Microgrid, which is one of the main foundations of the future grid, inherits many properties of the smart grid such as, self-healing capability, real-time monitoring, advanced two-way communication systems, low voltage ride through capability of distributed generator (DG) units, and high penetration of DGs. These substantial changes in properties and capabilities of the future grid result in significant protection challenges such as bidirectional fault current, various levels of fault current under different operating conditions, necessity of standards for automation system, cyber security issues, as well as, designing an appropriate grounding system, fast fault detection/location method, the need for an efficient circuit breaker for DC microgrids. Due to these new challenges in microgrid protection, the conventional protection strategies have to be either modified or substituted with new ones. This study aims to provide a comprehensive review of the protection challenges in AC and DC microgrids and available solutions to deal with them. Future trends in microgrid protection are also briefly discussed.

The dynamic behaviour of microgrid system under faulty conditions makes adaptive protection a general necessity for reliable microgrid operation. In design of adaptive protection, the grid-connected and islanded modes have immense importance including grid-connected mode without distributed generators (DGs) in microgrid. In this study, a new adaptive protection scheme is proposed based on the above-mentioned modes of microgrid operation. The proposed method considers nature of DGs connected, fault location detection and fault nature identification based on quadrature and zero sequence components of fault current considering impact of X/R ratio of DGs. The proposed methodologies for adaptive protection schemes are verified in Matlab-Simulink environment and the results are found to be satisfactory while various faults are simulated at different nodes of the microgrid model. At the time of verification of effectiveness of the proposed methodologies, the time derivative of quadrature and zero-axis components of fault current are considered sufficient to instantaneously detect the fault location and fault nature in microgrid system.

This paper presents a rule-based adaptive protection scheme using machine-learning methodology for microgrids in extensive distribution automation (DA). The uncertain elements in a microgrid are first analysed quantitatively by Pearson correlation coefficients from data mining. Then, a so-called hybrid artificial neural network and support vector machine (ANN-SVM) model is proposed for state recognition in microgrids, which utilises the growing massive data streams in smart grids. Based on the state recognition in the algorithm, adaptive reconfigurations can be implemented with enhanced decision-making to modify the protective settings and the network topology to ensure the reliability of the intelligent operation. The effectiveness of the proposed methods is demonstrated on a microgrid model in Aalborg, Denmark and an IEEE 9 bus model, respectively.

Over the last few decades, the inverse over-current protection scheme has been one of the most common schemes utilised in protecting the distribution networks (DNs). Although over-current relays (OCRs) give an excellent protection in extreme failure situations, but continuous integration of distributed generation (DG) and the multi-looped structure of modern DN has made it difficult for the existing industrial OCR's to secure suitable coordination between the different OCR's. The problem of coordinating over-current schemes in the presence of DG needs a complementary between the optimisation techniques employed and the limitation of the manufactured relays existed in the DN for proper discrimination between OCRs. This study investigates the influence of excessive fault current due to DG penetration on conventional IEC characteristics. A novel constraint had been proposed to be added to the formula of the coordination problem – taking into consideration – the limitation of conventional IEC tripping characteristics utilised in nowadays numerical relays. Furthermore, a new non-standard tripping characteristic is suggested to increase the applicability of the optimisation techniques with the existing OCR's. A benchmark IEC microgrid is implemented in ETAP Package. The genetic algorithm is employed to investigate the effectiveness of the two proposed approaches to the conventional optimisation techniques in different operational modes.

Increasing share of distributed energy resources (DERs) in power networks results in dynamic network topology and short-circuit fault levels. Low fault infeed in PV connected power network will rarely damage any power components, but detection of faults is of prime importance. The existing over-current, voltage, and impedance-based relays may fail to detect/clear the faults. However, it is observed that PV infeed networks are generally weak and experience a collapse in terminal voltage with increase in current during the system faults. In this article, variation in fault currents and voltages is utilised for formulating a new voltage-current-time inverse (VCTI) mathematical relay model. The proposed relay model enhances the operating time of the relays and maintains the protection coordination for weak power networks, especially powered with high PV generation. Using this relay model, a cluster-based protection coordination scheme for PV-dominant penetrated power network is presented. The performance of the proposed relay model and coordination schemes is compared with inverse definite minimum time (IDMT) relays and other relying models. Based on the proposed relay model, a hardware relay prototype model has been developed and it is found that hardware results are highly stable and are within 3% accuracy of simulated results.

Islanding detection in distributed generation (DG) interfaced to the microgrid is a key criterion for safety and power quality of the power system. This study proposes a transient monitoring function (TMF)-based islanding detection technique, which distinguishes between various islanding and non-islanding conditions in an inverter-based DG. Initially, the voltage samples of each phase retrieved at the target DG location are processed through Fourier–Taylor transformation (FTT). The TMF is the difference between the estimated voltage samples regenerated from the FTT and the actual sample values. Finally, the TMF index for islanding detection is computed by combining each phase TMF. Based on simulations carried out in PSCAD/EMTDC environment, the performance of the proposed method is examined in various critical conditions. Therefore, the proposed method provides generalised solution irrespective of active and reactive power mismatch and thus enhancing the non-detection zone. The proposed TMF-based anti-islanding relay is highly efficient and satisfies the speed criteria of the relaying function in performing the task.

The reactive power control (RPC)-based technique is a promising solution for islanding detection. The existing RPC-based islanding detection techniques have limitations of injecting unwanted reactive power during normal operation, almost same detection time for various percentage of unbalance in load and false detection of islanding for small disturbances when steady-state frequency is close to its threshold limit. To overcome the first limitation, the phase angle (θ) between the actual and nominal point of common coupling (PCC) voltage is added to the reference reactive power signal of the controller. The angle (θ) has zero value during normal system operation and increases prominently after islanding. To obtain proportionately faster detection for various percentage of unbalanced load conditions, the positive sequence PCC voltage is used in the detection scheme. Owing to major power imbalance between generation and load at steady state, the frequency may come close to its threshold. Under this situation, even a small disturbance will drag the frequency beyond threshold and false islanding detection will occur. To overcome this problem, discrete wavelet transform analysis of θ is considered in the decision-making criteria. The proposed scheme is tested for islanding conditions as well as under various non-islanding events.

In this study, a maximum power point tracking (MPPT) technique based on parabolic extrapolation has been presented for thermoelectric generator systems. Conventional MPPT methods require a closed-loop controller and perturb and observe (P&O) method to provide fast-tracking response. However, they produce power loss due to small oscillations in the steady state. The proposed method excludes the use of closed-loop controller and steady-state oscillations by directly estimating the coordinates of MPP using three random operating points on the parabolic P–I curve. To substantiate the effectiveness of the parabolic extrapolation-based MPPT algorithm, different conditions of temperature gradient and load have been applied. The results demonstrate that the proposed algorithm takes <15% of the time taken by P&O method to track the MPP.

This work introduces a novel damped fifth-order generalized integrator (DFOGI) based control algorithm for grid-integrated solar photovoltaic (PV) system. Moreover, for GMPPT (Global Maximum Power Point Tracking), HPO (Human Psychology Optimisation) algorithm is used. Here, a three-phase three wire, single-stage topology of grid-integrated partially shaded PV array is implemented. Global maximum power peak searching behavior of HPO algorithm is very rapid and accurate, which gives a satisfactory steady-state and dynamic performances, even in rapid solar irradiance change condition. The prime objective of proposed DFOGI control is to accurately transfer the solar power to the grid at unity power factor, even when grid suffers from abnormal and adverse conditions. During these conditions, on the grid side, the power quality is maintained, where the proposed control technique provides power factor correction, and harmonics mitigation. The proposed techniques are modeled, and their performances are verified experimentally on a developed prototype, in solar insolation variation conditions, as well as in different grid disturbances such as over and under-voltage, phase imbalance, harmonics distortion in the grid voltage etc. Test results have met the objectives of the proposed work and parameters are under the permissible limit, according to the IEEE-519 standard.

This study presents an improved power management control strategy of a hybrid direct current (DC) micro-grid (MG) system consisting of photovoltaic cell, wind turbine generator, battery energy storage (BES), fuel cell (FC), and electrolyser. Based on the voltage and state of charge of BES, FC, and electrolyser, the proposed control scheme improved the dynamics of the DC-link voltage and contributes a better power management between each generation/source and load. A gain control technique is implemented in the grid-side inverter controller to regulate the modulation index and improving the voltage stability of the DC-link. Furthermore, the PI-controller gains of BES are tuned dynamically based on the deviation in voltage and its derivative using Takagi–Sugeno-fuzzy control to enhance the transient response of the voltage. For a reliable operation of the DC MG under standalone or prolonged islanding mode of operation, a priority-based load shedding algorithm is proposed for maintaining proper power coordination between different energy sources and storage devices. Owing to smoother and faster voltage response, the proposed control schemes can comply with the grid code requirements of the changing configuration of the modern renewable energy integrated DC MG. The effectiveness of the proposed control strategy is tested by comparing the existing scheme through MATLAB/Simulink^®.

The microgrid with cascaded H-bridge micro-converters (cascaded-type microgrid) is an effective way to integrate the distributed generators (DGs) into medium/high-voltage distribution energy system. Just like the islanded microgrid composed of paralleled inverters, achieving accuracy in power sharing and high voltage quality is a serious challenge in cascaded-type microgrid without communication. In this article, a decentralised control scheme is proposed to share the active and reactive power accurately under the resistance-inductance and resistance-capacitance loads. The power factor angle of each DG is assigned to be consistent in the steady state via regulating both the frequency and voltage. The proposed scheme can be easily implemented only based on the local measured signals. Meanwhile, excellent load voltage quality is achieved. Small-signal analysis method is performed to verify the effectiveness of the proposed scheme, and a guide for designing the power sharing coefficient is given. The cascaded-type microgrid model is developed through simulations and experiments to verify the performance of the proposed scheme.

This study presents a new method for voltage unbalance compensation in islanded microgrids using distributed resources (i.e. distributed generations, storage systems) and responsive loads. The proposed method adopts the Newton trust-region approach to perform the load flow calculation. The sensitivity coefficients obtained from the load flow solution are incorporated into an optimisation problem whose cost function is defined based on voltage unbalance factor at all microgrid buses. The problem is solved using the particle swarm optimisation method. The results of the optimisation method in the form of correction commands are transmitted to the responsive loads and resources via a communication structure. The responsive resources even those with limited capacity and various types of loads including residential, commercial, and industrial are considered in the presented studies. In order to evaluate the performance of the proposed strategy, two different cases, namely, with and without energy storage devices, are simulated. Simulation results confirm the effectiveness of the proposed method in the voltage unbalance correction of islanded microgrids.

This study proposes a hybrid DC/AC radial distribution feeder architecture for the future microgrids. The architecture includes multiple distributed energy resource (DER) integrated household premises with AC and DC ports. Voltage unbalance (VU) and voltage drop (VD) in AC or DC distribution feeders are a research area that needs to be revisited in the light of the DERs integration. This study analyses the voltage variations occurring as a result of household DERs integration in the proposed low-voltage hybrid DC/AC distribution feeders. The impact of DERs rating and its location on DC or AC distribution feeder voltage is analysed by varying these parameters stochastically, via Monte Carlo algorithm. Sweep backward–forward load flow analysis technique is implemented to gain knowledge of distribution node voltage profiles for different source and load scenarios. The impact of cross-feeder VU, sag, and swell conditions is also evaluated here. The test case studies are primarily conducted for static conditions which are further extended to dynamic test profiles. This study would help in predicting a failure index of voltage fluctuations in the proposed hybrid DC/AC radial feeders in the presence of the DERs. The results are obtained through the MATLAB and PSCAD/EMTDC-based simulation studies.

High-voltage and high-power voltage source converters (VSCs) are usually operated at low switching frequencies to minimise the switching losses. This study presents a new control method suitable for low switching frequencies. It consists of two stages. In the first stage, a simple proportional controller is used to suppress any overvoltage or overcurrent during the transients. Then, after the control variables have settled to steady-state values, the iterative controller is activated to finely adjust the modulation index and firing angle of the converter to fulfil the control objectives. To justify the proposed control method, various control objectives are realised in a 10 MVA VSC system, and the results are validated by PSCAD/EMTDC and hardware-in-the-loop simulation. The results show that state variables converge within a few iterative steps, and the transient response is sufficiently fast. The proposed method is shown to perform better than the conventional PI control for eliminating the power imbalance and DC second harmonics due to the imbalance at the grid voltage.

This study proposes a data-driven distributionally robust framework for unit commitment based on Wasserstein metric considering the wind power generation forecasting errors. The objective of the constructed model is to minimise the expected operating cost, including the generating cost, start-up and shut-down costs, and also the reserve cost, which overcomes the shortcomings of the conventional model without optimising the reserve capacity. What is more important, different from the conventional robust optimisation methods, wind power big data is fully utilised in this model to construct the ambiguity set without any presumption about its probability distribution. This is realised by Wasserstein ball with an empirical distribution as the centre. Thus, the proposed robust model is actually data-driven and can immunise the solutions against the worst-case distribution in the ambiguity set. In addition, the scale of the historical data is very critical for this method, the larger the scale is, the smaller the ambiguity set is and the less conservative the result is. Numerical results and Monte Carlo simulations on a real 75-bus system demonstrate the superiority of the proposed model.

A data-driven multi-time scale robust scheduling model for a wind–hydro–thermal power system is presented in this study, according to the characteristic that wind power prediction accuracy increases with the decrease of the time scale. In day-ahead scheduling, a generation plan is formulated with the target of minimising the total operating cost, and a data-driven robust optimisation method based on the robust kernel density estimation (RKDE) is employed to deal with the uncertainty of wind power. That is, the distributional information of wind power is extracted by the RKDE from the big data, then the distributional information is incorporated into a data-driven uncertainty set, and finally, a robust optimisation model is formed. During the intraday scheduling stage, the objective is to minimise the total water spillage in cascade hydropower stations and the adjustment cost of thermal units, and the task is to readjust the outputs of units based on the base outputs obtained by the day-ahead scheduling, combined with the rolling forecast data of wind power and load. The real-time scheduling is aimed at satisfying the power balance with minimum power adjustment. Finally, a test system is carried out to verify the efficiency and practicability of the proposed framework.

The power flow equations usually have multiple solutions including a high-voltage solution and many low-voltage solutions, among which only the type-1 solutions (where the power flow Jacobian matrix has only one positive real-part eigenvalue) are closely related to the voltage instability phenomenon. This study proposes an efficient method to compute all the type-1 low-voltage power flow solutions in distribution systems. First, the geometric properties of the power flow solution space of distribution systems have been studied. Second, the propositions which can guarantee to locate all the type-1 power flow solutions have been suggested and proved. Finally, the conventional implicit Z-bus method is modified to compute all the type-1 germ solutions, based on the suggested propositions; and then the Newton–Raphson method is utilised to trace all the type-1 low-voltage power flow solution branches which originate from the known type-1 germ solutions. The 7-node, 33-node, and 69-node systems are used to validate and demonstrate the proposed method.

Weak AC grids are incapable of providing the reactive power needed by the line-commutated converter (LCC) converters to maintain an acceptable system voltage, which leads to several operational challenges. Although voltage source converter-high-voltage DC (HVDC) can solve these issues, its power rating has not matched that of the LCC technology as yet. The available alternative is the capacitor-commutated converter (CCC) technology, which is currently in operation using back-to-back configuration. In order to explore the capability of this technology for long-distance transmission, this study proposes a comprehensive non-linear state-space averaged phasor model of CCC-HVDC connected to weak grids on both sides of HVDC. To that end, the analytical model of CCC is revisited and the need of including the capacitor dynamics and the contribution of unbalanced capacitor voltages to get a closer match with the detailed switched model is investigated. The proposed model is used to present the root-cause analysis of the dynamic performance and quantify the interaction of various states in the system with different modes, which is not possible through a detailed switched model.

The study describes the quantifying impact of plug-in electrical vehicles (PEVs) and renewable energy sources (RES) for congestion management of power system. The proposed congestion management problem is formulated considering uncertainties of wind, solar, number of PEVs and load condition over a day. The uncertainty modelling of solar, wind and PEVs is presented using beta, Rayleigh and normal distribution functions, respectively. The PEVs uncertainty is dependent on its number and increases with escalation in number. The degree of uncertainties of RES is dependent on corresponding variable (wind and solar). These uncertainties result in large number of scenarios which increases the computational burden. The k-means clustering algorithm is applied to reduce the number of scenarios. The objective function is formulated to minimise generation cost, rescheduling cost and PEV cost for congestion management. This system is analysed by using Monte Carlo simulation. The proposed methodology relieves the congestion and reduces the generation cost, total power generation, total loss with increasing number of PEVs. The test result indicates that PEVs not only act as small storage unit but it also provides power during peak hours. The proposed approach is modelled in GAMS environment and implemented on modified IEEE 39-bus system.

The stability of future power systems will be challenged by high shares of converter-based generation technologies (CBGTs). To prevent instability problems, it is essential to explore new technologies and control strategies able to counteract the negative effects that CBGTs may have. In this regard, promising technologies are battery energy storage systems (BESSs), which can provide a wide range of benefits from a stability viewpoint. Current methodologies that quantify and allocate BESSs in electrical networks have been developed from an economic perspective considering a steady-state formulation of the system. Accordingly, these allocation approaches do not exploit all the benefits that BESSs can offer to system stability. This study proposes a novel optimisation methodology for efficient BESS allocation in systems with high levels of CBGTs. The model improves system stability by considering BESSs with voltage support capability during contingencies. The allocation is solved by a genetic algorithm considering transient voltages throughout the network busbars and their short circuit levels. The methodology was implemented in the 39-busbar New England system. Compared to traditional approaches, the proposed BESS allocation method enables significant improvements in the stability of the system during critical contingencies.