynamic processes of the power demands of the grid and the power limits of the DFIG have not been considered. Therefore, a vector equivalent model of the DFIG considering the controllability of the rotor voltage is established. The feasible power ranges that are limited by the electromagnetic process and controller response are deduced. The dynamic requirement and limitation of the reactive power under the wind speed variation are investigated. The emergency control strategy of reactive power contributed by the DFIG is proposed on the basis of the requirement and limitation. The simulation shows that the rapid adjustment capability of the DFIG can be fully utilised by the proposed method.

This study designs a passivity-based linear feedback control scheme of a permanent magnetic synchronous generator-based wind energy conversion system, which attempts to achieve a maximum power point tracking (MPPT) at generator-side voltage source converter (VSC) and enhance fault ride-through (FRT) capability at grid-side VSC simultaneously. A storage function is constructed based on the passivity theory, in which the actual role of each term is meticulously investigated while the beneficial ones are remained so as to significantly improve the transient responses. Then, an auxiliary input is employed in the form of linear feedback control to ensure a desired tracking error convergence. Moreover, the closed-loop system stability is thoroughly analysed, together with a detailed physical interpretation of the storage function. Three case studies are undertaken including step change of wind speed, stochastic wind speed variation, and FRT. Simulation results verify that the proposed approach can effectively achieve MPPT and dramatically improve the FRT capability under various operation conditions against that of vector control and feedback linearisation control.

This study proposes a two-stage glowworm swarm optimisation (GSO) algorithm for the economical operation of the inner plant of a hydropower station. Binary GSO and real-coded GSO (RCGSO) algorithms are applied with different types of encodings to solve the unit commitment sub-problem and the economic load distribution (ELD) sub-problem, respectively. Moreover, an improved dynamic patching mechanism is developed to avoid invalid calculations and enrich the diversity of the solutions. A luciferin transfer mechanism helps the algorithm escape the local optimum and a local research mechanism enhances the diversity of the solution space by selecting from among the derived solutions. The RCGSO algorithm uses a variable-step mechanism to avoid missing the optimal solution. In comparison with the genetic algorithm and particle swarm optimisation, the RCGSO is significantly robust and provides better solutions to ELD sub-problems. Numerical simulations exhibited the superiority of the two-stage GSO algorithm in terms of stably and quickly solving the economical operation problem of hydropower stations.

In electrical power system, economic load dispatch is a generic operation for optimal sharing of generation units to meet the system load. With the rapid development of the renewable infrastructure and wide encouragement for green energy have emerged hybrid generating systems in power systems. However, there continuous ever-increasing production is creating challenges as well as implicating economic factor also in operation. A collective cost function is considered with the conventional thermal generators along with the consideration of renewable energy sources to envisage the economic factor. For these renewable sources, like wind and solar, the proportional cost, their uncertainty and variability by overestimation and underestimation cost are considered. To achieve this economic day-ahead scheduling, dynamic operation in time scale of 1 h interval is performed. The stochastic nature of wind and solar is modelled by Weibull and Beta distributions, respectively. Moreover, economic optimisation is obtained by a newly developed algorithm called improved fireworks algorithm with non-uniform operator (IFWA-NMO). This introduces adaptive dimension strategy, limiting mapping operator and non-uniform operator. The effectiveness of proposed IFWA-NMO is investigated on standard dynamic economic load dispatch (DELD) system and also employed to solve conventional DELD with wind-solar system.

In order to improve the control effect on voltage and frequency of each distributed energy resource (DER) and improve the stability of the islanded micro-grid, a novel hierarchical control strategy combined sliding mode control (SMC) and consensus control is proposed in this study. The control structure is divided into two layers: the physical layer and the cyber layer. In the physical layer, in order to improve the dynamic control effect, an improved droop control based on consensus theory with considering packet loss is proposed in this study. And the control can be completed in finite time by selecting the approximate parameters in the controller; in order to improve the stability of voltage loop and current loop, an improved double closed-loop structure based on the SMC is proposed. Moreover, the control can also be completed in the finite time. In the cyber layer, an event-triggered SMC-virtual leader–following consensus protocol is proposed in this study to solve the problem of channel noise (disturbance). For improving the control effect, the protocol is used to complete the secondary control on voltage and frequency of each DER. Finally, the effectiveness of the novel control strategy is verified by simulation results.

Access to accurate power production prediction of a wind turbine in future hours enables operators to detect possible underperformance and anomalies in advance. This may enable more proactive and strategic operations optimisation. This study examines common Supervisory Control And Data Acquisition (SCADA) data over a period of 20 months for 21 pitch regulated 2.3 MW turbines. In this study, an algorithm is proposed to impute values of data that are missing, out-of-range, or outliers. It is shown that an appropriate combination of a decision tree and mean value for imputation can improve the data analysis and prediction performance by the creation of a smoother dataset. In addition, principal component analysis is employed to extract parameters with power production influence based on all available signals in the SCADA data. Then, a new data fusion technique is applied, combining dynamic multilayer perceptron (MLP) and adaptive neuro-fuzzy inference system (ANFIS) networks to predict future performance of wind turbines. This prediction is made on a scale of one-hour intervals. This novel combination of feature extraction, imputation, and MLP/ANFIS fusion performs well with favourably low prediction error levels. Thus, such an approach may be a valuable tool for turbine power production prediction.

This study focuses on the role of electric vehicle (EV) charging station (CS) to support the frequency of the islanded micro-grid (MG). Frequency deviation is a common issue in islanded MGs due to the intermittent nature of renewable energy sources (RES). This issue can be minimised by providing the inertia to the system. However, RES have low or no rotational mass and therefore, they are not able to provide the inertia to the system. In this study, the concept of virtual synchronous generator (VSG) mechanism using a CS is proposed to provide the inertia to the system, where a fleet of EVs parked in CS acts as an energy storage system for MG. Simulations have been performed in Matlab Simulink on the data taken from the MG on the island Kythnos in Greece. Case studies have been carried out to verify the frequency support of MG using VSG mechanism. In these case studies, irradiation level of the photovoltaic array and load of the MG system have been changed arbitrarily to induce the power mismatch in the system. Through simulation results, it has been verified that the frequency of the system can be supported by the bidirectional flow of power between the CS and MG using VSG control mechanism.

The large-scale integration of photovoltaic generation (PVG) on distribution systems (DSs) preserving their technical constraints related to voltage fluctuations and active power (AP) flow is a challenging problem. Solar resources are accompanied by uncertainty regarding their estimation and intrinsically variable nature. This study presents a new probabilistic methodology based on quasi-static time-series analysis combined with the golden section search algorithm to integrate low and high levels of PVG into DSs to prevent AP flow in reverse direction. Based on the analysis of two illustrative case studies, it was concluded that the successful integration of PVG is not only related to the photovoltaic-cell manufacturing prices and conversion efficiency but also with the manufacturing prices of power electronic devices required for reactive power control.

As wind energy is increasingly exploited worldwide, optimisation of wind farm layout becomes more crucial. To guarantee the economic efficiency and profit of a wind farm, the deployment of wind turbines has to be optimised before operation. Traditional methods usually assume that identical type of wind turbines are utilised in a layout design. In this study, multiple types of turbines are considered in wind farm layout optimisation in complex terrain, namely mixed-installation. By utilising different power generation characteristics, hub heights and rotor diameters, and cost models of different types of turbines, the efficiency of a wind farm can be further improved. A single-objective optimisation problem is firstly established by modelling all aforementioned factors, and the objective is to achieve a minimum cost per unit of energy. Subsequently, after using computational fluid dynamics to simulate the wind flow over complex terrain, a genetic algorithm-particle swarm optimisation optimisation algorithm is then proposed to determine the position and type of every individual turbine simultaneously. Eventually, extensive simulation studies are present to verify the feasibility of this scheme.

The objective of this study is to present an economic analysis (EA) of actual installed photovoltaic (PV) projects considering Gulf Cooperation Council countries climate conditions. The two analysed PV systems are commissioned in Kuwait and they were chosen to be the scope of this study since the availability of their characteristics. The first system is installed on a school and equipped with thin film (copper indium gallium selenide) solar modules of efficiency equal to 14% and the other system is installed on a commercial building and equipped with monocrystalline solar modules of efficiency equal to 17%. The EA consists of studying the financial parameters related to the two previous projects using two different calculation tools. One tool is developed and presented in this study that is the EA calculator and the other tool is the existing solar advisory model software. The two calculation tools’ results almost match with slight differences considered negligible, so the developed EA calculator is considered validated. As a conclusion, the impact of renewable energy (RE) costs on future investments in RE technologies and especially on PV projects is being evaluated.

Current power generation scenarios all over the world are not climate friendly as the generation systems are mainly dependent on fossil fuels that produce greenhouse gas (GHG). As renewable energies (especially solar and wind energies) have the sustainable characteristics, by replacing the conventional energy system with them, it is possible to significantly contribute to reducing the dependency on fossil fuels as well as GHG emissions. Though biomass is the primary renewable source in Bangladesh, this study is exploring new options in photovoltaic (PV) and on-shore wind in the 0.5–2 MW capacity range. This research presents environmental and socio-economic impacts of grid-connected hybrid (PV/wind) power systems by investigating the potentials of the solar and wind energy with considering various sensitivity variables for two different locations, namely Magnama and Dinajpur, in Bangladesh. The main goal of this research is to generate the lowest possible adverse socio-economic and environmental impacts ensuring a certain degree of monetary benefits. Three sizes of plants have been chosen for quantifying the variations in socio-economic and environmental impacts. It is found that the proposed model of the hybrid power system can maximise the benefits by reducing the adverse effects of conventional power plants.