The distributed power generation system (DPGS) with renewable energy sources is gaining popularity over conventional sources of energy. Although the renewable sources of energy are available in abundance and are eco-friendly, yet, they have intermittent nature. As a result, renewable energy-based DPGS face difficulty in power transfer during adverse atmospheric conditions. Also, if the interfacing unit is a single inverter, its switches have to withstand a large amount of heat and handle a high value of current. Henceforth, to ensure uninterrupted supply and reduce voltage stress on switches, the power inverters need to be connected in parallel. This study presents various current and power-sharing control strategies of parallel-interfaced voltage source inverters with a common AC bus. A detailed classification and analysis of wired and wireless (droop) controllers for parallel-connected voltage source inverters have been done. Moreover, the recent trends and improvements in traditional droop controllers are presented. Finally, a comparative analysis among the various wired and wireless controllers is done, listing their advantages and limitations.

The penetration of large-scale offshore wind farms in power grids has a negative effect on the operation of the conventional distance relays. In this study, a new protection technique is presented for uncompensated/compensated double-circuit transmission lines utilising one-end current measurements. The proposed protection technique does not require any information regarding the transmission line parameters, wind farms, or the compensation devices. In addition, the errors in phasor estimation due to the generated sub-harmonics and inter-harmonics by the wind generators are avoided as the current samples are directly used. Comprehensive studies are implemented on PSCAD/EMTDC software considering numerous cases of external and internal faults. In addition, the double-circuit transmission line is modelled using the frequency-dependent phase model and the mutual-coupling between both circuits are considered. The recorded results confirm the high efficacy of the proposed protection technique with respect to fault type, fault location, and fault resistance.

Hybrid photovoltaic-thermal (PVT) collectors have been widely investigated in recent decades since they ensure higher performances and compactness with respect to the two sub-components. In this study, a novel covered PVT water collector, specifically designed to be coupled with heat pumps, is presented. The PV-cells are directly laminated on the aluminium roll-bond absorber without the usual front glass layer, while a glass cover is added after the lamination, creating an air gap with the PV-absorber laminate. The novel collector has electrical features similar to those of uncovered PVTs, in which the front glass layer is laminated in contact with PV-cells and even better performances than a traditional covered collector, which has a second glass cover on top. The electric performances of the PVT, operated in stand-alone mode and solar-assisted mode, were monitored and compared with a traditional PV module, obtaining interesting results in both conditions. In particular, the novel PVT showed comparable and even better electric performances than the traditional PV when combined with the heat pump, thanks to PV-cells active cooling. The work proves that the proposed manufacturing technique could lead to a new generation of hybrid collectors, which may achieve competitive performances when integrated with heat pumps.

New disturbance-rejection control for an islanded microgrid (MG) is presented in this study. The proposed method can be used with MGs consisting of several distributed generation (DG) units and local loads with both grid-connected and islanded modes capability. The proposed controller utilises state feedback with integral control to track the desired voltage. The uncertainty of the load parameters is tackled as a disturbance. The proposed controller is designed using the method of invariant ellipsoids to guarantee robust stability, fast transient response and zero steady-state error. The controller synthesis is formulated as a convex optimisation problem that is effectively solved using linear matrix inequality methods. The performance of the proposed MG voltage controller is assessed by several simulations in the presence of random load variations, load unbalances and step changes in the voltage reference. Moreover, the power mismatch during accidental islanding event is also considered.

In this study, a novel trigeneration system is conceived to produce heat and electricity and to provide cooling for the health treatments and touristic facilities of a spa, based on the natural hot water and solar sources. The power generation components, individually considered, are commercially available ones, but their novel combination and the complex power flow management represented a challenge. The proposed system is composed of a low-temperature driven adsorption chiller, thermally activated by a low enthalpy geothermal source, and by hybrid photovoltaic/thermal panels. In this way, multiple objectives are achieved: produce electricity and thermal energy by renewable sources; optimise the use of different renewable sources (geothermal and solar); use the energy available for free from a geothermal source also during summer (otherwise wasted) to produce a cooling effect, and in so doing, avoiding the huge electricity consumption of conventional air conditioning units in summer; reduce the temperature of the fluids released to the environment (in a natural reserve); reduce the CO2 emissions by 45% with respect to the present configuration, limiting the global warming. The mathematical models were experimentally validated using a pilot plant built on purpose, and the performance of the whole system was analysed and discussed.

Recently, variable speed wind turbines (WTs) employing dual stator-winding induction generators (DSWIGs) have gained interest in related kinds of literature. A DSWIG has two sets of stator windings known as power-winding (PW) and control-winding (CW), where CW is connected to a pulse-width modulation converter called semiconductor excitation controller (SEC). This study, first, presents a topology for connection of DSWIG-based WT to the grid, in which unlike most of the related kinds of literature, both the PW and CW contribute in transmission of active power to the grid. In the study system, PW is connected to the diode rectifier-boost converter and CW to the SEC, and thus PW and CW active powers are controlled by the related boost converter and SEC, respectively. Hence, this study extends theoretical expression and presents a new relation for the generator torque as a function of the CW and boost converter currents, and then develops control structures for the study system. Next, mathematical expressions are presented for the selection of excitation capacitor at the PW terminals. Besides, the presented control strategy of the system is modified to enhance the WT-low voltage ride-through capability. In the end, simulation results are presented for examining the system performance and verifying the theoretical analyses.

This study proposes an optimal day-ahead (DA) electricity market offering model for a virtual power plant (VPP) formed by a mix of renewable distributed energy resources along with energy storage, such as electric vehicles. Two sources of uncertainty are considered, namely, wind power generation, modelled by an uncertainty set, and DA market price, modelled by scenarios. Opposite to classical robust optimisation approaches, the authors model maps minimal (worst-case) profits to a conservativeness parameter, while the classical robust optimisation maps conservativeness parameter to worst-case profits. In this regard, by using their optimisation framework, a VPP operator only deals with setting a minimum-profit constraint, which is more sensible and easy for interpretation, while the required conservativeness is endogenously determined. The proposed mathematical model for constructing the offering curve is a hierarchical four-level robust optimisation problem. The first level represents the optimal decision on the price–quantity offer bids; the second- and third-level relate to the optimal identification of conservativeness parameter; and the fourth-level represents the optimal operation of the VPP managed assets. The four-level model is reformulated as a single-level mixed-integer linear programming problem. The proposed approach and its applicability are verified using numerical simulations.

Wind and Solar photovoltaic power plants outputs are usually highly variable due to gusts of wind and sharp sun irradiance level variations caused by cloud shading effects. These effects negatively impact system security, especially in weak power networks. On the other hand, due to the recent technological progress and cost reductions, electrical energy storage systems are an attractive alternative that can be easily integrated into non-despatchable power plants to compensate for those power output fluctuations. This study proposes a methodology for optimal sizing of a hybrid (lithium-ion battery and ultracapacitor) energy storage system for renewable energy network integration. Special attention is paid to the battery cycling degradation process. It is shown that battery aging due to cycling is a major driver for optimal sizing. The resulting sizing problem is posed as a non-linear programming problem. Finally, real and illustrative case studies are presented for both, wind and photovoltaic power plants integrating a hybrid energy storage system. Results are reported by comparing different energy storage system configurations.

This study presents a fully decentralised robust optimisation (RO) approach for multi-area economic dispatch (MA-ED) in the presence of wind power uncertainty. Unlike traditional algorithms, the authors formulate this MA-ED problem as dynamic programming problem, and decompose the centralised robust MA-ED problem into a series of sub-problems based on approximate dynamic programming algorithm. The value functions are proposed for each area to iteratively estimate the impacts of its dispatches on the dispatches of other areas which make decisions subsequently. The proposed algorithm does not require a central operator but only needs to exchange a small amount of information among neighbouring areas to achieve fully decentralised decision-making. It is practical in cases where the centralised operator cannot be implemented considering the dispatch independence and the detailed data of one area is unavailable considering the privacy. Additionally, the accuracy, adaptability and computational efficiency of the proposed algorithm are illustrated using numerical simulations on two test systems and an actual power system.

Wind power is highly dependent on wind speed and operations offshore are affected by wave height; these together called turbine weather datasets that are variable and intermittent over various time-scales and signify offshore weather conditions. In contrast to onshore wind, offshore wind requires improved forecasting since unfavorable weather prevents repair and maintenance activities. This study proposes two data-driven models for long-term weather conditions forecasting to support operation and maintenance (O&M) decision-making process. These two data-driven approaches are long short-term memory network, abbreviated as LSTM, and Markov chain. An LSTM is an artificial recurrent neural network, capable of learning long-term dependencies within a sequence of data and is typically used to avoid the long-term dependency problem. While, Markov is another data-driven stochastic model, which assumes that, the future states depend only on the current states, not on the events that occurred before. The readily available weather FINO3 datasets are used to train and validate the performance of these models. A performance comparison between these weather forecasted models would be carried out to determine which approach is most accurate and suitable for improving offshore wind turbine availability and support maintenance activities. The entire study outlines the weakness and strength associated with proposed models in relations to offshore wind farms operational activities.

Integration of rooftop photovoltaic (PV) systems in a three-phase four-wire distribution network cause voltage-violations namely voltage-rise and voltage unbalance. This study investigates the factors that affect both the voltage-rise and voltage unbalance in low voltage distribution network integrated with the rooftop PV systems. The concerning factors are classified into active factors such as; loads active powers, PV active powers, and bus reactive powers, and passive factors such as; numbers of feeder buses and neutral-grounded resistances. The study also determines the factors conditions at which the highest values of both voltage-rise and voltage unbalance occurred. Moreover, the most and least significant effects of individual factors on both voltage-rise and voltage unbalance are studied. The studied system is simulated and implemented in MATLAB software environment and the feeder loads are modelled based on Back–Forward Sweep method. The simulation results identify that the conditions of the worst voltage-rise and voltage unbalance cases depend on the collective influence of the studied factors.

This study proposes a generic method for modelling and comparison analysis of grid-connected double-fed induction generator (DFIG)-based wind farms in a weak grid. A detailed model of DFIG in a weak grid is established and used as a benchmark. Specifically, the detailed model consists of the mechanical part, generator part, rotor side converter, and phase-locked loop (PLL). Two simplified models of grid-connected DFIG-based wind farms in a weak grid are compared with the detailed model to investigate the mechanism of sub-synchronous oscillation (SSO) as well as the influential factors through linear system analysis. The first simplified model ignores shafting dynamics, stator flux dynamics, and PLL dynamics. The same assumptions are used in the latter, except that PLL dynamics are considered. Time-domain simulation and eigenvalue analysis are used to verify the accuracy of the simplified models. The block diagrams of the linearised system are derived based on the proposed model, and the oscillation mechanism is explained by linear system analysis. Through eigenvalue analysis and the root locus method, the influences of the parameters of the DFIG and its controllers on the system stability are identified and analysed.

In a wind farm, individual turbines disturb the wind field by generating wakes, so wind speeds at various turbine locations are different. From the perspective of wind farm control, there is an interest in dynamic optimization of the power reference for each individual wind turbine, and the wind speed forecast at each turbine location is hence required. This paper develops a joint model of convolutional neural network (CNN) and the gated recurrent units (GRU) to forecast the wind speed at turbine locations. This model employs a two-layer architecture. At the lower-layer, the spatial features are automatically extracted by CNN. The extracted spatial features describe the spatial correlations among multiple wind turbines. At the upper-layer, GRU learns the temporal correlations across the extracted spatial features. This joint model is trained in an integrated manner. A salient characteristic of this model is that it extracts high-level spatial-temporal features from wind data. These automatically learnt features capture the spatial-temporal wind dynamics and interactions in a wind farm, thus being informative and appropriate for the forecasting at specific turbine locations. The simulation on actual data demonstrates the effectiveness of the presented model.

The participation of wind farms in grid emergency control is considered necessary to ensure the safe and stable operation of power systems with high-proportion wind generation. The grid voltage can be improved through the power control of doubly fed wind farm (DFWF) under grid faults. However, existing methods provide reactive power within the allowable power range (APR) under internal constraints of doubly fed wind turbine (DFWT). The precise control of DFWF is difficult to achieve, and its power controllability is underutilised because of the different operation status of DFWTs and the coupling between the APR and the grid voltage. Accordingly, an improved control method of DFWF is proposed to improve the voltage under grid fault. The power characteristics of DFWF are analysed. APR is described under internal constraints. The feasible power range (FPR) of DFWT under coupling is investigated. The connotations of FPR and APR are analysed, based on which a novel idea of active control of DFWF power is proposed. The maximum and optimal voltage operation points of DFWT under wind speed and rotor speed restrictions are calculated. The improved voltage control strategy is proposed. The simulation shows that the proposed method can significantly improve the voltage through the precise control of DFWF power.

In the unfolding-type interleaved two-switch flyback inverter (ILTFI) operating in discontinuous conduction mode (DCM), the hybrid control strategy combining the one-phase DCM and the two-phase DCM has a significant impact on improving the efficiency under all load conditions. In the situation where the marginal power of this control strategy is increased, the converter may enter CCM. Consequently, the converter cannot track the reference current and the output current quality is notably decreased. To tackle this issue, a novel hybrid control method is proposed which varies the switching frequency based on the output power. The proposed approach is immune to the loss analysis and power losses of the components. Hence, it is independent of components selection and their characteristics. In this case, the smooth transition between the one-phase and the two-phase operation modes is guaranteed without affecting the output power quality and the stability of the converter in DCM. The control complexity of the proposed scheme is low and the converter can be easily controlled in DCM. The performance of the flyback microinverter with the proposed hybrid control scheme is verified by the simulation and the experimental results together with the loss analysis.

Wind farms (WFs) can provide controlled inertia through synthetic inertial control (SIC) to support system frequency recovery after disturbances. This study proposes a model predictive control (MPC)-based SIC for a WF consisting of wind turbines (WTs) and a battery storage energy system (BESS). In the proposed MPC-SIC, the active power output of the WTs and BESS during the SIC are optimally coordinated in order to avoid over-deceleration of the WTs' rotor, and minimise the loss of extracted wind energy during the SIC and degradation cost of the BESS. The IEEE 39-bus system with a WF consisting of 100 WTs and a BESS is used to validate the performance of the proposed MPC-SIC. Case studies show that, compared with the conventional SIC, the minimum rotor speed among all WTs with MPC-SIC can be improved by 0.08–0.11 p.u., the loss of captured wind energy of WF with MPC-SIC can be reduced by 12–64% and the degradation cost of the BESS with MPC-SIC can be reduced by 72–83%. The results proves that with the proposed MPC-SIC, the WF can avoid the over-deceleration of the WTs' rotor and reduce the operation cost of the WF by improving the efficiency of wind energy usage and lifetime of the BESS.

This study addresses the validation of harmonic models for power generation units considering practical aspects associated with test-bench and on-site measurements. A new method is proposed for the harmonic model validation. This method has been developed within the joint research project ‘NetzHarmonie,’ which deals with the study of harmonic emissions of renewable energy sources in the power system. The main idea for model validation is to compare the harmonic voltage–current characteristic of the model with the measured harmonic voltage–current pairs. This idea is implemented in a practical process by considering measurement uncertainties as well as the time variation of harmonics. In the validation process, measurement data should meet the given requirements to verify whether they are suitable for the task of validation. The application of the proposed model validation process to photovoltaic and wind power generation units with different topologies and power classes is thoroughly addressed as well. The application of the proposed process is illustrated in detail for selected harmonic orders from these measurement campaigns. This study aims to provide a scientific foundation for the consideration of the harmonic model validation of power generation units in future standards.

A fully distributed hierarchical control strategy for multiple inverters-based AC microgrid is proposed. The developed controller provides real-time economic dispatch along with the network frequency and average voltage restoration. The controllers cooperate with neighbours using sparse communication network and perform local computations to achieve the assigned objectives. The economic dispatch control unit is embedded with the frequency restoration at the secondary level controller to change the active power supply from the inverter. The voltage controller along with reactive power controller ensures the proportional reactive power sharing as well as network average voltage regulation. The average voltage regulation aids the individual inverter to provide required reactive power demand, with a small, comprise at the terminal output voltages from the nominal values. The convergence of the proposed controller is proved using the Lyapunov stability criteria. The maximum allowable communication delay that obligate stable microgrid operation is derived. The simulation results verify the efficacy of the proposed controller. Experimental validation is also presented, and the controller ability to ride through communication failure and load perturbation condition is demonstrated.

The large-scale penetration of wind generation imposes challenges on the security of power system operation due to the intermittency and stochastic volatility. Hybrid energy storage system (HESS), which combines battery banks and super-capacitors, is applied in this study to smooth wind fluctuations to facilitate the grid-friendly integration. To optimally schedule HESS charge/discharge in an online receding horizon, a novel two-stage model predictive control (MPC) scheme is proposed. The first stage determines the initial charge/discharge profiles for battery banks at a large time scale, while the second stage quantifies the optimal amendment to update them and determines the operation strategies for super-capacitors at a smaller time scale. According to the rolling update of wind forecasting, the two-stage model is successively solved in a receding horizon to generate the most appropriate operation strategies. The proposed method also optimises the state of charge of HESS, yielding sufficient margin to cope with wind uncertainties, making HESS operation more reliable and robust. The case study demonstrates that the proposed model can enable an effective smoothing effect on wind generation volatility by fully utilising energy storage systems of various distinct characteristics, providing a powerful tool to facilitate the smooth integration in a large scale in practice.

The growing interest in connecting more distributed generation (DG) units to the utility grid, microgrids deal with the various challenges to satisfy a sufficient level of ancillary services such as active power oscillations (APOs), reactive power oscillations (RPOs), fault ride-through (FRT) capability, and overcurrent problem. Hence, for parallel operated grid-connected inverters (GCIs) based MG, this study presents a multi-objective control scheme that simultaneously ensures elimination of the collective APOs/RPOs at point of common coupling (PCC), overcurrent protection and reactive power injection. One of the significant parts of this study compared with similar existing studies is that provides reactive power support capability to fulfil the FRT requirements of the grid-connected multi-DG units and to remain grid-connected during asymmetrical grid faults. A current restraining control is also presented to ensure the safe operation of the MG system and to avoid overcurrent. The cancellation of the collective APOs and RPOs at the PCC for parallel operation of the GCIs has been achieved by using adjustable control coefficients and demonstrated with theoretical analyses in detail. Extensive case studies are presented and discussed to demonstrate the performance of the proposed ideas and to meet the shortcomings of the previous studies.

DC microgrid is becoming popular because of its high efficiency, high reliability and connection of distributed generation with energy storage devices and dc loads. The main objective in the dc microgrid is to keep the dc bus voltage constant and equalise per unit current sharing among converters. The conventional droop control is used to equalise per unit current sharing similar to reactive power sharing in an ac microgrid. Nevertheless, the problem in conventional droop control is that equal current leads to a reduction of dc bus reference voltage and voltage regulation becoming unequal across each node due to unequal line resistance drop. The proposed controller works on adaptive droop and voltage shifting technique, which equalises the current sharing whether line resistances are similar or not and controls each output voltage to follow the respective bus reference voltage. The isolated dc–dc converters are used to simulate and validate the proposed control technique.

The stochastic economic dispatch problem of power system with multiple wind farms and pumped-storage hydro stations is formulated as a specific stochastic dynamic programming (DP) model, i.e. stochastic storage model, it is impossible to obtain an accurate solution due to the curse of dimensionality. Based on the approximate DP (ADP) method, the stochastic storage model can be transformed into a series of mixed-integer linear programming (MILP) models by describing the approximate value functions (AVFs) as convex piecewise linear functions in post-decision states. The AVFs are first initialised using the results of the deterministic model under a forecast scenario of wind farm output and then trained by scanning stochastic sampling scenarios consecutively with the successive projective approximation routine algorithm. To obtain a near-optimal day-ahead dispatch scheme, the forecast scenario is substituted into the MILP models expressed by the trained AVFs and is solved forward through each time interval. The network constraints are incorporated by the while-loop detection of critical lines. Test results on an actual provincial power system and the modified IEEE 39-bus system, including the comparison among the ADP, DP, scenario-based and chance-constrained programming methods, demonstrate the feasibility and efficiency of the proposed model and algorithm.

Stability analysis in the power system is becoming more important than ever as more distributed energy resources penetrate in the system. This study presents a novel load pattern voltage stability index (LP_VSI) applicable to transmission and distribution systems. By considering the nominal value of voltages, the power network is converted into a two-bus equivalent system. Then, LP_VSI is derived by only the real-time measurement of the voltage and deviation of active and reactive power loads. Also, the assessment of distributed generation's penetration level on unbalanced systems, with maximum loadability and power loss reduction constraints, is performed with regard to daily load variations. The accuracy and efficiency of the proposed indicator are tested on an unbalanced 34-node radial distribution system. Obtained results in comparison with some other papers in the literature demonstrate that the proposed voltage stability index is fast and effective in identifying non-trivial instabilities in the power system networks.

Based on the non-parametric kernel density estimation technique, the probability distribution of wind power output and its forecast error are accurately modelled. The confidence interval and forecast error upper and lower bounds of wind power output are estimated to build a dynamic economic emission dispatch (DEED) model with wind power penetration. To effectively solve the DEED problem with multi-objective, high-dimensional, non-linear and strong constraints, based on the basic brainstorm optimisation (BSO) algorithm, three improvement mechanisms, namely random clustering centre, differential mutation operation and individual crossover operation are introduced to enhance the converging and diverging operation of BSO. Based on these improvements and external archive mechanism, an improved multi-objective BSO (IMOBSO) algorithm is proposed. Simulations on a classical test system with ten thermal units are performed, where two case studies are investigated carefully. The simulation results demonstrate that: (i) the proposed IMOBSO can optimise the cost and emission objectives simultaneously and have achieved better performance than other algorithms; (ii) the proposed DEED incorporating wind power model is reasonable and effective because it can achieve the optimal wind power output scheduling by adjusting the system's spinning reserve capacity and the confidence level of wind power prediction interval.

The operation and maintenance activity of off-shore wind turbines (WTs) increases the cost of the generated energy. Although significant efforts have been made to improve the reliability of the mechanical subassemblies, electrical and electronic subassemblies fail more frequently, causing undesirable downtimes and loss of revenues. Since off-shore WTs and wave energy converters (WECs) share the electrical and electronic subassemblies, the reliability of WECs is expected to be affected by the same causes. This study presents a robust model predictive control for a WEC consisting of an oscillating water column (OWC) installed in a point absorber. The control system is capable of dealing with open switch faults in one or two insulated-gate bipolar transistors of the same arm in any of the voltage source converters (VSCs), or even in both VSCs at the same time. The system allows the OWC WEC to generate energy, although under certain restrictions, thereby reducing the urgency of repair and loss of revenues. The performance of the proposed approach is tested for several cases of open switch faults, experimentally in the laboratory using an OWC WEC emulator.