China has experienced an unprecedented increase in hydropower development with the implementation of the ‘West–East Electricity Transfer’ project. Its total hydropower capacity has reached 350 GW, of which nearly one-third is transmitted to the load centre through an ultra-high-voltage power network. However, the absorption of abundant hydropower in southwest China is still a challenge, with increasing hydropower curtailment each year. This study provides an overview of the evolution of hydropower absorption, analyses the major problems and possible causes, and suggests several technical solutions. It is suggested to optimise the generation operations with power network limitations and transmission schedules to make full use of transmission channels and improve operational flexibility. Meanwhile, receiving power grids should coordinate the operations between southwestern hydropower and their local plants, and hydropower allocation among subordinate power grids. The differences in operation characteristics, regulation ability, and load demands will be helpful for efficiently absorbing large-scale outer hydropower. Reasonable economic incentives and compensation mechanisms are considered as another method to alleviate hydropower curtailment. Ancillary service market and discrepant electricity prices during different receivers and different periods are suggested. The overall analysis results indicate that there is great space for promoting hydropower absorption under existing transmission channel conditions.

One of the major challenges of integrating distribution level producers/consumers (prosumers) into the transactive energy market is that the prosumers are not able to precisely predict their energy exchange with the market. This is because small prosumers usually operate intermittent renewable sources and have highly uncertain consumptions. As a consequence, traditional wholesale market mechanisms cannot be implemented in the transactive environment, as they penalise the participants with uncertain energy transactions, and therefore discourage the small prosumers from participating in the market. To this end, novel market design is introduced in this study, which enables distribution system operator to incorporate both the submitted price–quantity bids and the associated risks of prosumers into the settlement process. The proposed market design maximises the social welfare while managing the undesired costs caused by the stochastic nature of participants. The settlement mechanism is formulated as a quadratic problem, which can be efficiently solved for the transactive energy markets with a large number of participants. To demonstrate the merits of the proposed approach, it is implemented on a sample transactive energy market, and the results are discussed.

Pack U-Cell (PUC) multi-level inverter is an attractive topology which widely investigated in the past few years for renewable energy conversion systems. This study introduces a modified five-level packed Unit-cell converter (MPUC5) for single-phase double stage grid-tied photovoltaic (PV) system with unity power factor. The proposed system operates as a single-phase active power filter able to compensate reactive power generated by non-linear loads connected to the point of common coupling, feeds the non-linear load by the generated PV power, and injects the extra power into the grid. The MPUC5 inverter has a special topology with two DC-link capacitors and six switches. One DC-link of the inverter is connected to the PV arrays through DC/DC Cùk converter to draw the maximum power. Therefore, finite-control-set model predictive control (FCS-MPC) for such configuration appears as a promising alternative for this inverter to work properly. Where the proposed FCS-MPC algorithm is designed to ensure a high grid current quality, taking into consideration the issue of the capacitor voltages balancing and the switching frequency minimisation. Both simulation results and experimental validation through real-time hardware in the loop system prove the validity and feasibility of the proposed control scheme, regarding PV power management and quality enhancement.

For the optimal operation of grid interfaced solar photovoltaic (PV) system, a neural network-based Quickprop control algorithm is presented in this study. The solar PV array supplies maximum power by utilising an incremental conductance-based maximum power point tracking technique to the grid and the load. When the solar power is not present, during cloudy days or at night, the distribution static compensator (DSTATCOM) operation is performed by harmonics mitigation and reactive power compensation of the loads connected at the point of intersection. The proposed system improves power quality when solar PV power is present, along with active power transfer from solar PV array to grid/load. Thus, a smooth transition is provided between these modes with neural network-based Quickprop control algorithm. Moreover, the neural network-based control technique offers enhanced accuracy due to the combinational neural structure in the estimation process. A laboratory prototype is developed for validation and experimental results corroborate reliable operation for modes of operation as DSTATCOM and grid interfaced PV system at varying load and solar insolation condition.

The growing popularity of renewable-based generations along with loads fluctuation and network topology variation has exposed distribution systems to high uncertainties, causing difficulties in operating and planning decisions. In addition, the correlation among various uncertain variables has introduced more complexity to this problem. The probabilistic assessment of power systems with various uncertain variables and with any correlation between them can be efficiently handled by Monte–Carlo simulation (MCS) method, but the calculation burden in this method is heavy and thus it is not appropriate in online applications. Keeping the accuracy of the results, data clustering techniques can be efficiently substituted for this method with much less calculation time and burden. In this study, two methods based on data clustering which can consider the correlation between different variables in a straightforward manner are presented for the probabilistic power flow of distribution systems. In order to demonstrate the efficiency of the proposed methods, IEEE 37 node test feeder and IEEE 123 node test feeder were selected as the case study. The results obtained by the proposed methods were compared with those of the MCS method in terms of accuracy and calculation time.

This study presents the temperature impacts on the performance parameters of PV systems based on measurements of two grid-connected PV systems installed at different climatic locations in Palestine. PV system-1 is installed in the northern West Bank, where the measured maximum module temperature varies during system operation from 30.5°C in winter to 52.5°C in summer, while PV system-2 is installed in the Jordan Valley, where the maximum module temperature varies from 35.5°C in winter to 62.6°C in summer. Measurements of a complete year obtained from PV system-1 of 4.2 kWp are analysed to show the negative effect of temperature on the performance parameters of the PV system. At solar radiation of 800 W/m², the output power varies from 3.0 kW in the cold month of December to 2.7 kW in the hot month of July. The efficiency and the performance ratio dropped from 13.1 to 11.75% and from 93 to 84% between December and July, respectively, due to the higher ambient temperature during the summer months. The measured annual final yield amounts to 1641.1 kWh/kWp and the annual capacity factor amounts to 18.71%. The measured drop in voltage according to module temperature in PV system-2 amounts to 1.93 mV/°C-Cell.

Health state assessment of wind turbine components has become a vital aspect of wind farm operations in order to reduce maintenance costs. The gearbox is one of the most costly components to replace and it is usually monitored through vibration condition monitoring. This study aims to present a review of the most popular existing gear vibration diagnostic methods. Features are extracted from the vibration signals based on each method and are used as input in pattern recognition algorithms. Classification of each signal is achieved based on its health state. This is demonstrated in a case study using historic vibration data acquired from operational wind turbines. The data collection starts from a healthy operating condition and leads towards a gear failure. The results of various diagnostic algorithms are compared based on their classification accuracy.

This study presents a methodology for reactive power compensation provided by distribution static synchronous compensators (DSTATCOMs) to mitigate the voltage fluctuation and increase the solar energy harvest via photovoltaic (PV) inverters accordingly in medium-voltage distribution systems. An approximate method that uses one power flow run with the base-case system is analytically developed to offer a fast estimation of the location and size of DSTATCOMs to enhance the local voltage controllability over sunshine hours. This study then presents daily local reactive power control, where the control signals are calculated based on instantaneous measurements of the voltage at each bus without requiring any communications. The control method involves estimating the amount of reactive power support by the installed DSTATCOMs to reduce the voltage fluctuation during daytime while PV inverters are fully used to harvest the solar energy. At night-time, the DSTATCOMs operate as a master in voltage-controlled mode with other PV inverters in load mode if required to minimise the energy loss while improving the voltage profile. The proposed method has been tested on a 69-bus distribution system with 1 min load and 1 s solar power profiles and validated using a repeated power flow-based exact solution.

The multiple power peaks obtained in the power–voltage (P-V) curve of a photovoltaic string under partially shaded condition results in a complicated maximum power point tracking (MPPT) process. Under this condition, the conventional MPPT methods are not acknowledged as they result in false and slow tracking. In this study three novel global MPPT (GMPPT) methods have been proposed and validated. These are named as large and small duty step (LSDS), large and mutable duty step (LMDS) and fast and intelligent GMPPT (FI-GMPPT). The LSDS method sweeps almost the entire P-V curve using a combination of LSDS. Small duty steps are used in predefined areas near all local maximum power points of the P-V curve. LMDS is a further improved method, which uses a combination of LMDSs. The FI-GMPPT is an advance true GMPPT method which limits the area to be swept during the search process. This results in a further reduction in sweep time. In this method, the unnecessary area is skipped during the sweep process. The improved performance of the projected methods has been demonstrated and validated using MATLAB/SIMULINK and hardware implementation.

As the offshore wind farm (OWF) is often difficult to access due to weather restrictions, the scheduling strategy of operation and maintenance (O&M) becomes a highly complex task. This study presents an O&M scheduling strategy model considering the priority of O&M task and practise constraints of O&M scheduling. First, the two-stage maintenance division method is introduced to consider the priority of O&M task. The initial division of wind turbines to be maintained is obtained by considering operation status. The second-stage maintenance division considering the available maintenance resources is formed adopting the cluster analysis, which the warning and delayed maintenance wind turbines have become a priority. Then, the practise constraints such as the O&M vessels, technicians, daily working time, maintenance route and others are taken into account in the O&M task scheduling model. The proposed model aims to provide the optimal sequence and routeing of maintenance task such that the maintenance scheduling cost is minimised. Simulation results in the scheduling of O&M task for an OWF confirm the feasibility of the proposed model. Sensitivity analysis is undertaken with respect to daily maintenance time, vessel and technician configuration and weather condition.

This essay performs a reliability constraint stochastic model for unit commitment problem by considering generation and transmission constraints with high wind penetration and volatility of load demands. This query is expressed as a MILP that is based on the linear direct current model. The proposed approach models uncertainty of wind generators output power, load demand fluctuations and stochastic elements outage of the system like generators and transmission lines. In this paper, stochastic interdependence between random variables like wind speed and load demand is recognized. To establish the probability distribution of these correlated random variables, Copula theory is applied. Correlation structure between wind speed of different locations and a group of loads existing in the same area is investigated and studied based on historical data. For representing these uncertainties in the stochastic unit commitment problem, possible scenarios are generated with Monte Carlo simulation method. The reliability constraints are utilized in each scenario to evaluate the feasibility of solutions from a reliability point. The introduced stochastic UC is executed on the RTS 96-bus test system. Numerical results demonstrate the advantages of implementing stochastic programming on the UC problem by taking into account the intermittent behavior of wind energy and load inconstancy.

As power systems grow reliant on large-scale integration of renewable energy sources, pumped-storage power stations are being called on to provide greater adjustment capabilities. This study focuses on the stability and fast–slow dynamics of a pump-turbine governing system (PTGS). First, the time delay τ is considered into the PTGS since a lag in time exists between the signal and response in the hydraulic servo system. Second, six analytical expressions for the transfer coefficients of the guide vane opening in the process of wide-ranging load decrease are obtained utilising the complete characteristic curve of pump turbines. The effects of the time delay τ on system stability for the transient process are explored. Furthermore, considering the dynamic transfer coefficient e is a variable during the operation, the authors introduce the intermediate variable e as a periodic excitation into the PTGS. From the perspective of non-linear dynamics, the bursting oscillation behaviours of the multi-scale coupling system are analysed in detail. On the basis of practise engineering, the stability of the PTGS is studied in depth. All of the above methods and results supply the theoretical basis for maintaining a stable operation of pumped-storage power stations.

DC Grid does not transmit reactive power, nor does it offer frequency and phase issues. This results in malfunction of conventional islanding detection methods (IDMs) that rely on the above quantities. At present, the DC IDMs proposed in literatures are mostly active power injection methods based on DC/DC converters. However, in a multi-photovoltaic (PV) DC system, the synchronization of the injection of converters is difficult to guarantee, which may lead to detection failure. This paper proposes an impedance measurement IDM for multi-PV DC micro grid based on the analysis of high-frequency impedance model of power electronic devices. The impedance of DC converters is constant at high frequency and the harmonic impedance of the system changes significantly after islanding, so that islanding operation can be accurately detected. The harmonic impedance change directly reflects the structural changes of the system, and its calculation requires less disturbance intensity, which avoids the requirement of synchronous injection. Therefore, the method uses a DC/DC converter for injection, without the need for additional injection equipment. The method has little influence on power quality and theoretically has no none detection zone. Simulation results show that the method can perform accurately islanding detection in multi-PV DC micro grid.

Tidal as renewable energy is increasingly utilised for power generation in many countries. Integration of tidal plants into the power system requires considering the intermittent nature of the generated power caused by variable tide levels. Thus, the reliability studies of power systems including high tidal generation affected by the uncertain nature resulting from the variable water levels are investigated. The reliability model of a tidal power plant on a barrage is proposed. In the proposed model, the failure rates of the composed components (especially the hydro pump and gate) and the effect of tidal height variation on the components failure rate are considered. Also, the reliability model with numerous states resulting from the variability of the generated power through the fuzzy C-means clustering technique and Xie-Beni index is reduced to a multi-state reliability model. The resulting reliability model is utilised for the generation adequacy studies of a power system containing large-scale tidal power plants. Moreover, different reliability indices are calculated for future generation expansion planning. The proposed technique is applied to the two-test systems, and sensitivity analysis is performed. Besides, the effects of the hydro pump, maintenance, ageing of the components and peak load are investigated.

The multi-energy microgrid (MEMG) comprises heterogeneous distributed generators (DGs) such as wind turbines, diesel generators, combined cooling, heat and power plants etc. Proper placement of these DGs is critical for the system energy efficiency and network reliability performance. This study proposes a two-stage coordinated method for optimally placing heterogeneous DGs in an MEMG project considering the uncertainties from renewable energy sources (RESs). Apart from optimising the traditional DG size and location, this method considers the optimal DG type and investment year simultaneously by maximising the project net present value (NPV), which consists of investment costs and operation costs. The whole problem is modelled as a two-stage coordinated stochastic optimisation model, where the long-term DG investment is determined at the first stage and operation decisions are determined at the second stage. The proposed method is verified on a test MEMG system. The simulation results show that its NPV is positive, which means the method is effective and should be implemented. Compared with the conventional DG placement approaches, the proposed method is more robust against the RES uncertainties and can better coordinate the heterogeneous energies with higher dispatch flexibility and economic profits.

This work presents a maiden approach of coordinated frequency control of novel solar tower (ST)-Archimedes wave energy conversion (AWEC)-geothermal energy conversion (GEC)-biodiesel driven generator (BDDG)-energy storage (ES) units and direct current (DC) links based independent three-area interconnected microgrid system (ImGS). A recent metaheuristic technique, named butterfly optimisation algorithm (BOA) is applied to obtain the optimal gains of the controllers employed with the ImGS and system participation factors. The dynamic performance of proportional–integral derivative (PID), PID with filter (PIDN), proportional–fractional-order integral derivative (PFOID) controllers with their gains tuned by different algorithms such as particle swarm optimisation (PSO), firefly algorithm (FA), whale optimisation algorithm (WOA), and BOA have been compared. Further, the effect of ES units and DC links in all the areas is analysed first time in ImGS. The results have established the superiority of the BOA-based PFOID controllers under different real-world scenarios in terms of frequency deviation, tie-line power, and objective functions. Finally, rigorous sensitivity analysis has been conducted to evaluate the superiority of BOA-optimised PFOID controller towards preserving system stability of ImGS with ±25% change in synchronising tie-line coefficients and bias values, and +20% change in loading condition without resetting the nominal condition gain values.

Estimation of the output power of a solar photovoltaic (PV) system during partial shading condition (PSC) is an interest of PV installers. Owing to the inclusion of a bypass diode, the occurrence of local and global maximum peak creates more difficulty in the calculation of maximum power output from a PV array. In this study, a mathematical model is established to estimate the percentage of degradation of a square total cross tied (TCT) connected PV array at different PSCs. By employing the proposed algorithm, it will be easier to estimate the maximum power point of a () TCT connected PV array during PSC without using any measuring instrument except for the measurement of irradiance and temperature. A better correlation is obtained when the output of the proposed model is compared with MATLAB simulated output. The output of the proposed model is compared with the experimental output performed on an array comprises of 3 W PV modules and the comparison shows the satisfactory result.

A forecast interval is effective for handling the forecast uncertainty in solar photovoltaic systems. In estimating the forecast interval, most available approaches apply an identical policy to all the point forecasts. This results in an inefficient interval (e.g. an unnecessarily wide interval for an accurate forecast). They also adopt a complex model and even require modification of the available deterministic forecasting model, which may adversely affect their application. To overcome these limitations, the authors introduce a forecast uncertainty-aware forecast interval. They calculate a forecast accuracy-related uncertainty metric from an ensemble method based on the dropout technique. The dropout technique is widely used in deep learning models. This implies that the proposed approach can be applied to available deep learning forecasting models without modifying them. Using the uncertainty metric and relevant data of previous forecast results, they estimate the uncertainty-aware forecast interval. Through experiments using real-world data, they first demonstrate the close relation of their uncertainty metric to the forecasting accuracy. Then, they demonstrate that the uncertainty-aware forecast interval reduces the mean interval length by up to 25.7% and decreases the prediction interval coverage probability by 4.07%, compared to available approaches. This illustrates that their approach results in an effective interval.

This study presents a novel approach for ensuring the power quality and reliability of supply required for low-voltage networks with distributed energy sources. For this purpose, a control strategy which enables active network uninterrupted transfer to islanded mode and continues its operation when the quality of supply from the utility grid is insufficient is proposed. Islanded operation is initiated based on an immunity curve, which defines the tolerance of the considered system for different voltage disturbances. The proposed strategy is based on expanding the functionality of the energy storage system and the application of a central controller. Moreover, it does not interfere with the method for control of energy sources. An original mechanism was developed and implemented to reserve power and energy in the ESS, enabling the switch to islanded mode at any time. A simulation model of the considered network was developed using the PSCAD/EMTDC platform to verify the proposed strategy. Simulation results illustrating the functioning of the state of charge storage control mechanism and demonstrating the flexible operation of the network in grid-connected and islanded modes are provided. The results clearly confirm the effectiveness of the proposed control strategy for improving the quality and reliability of power supply.

Microgrids have been recognised as one of the most promising but challenging research topics over the last decade. The optimal energy scheduling problem is regarded as the most essential aspect in the tertiary level control in microgrids. However, most existing centralised or distributed scheduling models only focus on the logical and dynamical feature of microgrids’ operation or the non-linear power flow constraint, which would overact the system performance without considering appropriate unit commitment requirements. Moreover, applying decomposition and iteration technics to complex scheduling problems would encounter convergence issues. To address this concern, this study presents a mixed integer linear reformulation to characterise the operation of different controllable devices and convex relaxation techniques to cope with non-linear power flow constraints, leading to a mixed integer second-order cone programming framework in a concordant yet computationally efficient pattern, capturing non-linear, logical and dynamical properties of the optimal energy scheduling problem in microgrids. The effectiveness of the proposed framework is validated on the IEEE 33-bus distribution network with both grid-connected and islanded modes.

The penetration rate of grid-tied wind turbine generator systems (WTGSs) into the existing electricity networks is rapidly increasing worldwide. Huge efforts are exerted for improving the characteristics of variable-speed wind energy conversion systems. This study exhibits a novel symbiotic organisms search algorithm (SOSA)-based optimal control strategy for achieving efficient operation of a grid-connected WTGS. The optimal control strategy relies on proportional–integral (PI) controllers, which are properly fine-tuned using the SOSA. The simulation-based optimisation method is considered in the optimisation process, where the integration of the square error criterion is selected as a fitness function. To obtain realistic performances, practical wind speed data extracted from the Zaafarana power plant in Egypt are used in the analyses. The efficacy of the SOSA-based optimal PI control strategy is compared with that realized using the grey wolf optimiser algorithm (GWOA)-based PI control scheme, considering network disturbances. The feasibility of the proposed control strategy is validated using the simulation studies, which are implemented using MATLAB/Simulink software. Notably, the proposed SOSA-based optimal control strategy is considered to be a precise means of improving the behaviour of grid-tied WTGSs.

With the increase of wind power penetration in the power system, wind farm (WF) needs to limit active power and accurately track the instructions from the dispatch centre. Since a WF has many distributed wind turbines (WTs), it is a crucial issue to reasonably distribute power reference values to WTs. In this study, a novel active power dispatch (APD) strategy based on dynamic grouping of WTs is proposed. This strategy considers the characteristics and operating conditions of WTs, which can smoothen the power reference values to WTs and reduce fluctuations of key parameters of WTs. Then, a distributed dispatch strategy based on multi-agent system consistency algorithm (MASCA) is applied for APD, which provides a dispatch strategy for WTs that does not require a centralised control centre. And the segmental virtual consistency algorithm is presented as an improvement of MASCA, which innovatively allows MASCA to support the grouping strategy for APD. Finally, the simulations show that the proposed strategy can enable WTs to obtain smoother reference power to track the dispatching instruction while reducing fluctuations of rotor speed and pitch angle, which is helpful to alleviate the fatigue of WTs. The dispatch strategy also shows good robustness when some communication is interrupted.

This study proposes a two-stage stochastic optimisation model to co-optimise the onsite flexibility resources of large customers with the decisions to purchase power from bilateral contracts and forward energy market, while considering the uncertainty of onsite solar generation and market prices. In addition to onsite solar generation, the proposed model integrates the energy flexibility provided by the customers’ flexible loads, onsite energy storage and onsite dispatchable generation. The uncertainty of onsite solar generation and market prices are characterised by autoregressive integrated moving average models, which are used to generate scenarios for the proposed stochastic optimisation model. The proposed two-stage stochastic optimisation model, formulated as a mixed-integer linear programming problem, minimises the expected energy procurement cost of the customers by optimising the first and second stage decisions. The first stage decisions include the utilisation of bilateral contracts, flexible loads and onsite energy storage and dispatchable generation, while decisions to purchase power from, and selling excess solar power to the energy market are optimised in the second stage. The proposed model integrates conditional value-at-risk as a risk measure that would enable the customers to manage the financial risks associated with the uncertainty of onsite solar generation and market prices. The proposed model is implemented on a test large industrial customer, where the advantages of the proposed model are investigated through multiple case studies.

With the increasing penetration of renewables, the modern power system is facing emerging stability issues. A prime example of such issues is the subsynchronous oscillation (SSO) phenomenon observed in large-scale wind farms. It has been studied that hydrogen production equipment (HPE) has the potential to smooth the fluctuation in generated power. However, it is still unclear whether the HPE can be used to mitigate dynamic stability problems induced by wind generators. This study is aimed at filling the gap by exploring the capability of HPE to damp the emerging SSO. Firstly, the overall configuration of an MW-scale HPE is presented. Then its electromagnetic transient model is established for system dynamics studies. Next, a supplementary subsynchronous damping control (SSDC) of HPE is proposed to modulate its power absorption at the subsynchronous frequency, which can improve system damping and stabilise SSO. Finally, the effectiveness of the proposed SSO mitigation strategy is verified on the simulation model of a practical wind power system that suffered actual SSO incidents. The results demonstrated that the HPE with properly designed SSDC, along with suitable capacity and location, can efficiently mitigate the SSO, thus offers a new option to improve the dynamic stability of renewable power systems.