This study examines experiences of grid operators to successfully integrate very high penetrations of wind and solar photovoltaic (PV) resources. The variability of these resources creates challenges in balancing the system generation and demand, and ensuring resource adequacy and essential reliability services. The inverter-based nature of wind and solar PV leads to challenges in frequency, transient, and small-signal stability. In this study, seven system operators demonstrate the ability to manage these challenges in a variety of power systems, from stand-alone island systems to larger island systems that are interconnected to neighbours, to balancing authorities that are strongly interconnected within very large synchronous systems. They operate within a variety of market constructs, from full regional transmission operators to vertically-integrated utilities. All are experiencing increases in the penetration of inverter-based, variable energy resources and finding creative solutions to these challenges.

Following from smaller-scale investigations of grid-forming converter control applied to wind turbines in 2017–2018, this study describes a much larger trial involving an entire wind farm, owned and operated by ScottishPower Renewables. To the authors’ knowledge, this was the first UK converter-connected wind farm to operate in grid-forming mode, and the largest in the world to date. The 23-turbine, 69 MW farm ran in the grid-forming mode for 6 weeks, exploring inertia contributions between H = 0.2 s and H = 8 s. A number of unscheduled frequency disturbances occurred due to interconnector, combined cycle gas turbine (CCGT) and other trips, to which un-curtailed turbines were able to respond. In addition, several deliberate tests were carried out. The turbines were able to provide a stable and appropriate response at relatively high inertia levels to the frequency events commonly occurring today. The captured responses stimulated a debate as to whether external damping power might be required in a grid-forming converter, or whether internal damping is sufficient to allow stable and robust power-sharing with parallel devices in all grid event scenarios. Analysis in this study suggests that, practically, internal damping is probably appropriate, and that any deficiency in external damping power can be more than mitigated by reactance and/or droop-slope response-time management in the grid-forming converters.

This study investigates the effects of converter-interfaced generation integration on the dynamic response of the power system of Continental Europe. The system is analysed with a large-scale dynamic model of the entire synchronous area, considering different instantaneous integration percentages of converter-interfaced generation across the system. The study focuses on the reduction of the overall available kinetic energy and the impact on frequency dynamics and system oscillations. The dynamic model of the system originally provided by ENTSO-E is further developed according to a specific methodology, replacing a determined amount of synchronous generation and introducing a corresponding amount of converter-controlled current sources. The reference incident of a generation loss in Western Europe specified by ENTSO-E is considered in the analysis. The results of time-domain simulations and modal analysis show how the integration of non-synchronous generation affects the frequency dynamics of Western, Central and Eastern Europe, bringing to attention some relevant effects of the spatial distribution of different generation sources within an extensive system as the Continental Europe synchronous area.

In recent years, there has been considerable interest in convertor-based generating solutions which to a greater or lesser extent mimic the behaviour of synchronous machines, thus overcoming many of the disadvantages of the existing technologies which are potentially destabilising at high penetration. Such solutions are frequently referred to as grid-forming convertors (GFCs). This study focuses on the application of GFC technologies in offshore windfarms, where installation, maintenance and/or modification of any offshore equipment is very expensive and carries greater commercial risks, requiring extensive testing and confidence building prior to deployment in real applications. This is time consuming and particularly significant for GB and where there are large quantities of offshore generation. Onshore solutions to stability are therefore desirable for off-shore transmission owners (OFTOs), especially, if they could be applied by retrofitting to existing conventional converter plant. Consequently, this study proposes and investigates the performance of hybrid solutions for offshore networks where the conventional STATCOM onshore unit is replaced by alternative options such as synchronous compensator and virtual synchronous machine converter of similar (or appropriate) rating with the aim of achieving grid-forming capability. A laboratory-scale implementation of the proposed control algorithm is also presented with selected validation test results.

This study provides an analysis of the potential for a sub-energy system to provide an electricity balancing service to, in this case, a national energy system with a large share of variable renewable electricity generation. By comparing electricity balancing capacity, CO_{2, eq}-emissions, and costs, three different local residential energy system setups are assessed. The setups contain different combinations of district heating, combined heat and power, thermal energy storage, electric battery storage, heat pumps, and electric boilers. The analysis focuses on system-level integration, heat and electricity cross-sectoral operations, and unconventional production strategies for district heating production. The results show that local sub-energy systems with heat pumps, combined heat and power, and thermal energy storage has the potential to reduce national electricity balancing demand in an economically feasible way, and with modest CO_{2, eq}-emissions. It was also shown that electricity-based heat production without district heating is economically unfavourable, even in the most optimistic scenario; it is not likely to be feasible within a 30-year period.

The energy produced by bifacial photovoltaic (PV) arrays can be augmented via albedo enhancements. However, the value of the additional energy must outweigh the costs for such modifications to be economically viable. In this work, the electrical performance and economic value of six 13 kWp crystalline-silicon (c-Si) PV arrays with distinct configurations are evaluated. The system designs include horizontal single axis trackers (HSAT) and 25° fixed-tilt structures, monofacial and bifacial PV panels, and low and high ground albedo. The value of the system designs is assessed using onsite electrical measurements and spot prices from the Nord Pool electricity market. We find that HSAT systems increase the annual value factor (VF) by 4% and decrease levelized cost of energy (LCOE) by 3.5 EUR/MWh relative to fixed-tilt systems. The use of bifacial panels can increase the VF by 1% and decrease LCOE by 4.0 EUR/MWh. However, a negligible VF increase and modest LCOE decrease was found in systems with bifacial panels and ground albedo enhancements. Although our results show that albedo enhancements result in lower LCOE than designs without, the uncertainty in upfront and ongoing costs of altering the ground in utility-scale PV parks makes the solution presently unadvisable.

Variable renewable generation technologies such as wind and solar photovoltaic have introduced new challenges in power system management. Grid operators have traditionally used ancillary services such as reserve and reactive power to help to maintain a secure and reliable power system. In addition, conventional synchronous generators have inherent characteristics such as inertial response, which help the power system to recover after an event. In Ireland and Northern Ireland, a weakly-interconnected island system with a high penetration of wind, new ancillary or ‘system’ services have been devised to address the technical scarcities arising from the displacement of synchronous generation. Twelve system services addressing the frequency and voltage control needs have been procured to date, allowing the power system to be operated with up to 65% instantaneous non-synchronous renewable power on the system. This study outlines the implementation of the innovative system services framework. It includes an overview of the services procured and examples of the changed behaviour of existing units in response to financial incentives. It also examines how new technologies have responded to the signals provided, questions how aspects of the arrangements may be further improved and details the challenges of integrating the use of these innovative services into operational policy.

Since the harmful effects of climate warming on our planet were first observed, the use of renewable energy resources has been significantly increasing. Among the potential renewable energy sources, photovoltaic (PV) system installations keep continuously increasing world-wide due to its economic and environmental contributions. Despite its significant benefits, the inherent variability of PV power generation due to meteorological parameters can cause power management/planning problems. Thus, forecasting of PV output data (directly or indirectly) in an accurate manner is a critical task to provide stability, reliability, and optimisation of the grid systems. In considering the literature reviewed, there are various research items utilizing PV output power forecasting. In this study, a systematic literature review based on the search of primary studies (published between 2010 and 2020), which forecast PV power generation using machine learning and deep learning methods, is reported. The studies are evaluated based on the PV material used, their approaches, generated outputs, data set used, and the performance evaluation methods. As a result, gaps and improvable points in the existing literature are revealed, and suggestions which include novelties are offered for future works.

Sub-synchronous oscillation (SSO) causes significant damage and performance degradation in wind farms (WFs). The root causes and mitigation methods of SSO have been identified as a result of many studies. An overview that analysed and summarised those findings in different perspectives helps optimise the existing solutions and to find alternative approaches to mitigate SSO. Therefore, a comprehensive overview of the SSO analysing techniques, mechanisms, and mitigation strategies are presented in this study. This overview is focused on the WFs based on type 3 and 4 wind turbine generators and the WFs connected to high-voltage DC transmission systems. The dominant SSO modes in each application have been presented along with the identified root cause, influencing factors, and the mitigation methods. Besides that, a comprehensive comparison between the existing mitigation strategies is presented to identify the alternative approaches and improvements. The identified improved methods are presented along with the time and frequency domain simulation results to validate their applicability. Finally, an insight into the future direction of this research is presented along with the conclusions.

Deployment of distributed energy resources has rapidly increased during the last few years. The uptake of renewable energy and especially photovoltaic (PV) systems are of interest to utilities in remote and rural areas where the use of conventional power generation is costly. Investigating the effects of such PV systems on isolated power systems at different penetration levels is a relevant research topic. This study reports on the data acquisition system deployed in a remote town in Western Australia and presents some of the findings and observations extracted from the captured real data. It highlights the maximum PV output variations and investigates the underlying factors. The impact of the PV systems on the voltage across the network is also analysed in this study. The studies show that inverter tripping events have led to larger PV output variations in shorter intervals while the cloud movements have contributed to variations in longer intervals.

This paper presents a hierarchical coordination method to improve operation conditions between the medium-voltage distribution network (MVDN) and low-voltage distribution networks (LVDN) considering high-level residential PV units integrating LV distribution networks. It addresses the following issues: (i) rapid power fluctuations and unbalance caused by high-level residential PV units passes to the MV distribution network via the point of common coupling (PCC) and thus increase operation challenges; (ii) power fluctuations, voltage violations and unbalance simultaneously happens in the LVDNs. A hierarchical coordination structure the MVDN and LVDN is proposed. In the MV level, a centralized dispatch method based on three-phase optimal power flow is developed in the timescale of minutes to minimize the power losses, unbalance and PCC adjustments, which are achieved by regulating energy storage systems (ESSs) and PV inverters in the LV level. In the LV level, a distributed control model based on a consensus algorithm is proposed in the timescale of seconds to track the reference active (or reactive) power at the PCC given by the MV level and mitigate its fluctuations. Simulations studies are performed to verify the proposed method.

The integration of variable and unpredictable renewable energy sources into the current power networks introduces considerable changes in system operations. This poses enormous threats to the stability of the power system. Hence, it is essential to analyse the necessary adjustments in operation strategies in preparation for increased amounts of variable generation in existing power systems. The present study describes the dynamic modelling and integration of solar photovoltaic and wind power generation systems into a transient stability analysis toolbox. In view of the inherent connection of renewable energy generators to the electrical network through converter systems, the main contribution in the present study is the development of high-level control functions to model converter interfaces with reference to standard grid operation codes. The dynamic models and corresponding control functions are tested using a network representing the transmission grid of the Baden-Württemberg state in Germany as part of the assessment process to analyse the capability of the control functions for grid stability support. The simulation results show that the proposed converter control functions can equip renewable energy generators with equivalent features from a functional point of view to those of synchronous generators.

This study presents a method for small-signal analysis of an advanced, multi-variable control system for variable speed hydropower (VSHP) plants. A model predictive controller (MPC) optimises the power plant performance. In parallel, a virtual synchronous generator-type (VSG) converter control ensures that the VSHP contributes to virtual inertia and frequency control of the power system. The aim of the small-signal analysis is to parametrise the cost function of the MPC to minimise oscillatory modes between the VSHP hydraulic system and the power system. A state-space representation of the MPC is developed by assuming a stable steady-state operating point equal to the reference values of the MPC cost function, and that no constraints are active. This state-space representation allows for small-signal analysis of the power system, including the MPC. The results show that the modes between the hydraulic system and the power system are well-damped and negligible when the costs of deviations in the hydraulic system are low compared to the cost of deviations in the VSG power reference. Thus, these modes do not constrain the tuning of the VSG. The VSHP power output can, therefore, be optimised independently through the VSG controller to damp power oscillations and reduce frequency deviations.

This study proposes an approach to determine the optimal placement and sizing of renewable distributed generations (DGs) and shunt capacitor in the distribution network (DN) with a pre-defined energy contract strategy with the main grid by using fuzzy integrated mixed-discrete particle swarm optimisation (MDPSO) based method. The proposed pre-defined contract strategies are implemented in this study using the modified Newton–Raphson (NR) load flow method with the concept of the zero bus. The modifications in the Jacobian matrix in the NR load flow method is developed. MDPSO method is adopted to take care of both the continuous and the discrete variables, to determine the proper locations and the sizes of renewable DGs and shunt capacitor by reducing the active power loss, improving the voltage profile, and reducing the annual costs of the DGs. The multi-objective optimisation is solved by using the fuzzy max–min-based technique. The proposed method of DG placement is tested on 69 bus DN for three different scenarios. The results obtained from the proposed multi-objective method are also compared with the weighted sum approach for different combinations.

This study presents a travelling wave (TW)-based method for locating DC line faults in a modular multilevel converter (MMC)-based high-voltage direct current (HVDC) system by using local information. Pole voltage signals are adopted and denoised via stationary wavelet transform (SWT) with improved threshold functions. Hankel matrix-based singular value decomposition (SVD) is utilised to detect TW arrivals. The arrival times of incidental and reflected wave heads are observed in SVD result. The reflected wave heads from the fault point and the opposite end can be discriminated by comparing surge polarities in SVD result. The proposed method relies on the TW principle but is independent of TW propagation velocity. The feasibility of the proposed algorithm is evaluated considering potential factors, such as fault resistance, close-in fault, remote fault, sampling rate and noise. The superiority of this method is validated by comparing it with other signal-processing techniques and TW-based fault location principles. Electromagnetic transient simulation of the multi-terminal HVDC system on Power Systems Computer Aided Design / Electromagnetic Transients including DC (PSCAD/EMTDC) is conducted to provide fault TW signals, which are analysed in MATLAB. A corresponding equivalent test model developed in a real-time digital simulator is also provided for conducting a supplementary study to verify and further research this fault location method.

The universal power demand is increasing every day because of population growth, industrial revolution and high standards of living. The renewable energy technologies (RETs) are environmental and cost-effective solutions to satisfy dynamic load profile based on the application of multiple components of a standalone microgrid system that encompasses photovoltaic (PV), diesel generator (DG), electric storage system and wind turbine generator (WTG). This study reveals more insight into various research questions that have not been completely addressed in past investigations. The contributions of the study are expressed: (i) analyse the effects of RETs on the total cost, (ii) test the total benefits, (iii) estimate the market benefit share of the unit that reflects the benefit of one generation unit in proportion to the whole power system, (iv) maximise and minimise the yearly average power generated by (PV and WTG) and DG. This research work presents the application of fmincon optimising tool to accomplish the objectives of the study owing to some significant characteristics. The outcomes of the work show the validity of the model and can assist the utilities to minimise the costs that are identified with the optimal operation of their power systems.

The transmission lines between the sending and receiving ends of an interconnected power grid may be tripped under severe disturbance or equipment failure. The tripping will result in a large power imbalance in the sending grid, thereby causing frequency instability and even collapse. The effect of existing emergency control methods based on synchronous generators, such as generator tripping control, is limited due to the lack of available tripped generators, high cost and long recovery time. The high penetration and flexible controllability provide wind farms with the potential to participate in system emergency control. To evacuate excess power from the tripping of transmission lines and improve frequency stability, an emergency power balance control method based on the doubly-fed induction generator (DFIG)-based wind turbine (DFWT) is proposed in this study. A novel idea of the motor operation of DFWT is presented and applied to the proposed method. To realise steady and adjustable power consumption of DFWT under motor operation, a rotor virtual resistor control and supplementary pitch control are introduced under safety constraints. The validity of the proposed method is verified by case studies on single DFWT and DFWT-based wind farms.

The uncertain demeanour from wind generators and loads adversely affect the grid operational stability. Various control approaches have been explored to remedy the system uncertainties while maintaining generation and load demand balance. This study proposes a fuzzy-based proportional–fractional integral–derivative with filter controller to sustain frequency stability in wind integrated power systems having different configurations. The controller parameters have been tuned using a recently developed coyote optimisation algorithm (COA). The proposed control approach is executed and validated on three distinct configurations of two-area power systems. All test models are integrated with a doubly fed induction generator (DFIG) type wind turbine units (WTUs). Different case scenarios have been considered to analyse the efficacy of the proposed control strategy in the presence of WTU. Furthermore, the impact of inertial support delivered by the DFIG-WTU and higher penetration of wind energy in the power system has been studied. The analysis reveals that the control scheme in coordination with WTU support reduces the stress on a wind turbine during the inertial control scheme and maintains the grid frequency stability under unexpected load disturbances. Stability and robustness analysis are also conducted to verify the validity of the introduced control approach.

Wind farms are increasingly important nowadays since in some countries can surpass conventional power sources. However, in countries where the exploration of this renewable source started recently, the lack of knowledge related to maintenance routines and efficient operation has led to fast performance degradation. In this context, wind turbine condition monitoring can detect anomalies in its performance as an unexpected failure, avoiding financial loss. In this study, machine learning approaches are applied as an online tool to detect abnormal wind turbine operation modes, evaluating the wind turbine operation in all regions of the power curve. The methodology has been validated with an original and real dataset collected from a large-scale onshore wind turbine in Northeast Brazil. The results exhibit an expressive reduction of energy loss and indicate the ability of the proposed approach to assessing the abnormal modes even when a small number of recorded data are available. The standard classifiers reached on average 98.64% accuracy in the holdout data set. Additionally, an ensemble of classifiers is proposed which helped to improve in 12% the accuracy of the best classifier alone, increasing the confidence of alarms raised by the predictive maintenance tool.

This study presents a three-phase four-wire grid-tied solar photovoltaic (SPV) energy conversion system with a reduced ordered generalised integrator frequency locked loop (ROGI-FLL) based control for the extraction of fundamental grid voltages. The reference grid currents are extracted by using the proposed filtered-s least mean square (FsLMS) current control for voltage source converter (VSC) switching. This VSC is capable of performing multifunctions like compensating harmonics of non-linear loads, the grid currents balancing, mitigation of neutral current and it stabilises the DC link voltage. The SPV array generates the active power for feeding loads and to the grid. Matlab environment is used to develop the model of the proposed scheme and simulation results of the system are validated with test results on a developed prototype. Test results have shown the good responses at different environmental conditions. Moreover, a comparative study is performed with ROGI-FLL controller and other controls at the grid voltages unbalancing, DC offset and the grid voltages distortion conditions. This FsLMS-based control has shown fast convergence, less oscillations and low settling time as compared with other controls at load unbalancing. Simulated and test results have satisfied the objective of this system and it meets the IEEE-519 standard.

Compressed air energy storage (CAES) can balance the fluctuation of renewable generation. In this study, the CAES is used as an energy storage system in the distribution network with wind power generation (WPG). Considering the forecasting error of WPG and load, a robust day-ahead optimal dispatch scheme for the CAES is developed in this study. The characteristics of the CAES are modelled in detail for the expansion/compression operation during the robust optimal dispatch (ROD). The aim is to minimise the fluctuation of the net load in the distribution network. Robust dynamic programming is employed to solve the optimisation problem in this study. The developed scheme is tested with the IEEE-RBTS load profile and real WPG data. The deterministic and ROD-ahead dispatch scheme are simulated and compared. The fluctuation of the net load is compared with different weights of the variation and uncertainty set.

Accurate prediction of wind direction can improve wind energy utilisation and extend the life of the wind turbine yaw system effectively. The ‘measurement–sharing–correlation–prediction–verification’ (MSCPV) wind parameter sensing technology based on the turbine network provides a new perspective for wind direction prediction, in which the screening of correlated wind turbines is a key step. In this study, a calculation method of wind direction spatial correlation is proposed, and correlated turbines are selected according to the correlation strength. The study first analyses the limitations of traditional wind direction correlation based on the principle of MSCPV and proposes the concept of correlated turbine screening based on yawing correlation. Then, the modelling and calculation of yawing correlation are analysed in detail. The prediction based on correlated wind turbines selected by different methods is evaluated. Case study results show that compared to wind direction correlation and wind turbines' distance, the yawing correlation has a better effect on the screening of the spatially correlated turbines. The prediction accuracy based on different correlated turbines is positively correlated with the degree of yawing correlation, indicating the proposed screening method is effective.

Combined cooling, heating, and power (CCHP) microgrids are a special form of a microgrid that is attracting increasing attention. This study contributes to the goal of minimising the operation cost of CCHP microgrids by proposing a hierarchical two-stage robust optimisation dispatch model for multiple CCHP microgrid systems. The uncertainties associated with wind power output, electric power, heating, and cooling loads, and transmission line failures are considered in the proposed model. Moreover, the electricity purchasing and selling prices of each microgrid are independently determined. The proposed model applies the outputs of fuel cells, energy storage devices, and gas turbines, the distribution factor of waste heat, and the power transmission between the microgrids and an external grid as control variables. The optimised dispatch problem is solved using McCormick envelopes relaxation and a novel column and constraint generation algorithm that provides enhanced optimisation performance by implementing co-evolutionary theory. In this way, the microgrid system is divided into several sections, and each section is represented as an individual min–max–min problem. The rationality and validity of the proposed model and the superiority of the solution performance of the improved algorithm are verified through simulation case studies involving a system composed of four CCHP microgrids.

Penetrating distributed generation units into the radial distribution networks has been making the protection schemes become ever so complex. The microgrid modes of operation have also increased this complexity. Moreover, the ‘loop-based microgrid’ concept has made it extremely difficult or even impossible to utilise the available radial protection schemes. Although redesigning a new protection structure solves the problem, there should be an affordable way for surviving today's protection systems; accordingly, this study equips the ordinary overcurrent relays with intelligent electronic devices to act as an agent within a multi-agent framework. Given these devices are equipped with an efficient communication infrastructure for industrial applications, named IEC 61850 standard, the proposed approach uses a communication-based strategy, named token, to securely protect the system without any conflict in relays’ operation. The simulation results prove the effectiveness of the proposed method for any DG's penetration, network configuration and fault location. This kind of flexible strategies can affordably differ the expensive protection system replacement in the future revolutionary network upgrades.

Good power quality cannot be achieved without the frequency stability of power systems. Nevertheless, with the large-scale integration of highly flexible renewable energy sources (RESs) such as wind and solar energy, frequency stability is threatened by RES-generated disturbances due to their volatility and intermittency especially in the very short time scale. Therefore, a new frequency control scheme should be designed to attenuate these highly time-varying disturbances. In this study, load frequency control (LFC) considering flexible RES suppression is investigated. Inspired by the idea of disturbance estimation and compensation, a novel active disturbance rejection (ADR) scheme is proposed. Unlike previous ADR-based LFC schemes, the proposed one can significantly reduce estimation errors via a novel extended state observer. Then a sliding mode control scheme is used to force the area control error to slide along the equilibrium, thus significantly attenuating the power imbalance. Simulation results show the effectiveness of the proposed schemes.

The advancement in sensing technologies and infrastructure allows real-time condition monitoring on wind turbines (WTs), which helps improve the power generation efficiency, lower the maintenance costs of wind farms (WFs). Practically, the real-time measurements could be unavailable at the Supervisory Control and Data Acquisition end due to unintended events such as sensor faults and communication loss, which significantly depreciates the condition monitoring and fault detection performance. Aiming to mitigate the missing data impact on data-driven WF applications, this study develops a robust anomaly detection approach for WT fault detection using a denoising variational autoencoder. In presence of missing measurements, the proposed approach can not only sustain high fault detection performance but also recover the missing data as an auxiliary function. The proposed approach is tested on a realistic offshore WF and compared with other autoencoder variants and traditional anomaly detection methods. The testing results verify the outstanding robustness of the proposed approach against missing data events and demonstrate its great potential in missing data recovery.

Wind is a highly unstable renewable energy source. Accurate forecasting can mitigate the effects of wind inconsistency on the electric grid and help avoid investments in costly energy storage infrastructure. Basing the predictions on open-source forecast models and climate data also makes them entirely free of charge. The present work studies the feasibility of using two machine learning (ML) models and one deep learning (DL) model, random forest (RF) regression, support vector regression (SVR), and long short-term memory (LSTM) for short-term wind power forecasting based on the publicly accessible ERA5-Land dataset. For each forecast model, a selection of hyperparameters is first tuned, followed by determining the best performing input data structure using surrounding data grid points and increasing the time interval of data affecting a single prediction. Both the ML models and the DL model perform better than the baseline (BL) model when forecasting wind speed up to 24 hours ahead. However, a reduced forecast duration is needed to achieve satisfactory wind turbine (WT) power output forecast accuracy. Most notably, the RF is able to produce 3-hour forecasts with the combined WT power output prediction error amounting to less than 10 % of the WT's nominal power.

In this study, a new double-rotor permanent magnet vernier induction wind generator is proposed, which can be connected directly to a double-turbine system used in urban areas. The proposed generator consists of a vernier machine and a squirrel-cage induction machine, which includes a stator and two concentric rotors (a permanent magnet outer rotor and a squirrel-cage inner one). Accordingly, first, the relationships governing the structures of each rotor are presented and investigated. Then, the performance of the generator connecting to a double-turbine system is analysed. Optimisation of the presented generator is investigated by finite-element analysis and also a laboratory prototype is constructed. Finally, both simulation and experimental results are compared, and the reliability of the proposed generator is validated.

Islanded microgrids (IMGs) are embedded power networks with distributed energy resources (DERs) providing a reliable and flexible energy option for off-grid customers. This work addresses the planning model of renewable-based IMGs feeding multi-energy demands considering investment and emission related objectives. The proposed solution is to determine the optimal mix and sizing of various energy sources in IMG, including renewables; for multiple energy demands. This study also presents a hybrid-scenario and Monte Carlo approach to gauge the uncertainty involved in multi-energy demands, i.e. electrical, heating, and cooling loads; together with correlation among wind and solar generations. The spatial interdependence among renewable generations is implemented using copula; that generates a synthetic set of stochastic correlated data. The combined load scenarios for multi-energy demands and renewable samples are implemented with the proposed hybrid approach in the formulated stochastic planning model. In this work, the formulated problem is proposed to solve using meta-heuristic multi-objective ant lion optimiser algorithm, that is validated on the test system. The superiority of the proposed approach is highlighted in comparison with other multi-objective optimisers. The multi-energy dispatch between associated sources and loads were simulated to show how the obtained capacity can suffice the seasonal multi-energy demands of a typical day considered.

The characteristics of subsynchronous oscillation (SSO) in series-compensated wind power systems are significantly affected by the system's operating condition. Besides the variation in the magnitude and frequency of the SSO during an SSO event, the fundamental frequency may also deviate from its nominal value. This study aims at capturing the dynamics of both subsynchronous and fundamental frequency components simultaneously. This work first explores the strengths and weaknesses of various Kalman filtering based frequency tracking algorithms for joint tracking of the fundamental and SSO components. Then, it proposes a novel adaptive extended Kalman filtering (AEKF) algorithm, in which the process noise covariance is updated online by maximising the probability density function of the predicted error. The process noise covariance factor changes to positive non-zero value whenever the frequency of the fundamental component deviates. Thus, the proposed AEKF extracts the time-varying subsynchronous component while also tracking the small variations in the fundamental frequency. The tracking performance of the AEKF is validated on computer-generated test signals as well as on the electromagnetic transient simulation model of an actual wind power system facing SSO. The captured fundamental and subsynchronous dynamics can be used for designing monitoring, protection, and control schemes for the SSO.

In engineering practice, the Voronoi diagram is often used to plan electric vehicle (EV) fast-charging stations but it does not consider the spatio-temporal distribution of EV trip. Research on the spatio-temporal distribution of EVs mainly uses the shortest path method to reduce the amount of iterative calculations. The influence of the external environment and traffic congestion on the travel of EVs has not been considered, which results in the inaccuracy of the EV charging demand. To solve the above problems, this study proposes a method for predicting the spatio-temporal distribution of EVs based on quasi-dynamic traffic flow. This method takes into account the external environment and the impact of traffic congestions on EV trips and balances the problem between simulation accuracy and calculation efficiency. Based on this, the particle swarm optimisation algorithm is used to optimise the travel cost of the EVs and the cost of the construction and operation of the charging infrastructure. An optimal siting and sizing model for the fast-charging station based on quasi-dynamic traffic flow is established. Simulation results verify the effectiveness of the model.

Battery energy storage systems (BESSs) are being presented as a prominent solution to the various imminent issues associated with the integration of variable renewable energy sources in the distribution system. Performance of grid-connected BESS depends on the type of application (power and energy), grid operating parameters and level of interconnection point, which shows the necessity of benefit assessment of BESS technology and its applications in terms of economic and grid operations before investment at a larger scale. In this study, real-time analysis and evaluation of two different battery technologies connected in a medium voltage (MV) distribution system are carried out. The main objective of this paper is focused on the impact analysis of large-scale BESS on MV distribution feeder. A real case of installation of lithium-ion and advanced lead-acid battery systems into the Indian distribution system has been considered for this study. Different operational strategies of BESS such as frequency regulation and energy time-shift have been performed with real-time data. The effectiveness in usage of BESS with these functionalities has been verified in terms of peak shaving, improvement in voltage profile and reduction in power losses in the feeder.