IET Renewable Power Generation

To succeed over the sudden load‐frequency variations in interlinked power systems, an equilibrium must be maintained between power generations and losses. The major problem associated to manifold interlinking arenas of power systems is load frequency control. In this paper, a multiple‐arena scheme is examined which encompasses thermal and split shaft gas turbine plants. Here, artificial rabbit optimization (ARO) is applied to procure the premium standards of the supplementary controller. The projected controller is the amalgamation of integer order integral‐derivative with filter (IDN) and fractional order integral‐derivative (FOID). So, the amalgamation is IDN‐FOID. Henceforth, the ARO augmented IDN‐FOID controller is recognized. The ARO augmented IDN‐FOID supplementary controller delivers enhanced outcomes related to additional secondary controllers like I, PI, and PIDN. Valuation articulates about the improved act of ARO over added algorithms using the IDN‐FOID controller related to converging nature, transient profile, and steady‐state assessment. Assessment is done in the presence of non‐linearities in generation rate constraints and time delay. It is also detected that scheme potent outcomes with the IDN‐FOID controller are superior when the scheme is instructed with solar photovoltaic, electric vehicles, solid oxide fuel cells, and ultra‐capacitor. The ARO optimized IDN‐FOID controller is the anticipated arrangement for the measured scheme.

This paper proposes an operational planning model based on optimal active and reac-tive power control strategies to enhance solar photovoltaics (PV)' hosting capacity in distribution networks. Reactive power control is carried out through optimum static VAr compensators (SVCs) placement, while active power control is performed through flexible loads, particularly shiftable and interruptible loads. The first stage of the proposed two-stage stochastic model assigns decision-making regarding calculating PV hosting capacity at different nodes, in addition to the allocation and capacity of SVCs. In the second stage, the first stage decisions are assessed to ensure the power flow constraints under various uncertainties such as daily load and stochastic PV generation. The presented model is investigated through numerical analyses on modified IEEE 15-bus and IEEE 33-bus distribution systems considering different active-reactive strategy cases. While most previous works only rely on one type of active or reactive power control strategy, this study investigates the challenges of the respective application of active and reactive power control in various modes of fundamental practices. The obtained results prove the superiority of the proposed hybrid active-reactive control strategy for enhancing PV hosting capacity compared to respective active or reactive power controls.

A review of the state‐of‐the‐art in short‐term Solar Power Forecasting (SPF) methodologies is presented in this paper. Over the last few years, developing and improving solar forecasting models has been the main focus of researchers, considering the need to efficiently increase their forecasting accuracy. Forecasting models aim to be used as an efficient tool to help with the stability and control of energy systems and electricity markets. Intending to further comprehend the factors affecting the quality of SPF models, this paper focuses on short‐term solar forecasting methodologies since they pose a crucial role in the daily operation and scheduling of power systems, since they focus on forecasting horizons typically ranging from 1 h to 1 day. The reviewed works are classified according to the climatic conditions, technical characteristics, and the forecasting errors of the different methodologies, providing readers with information over various different cases of SPF. Considering the need to improve the SPF efficiency, such classifications allow for important comparative conclusions to be drawn, depending on the location of each case and the meteorological data available. Future directions in the field of short‐term solar power forecasting are proposed considering the increasing development of SPF models’ architecture and their field of focus.

The clean Energy router based on advanced adiabatic compressed air energy storage (AA‐CAES) has the characteristics of large capacity, high efficiency and zero carbon emission which are an effective mitigation scheme for the integration of renewables and peak‐shaving and a new clean energy technology for storing energy in the world. A novel solar‐thermal‐assisted AA‐CAES (ST‐AA‐CAES) is proposed in this paper, integrating variable thermal energy storage to improve the system electric to electric (E2E) and round‐trip efficiency (RTE). The efficiency and exergy evaluation of ST‐AA‐CAES are carried out to determine the performance of ST‐AA ‐CAES. The results illustrate that E2E, RTE, and exergy efficiency can reach 56.4%, 95.5%, and 55.9%, respectively. Meanwhile, the details of exergy efficiency and destruction of each subsystem are demonstrated. Particle swarm optimization algorithm is applied to analyse the economy of optimally integrated energy systems which has the advantages of high accuracy, convenient implementation and fast convergence. The system can be applied in abundant solar energy resources area with high efficiency and multi‐energy supply capability.

Deployment of Energy Hubs (EHs) across the power grid can alleviate the Transmission System (TS) capacity and substitute the conventional fossil fuel‐based thermal units. Therefore, this paper presents a tri‐level multi‐stage Joint Expansion Planning of the Transmission system and EHs (JEPT&EHs). In this approach, the Cholesky decomposition technique combined with the Nataf transformation is applied to make the uncertain input parameters correlated. Then, the k‐means data‐clustering method is employed to reduce the initial correlated samples. In the first level, the Transmission System Operator (TSO) optimizes the planning and scheduling strategies associated with the TS capacity requirements and operation costs of the conventional generators. In the second level, the financers specify the expansion of the EHs based on the Locational Marginal Prices (LMPs). In the third level, the Direct Current Optimal Power Flow (DCOPF) is determined to update the LMPs by the Independent System Operator (ISO). The optimization problem is an Equilibrium Problem with Equilibrium Constraints (EPEC) since there are multiple financers across the TS. The proposed model is implemented on the IEEE standard 30 bus TS to present the effectiveness of the EHs' deployment and the impact of the correlations in the total costs of the TSO and financers.

The penetration of wind power generation into the power grid has been accelerated in recent times due to the aggressive emission targets set by governments and other regulatory authorities. Although wind power has the advantage of being environment‐friendly, wind as a resource is intermittent in nature. In addition, wind power contributes little inertia to the system as most wind turbines are connected to the grid via power electronic converters. These negative aspects of wind power pose serious challenges to the frequency security of power systems as penetration increases. In this work, an approach is proposed where an energy storage system (ESS) is used to mitigate frequency security issues of wind‐integrated systems. ESSs are well equipped to supply virtual inertia to the grid due to their fast‐acting nature, thus replenishing some of the energy storage capability of displaced inertial generation. In this work, a probabilistic approach is proposed to estimate the amount of inertia required by a system to ensure frequency security. Reduction in total system inertia due to the displacement of conventional synchronous generation by wind power generation is considered in this approach, while also taking into account the loss of inertia due to forced outages of conventional units. Monte Carlo simulation is employed for implementing the probabilistic estimation of system inertia. An ESS is then sized appropriately, using the system swing equation, to compensate for the lost inertia. The uncertainty associated with wind energy is modeled into the framework using an autoregressive moving average technique. Effects of increasing the system peak load and changing the wind profile on the expected system inertia are studied to illustrate various factors that might affect system frequency security. The proposed method is validated using the IEEE 39‐bus test system.

A representative frequency dependent model (FDM) of an HVDC cable is important in ensuring HVDC systems are designed harmoniously. A model of the proposed and very long, 4200 km, AAPowerLink cables is implemented in PSCAD/EMTDC. The suitability of approximations required for efficient simulation are evaluated according to the anticipated cable specifications. In practice, a submarine cable of this length has between 40 to 80 field joints (FJ), which can be onerous to individually express and computationally expensive to model. Frequency domain analysis shows a minimum of 8 FJs is sufficient to represent the behaviour of the core voltage. The sensitivity of the FDM to cable parameters and operating conditions is assessed. The FDM is applied to establishing single ended fault location techniques. For a 4200 km cable these techniques can determine fault location to within 200 km (≈5%). Additional methods are explored to refine fault location estimates.

Photovoltaic (PV) modules are prone to short circuits, open circuits, cracks, which can bring serious harmful effects. It is difficult to establish the corresponding PV fault models to diagnose the status of PV strings. The paper proposes a machine learning‐based stacking classifier (MLSC) for accurate fault diagnosis of PV strings. Specifically, for the operating state of PV modules, the parameter sensitivity algorithm is used to analyze the impact of characteristic factors on the characteristics of PV modules. Then based on the characteristic factors (irradiance, temperature, current, and power), MLSC is proposed to realize the accurate fault diagnosis of PV strings. This structure of MLSC is to integrate all kinds of classifiers by stacking to play the characteristics of each classifier. It combines the characteristics of various types of machine learning algorithms to improve the overall classification effect by using the advantages of each classifier. Finally, experiments reveal that MLSC improves the accuracy of PV fault diagnosis.

Aiming at the problem of multi‐point power source layout planning for power systems, the output characteristics of a power system composed of wind power, photovoltaic power, hydropower, traditional thermal power, concentrated solar power and electrochemical energy storage are comprehensively analyzed. A power source multi‐point layout planning model for a power system based on complex adaptive system theory is proposed with a focus on the complementarity among these different energies and the combination of power optimization planning and complex adaptive system theory. With the minimum construction unit of various types of power sources as the ‘agent’, considering the interactions among agents and the accumulation of experiences, the behaviour rules of the model are constantly changed, the grid‐connected positions of various types of power sources are adjusted, and the optimal layout schemes for all kinds of power capacities for each node are obtained. In addition, an agent modelling method based on complex phenomena emergence with simple rules is proposed, which reveals the core idea of the complex adaptive system theory: adaptability creates complexity. Taking the new energy construction base in Northwest China as an example, the proposed method is verified to have a significant effect on improving energy consumption in the new system. Based on the current power capacity layout and the future grid structure in this area, the future power optimization scheme is determined, and it provides guiding significance for actual engineering construction.

This study provides an optimal and efficient energy management strategy (EMS) for the cost‐effective performance of a combined solar and green energy microgrid in both independent and grid‐connected modes. A microgrid is formed by the system that includes a fuel cell (FC), a battery storage (BS), and a photovoltaic system (PV). Evidently, the unguaranteed features of the renewable energy and load electricity generate instability problems as well as economic ones, like operational expenses. To tackle these issues, a novel procedure is proposed that has been improved by a modified metaheuristic algorithm, called chaotic map‐based chameleon Swarm Algorithm (CCSA). In this method, the simulation is based on a one‐day planning perspective. The method aims to supply the power requirements of the load at the lowest possible cost under a constant DC bus voltage, protect the battery from overcharging and depletion, and improve the efficiency of the total system. To illustrate the suggested method's effectiveness, the simulation results of CCSA are compared with some studied methods in the literature, including GAMS, bald eagle search optimization algorithm (BEOA), original chameleon swarm algorithm (CSA), and grey wolf optimization algorithm (GWOA).

A severe voltage sag on the wind‐farm grid side with doubly fed induction generators (DFIGs) can induce a peak inrush current in the rotors and damage converters, resulting in wind‐turbine disconnection from the grid. To prevent this from happening, a variable‐step model predictive control (VS‐MPC) strategy is proposed for improving the wind turbines’ ability to operate without disconnecting themselves from the grid when a fault occurs. First, the predictive‐control state‐space model of a doubly fed wind farm is established according to its working principle. Second, model predictive control (MPC) is applied on the rotor side of the DFIG to realize the rapid tracking of the rotor current to the reference value during low‐voltage ride‐through (LVRT) of the DFIG. Finally, a variable‐step size algorithm is introduced into the MPC controller to change the step size continuously during the LVRT period. This increases the control accuracy, realizing rapid attenuation of each transient component, whereby the wind‐farm LVRT capability is enhanced. The proposed control strategy was simulated and verified using MATLAB/Simulink. The simulation results indicated that VS‐MPC can effectively handle LVRT, allowing the recovery of a wind farm that uses DFIGs and improving the performance of wind‐farm.

The market clearing pricing (MCP) model is used to operate electricity, gas, and heating networks (EGHNs) with flexible energy hubs (EHs) in the day‐ahead energy market. It's two‐level optimization. Its higher level refers to EHs' participation in the market and their profit maximization bound by the operational model of power sources, storage devices, and responsive loads in the form of EHs and their flexibility limit. In the lower‐level problem, the MCP model calculates energy price and evaluates EH performance's effects on the networks' technical and economic indices. It optimizes power flow in the networks to reduce centralized generator operating costs. This approach is linear approximation. Unscented transformation (UT) model load and renewable power uncertainties. This technique contains the fewest situations, reducing issue volume and computing time. Benders decomposition (BD) technique calculates energy prices, EHs, and networks. Finally, the numerical results show that the proposed scheme can extract the optimal economic and flexibility states of EHs. EHs' optimal performance enhanced energy networks' economic and operating status compared to power flow studies and promoted societal welfare by lowering energy prices.

As the grid‐connected interface device of wind and photovoltaic power generation, the voltage source converter (VSC) must pass the low voltage ride‐through (LVRT) test. However, existing literature demonstrates that under weak grid conditions, there is a possibility of severe fluctuations in the terminal voltage of VSC during transiting from the pre‐sag normal operation mode to the LVRT mode. Unfortunately, it will make VSC frequently enter and exit LVRT mode. In order to address the tricky problem of transient instability of VSC riding‐through severe grid voltage sag under the weak grid, in this paper, the full‐order large‐signal model of VSC is established first. Then, based on the established model, the analysis indicates VSC will face the risk of losing stability under the weak grid during LVRT due to current transients. By studying the impacts of low short circuit ratio (SCR) on the basin of attraction of the post‐sag equilibrium point, the intrinsic mechanism of VSC losing stability under weak grid conditions is revealed. Moreover, considering that the transient stability is related to the current control, the stability‐enhanced LVRT control ensuring both small‐signal stability and transient stability of VSC during LVRT is proposed. Finally, the correctness of the above theoretical analysis is verified by real‐time simulation

The existing static voltage stability margin evaluation methods cannot meet the actual demand of current power grid well in terms of calculation speed and accuracy. Thus, this paper proposes a static voltage stability margin prediction method based on a graph attention network (GAT) and a long short‐term memory network (LSTM) to predict the static voltage stability margin of a power system accurately, fast, and effectively, considering new energy uncertainty. First, an innovative machine learning framework named the GAT‐LSTM is designed to extract highly representative power grid operation features considering the spatial‐temporal correlation of the power grid operation. Then, a static voltage stability margin prediction method based on the GAT‐LSTM is developed. Particularly, considering the influence of new energy power uncertainty, two loss functions of certainty and uncertainty are used in the proposed method to predict the voltage stability margin and voltage fluctuation range. Finally, the IEEE39‐bus power system and a practical power system are employed to verify the proposed method. The results show that the computational speed of the proposed method is greatly improved compared to the traditional methods not based on machine learning; the computation results are more accurate and reliable than the existing machine learning methods. Compared with the existing methods, the proposed method has higher scalability and applicability.

In distributed networks, wind turbine generators (WTGs) are to be optimally sized and positioned for cost‐effective and efficient network service. Various meta‐heuristic algorithms have been proposed to allocate WTGs within microgrids. However, the ability of these optimizers might not be guaranteed with uncertainty loads and wind generations. This paper presents novel meta‐heuristic optimizers to mitigate extreme voltage drops and the total costs associated with WTGs allocation within microgrids. Arithmetic optimization algorithm (AOA), coronavirus herd immunity optimizer, and chimp optimization algorithm (ChOA) are proposed to manipulate these aspects. The trialed optimizers are developed and analyzed via Matlab, and fair comparison with the grey wolf optimization, particle swarm optimization, and the mature genetic algorithm are introduced. Numerical results for a large‐scale 295‐bus system (composed of IEEE 141‐bus, IEEE 85‐bus, IEEE 69‐bus subsystems) results illustrate the AOA and the ChOA outperform the other optimizers in terms of satisfying the objective functions, convergence, and execution time. The voltage profile is substantially improved at all buses with the penetration of the WTG with satisfactory power losses through the transmission lines. Day‐ahead is considered generic and efficient in terms of total costs. The AOA records costs of 16.575M$/year with a reduction of 31% compared to particle swarm optimization.

When a wind farm (WF) approaches the end of its life cycle, repowering is another opportunity for wind energy to prove its value. This paper proposes an optimization framework to guide the WF repowering, considering the power generation, the economic cost, and the aesthetic of the WF when various types of new wind turbines (WTs) are added. When calculating the wake deficits inside the WF, a three‐dimensional (3‐D) Gaussian wake model is applied which considers the height differences among the new WTs. A harmony pattern metric is used to assess the visual impact of the rebuilt WF. This optimization problem is formulated as an integer programming (IP) problem and is tackled by the integer particle swarm optimization (IPSO) algorithm. The wind data used for this optimization procedure is predicted by the auto‐regressive (AR) model. The case study on the OWEZ WF verifies the effectiveness of the proposed method. It is also validated that the application of predicted wind data is better than the historical data for WF repowering optimization.

Efficient voltage/VAR feasible boundary (VVFB) assessments for large‐scale wind farms can help reduce cascading trip risks. An accurate VVFB assessment result for such large‐scale wind farms, which is essentially a transient security constrained optimal power flow (TSCOPF) problem, may involve dynamic characteristics of all wind turbines in a whole wind farm, and the TSCOPF scale of VVFB assessment is quite huge and difficult to use directly for online computations. Therefore, this paper proposes an adaptive parameter aggregation dimensionality reduction equivalence (APA‐DRE) method to efficiently and accurately solve the VVFB problem. First, the core parameters are combined into an adjacency matrix as an input aggregation sample based on automatic weighting factors. The proposed between‐within proportion index is used to evaluate the aggregation result and determine the optimal aggregation number of input samples. Then, the original VVFB model is accurately transformed into a small‐scale problem represented by equivalent wind turbines with optimal numbers based on the principle of equal voltage differences. Finally, actual wind farm results validate that the proposed approach reduces the computation scale of the VVFB and improves the computation efficiency of the original model by approximately three times while ensuring accuracy.

Subsynchronous oscillation events of grid‐connected voltage source converter (VSC) pose a potential risk to the security and stability of power systems. It is particularly challenging to reveal the subsynchronous oscillation mechanism precisely because the grid‐connected VSC consists of non‐linear components such as saturation. The describing function and the generalized Nyquist criterion are used in this paper to investigate the bifurcation types and dominant parameters of grid‐connected VSC. First, the condition of a supercritical Hopf bifurcation is investigated, which generates subsynchronous oscillations. Then, the dominant parameters of supercritical Hopf bifurcation are deduced, including the grid strength, the active power reference, or the proportional coefficient of phase‐locked loop. Finally, it is examined how the saturation's limit value impacts the limit cycle's amplitude and stability. The findings demonstrate that a system's stability depends on whether its closed‐loop frequency characteristic curve intersects the describing function's negative reciprocal curve. When the two curves intersect, the supercritical Hopf bifurcation appears, and changing the limit value can affect the limit cycle's amplitude. However, if the two curves do not intersect, altering the limit value cannot cause the supercritical Hopf bifurcation, and the subsynchronous oscillation will not happen. Variations in grid strength, active power reference, or proportional coefficient of phase‐locked loop will significantly affect the Nyquist curve, determining if supercritical Hopf bifurcation appears.

Quasi‐Z source inverters (qZSI) can overcome the disadvantages of conventional wind power systems, because of achieving the balanced DC‐link voltage by using its boost ability. The majority of currently used control methods for conventional inverters are susceptible to parameter changes, and the qZSI are controlled by finite switching sequence model predictive control (FSS‐MPC). In this paper, a Variable weight coefficient Model Predictive Control (V‐MPC) strategy is proposed. First, based on the optimal scheduling algorithm, according to the priority of the controlled object. The proportioning weights of the global feasible solution and the local optimal solution in the algorithm are coordinated. Then, using the equality‐constraints quadratic programming method, according to the relationship between the system frequency and the reference value, increase the weight of the qZSI algorithm or increase the proportion of VSG control to improve the ability to suppress frequency fluctuations. It can give full play to the short‐term power support role of the virtual synchronous wind turbine. The modeling and testing findings validate the suggested control strategy. When compared to the conventional finite control set model predictive control (FCS‐MPC), the V‐MPC can minimize the big store inductance current ripple and have lower THD in the load currents waveform.

The construction of a household integrated energy system will reduce greenhouse gas emissions and promote sustainable development. Firstly, a household energy system is proposed, which consists of a photovoltaic, wind turbine, electrolysis cell, hydrogen storage tank, and hydrogen‐fired gas turbine. The proposed system is modelled as a bi‐objective optimization problem in which the minimum daily system economic cost, and the minimum loss of energy supply probability. Secondly, a novel multi‐objective egret swarm optimization algorithm with strong search capability and fast convergence is proposed. Thirdly, a household load corresponding to a typical day in spring is chosen as the study case. The optimization results show that the daily system economic cost with the optimal number of devices is 97.48 RMB, and the loss of energy supply probability is 8.33% at the lowest. Finally, to validate the efficiency of the proposed method, the proposed method is compared with NSGA‐II (a widely used multi‐objective evolutionary algorithm). The comparison indicates that the proposed method has a better diversity due to the random searchability. As a consequence, the proposed method can be used in the optimization capacity design of the integrated energy system.

This paper addresses the cybersecurity of hierarchical control of AC microgrids with distributed secondary control. The false data injection (FDI) cyberattack is assumed to alter the operating frequency of inverter‐based distributed generators (DGs) in an islanded microgrid. For the microgrids consisting of the grid‐forming inverters with the secondary control operating in a distributed manner, the attack on one DG deteriorates not only the corresponding DG but also the other DGs that receive the corrupted information via the distributed communication network. To this end, an FDI attack detection algorithm based on a combination of Gaussian process regression and one‐class support vector machine (OC‐SVM) anomaly detection is introduced. This algorithm is unsupervised in the sense that it does not require labelled abnormal data for training which is difficult to collect. The Gaussian process model predicts the response of the DG, and its prediction error and estimated variances provide input to an OC‐SVM anomaly detector. This algorithm returns enhanced detection performance than the standalone OC‐SVM. The proposed cyberattack detector is trained and tested with the data collected from a 4 DG microgrid test model and is validated in both simulation and hardware‐in‐the‐loop testbeds.

The emergence of micro-sources in the energy market to reduce carbon emissions and exploit more renewable generations increases the frequency oscillations of the system. Hence, this paper attempts to develop a robust nonlinear fractional order proportional integral derivative (NLFOPID) controller for frequency regulation of restructured energy systems. A hybrid atom search-particle swarm optimization (AS-PSO) is proposed to optimize the gain values of the NLFOPID controller. The proposed control approach enhances flexibility by providing robustness against the RE intermittency. The optimization technique is coded in MATLAB and applied for frequency regulation of a multi-area energy system developed in Simulink. Initially, the effectiveness of the hybrid algorithm is validated for standard benchmark functions and then implemented for frequency control. The results demonstrate that the proffered AS-PSO technique performs significantly for various types of transactions in a deregulated energy market than state-of-the-art. The real-time applicability of the proposed controller is validated using hardware-in-the-loop simulation of the open energy market in OPAL-RT. The frequency response obtained from the AS-PSO optimized NLFOPID controller is remarkable compared to other techniques. Furthermore, the closed-loop stability of the system is examined using bode analysis through an improved Oustaloup approximation technique.

With the integration of wind, electricity and heat, the combined heat and power system has the characteristic of multi‐time scales, in which the wind power has high uncertainty, the thermal system is difficult to synchronize with the power system for the large inertia and delay. Therefore, the fixed time‐scale dispatching is unable to guarantee the reliability of the system. Given the above problems, this paper proposes a time‐scale adaptive dispatching strategy of the combined heat and power system considering thermal inertia. This strategy analyzes the time characteristics of wind power forecasting error and the thermal inertia, while later establishing time‐scale adaptive dispatching sub‐main model of combined heat and power system. Specifically, in the time‐scale adaptive method, the system available reserve model is improved due to the power support of the thermal inertia. Based on the existing fixed time‐scale dispatching, this method dynamically adjusts the steps of forecasting and the time‐scales of dispatching when the system available reserve is inadequate to compensate for the wind power forecasting error. Taking the 4‐h fixed time‐scale dispatching as an example, the simulation results suggest that the proposed strategy achieves the dynamic dispatching of the combined heat and power system, while improving its reliability and stability.

Electric grid is vulnerable to power imbalance and inertia is the grid's response to overcome such disturbance. Augmentation of power electronic converter based renewable energy technologies like Photovoltaic Generators (PVG) and batteries in utility grid significantly reduces inertia. Inertia degradation is indicated by sharp Rate of Change of Frequency (ROCOF) events due to any grid component failure or imbalance. Fixed gain feedback Proportional Integral Derivative (PID) control is insufficient to deal with varying ROCOF events. This work proposes Sliding Mode (SM) robust droop control scheme assisted by Artificial Neural Network (ANN) algorithm for an islanded PVG integrated microgrid. Droop response is governed by swing equation that uses PVG Maximum Power Point (MPP) forecasted by ANN. ANN forecast is compared with optimized Gaussian process regression algorithm based on mean squared error and speed of training as key performance indicator. The algorithms are trained and validated based on climate dataset of Islamabad, Pakistan. SM control performance is compared with various PID gain settings and qualified as the most suitable against variable source, load and ROCOF scenarios. Finally, significance of accurate MPP forecast for droop control is established by comparing the ANN and deterministic forecaster assisted droop response in a microgrid case study.

The penetration of renewable resources in distribution networks have led to the creation of a concept called microgrids. Microgrids act as a bus with the ability to control the amount of consumption and generation, from the viewpoint of system operator. If the amount of capacity that can be provided by microgrids is known, they can be effective in providing resource adequacy and the required capacity of the network. This paper contains two contributions. Modelling of capacity value of a microgrid that includes wind turbines, photovoltaic, non‐renewable generators, loads and batteries is the first innovation of this paper. Here, a capacity value model for microgrids is created for the first time. The application of microgrid capacity value model to a long‐term issue, such as ensuring resource adequacy in the capacity market, constitutes the second innovation of this paper. Consequently, there are two distinct innovations in this work. The presence of microgrids in the capacity market has increased competition and reduced costs, and on the other hand, it will help the microgrids development through their long‐term generation revenues. The proposed method has been tested on IEEE 57‐bus test system. Results have shown the efficiency of using microgrids in the capacity market.

This research work proposes an adaptive grid integrated photovoltaic (PV) system with perturb adaptive T–S fuzzy approach‐based sliding mode controller (SMC) (T–S fuzzy SMC) as a maximum power point tracker (MPPT). In this work, a grid‐connected single‐phase H‐bridge microinverter is employed which is controlled using sine pulse width modulation (SPWM). The proposed variable step sliding mode controller‐based MPPT has high PV tracking efficiency, strong adaptive capability, high precision, simpler hardware implementation, better transient performance, and rapid convergence velocity compared to other recent MPPT algorithms under discontinuous non‐linear operating conditions. The Takagi–Sugeno (T–S) based perturb adaptive sliding mode controller MPPT generates adaptive perturbation and has high‐performance efficiency under changing solar irradiance levels compared to the Mamdani approach. The proposed inverter controller provides better power quality performance with less total harmonic distortion (THD) as the SPWM technique can minimize lower‐order harmonics. Experimental results justify the control system design with high performance and PV system MPPT efficiency is found 98.45% using proposed T–S fuzzy SMC method. From practical results THD of grid voltage/current is found according to IEEE 519 coder, which is less than the 5% limit.

In order to improve the stability of doubly‐fed induction generator (DFIG)‐based wind turbines under weak grid, previous works have proposed the power synchronization control strategy in the rotor side converter (RSC). However, the potential instability risk of dc‐link voltage control in the grid side converter (GSC) has been neglected. Here, DFIG with power synchronization and phase‐locked synchronization for RSC and GSC respectively, is called as hybrid synchronization mode (HSM)‐controlled DFIGURE and, the full‐order state space model of HSM‐controlled DFIG are established. Eigenvalue and participation factor analysis shows that the HSM‐controlled DFIG is unstable under extremely weak grid due to the interaction of phase‐locked loop (PLL) and current‐loop in GSC. Furthermore, dominant pole analysis indicates that the HSM‐controlled DFIG exhibits weak damping at the AC side in strong grid. To enhance the stability of HSM‐controlled DFIG under extremely weak grid, an improved hybrid synchronization control strategy is proposed. The improved control strategy utilizes active power and dc‐link voltage to achieve PLL‐free operation of DFIGURE. The effects of the parameters in dc‐link voltage synchronous loop on control bandwidth, system stability and dynamic coupling are analysed. Finally, the validity of the theoretical analysis is investigated with experiments based on 2MW control‐hardware‐in‐loop platform.

In terms of microgrids (MGs) operation, optimal control and management are vital issues that must be addressed carefully. This paper proposes a practical framework for the optimal energy management and control of renewable MGs considering energy storage (ES) devices, wind turbines, and microturbines. Due to the non‐linearity and complexity of operation problems in MGs, it is vital to use an accurate and robust optimization technique to control the power flow of units efficiently. To this end, in the proposed framework, teacher learning‐based optimization (TLBO) is utilized to solve the power flow dispatch in the system efficiently. Moreover, a novel hybrid deep learning model based on principal component analysis (PCA), convolutional neural networks (CNN), and bidirectional long short‐term memory (BLSTM) is proposed to address the short‐term wind power forecasting problem. The feasibility and performance of the proposed framework and the effect of wind power forecasting on operation efficiency are examined using the IEEE 33‐bus test system. Also, the Australian Woolnorth wind site data is utilized as a real‐world dataset to evaluate the performance of the forecasting model. The results show that the proposed framework can be used to schedule MGs in the best way possible.

This paper presents a novel control scheme for combined frequency and voltage stabilization of an islanded multi-generator hybrid microgrid (IHμG). The control concept incorporates an improved virtual inertia support scheme (IVIS) and the recently developed yellow saddle goatfish technique (YSGA) to obtain optimal control parameters. IHμG model consists of an AVR-based voltage compensating loop for synchronous biodiesel generator, wind generator, wave generator, photon exchange membrane fuel cell (PEMFC), and controllable heat pump and freezer. An integer order proportional-integral-derivative (IOPID) controller is leveraged for frequency-voltage stabilization. A comparative response assessment has been performed with/without IVIS. The utilization of YSGA has been justified by comparative assessment with particle swarm optimization, firefly, and sine-cosine techniques. A meticulous performance evaluation of YSGA optimized IOPID control scheme in the IHμG has been conducted through several case studies. Furthermore, the rigorous sturdiness assessment of YSGA optimized IOPID controller was performed under different uncertainties such as: variation of amplifier gain, ±30% variation in demanded loading magnitude, moment of inertia and droop coefficient. Finally, real-time hardware-in-the-loop (HIL) simulation platform is utilized to validate the proposed control approach.

This paper proposes a new framework for the planning of both distributed generators (DGs) and electric vehicle charging stations (EVCSs). The proposed method efficiently produces a unified solution for the joint planning of DGs and EVCSs for both grid‐connected and islanded scenarios. The problem is formulated as a novel two‐stage planning problem. The first stage determines the locations and sizes of the DGs with locations of EVCSs in grid‐connected scenario, whereas the second stage planning identifies the optimal islands under the islanded microgrid scenario. A non‐dominated sorting genetic algorithm (NSGA‐II) is applied to solve the first stage planning problem; in this stage, the algorithm minimizes two objective functions: the system‐losses and total cost. In the second stage, another single objective optimization problem is designed which minimizes supply voltage variations to find optimal islands for the DGs and EVCSs to ensure a secure supply of power for EVs. The proposed framework is implemented on the IEEE 33‐bus system and verified with four test cases. The results demonstrate the effectiveness of the proposed method and show that the sizes and locations of DGs, and locations of EVCSs are adequate for both grid‐connected and islanded microgrids.

Almost the majority of power dispatch strategies are based on the economic, energy‐efficient or environmental consideration. It has never been studied the effect of power dispatch on dynamic stability of turbine shafts torsional responses. A 9‐bus isolated grid is thus studied in the paper by focusing on such an effect by using the DIgSILENT PowerFactory. It is found the dynamic stability will be affected when a large scale photovoltaic (PV) farm is incorporated into an isolated system. When the system is operating under the low load situations, the traditional generators need to operate with a small rating factor to counterbalance the PV power. That might lead to the torsional modes of the traditional generators become unstable. The power dispatch thus becomes very important for maintaining the stability. Here, some of the commonly used power dispatch approaches have been studied to evaluate their effect on dynamic stability of turbine shafts torsional responses. A moderate dispatch approach then is proposed specifically for improving the torsional responses stability under the system low load situations.

Artificial intelligence (AI) methods have been used widely in power transformer fault diagnosis with notable developments in solutions for big data problems. Training data is essential to accurately train AI models. The volume, scope and variety of data samples contribute significantly to the success and reliability of diagnostic outcomes. This paper provides a comprehensive review and comparison of different augmentation methods used to generate reliable data samples for minority and majority classes to balance the diversity and distribution of dissolved gas analysis (DGA) datasets. The augmentation method presented in this paper combines three common AI models—the Support Vector Machine (SVM), Decision Tree, and k‐Nearest Neighbour (KNN)—to assess performance for diagnostic fault determination and classification, with comparator assessment using no data augmentation. Comparative analysis of the hybrid models uses evaluation metrics including accuracy, precision, recall, specificity, F‐score, G‐mean, and the area under receiver operation characteristic (Auc). Experimental results presented in this paper confirm that the data augmentation applied to AI models can resolve difficulties in imbalanced data distribution and provide significant improvements for fault diagnosis, particularly for minority classes.

Long‐term storage will play a crucial role in future local multi‐energy systems (MES) with high penetration renewable energy integration for demand balancing. Local MES planning with long‐term energy storage is essentially a very large‐scale program because numerous decision variables, including binary variables, should be used to model long‐term energy dependencies for accurate operational cost estimation. How to largely reduce decision variables as well as guarantee the planning model accuracy becomes one main concern. To this end, this paper proposes a novel efficient aggregation and modeling method for local MES planning. The aggregation method first decomposes input time series data (renewable energy output and energy demand) into hourly and daily components, based on which more accurate aggregation results with a few typical scenarios can be derived. By incorporating similar decomposition into the operation model of energy devices, the planning model can describe the long‐term energy cycle and the hourly operation characteristic at the same time and yield accurate optimization results with limited complexity. Experimental results show that the proposed method can considerably decrease the complexity of the problem while maintaining agreement with the results based on the optimization of the full‐time series.

Energy Storage Resources (ESRs) can help promote high penetrations of renewable generation and shift the peak load. However, the increasing number of ESRs and their features different from conventional generators bring computational challenges to operations of wholesale electricity markets. In order to improve the computational efficiency, this paper tightens the generic ESR formulation for unit commitment. To avoid the complexity caused by ESR operations in both discharge and charge directions, a novel “decoupled analysis” is conducted to analyze one direction at a time. For each direction, ESRs over two and three time periods are categorized into several types based on their parameters. For each type, our recent four‐step systematic formulation tightening approach is used to construct the corresponding tight formulation. In order to consider more periods without analyzing all the drastically increased number of types, a series of major types are selected based on how many periods an ESR is able to discharge (charge) consecutively at the upper power limit. A related generic form of tight constraints over multiple periods is established. Moreover, validity and facet‐defining proofs of our tight constraints have been provided. Numerical testing results illustrate the tightening process and demonstrate computational benefits of the tightened formulations.

Local integrated energy system (IES), usually a multi‐building heating and cooling system incorporating cogeneration systems and distributed energy resources (DERs), is becoming an efficient energy infrastructure for energy system decarbonization. However, for small and medium‐scale local IESs, the fluctuation of user loads seriously influences energy system operation, and the increasing DER penetration also enlarges the influence. Given the necessity of exploring an efficient way to handle the uncertainties in local IES, this paper proposes a reinforcement learning (RL) approach based on the improved TD3 algorithm. The mathematical model of local IES is first established considering supply‐ and load‐side flexible resources. The local IES dispatch problem is formulated as a Markov decision process (MDP), in which multi‐type uncertainties of renewable generation, electric load and heat load are considered. For solving the MDP, an improved twin delayed deep deterministic policy gradient (TD3) algorithm is proposed with a dynamic balance mechanism of exploration noise. Based on a local IES testbed in the Nantong Central Innovation District, China, a comparison analysis is conducted to verify the promoting effect of flexible resources on the operation economy and renewable energy consumption. The system operating cost reduces by 18.46%, and surplus renewable energy can all be accommodated considering flexible resources. The dispatch policies obtained by the deep deterministic policy gradient (DDPG), the improved TD3, the original TD3 and traditional optimization algorithms are also compared. The results reveal that the convergence stability and solving accuracy of the improved TD3 outperform the other two RL algorithms. Specifically, the system operating cost of the improved TD3 reduces by 2.76% compared with the DDPG, and the energy supply imbalance decreases by around 88%. Meanwhile, the improved TD3 exhibits better operation economy and adaptability to the uncertain environment than the deterministic optimization and intraday rolling algorithms.

Due to the advantages of micro‐grids including power losses and voltage drops reduction, reliability improvement and reduction in transmission network cost, numerous clean micro‐grids including renewable resources such as wind, tidal and solar are developed in different countries of world. The investment cost of the renewable energy‐based generation units especially tidal barrages is high, and to generate the cost‐effective electricity from the renewable resources, the optimization process should be performed. In this paper, optimal planning of a micro‐grid containing energy storage device and new mixture of renewable resources including tidal barrage and photovoltaic system is performed. For this purpose, optimal characteristics of barrage type tidal plant including number of turbines, sluices and hydro‐pumps, turbine tip and hub diameters and sluice width are determined to maximum energy of tidal plant is generated. Then, number of photovoltaic systems and batteries are obtained to supply the required load in minimum cost. To optimize the objective functions, different heuristic approaches including particle swarm, genetic and imperial competitive algorithms (ICAs) are applied, in the paper. To study the effectiveness of the proposed approach, optimal planning of a micro‐grid is performed. It is deduced from the numerical results that the particle swarm method has performed best in determining the optimal solution.

Heat recovery by combined heat and power (CHP) systems to provide heat load reduces the cost of microgrid (MG) energy management. However, heat transfer from CHP systems to consumers is limited due to the heat loss of pipelines. The amount of heat loss varies depending on pipeline material and environmental characteristics. Here, a day‐ahead energy management of CHP‐based microgrid, including various types of CHP systems to supply electrical and thermal loads, is performed. In this regard, the accurate model of heat transfer losses (HTL) through pipelines in the energy management problem is considered. Moreover, the optimal scheduling of different types of MGs in terms of HTL specifications is compared with each other. Evaluating the simulation results, it is inferred that accurate modeling of HTL in the energy management problem has a significant impact on the optimal scheduling and operation cost of MG. To solve the optimization problem, a new enhanced imperialist competitive algorithm (E‐ICA) is examined and validated. The E‐ICA method improves the efficiency of the classical ICA method to prevent getting stuck in local optimums by enhancing exploration and exploitation. The results of the energy management problem show E‐ICA superiority compared to other evolutionary algorithms.

Recently, due to the increase of the frequency of high impact low probability (HILP) events (manmade and natural events), assessment and enhancement of resilience has become very important in the operation and planning procedures of future power systems. In this regard, several researches have been conducted on operation and planning strategies of the power system to improve resilience. Here, assessment and enhancement approaches of power system resilience by focusing on the role of wind farms and wind turbines are reviewed. To do so, a comprehensive review of the related metrics, hazard types, modelling approaches, assessment methods, and enhancement solutions is presented. Then, the assessment and enhancement methods of power system resilience are categorized in three phases before event (preventive proactive actions), during event (corrective active actions) and after event (restorative reactive actions) from the point of view of operation and planning actions. Finally, the current challenges, research gaps and future trends are discussed.

Considering both the DC fault ride‐through (FRT) and lightweight requirements of modular multilevel converter (MMC), this paper proposes a hybrid arm‐multiplexing modular multilevel converter (HAM‐MMC). The phase leg of the proposed MMC topology is divided into upper, middle, and lower arms, configured with full‐bridge submodules (FBSMs), half‐bridge submodules (HBSMs), and FBSMs, respectively. The time‐division multiplexing of middle arms between upper and lower arms is achieved by introducing arm selection switches, which considerably enhances the submodule utilization and thus minimizes the submodule number. The structure and zero voltage switching strategy of arm selection switches are presented, and a modified sorting algorithm is developed for capacitor voltage balance. The negative level output capability of FBSMs in upper and lower arms allows the DC voltage to be reduced in response to DC faults. Compared with the conventional hybrid MMC composed of HBSMs and FBSMs in a 1:1 ratio, the HAM‐MMC has the same power quality and DC FRT capability, but uses 25% fewer capacitors for lightweight. Simulation results demonstrate that the proposed HAM‐MMC can smoothly ride through DC voltage dip without interrupting power transmission, and can also rapidly resume normal operation after a zero‐voltage disturbance.

This paper provides a comprehensive review of the research work related to Reliability Assessment Methodologies for grid‐connected photovoltaic (PV) systems performed in recent literature. Solar power is emerging as the fast growing source of energy in the world as a result of rising environmental concerns regarding the hazards of climatic change linked with the production of electricity using fossil fuels. Although PV systems can support small businesses and households on their own, many people prefer a grid‐connected PV system (PVS) because of the net profit it provides. Grid‐integrated PV system, however, comes with many reliability issues. Evaluating the reliability of grid‐integrated photovoltaic system is thus an important area of research. The article presents a critical survey of the state‐of‐art technologies for assessing the reliability of a PV system. Issues related to the reliability of the grid‐integrated PVS are spotted along with the solution techniques. Reliability indices for analyzing the PVS performance are also discussed.

This paper proposes a zero‐sum dynamic game (ZSDG) design to mitigate frequency deviations in the secondary control layer of islanded AC microgrids. By defining a min‐max optimization problem, where the control input minimizes the frequency deviations while the external disturbances maximizes the cost function, a robust control law is designed. The outcome leads to setting the frequency to its desired value and at the same time effects of disturbances are attenuated by applying the H∞$H_{\infty }$ optimal controller in the secondary control level of AC microgrids. Since the basic ZSDG controller cannot eliminate adverse effects of external disturbances on the system frequency, an extended‐ZSDG (EZSDG) method is introduced to design an effective H∞$H_{\infty }$ controller. Due to the lack of access to the state variables of the microgrids, a full‐order observer is designed to estimate these variables based on the measured output. In simulation results, the validity of the proposed EZSDG is investigated by considering uncertainties in both the communication and physical layers.

Frequency‐adaptive prefilters are widely used in the phase‐locked loop (PLL) to suppress input disturbances. However, the parameters of the PLL and prefilters are often designed separately or based on an inaccurate model. The positive feedback effect introduced by the frequency adaption (FA) is usually ignored, leading to non‐optimal performance. Based on the accurate small‐signal model with a particular focus on the FA, this paper proposes an optimal design method for these advanced PLLs. The PLL based on dual second‐order generalized integrator (DSOGI) is taken as an example to demonstrate the principle. With different gain margins, the optimal damping factors of DSOGI and PLL, as well as the natural frequency of PLL, are obtained using the proposed method. Then the global optimal parameters are obtained by evaluating the settling time and overshoot. The analysis shows that the truly optimal parameters are quite different with those provided in the literature, and the dynamic performance is significantly enhanced without degrading the steady‐state performance. Experimental results are presented to verify the theoretical analysis.

The utilisation of offshore wind turbines has rapidly increased in the last decade, which has resulted in a steady increase in wind turbine sizes. The global average offshore wind turbine size has increased from 1.5 MW to 6 MW in the last two decades. The research community has started to investigate huge 10 to 15 MW offshore wind turbines in recent years, resulting in the study of very innovative floating wind turbines using various substructure technologies. With this backdrop, this paper will investigate and thoroughly compare the power performance of extreme load effects of a large offshore 10 MW turbine installed on the monopile, spar, and semisubmersible substructures. This is performed by using the average conditional exceedance rate (ACER) and Gumbel methods to predict the extreme responses under the operating conditions of 8, 12, and 16 m/s mean wind speed, representing the below‐rated, rated, and above‐rated regions, respectively. The results show that the power performance and extreme loads experienced depends significantly on the operating regions. The mean power generation between the three different types of offshore wind turbines (OWTs) are closely in the whole operating range, which standard deviations differ significantly. Large standard deviations of power generation appear in the spar turbine under the below‐rated condition. Further, it was observed that the spar wind turbine generally experiences larger extreme loads due to larger platform pitch motion. In addition, the ACER method shows a better prediction for the 1, 2 and 5‐year extreme responses than the Gumbel method, which is due to the relatively poor data fitting of the Gumbel method at the upper tail. The study is believed to consolidate and close the knowledge gap in understanding wind turbine responses across the most common offshore substructure technologies and provide a basis for design and deployment of OWTs.

Integrating renewable energy sources into an unbalanced distribution network requires fast and accurate extraction of fundamental frequency positive‐ and negative‐sequence components from the unbalanced three‐phase grid voltage signals. For this purpose, various methods are already available in the literature. Out of them, delayed signal cancellation (DSC) is prevalent. Conventional DSC can separate the sequence components using a quarter‐cycle delay. Fast DSC tools can achieve the same with less than a quarter‐cycle delay. However, neither conventional nor fast DSC can handle DC offset without requiring additional delayed signals. This article addresses this issue by proposing a modified DSC to estimate the sequence components with DC offset rejection and having arbitrarily fast convergence speed, that is, low memory requirement. Two equidistant delayed samples of the measured grid voltages/currents are required to implement the proposed technique and can easily be applied in a phase‐locked loop (PLL). Comparative experimental results demonstrate the suitability of the proposed approach over other DSC methods.

To guarantee an uninterruptable power supply, a microgrid must be able to operate in both islanded and grid‐connected modes. Hence, it is required to synchronize the voltage phasor of the point of common coupling of the microgrid with the utility grid. Therefore, a distributed cooperative hierarchal control structure is proposed that seamlessly synchronized an islanded microgrid with the utility grid. A proposed method uses a leader‐follower based consensus to regulate the voltage phasor while ensuring proportionate load sharing. A synchronization controller is designed that uses an adjustable parameter to eliminate the voltage magnitude and phase mismatch, and transmit the regulated compensation signal to the leader DGs. The leader DGs transmits these signals to the follower DGs through a cyber‐channels. Moreover, a Massachusetts Institute of Technology rule and Lyapunov function based adaptive controllers are designed to update the secondary control law parameters in case of any disturbance or uncertainty. These adaptive techniques do not require prior knowledge about the microgrid topology, loads, and impedances thus enhancing the system performance. Furthermore, a small signal stability analysis is presented to facilitate the controller parameter design criteria. Finally, the robustness and effectiveness of proposed structure is verified through simulation results in different scenarios.

The harmonic currents produced by nonlinear loads and renewable energy sources (RES) like PV and wind energy have had a growing impact on industrial distribution networks in recent times. Along with harmonic distortions, lack of adequate reactive power support in a heavily loaded renewable-rich industrial network has made the operation vulnerable to fault-induced delayed voltage recovery (FIDVR). Due to poor post-fault voltage recovery , an industrial microgrid is exposed to cascading loss of distributed generators (DGs). Dynamic var sources are being used to prevent cascade tripping. However, they may result in deterioration of the network's THD scenarios. Single tuned passive filters are widely used to suppress harmonic contents. The goal of this work is to design filters so that these become capable to lower overall harmonic distortion while also minimizing the risk of blackouts caused by inadequate post-fault voltage recovery. To determine the allocation and rating of single tuned passive filters in a renewable-penetrated industrial microgrid, an optimization model is developed. To show the feasibility and efficacy of the proposed formulation, simulations are performed on a modified IEEE 43 bus system. The findings suggest that the proposed design methodology produces effective solutions to minimize the necessity of additional reactive power sources.

Dispatchable energy storage system (ESS) plays a critical role in the smart grid through energy shift and power support. However, it exhibits different operational strategies and economic benefits in different application scenarios due to its inherent degradation behaviour. This paper aims to explore the technical and economic feasibility of the flexible traction power supply system (FTPSS) integrating ESS and renewable energy sources (RES) based on the traction load characteristics. First, a battery degradation model applicable in its frequent charging and discharging operating conditions is derived. Then this paper develops an operational‐sizing co‐optimization framework for the ESS in the FTPSS, where the operation decisions are made considering the degradation costs varying with the sizes and energy throughput. To solve this large‐scale non‐linear intertemporal decision‐making problem, an iterative method with a linear programming (LP) core is proposed to achieve the trade‐off between computational efficiency and accuracy. The results of the extensive comparative cases show that the proposed approach can achieve approximately 10% higher economic benefits than the existing bi‐level sizing strategies for FTPSS.

Due to the increasing global warming, it is anticipated that the number and severity of natural disasters will increase in the coming years. In this regard, this paper proposes a planning model to improve the resilience of distribution systems against natural disasters. A mathematical model is developed to determine the optimal locations of remote‐controlled switches (RCSs), distributed generation units (DGs), and tie lines in distribution systems with complex topologies or lateral branches. Simultaneous occurrence of multiple faults is considered to better simulate the extreme events. Moreover, the concept of multi‐microgrids is used to supply the maximum possible interrupted load after the fault occurrence. To manage the system risk and different failure scenarios, the conditional value at risk (CVaR) is added to the planning model. The optimization model has been formulated as a mixed integer linear programming (MILP) problem, which can be easily solved using various commercial solvers and give the global optimal solution. Finally, to illustrate the effectiveness of the proposed planning model on improving the distribution system resilience, it is implemented on the IEEE 33‐bus system using different case studies and sensitivity analyses.

Abstract Variability and uncertainty in wind resources pose significant challenges to the expansion planning of wind farms and associated flexible resources. In addition, the spatial smoothing effect, indicating the impact of wind farm scale on aggregated wind power prediction errors, further aggravates the challenge. This paper proposes a chance‐constrained co‐expansion planning method considering the spatial smoothing effect, where the expansion of wind farm capacity, batter energy storage capacity, and power transmission lines are co‐optimized. Specifically, a decision‐dependent uncertainty (DDU) model is established capturing the dependency of wind power uncertainties on wind farm expansion decisions under the spatial smoothing effect. Unlike traditional optimization diagram where decisions are made under only decision‐independent uncertainty (DIU) with fixe properties, properties of decision‐dependent uncertain parameters would be inversely altered by decisions. To effectively tackle the coupling relation between decisions and DDU, DDU‐based chance constraints are formulated in an analytical manner, where the decisions and decision‐dependent uncertain parameters are expressed in a closed form. Eventually, with piecewise linearization of the DDU model and the polynomial approximation of cumulative distribution function of uncertain parameters, the proposed chance‐constrained optimization model with DDU is converted into a mixed‐integer second‐order cone program (MISOCP). Case studies verify the effectiveness of the proposed method.

Inertial response from grid‐followers (GFLs) is deemed to be “synthetic” due to a slow response. In contrast, grid‐forming (GFM) inertial response is deemed to be faster and therefore “true” and more useful for frequency stability. This paper explores the differences and similarities between an established example of a GFM and a GFL inertial controller by carrying out parametric sweeps at different operating conditions. The analysis aims to assist the ongoing efforts to quantify grid stabilising phenomena, particularly the recent adaptation of the British grid code to incorporate GFM converters. The optimal tuning configurations are identified, showing that some configurations of the GFL can achieve fast inertial provision on strong grids. These configurations are shown to contain the grid frequency as effectively as the GFM, despite the opposing consensus in the literature. The results also highlight the importance of voltage‐source behaviours in determining the initial evolution of grid frequency. Although a blanket inclusion of all GFL inertial configurations is not appropriate, equally, the existing blanket disqualification could limit the assets available to support GFMs (who will certainly be required to stabilise the grid in a fundamental sense) and could inhibit the rate that the net zero transition can occur.