Ocean waves are an abundant source of energy. This energy of the ocean can be converted into useful electrical energy using electrical generators. Linear generators have received tremendous attention in energy harvesting technology due to its unique ability to convert the energy without any intermediate converter. The principal objective of this study is to present the various types of linear electrical generators which have been investigated so far for direct drive ocean wave energy conversion and describe the working principle of each type with the difference. In this study, after a brief description of the basic electrical generator, various types of generators, including the linear and flat generators available in the literature are reviewed and discussed based on the design configuration of different types of magnet arrangements. Linear generators have been compared in terms of core type, flux path, the location of PMs, etc. The research gaps have been identified and future research directions have been suggested.

The lipid-extracted residues of Desmodesmus sp. cultivated in anaerobically digested effluents (LERDADEs) were determined in real time using a thermogravimetric analyser and pyrolysis-gas chromatography–mass spectrometry coupling technology over a range of temperature (300–800°C). The composition analysis results indicated that LERDADE has potential for energy application due to its high carbon content and relatively low nitrogen content. The main decomposition temperature of LERDADE was 319.9°C, at which up to 68.4% of the mass was lost. The fast pyrolysis of LERDADE at 800°C produced the maximum yield (36.6%) of bio-oil compared with 29% at 700°C. However, the number of harmful pollutants (polycyclic aromatic hydrocarbons and nitrogen compounds) released at 800°C (41.7%) was much higher than that released at 700°C (28.3%), which caused a relative increase of 32.1%. Considering the reasonably high bio-oil production and minimum pollutant emission, a lower temperature (∼700°C) was found to be optimum for producing biofuel from LERDADE.

In this study, the authors are interested in estimating how much a PV system underperforms than expected by exploiting forecast uncertainty. For this, they first study a forecast accuracy-related forecast uncertainty metric using the ensemble method based on the dropout technique, which is widely used in deep learning forecasting models. Given the forecast accuracy-related uncertainty metric, the rationale of the authors' approach is that forecast accuracy is likely to decrease compared to the normal case of similar uncertainty metric values if any performance degradation happens. It is because similar uncertainty metric values are likely to show similar forecast accuracy. Therefore, they generate a standard table by simulating possible performance degradation cases and conduct the performance degradation diagnosis by looking up the standard table based on the uncertainty metric. From the experiments, in the case of persistent degradation, they show that their approach estimates the performance degradation with the estimation error of around 1% while an uncertainty-unaware approach shows the estimation error of up to 5%. In the case of temporal degradation, their approach shows the estimation error of around 3%, while the uncertainty-unaware approach does not show meaningful result.

This study proposes the optimal bidding of a price-maker energy hub. It employs game theory for optimal bidding strategy in a competitive and equilibrium market. The high flexibility of the energy hub helps it to make a profit from participation in the energy market. This price-maker energy hub has some energy converters and combined heat and power to deliver energy to its loads. The strategy is that the proposed energy hub maximises its profit with the presence of other rival energy hubs. There are price uncertainties modelled by the Monte Carlo simulation. Incomplete information in the competitive market is modelled by the game theory, which causes the problem to be stochastic and equilibrium programming. The proposed solution for this problem uses stochastic bi-level programming. The optimal bidding strategy of price-maker energy hub is modelled in the upper level, and modelling of market clearance is at the lower level. The proposed algorithm has been evaluated in comparative case studies. The obtained results show the efficiency of the equilibrium environment in profit maximisation. The effect of price uncertainty in profit maximisation is also compared with the deterministic case study.

Maximum power point tracking (MPPT) is essential in off-grid and grid-tied photovoltaic (PV) applications to extract the maximum available power. This study presents a new MPPT technique named irradiance and temperature (I&T) method. This technique uses the estimation of irradiance and measurement of temperature to define the MPP. It is based on the observation of irradiance effects in PV module current and temperature dependency of PV module voltage. The irradiance will be evaluated through the measurement of short-circuit current. In addition, temperature reading is employed to determine its effects in temperature-dependent variables, thus providing higher efficiency to the I&T method. Good stability in steady state and fast tracking speed, in different conditions of irradiance and temperature, are important features of this technique. Experimental tests compared the performance of the I&T method with conventional and parameter-based methods. The performed tests highlight the effectiveness of the proposed method at uniform and dynamic weather conditions.

Inverter interfaced distributed generation units contribute limited fault current and thereby the current magnitude-based protection schemes find limitation for such situations. Change in penetration level of renewable energy sources with time makes available protection schemes more challenging. The distinction between load and fault current is an issue and with high resistance fault, level of fault current and load current becomes comparable. In this paper, local measurement-based protection technique is proposed that uses current and voltage signals to derive accurate protection decision for a distribution system in the presence of distributed generation units. The method uses real and reactive power calculated at the relay point for the decision. The performance of the algorithm is tested for a 33 bus distribution system simulated using DIgSILENT PowerFactory. Considering such units, the method is tested for both islanded and grid-connected modes, the varying level of distributed generation penetration and for different fault resistances. The results demonstrate the strength of the proposed method over available techniques. The proposed method is also validated using hardware-in-loop testing using Arduino NANO microcontroller and OPAL-RT simulator in real-time.

In order to improve the utilisation rate of renewable energy and flexibility of the system, the AC–DC distribution network with multiple park integrated energy subnetwork (PIESN) based on multi-port power electronic transformer (MPPET) is proposed. First, a power-sharing model of interconnected PIESNs based on MPPET is built, in which the port power of MPPET can be controlled. Then the flexibility demand and supply of electricity, heat and cooling are analysed and a flexibility-sharing model among multi-PIESN is established. Multi-energy flexibility conversion mechanism is proposed and the flexibility index is established. Moreover, the optimal operation problem is formulated and solved. The objective function is the comprehensive operation cost, which includes generation cost, energy cost of PIESN, network loss cost, MPPET loss cost, carbon emission cost and electricity storage system operation cost. Finally, simulation on the modified IEEE33 system shows that the proposed model can make the system be in an adequate flexibility, improve the utilisation rate of renewable energy, reduce load shedding and realise the power sharing among multi-PIESNs.

A Gaussian process regression (GPR) with active learning is proposed for developing the solar irradiance point and interval forecasting models, which consider the spatial-temporal information collected from a targeted site and a number of neighbouring sites. To enhance the performance of the GPR-based model an active learning process is developed for constructing an ad-hoc input feature set, selecting training data points, and optimising hyper-parameters of GPR models. To validate the advantages of the proposed method, a comprehensive computational study is conducted based on solar irradiance data collected from the northwest California area. In the point forecasting, the proposed method beats the state-of-the-art benchmarking methods including classical statistical models and data-driven models according to values of the normalised root mean squared error, normalised mean absolute error, normalised mean bias error, and coefficient of determination. In the interval forecasting, the proposed method outperforms the persistence model, autoregressive model with exogenous inputs, generic GPR, as well as two recently reported forecasting methods, the bootstrap-based extreme learning machine and quantile regression, in terms of the forecasting reliability. Computational results show that the proposed method is more effective than well-known existing benchmarks in the point and interval forecasting of the solar irradiance.

This study presents a robust, coordinated control methodology for damping sub-synchronous oscillations (SSOs) while considering power output variations from multiple wind farms. The proposed damping control strategy utilises the mixed H ₂/H _∞ control with regional pole placement to suppress the oscillations. For ensuring applicability over a wider operating range, the convex polytopic theory is utilised by using different operating points as the vertices of a convex polytope. The centralised, coordinated controller for damping SSOs is designed using linear matrix inequalities. Furthermore, unmeasurable state variables, if present, are represented by corresponding output variables. The damping signal is implemented as active power and reactive power modulation of the rotor-side converter of the doubly fed induction generators. A 4-machine, 2-area system and a 39-machine New England system are used to demonstrate the performance of the proposed control. The simulation results show that the polytopic controller can not only provide requisite damping to the SSO modes of interest, but also has good control performance when the wind power outputs change over a wide range.

In this study, a proportionate power sharing among the parallel inverters operating in an islanded microgrid is achieved using droop and virtual inductance control. The same droop coefficients are used to achieve the frequency regulation as well. The frequency changes which are inevitable in droop control are measured and used to emulate the behaviour of damping and inertia to the DC link voltage using hybrid energy storage system consisting of battery and supercapacitor units. The proposed DC link voltage regulator restores the DC link voltage quickly by providing power corresponding to the rate of change of frequency and frequency deviation. This reduces the impact of voltage variations on the DC-load and keeps modulation index within the linear range for voltage source inverter. The design aspects of DC link voltage regulator, damping and inertia constants, selection of battery and supercapacitor units based on rating of the DC link voltage are discussed. The proposed decentralised droop control and DC link voltage restoration methods are validated through detailed simulation and experimental studies.

The fluctuation and intermittence of the distributed renewable energy sources (RESs) limit the connection to the main grid and cause large-scale power curtailment. This issue can be addressed by the application of a hydrogen production unit (HPU), which is compatible with the disadvantages of RESs and can achieve large-capacity and long-term absorption of the electric energy. A decentralised coordination control strategy among the generation, storage and HPU are proposed in the photovoltaic (PV) dominated islanded AC microgrid. Firstly, the energy conversion efficiency from electric energy to hydrogen energy of HPU is derived, based on which the efficiency adaptive control is proposed to regulate its power consumption by adjusting the efficiency reference according to the bus frequency. Secondly, the state of charge and the instantaneous power of the storage are considered in the control strategy, which can avoid the storage being overused or damaged. Thirdly, the control strategy of the PV generator is designed to adaptively regulate its output power from maximum power point tracking mode to the reference power point mode seamlessly. Finally, the stability of the whole control strategy is analysed and the control effectiveness is verified based on the RTLAB experiment platform.

For low-voltage autonomous microgrids, the essential goal is maintaining stable voltage and frequency throughout its network for the smooth operation of distributed loads. The other goal is to maintain proportionate power-sharing between multiple sources connected through voltage source converters (VSCs). For the particular case of a heavily loaded microgrid, any further increase in demand poses new problems. The VSCs proximate to the loads may overshoot beyond its maximum ratings. Owing to which the stability of the power supply, as well as the longevity of VSCs, may get compromised. Also, these sources provide power under varying composite loading conditions comprising both linear and non-linear loads resulting in unbalancing in its network. To overcome these problems, a decentralised fast terminal sliding mode control strategy for active power-sharing between parallel VSCs has been proposed. A detailed mathematical model based on Lyapunov's stability theory-based study is designed to establish the operating stability of the proposed controller. To validate the proposed control strategy, MATLAB-based simulation results are provided for possible loading scenarios. A comparative study for performance evaluation between the proposed controller and a conventional proportional -integral-based controller is further carried out. Finally, the performance of the proposed controller is also validated in real-time scenario.

This study proposes an earliest approach toward coordinated frequency stabilisation of wind turbine driven generator-tidal power generation-biodiesel driven generator-micro-turbine generator-based islanded two-area interconnected microgrid system with demand response support (DRS) mechanism. A recent bio-inspired optimisation technique, named yellow saddle goatfish algorithm (YSGA) is employed to optimally tune the controller gains. The comparative dynamic performance of conventional proportional–integral–derivative (CPID), fractional order (FO) PID, dual-stage PIFOD-one plus PI [PIFOD-(1 + PI)] controllers’ parameters optimised by several algorithmic tools such as particle swarm optimisation, firefly algorithmic tool, salp swarm technique and YSGA clearly designates the superiority of YSGA-PIFOD-(1 + PI) controller under different scenarios (considering the real-time recorded wind and load data) in terms of change in frequency, tie-line power fluctuation and objective function. Furthermore, the impact of the DRS mechanism in both areas is analysed first time under real-time wind and load disturbances. Finally, the rigorous sensitivity analysis of YSGA-optimised PIFOD-(1 + PI) controller has been conducted with the variation of wind turbine driven generator gain, ±30% change in synchronising tie-line factor, frequency bias value, microgrid system time constant and  + 30% change in loading magnitude without retuning the optimal base condition values.

Guide vane closure scheme (GVCS) optimisation in hydroturbine generating unit (HTGU) under extreme conditions is one of the most important issues in hydropower plant design and operation. It is a kind of multiobjective constrained optimisation problem that contains the coordination of hydraulic and mechanical dynamic processes of many system states. In such a problem, the traditional optimisation objectives often include the control of the rotational speed of the HTGU and the suppression of the fluctuation amplitudes of the hydraulic pressure at different locations. In order to improve the overall control performances in load rejection process, an improved multi-objective two-archive evolutionary algorithm (TAEA) is put forward for GVCS with chaotic operators. The TAEA-based multi-objective optimisation carefully takes the multiple objective functions and the relevant constraint treatments including the limits on rotational speed peak, speed fluctuations, surge tank water levels, speed governor movement and hydraulic pressure oscillations into consideration. Simulation experiments of a real hydroturbine unit under load rejection condition are conducted with the proposed optimisation scheme and comparative methods. The results indicate that TAEA algorithm can achieve better overall performances and contribute to the operational stability of HTGU.

Unpredictable system component contingencies have imposed vulnerability on power systems, which are under high renewables penetration nowadays. Intermittent nature of renewable energy sources has made this unpredictability even worse than before and calls for excellent adaptability. This study proposes a flexible security-constrained structure to meet the superior flexibility by coordination of generation and demand sides. In the suggested model, demand-side flexibility is enabled via an optimum real-time (RT) pricing program, while the commitment of conventional units through providing up and down operational reserves improves the flexibility of supply-side. The behaviour of two types of customers is characterised to define an accurate model of demand response and the effect of customers’ preferences on the optimal operation of power networks. Conclusively, the proposed model optimises RT prices in the face of contingency events as well as wind power penetration. System operators together with customers could benefit from the proposed method to schedule generation and consumption units reliably.