Search

Abstract Power systems are currently experiencing a transition towards decarbonisation through the large‐scale deployment of renewable energy sources. These are gradually replacing conventional thermal power plants which today are the main providers of black start services. Consequently, in case of a total/partial blackout, conventional black‐start resources may not be ready for operation. Offshore wind farms (OWFs), with their large capacity and fast controllers, have potential as innovative black‐start units, thus, the need for a new design for OWFs. Here, challenges and possible solutions in integrating black start services into offshore wind farms will be presented. The first challenge is represented by the self‐start capability. The self‐start unit should be capable of forming the wind farm power island and withstanding transient phenomena due to the equipment energisation. The investigated solution comprises grid‐forming (GFM) converters in the wind farm design, which could be battery energy storage systems (BESSs) to also increase the service availability. The challenges are analysed using simulations on a wind farm, and the proposed solutions are discussed. It can be concluded that a hybrid system comprised of a BESS and an OWF, with GFM control, applying soft‐charging, etc., represents a good proposal to provide black start services by OWFs.

Fault behaviours of converter-interfaced renewable energy sources (CIRESs) are greatly diverse from those of synchronous generators (SGs), so the traditional proportional restraint differential protection may fail to be activated. In order to deal with this issue, a comprehensive current amplitude ratio-based pilot relay is proposed. Since the fault current from CIRESs is much smaller than that of SGs, so phase current amplitude ratio on both end is lower than 1. In order to improve protection sensitivity for high resistance faults and grounding faults, sequence current ratio is also introduced to constitute a comprehensive protection criterion. The proposed method only uses the amplitude feature, so it has a lower time synchronization requirement for the currents on both terminals. Meanwhile, it can be applied for different fault ride through (FRT) strategies because the current limiting is always satisfied. Furthermore, the proposed method is easy to be deployed in protection devices after a small revision is done for the original protection algorithm. PSCAD simulation demonstrates that the proposed method is effective for different fault scenarios.

Converter-interfaced renewable energy sources (CIRESs) may have a different fault current angle with synchronous generators (SGs), so the large angle difference on both sides will cause low sensitivity or failure for traditional proportional braking differential protection. So as to cope with this issue, a new fault control strategy is put forward. By analyzing the phasor diagram during asymmetric faults, a reasonable phase angle of the CIRES fault current is determined to reduce the angle difference below 60°, and corresponding current references of the controller are calculated. In this way, the proposed method can make CIRESs compatible with differential protection, so it is economical since the protection device is not necessary to be updated. In addition, it also shows good performance for different fault points, high-resistance faults and different power factors before the fault. Moreover, typical fault ride through (FRT) conditions including fault current restriction and reactive power support are still being satisfied while the controller structure does not require to be altered. Simulation results in PSCAD validate the proposed approach can be used for different fault scenarios.

The increasing penetration of distributed generation (DG) to power distribution networks mainly induces weaknesses in the sensitivity and selectivity of protection systems. In this manner, conventional protection systems often fail to protect active distribution networks (ADN) in the case of short-circuit faults. To overcome these challenges, the accurate detection of faults in a reasonable fraction of time appears as a critical issue in distribution networks. Machine learning techniques are capable of generating efficient analytical expressions that can be strong candidates in terms of reliable and robust fault detection for several operating scenarios of ADNs. This paper proposes a deep group method of data handling (GMDH) neural network based on a non-pilot protection method for the protection of an ADN. The developed method is independent of the DG capacity and achieves accurate fault detection under load variations, disturbances, and different high-impedance faults (HIFs). To verify the improvements, a test system based on a real distribution network that includes three generators with a capacity of 6 MW is utilized. The extensive simulations of the power network are performed using DIgSILENT Power Factory and MATLAB software. The obtained results reveal that a mean absolute percentage error (MAPE) of 3.51% for the GMDH-network-based protection system is accomplished thanks to formulation via optimized algorithms, without requiring the utilization of any feature selection techniques. The proposed method has a high-speed operation of around 20 ms for the detection of faults, while the conventional OC relay performance is in the blinding mode in the worst situations for faults with HIFs.

SEV, the Faroese Power Company, has a vision to reach a 100% renewable power system by 2030. SEV is committed to achieve this, starting from a 41% share of renewables in 2019. A detailed expansion plan for the generation, storage and transmission is needed to reach this goal. This is the focus of this study. Practical constrains e.g. resource potential and available space must be considered. Balmorel, an optimisation tool, has been used to optimise investments and dispatch. A method to translate optimal results to a realistic RoadMap was developed and applied. The impact of different technologies and costs has been investigated through multiple scenarios. In ratios of average consumption in 2030, installed power will be 224% wind, 105% solar with 8–9 days of pumped hydro storage according to the proposed RoadMap. The plan is economically favorable up to 87% of renewables, but in order to reach a 100% renewable production in an average weather year, the renewable generation capacity has to be increased by 80%. The study also shows that if biofules or tidal technologies become viable, these will be game changers needing a significantly lower total sum of installed renewable power.

Synchronous reference frame phase-locked loops (SRF-PLLs) are widely used in different technologies, such as wind turbines, electric vehicles, more electric aircraft, and motor drives, to estimate system's variables. The SRF-PLL is an adaptive notch filter that is used to estimate a sinusoidal signal's amplitude, phase angle, and frequency. However, when the input signal is subjected to a considerable variation or includes a significant noise, its stability and performance become challenging. In this paper, the stability of the SRF-PLL for substantial changes in the input signal's variables are investigated. To do so, the nonlinear time-varying (NTV) model of the system is proposed and is used for the large-signal stability assessment. Then, an adaptive tuning method, based on the proposed NTV model, is designed to improve its transient performance during and after the variation. Simulation and experimental results are used to validate the proposed method.

This study presents an investigation of the impact of the quasi-stationary voltage support provided by a voltage source converter (VSC) connected to a single point of a power system. Based on the directional derivative concept, an analytical method is developed to quantify the sensitivities of the AC bus voltage with respect to the VSC reactive power control modes. Based on a real case study, it is shown that the method applies to VSC units that are part of VSC-HVDC systems, which can operate in a point-to-point or multi-terminal configuration. Time-domain simulations are performed to verify the findings from the application of the analytical method on a reduced size power system.

By the proliferation of electric vehicles (EVs) in power systems, it is needed to manage their demand energy within a regulated market framework. From the market perspective, integration of different market players, such as the energy producers, aggregators, and loads, could complicate the system operation and management. Therefore, an appropriate model of the market that shows the exact behavior of the system components is needed. In this paper, a new tri-level game theoretical approach for energy management of EVs and EV charging stations (EVCSs) as independent decision makers for their energy scenarios is proposed. To make it practical for a real power system, the system operator is also included in the proposed method as a master decision maker. Therefore, EVs’ and EVCSs’ objectives are to maximize their financial profits, while the system operator indirectly controls their energy scenarios in order to fulfill the system’s technical constraints. To do so, at the highest level of the proposed method, technical goals of the system, which are related to the system operational condition, will be followed as the objective criteria. At the second level of the designed model, the EVCSs financial objectives are optimized. In the third level of the proposed method, it is tried to minimize the EVs’ cost function. The method is tested on an IEEE 9-bus standard system, and the results show a superior performance of the proposed energy management system (EMS) compared with the conventional EMS methods in terms of technical and financial objectives. In this way, it is shown that in the case of considering only one aspect of the system, either financial or technical, the other aspects of the system may not be satisfied. Hence, it is essential to consider both the financial and technical aspects of the system simultaneously, in order to operate the system optimally and securely.

In this paper, a methodology for optimal decision making for electrical systems is addressed. This methodology seeks to identify and to prioritize the replacement and maintenance of a power asset fleet optimizing the return of investment. It fulfills this objective by considering the risk index, the replacement and maintenance costs, and the company revenue. The risk index is estimated and predicted for each asset using both its condition records and by evaluating the consequence of its failure. The condition is quantified as the probability of failure of the asset, and the consequence is determined by the impact of the asset failure on the whole system. Failure probability is estimated using the health index as scoring of asset condition. The consequence is evaluated considering a failure impact on the objectives of reliability (energy not supplied -ENS), environment, legality, and finance using Monte Carlo simulations for an assumed period of planning. Finally, the methodology was implemented in an open-source library called PywerAPM for assessing optimal decisions, where the proposed mathematical optimization problem is solved. As a benchmark, the power transformer fleet of the New England IEEE 39 Bus System was used. Condition records were provided by a local utility to compute the health index of each transformer. Subsequently, a Monte Carlo contingency simulation was performed to estimate the energy not supplied for a period of analysis of 10 years. As a result, the fleet is ranked according to risk index, and the optimal replacement and maintenance are estimated for the entire fleet.

This paper proposes an equivalent circuit model to obtain the transient electrical stress quantitatively in medium voltage medium frequency transformers in modern power electronics. To verify this model, transient simulation is performed on a 1.5 kV/1 kHz transformer, revealing voltage overshoot quantitatively between turns and layers of the transformer’s HV winding. Effects of rise time of the input pulse voltage, stray capacitance of the winding insulation, and their interactions on the voltage overshot magnitude are presented. With these results, we propose limiting the voltage overshoot and, thereafter, enhancing medium voltage medium frequency transformer’s insulation capability, which throws light on the transformer’s insulation design. Additionally, guidance on the future studies on aging and endurance lifetime of the medium voltage medium frequency transformer’s insulation could be given.

It is well-known that ac power flows of a power system do not have a closed-form analytical solution in general. This paper proposes a multi-dimensional holomorphic embedding method that derives analytical multivariate power series to approach true power flow solutions. This method embeds multiple independent variables into power flow equations and hence, can, respectively, scale power injections or consumptions of selected buses or groups of buses. Then, via a physical germ solution, the method can represent each bus voltage as a multivariate power series about symbolic variables on the system condition so as to derive approximate analytical power flow solutions. This method has a non-iterative mechanism unlike the traditional numerical methods for power flow calculation. Its solution can be derived offline and then evaluated in real time by plugging values into symbolic variables according to the actual condition, so the method fits better into online applications, such as voltage stability assessment. The method is first illustrated in detail on a 4-bus power system and then demonstrated on the IEEE 14-bus power system considering independent load variations in four regions.

Loss of excitation (LOE) relay is one of the most essential protection elements for synchronous generators in power plants. During the last few decades, several LOE detection methods have been proposed, while limited schemes such as admittance- and impedance-based ones have been adopted for industrial applications. This study investigates and compares the behavior of some practical LOE detection schemes through extensive simulation scenarios, and from the reliability, speed, and security points of view. The simulation scenarios are accomplished by using the real-time-digital-simulator, where the phase domain model of the synchronous generator is used to develop a realistic and typical power generation system. Employing such a system, different types of complete and partial LOE incidents can be applied according to IEEE Standard C37.102-2006, while the performance of any scheme can be assessed through accurate and realistic LOE scenarios.

A review of the ongoing research on black start (BS) service integrated with offshore wind farms (OWFs) is presented in this paper. The overall goal is to firstly gain a better understanding of the BS capabilities required by modern power systems. Subsequently, the challenges faced by OWFs as novel BS service providers as well as an outlook on the ongoing research which may provide solutions to these are presented. OWFs have the potential to be a fast and environmentally friendly technology to provide BS services for power system restoration and, therefore, to ensure resiliency after blackouts. As a power electronic-based system, OWFs can be equipped with a self-starter in the system in order to perform BS. The self-start unit could be a synchronous generator (SG) or a power electronic unit such as a grid-forming (GFM) converter. Preliminary BS studies performed in PSCAD/EMTDC are presented in a simplified OWF system via an SG as the self-start unit. Consequently, technical challenges during the BS procedure in an OWF benchmark system are outlined via theoretical discussions and simulations results. This is useful to understand the threats to power electronics during BS. Finally, the most relevant GFM strategies in the state-of-the-art literature are presented and their application to OWF BS is discussed.

The equivalent model of offshore DC power collection network for the harmonic susceptibility study is proposed based on the discrete time-domain modelling technique and frequency scan approach in the frequency domain. The proposed methodology for modelling a power converter and a DC collection system in the frequency domain can satisfy harmonic studies of any configuration of wind farm network and thereby find suitable design of power components and array network. The methodology is intended to allow studies on any configuration of the wind power collection, regardless of choice of converter topology, array cable configuration, and control design. To facilitate harmonic susceptibility study, modelling DC collection network includes creating the harmonic model of the DC turbine converter and modelling the array network. The current harmonics within the DC collection network are obtained in the frequency domain to identify the resonance frequency of the array network and potential voltage amplification issues, where the harmonic model of the turbine converter is verified by the comparison of the converter switching model in the PLECS™ circuit simulation tool and laboratory test bench, and show a good agreement.

This paper focuses on the modelling of the series resonant converter proposed as a DC/DC converter for DC wind turbines. The closed-loop control design based on the discrete time domain modelling technique for the converter (named SRC#) operated in continuous-conduction mode (CCM) is investigated. To facilitate dynamic analysis and design of control structure, the design process includes derivation of linearized state-space equations, design of closed-loop control structure, and design of gain scheduling controller. The analytical results of system are verified in z-domain by comparison of circuit simulator response (in PLECS™) to changes in pulse frequency and disturbances in input and output voltages and show a good agreement. Furthermore, the test results also give enough supporting arguments to proposed control design.

Photovoltaic distributed generation (PVDG) has seen tremendous growth in recent years, especially in the residential sector. Among other concerns, the voltage rise in AC networks is considered the most limiting factor in achieving increased PV penetration levels. A steady-state impact study is performed on a CIGRE low-voltage (LV) residential network. This paper compares six techniques to increase the PV penetration limit in the LV residential network, namely single-phase penetration (SPP), Distribution Scheme 1 (DS1), Distribution Scheme 2 (DS2), alternate phase penetration (APP), offline tap adjustment (OTA) and switched on-load tap adjustment (SOLTA). PSCAD software is used for this study. The best results are obtained for the DS2-SOLTA case that gives the minimum voltage magnitude and voltage unbalance in the system. The steady-state results are validated by a dynamic data study using measured solar irradiance and residential load data. A novel approach is also proposed for calculating the worst day from the data set. The obtained results verify the effectiveness of the proposed approach.

This paper presents a selective harmonic-based pilot protection scheme for detecting faults happened in the DC transmission section of VSC-MTDC grids with pulse width modulation (PWM) voltage source converters (VSCs). When a DC fault occurs in VSC-MTDC grids with PWM converters, first carrier frequency harmonic (FCFH) currents will be generated by all VSCs through the grid. FCFH currents have different flowing directions depending on the characteristics and the location of the fault. According to the characteristics of the existing FCFH in the fault currents, a selective pilot protection algorithm is designed for VSC-MTDC grids. Considering the internal and external DC transmission faults for specific zones, and the circulating flow of FCFH current in the DC link capacitors, the relays cannot detect FCFH currents for external faults, while for the internal faults, FCFH currents are clearly detected. To design the selective protection algorithm, Hilbert-Huang transform (HHT) is used to detect the instantaneous frequency and the instantaneous amplitude of the high frequency intrinsic mode function (IMF)s, which are extracted from the fault current waves. Multiple faults with different characteristics are applied to CIGRE DCS-2 VSC-MTDC grid with two-level and three-level VSCs modeled in PSCAD, and the HHT-based selective protection scheme is designed in MATLAB. According to the results, the proposed algorithm can truly discriminate between internal and external faults.

In this paper, a Lyapunov-based method is used in order to determine the stability boundaries of the grid-connected voltage source converter (VSC). To do so, a state space model of the VSC is used to form the Lyapunov function of the system. Then, by using the eigenvalues of the Lyapunov function, the system stability boundaries will be determined. It is shown that the grid-connected VSC works in its stable mode when all of its Lyapunov function’s eigenvalues are positive. The proposed model validity is tested by time-domain simulation. Simulation results show that the method is credible in determining the stability margin of the grid-connected VSC.

With the increasing integration of renewable energy resources and other forms of dispersed generation, more and more variances and uncertainties are brought to modern power systems. The dynamic security assessment (DSA) of modern power systems is facing challenges in ensuring its accuracy for unpredictable operating conditions (OC). This paper proposes a novel approach that uses random forest (RF) for online DSA. Hourly scenarios are generated for the database according to the forecast errors of renewable energy resources, which are calculated from historical data. Fed with online measurement data, it is able to not only predict the security states of current OC with high accuracy, but also indicate the confidence level of the security states one minute ahead of the real time by an outlier identification method. The results of RF together with outlier identification show high accuracy in the presence of variances and uncertainties due to wind power generation. The performance of this approach is verified on the operational model of western Danish power system with around 200 transmission lines and 400 buses.

In islanding, a portion of the power network, comprised of the loads and distributed generation (DG) units, is isolated from the rest of the power grid and forms a micro grid (MG). In this condition, it becomes essential for the islanded MG to operate in a stable and controlled manner by providing ancillary services. When the MG is cut off from the main grid, the islanding must be detected by the DG units. In this condition it is essential that one of the controllers should be switched to the voltage-frequency (VF) control mode. In islanding, the network loses it slack reference and this reference is established by a VF controller. The voltage and the frequency of the islanded MG deviate when disconnected from the transmission grid and these deviations are caused by the load-generation imbalance. The voltage and the frequency of the islanded MG can be restored to the permissible limits if the desired/exceeded amount of active and reactive power is injected/absorbed by the locally available sources in islanded MG. This paper proposes a control strategy which can compensate the voltage and the frequency deviations in the islanded portion of the International Council on Large Electric Systems (CIGRE) low voltage distribution network by using advanced power electronics devices such as STATCOMs) for Photovoltaic (PV) Units and Battery Energy Storage Systems (BESS) STATCOMs (for battery units). The selection of the VF controller for the most suitable DG unit of this test network is also presented in this paper, and the effectiveness of the controllers is verified by presenting simulation results using DIgSILENT (DIgSILENT GmbH, Gomaringen, Germany) power factory software version 15.0.

As a long term bidding behavior, bid shading is exhibited by wind farms participating in real Uniform Price (UP) markets. This signifies that the wind farm owners bid far below their true long run marginal cost. In this paper, a method is proposed to consider the uncertainty of bidding admission in the long term expected revenue of wind farms. We show that this consideration could perfectly explain the observed bid shading behavior of wind farm owners. We use a novel market price model with a stochastic model of a wind farm to derive indices describing the uncertainty of bidding admission. The optimal behavior of the wind farm is then obtained by establishing a multi objective optimization problem and subsequently solved using genetic algorithm. The method is applied to the analysis of long term bidding behavior of a wind farm participating in a Pay-as-Bid (PAB) auction such as Iran Electricity Market (IEM). The results demonstrate that wind farm owners change their bid shading behavior in a PAB Auction. However, the expected revenue of the wind farm will also decrease in a PAB auction. As a result, it is not recommended to make an obligation for the wind farms to participate in a PAB auction as a normal market player.