Abstract: This paper deals with the transient stability of a grid-forming converter while embedding a current reference saturation strategy. The novelty of this work consists in investigating the impact of the current reference angle on the transient stability. In case of a balanced voltage sag, analytical formulas to estimate the critical clearing angle (CCA) and critical clearing time (CCT) while considering different values of the current reference angle are derived. It is demonstrated that the choice of this angle is constrained by the ability of the power converter to switch back to the voltage control mode. Based on that, its optimal value that enhances the transient stability and allows a switching from the saturated current control mode to the voltage control mode is calculated. Thereafter, the effectiveness of this optimal choice to guarantee the stability in case of a phase shift caused by a line re-closing event is verified. Time-domain simulations and experimental tests validate the correctness of the presented theoretical approaches.

Abstract: Grid-synchronization stability (GSS) is an emerging stability issue of grid-tied voltage source converters (VSCs), which can be provoked by severe grid voltage sags. Although a qualitative understanding of the mechanism behind the loss of synchronization has been acquired in recent studies, an analytical method for quantitative assessment of GSS of grid-tied VSCs is still missing. To bridge this gap, a dedicated Lyapunov function is analytically proposed, and its corresponding stability criterion for GSS analysis of grid-tied VSCs is rigorously constructed. Both theoretical analysis and simulation result demonstrate that the proposed method can provide a credible GSS evaluation compared to the previous EAC/EF-based method. Therefore, it can be applied for fast GSS evaluation of the grid-tied VSCs as is exemplified in this letter, as well as an analytical tool for some GSS-related issues, e.g., GSS-oriented parameter design and stabilization control.

Abstract: F.D

Abstract: This letter develops an equivalent circuit model of a grid-forming (GFM) converter with a circular current limiter and analyzes its transient stability. It is revealed that the inner control loop can be simplified as a voltage source behind an equivalent resistor. Based on the developed model, theoretical analysis and experimental tests demonstrate that the transient stability of a $P-f$ droop-controlled GFM converter with the circular current limiter can be assured whenever there exist stable equilibrium points.

Abstract: Accurate event identification is an essential part of situation awareness ability for power system operators. Therefore, this work proposes an integrated event identification algorithm for power systems. First, to obtain and filter suitable inputs for event identification, an event detection trigger based on the rate of change of frequency (RoCoF) is presented. Then, the wave arrival time difference-based triangulation method considering the anisotropy of wave propagation speed is utilized to estimate the location of the detected event. Next, the two-dimensional orthogonal locality preserving projection (2D-OLPP)-based method, which is suitable for multiple types of measured data, is employed to achieve higher effectiveness in extracting the event features compared with traditional one-dimensional projection and principle component analysis (PCA). Finally, the random undersampling boosted (RUSBoosted) trees-based classifier, which can mitigate the data sample imbalance issue, is utilized to identify the type of the detected event. The proposed approach is demonstrated using the actual measurement data of U.S. power systems from FNET/GridEye. Comparison results show that the proposed event identification algorithm can achieve better performance than existing approaches.

Abstract: With the increasing penetration of renewable energy, frequency response and its security are of significant concerns for reliable power system operations. Frequency-constrained unit commitment (FCUC) is proposed to address this challenge. Despite existing efforts in modeling frequency characteristics in unit commitment (UC), current strategies can only handle oversimplified low-order frequency response models and do not consider wide-range operating conditions. This paper presents a generic data-driven framework for FCUC under high renewable penetration. Deep neural networks (DNNs) are trained to predict the frequency response using real data or high-fidelity simulation data. Next, the DNN is reformulated as a set of mixed-integer linear constraints to be incorporated into the ordinary UC formulation. In the data generation phase, all possible power injections are considered, and a region-of-interest active sampling is proposed to include power injection samples with frequency nadirs closer to the UFLC threshold, which enhances the accuracy of frequency constraints in FCUC. The proposed FCUC is investigated on the IEEE 39-bus system. Then, a full-order dynamic model simulation using PSS/E verifies the effectiveness of FCUC in frequency-secure generator commitments.

Abstract: Despite major advancements in nonlinear programming (NLP) and convex relaxations, most system operators around the world still predominantly use some form of linear programming (LP) approximation of the AC power flow equations. This is largely due to LP technology's superior reliability and computational efficiency, especially in real-time market applications, security-constrained applications, and extensions involving integer variables, in addition to its ability to readily generate locational marginal prices (LMP) for market applications. In the aim of leveraging the advantages of LP while retaining the accuracy of NLP interior-point methods (IPMs), this paper proposes a sequential linear programming (SLP) approach consisting of a sequence of carefully constructed supporting hyperplanes and halfspaces . The algorithm is numerically demonstrated to converge on 138 test cases with up the 3375 buses to feasible high-quality solutions (i) without AC feasibility restoration (i.e., using LP solvers exclusively), (ii) in computation times generally within the same order of magnitude as those from a state-of-the-art NLP solver, and (iii) with robustness against the choice of starting point. In particular, the (relative) optimality gaps and the mean constraint violations are on average around $10^{-3}$ % and $10^{-7}$ , respectively, under a single parameter setting for all the 138 test cases. To the best of our knowledge, the proposed SLP approach is the first to use LP exclusively to reach feasible and high-quality solutions to the nonconvex AC OPF in a reliable way, which paves the way for system and market operators to keep using their LP solvers but now with the ability to accurately capture transmission losses, price reactive power (Q-LMP), and obtain more accurate LMP.

Abstract: A rapid deployment of renewable generation has led to significant reduction in the rotational system inertia and damping, thus making frequency control in power systems more challenging. This paper proposes a novel control scheme based on Model Predictive Control (MPC) for converter-interfaced generators operating in a grid-forming mode, with the goal of exploiting their fast response capabilities to provide fast frequency control service to the system. The controller manipulates converter power injections to limit the frequency nadir and rate-of-change-of-frequency after a disturbance. Both centralized and decentralized MPC approaches are considered and compared in terms of performance and practical implementation. Special attention is given to the decentralized controller by generating an explicit MPC solution to enhance computational efficiency and reduce hardware requirements. Simulation results obtained from a detailed dynamic model of the IEEE 39-bus system demonstrate the effectiveness of the proposed control schemes.

Abstract: The increasing of electric vehicles (EVs) alleviates the faced environmental problems but brings challenges to the optimal operation of transportation network (TN) and distribution network (DN). However, the most of existing research works consider EV charging station assignment and navigation services in the TN separately from charging station power scheduling services in the DN. To overcome this research gap, this paper proposes a collaborative optimal routing and scheduling (CORS) method, providing optimal route to charging stations and designing optimized charging scheduling schemes for each EV. In the order of reporting, whenever an EV reports its charging demand, a CORS optimization model is built and solved so that a specific charging scheme is designed for that EV. Then, the TN and DN status is updated to guide the subsequent EVs operating. The proposed CORS integrates the real-time state of the TN and DN, and effects positive benefits in helping EVs to avoid traffic congestion, improving the utilization level of charging facilities and enhancing charging economy. The combined distributed biased min consensus algorithm and generalized benders decomposition algorithm are adopted to solve the complex nonlinear optimization problem. Through comparing with the existing methods, better effectiveness is verified by simulation results.

Abstract: With the increasing deployment of offshore wind power plants (WPPs), the grid-forming (GFM) battery energy storage system (BESS) has recently emerged as an attractive solution to improve the dynamic performances of WPPs. However, the control interactions of the GFM-BESS and offshore WPP, under different grid strengths, tend to complicate the controller-parameter tuning. This paper presents a modeling method for analyzing control interactions of offshore WPP and GFM-BESS, which sheds clear insights into the critical controller parameters to the system dynamics. Differing from conventional methods, a frequency-domain model of GFM-BESS, obtained by taking the Laplace transform of the corresponding state-space model, is developed first. This allows the impedance model of offshore WPP, including a black-box model of long transmission cable, to be flexibly integrated. Based on the model, both closed-loop transfer-function and pole-based dynamic analyses are then performed. Electromagnetic transient simulations corroborate the effectiveness of the model and analysis.

Abstract: The forecasting of the day-ahead electricity price (DAEP) has become more of interest to decision makers in the liberalized market, as it can help optimize bidding strategies and maximize profits with the gradual market expansion. Deep learning (DL) is a promising method for its strong nonlinear approximation capabilities. However, it is challenging for traditional DL models to obtain a high forecasting precision for the DAEP, due to its internal temporal and feature-wise variabilities. To address the issue, this paper proposes a dense skip attention based DL model. In this model, to tackle the feature-wise variability, a mechanism of dense skip attention is proposed to efficiently assign learnable weights on the features for training. In terms of the temporal variability, a drop-connected structure based on the advanced residual unshared convolutional neural network (ARUCNN) and gate recurrent units (GRUs) is further proposed. In this structure, the ARUCNN is developed by embedding advanced activations to deal with the short-term dependencies and degradation while GRUs addressing the long-term ones, and they are integrated via a drop connection to reduce the overfitting. Through validating on real DAEP data in day-ahead markets of Sweden, Denmark, Norway and Finland, the results verify our proposed approach outperforms the existing methods in the deterministic and interval forecasting of DAEP.

Abstract: Accurate solar photovoltaic (PV) generation forecast is critical to the reliable and economic operation of a modern power system. In practice, due to various faulty issues in the sensor, communication, or database system, the historical and online measurement data may not be always complete, and the missing data could dramatically degrade the forecasting model's accuracy. To solve this problem, this paper proposes an integrated missing-data tolerant model for probabilistic PV power generation forecasting. Taking historical PV generations as input, this model is based on a recursive long short-term memory network (Rec-LSTM), which can provide multi-step ahead forecasting of the probability distribution of PV generation. The unobserved input data will be imputed recursively based on the model output at the previous time step. During the training process, the imputations and forecasting values are iteratively updated by the negative log-likelihood loss function. As a salient advantage, this method can deal with data missing scenarios at both offline and online stages. Numerical experiments are conducted on two one-year datasets from Australia and Singapore, respectively. Probabilistic forecasting for both large-scale and small-scale building-level PV power generation is tested at the time resolution of 15 mins. Testing results show the proposed method can achieve superior probabilistic prediction accuracy as well as strong robustness under various data missing scenarios, compared to other state-of-the-art methods.

Abstract: This paper proposes a holistic framework of data-driven distributionally robust joint chance constrained economic dispatch (ED) optimization, which seamlessly incorporates deep learning-based optimization for effective utilization of renewable energy in power systems. By leveraging a deep generative adversarial network (GAN), an f -divergence-based ambiguity set of wind power distributions is constructed as a ball centered around the probability distribution induced by a generator neural network. In particular, the GAN is well suited for capturing complicated spatial and temporal correlations of wind power. Based upon this ambiguity set, a distributionally robust joint chance constrained ED model is developed to hedge against distributional uncertainty present in multiple constraints, without assuming a perfectly known probability distribution. The proposed deep learning based ED optimization framework greatly mitigates the conservatism inflicting on distributionally robust individual chance constrained optimization. Theoretical a priori bound on the required number of synthetic wind power data generated by GAN is explicitly derived for the multi-period ED problem to guarantee a predefined risk level. The effectiveness and scalability of the proposed approach are demonstrated in the six-bus and IEEE 118-bus systems by comparing with the state-of-the-art methods.

Abstract: This letter presents a novel sequential black-start restoration model to improve the system resilience after a natural disaster in an active distribution network. Two challenges have been addressed: First, a new set of radiality constraints is designed as the network topology is changing along the restoration paths; Second, a black-start restoration model is proposed where multiple backup black-start units in the system are coordinated to find the best restoration paths. Case studies on two IEEE test systems verify the effectiveness of the proposed model.

Abstract: Grid-forming (GFM) inverters are required to operate robustly against grid faults. However, due to the limited over-current capability of inverters, current-limiting controls are usually applied to protect these semiconductor devices, which may prevent GFM inverters from a successful fault recovery. To understand this phenomenon, this study analyzes the fault recovery process of a GFM inverter with a priority-based current limiter. According to whether the GFM inverter can ensure transient stability and exit the current-limiting mode after fault clearance, three post-fault scenarios are identified, including normal operation, current limitation, and oscillations . Further, the impacts of the short-circuit ratio and control parameters on the post-fault behavior of GFM inverters are demonstrated. To illustrate the implications of these theoretical results, typical numerical examples are presented. Finally, the theoretical findings are validated through experimental tests.

Abstract: Natural disasters which include major storms, floods, tornados, and hurricanes can seriously threaten the resilience of large and coupled infrastructures such as electric power distribution and transportation systems. This paper proposes a planning (i.e., investment + operation) method for enhancing the resilience of coupled power distribution and transportation systems. The proposed investment method includes capacity expansions of power lines, roads, and charging stations, and the hardening of roads and power lines. A tri-level problem is proposed and formulated to accommodate random natural disasters. The proposed model is solved by applying the Benders decomposition and the column-and-constraint generation (C&CG) algorithms. Benders decomposition will decompose the tri-level coupled problem into a single-level master problem and a bi-level subproblem. However, the latter with binary variables is not a convex problem and cannot be converted to the maximization problem. The C&CG algorithm is applied to solve the bi-level subproblem with binary variables. The proposed resilience enhancement algorithm is tested using a coupled power distribution and transportation system with 21 electric buses and 20 roads and numerical results are analyzed to validate the effectiveness of the proposed planning method.

Abstract: This paper utilizes a fully model-free and data-driven deep reinforcement learning (DRL) framework to develop an intelligent controller that can exploit information to optimally schedule the energy hub with the aim of minimizing energy costs and emissions. By posing the energy hub scheduling problem as a multi-dimensional continuous state and action space, the proposed deep deterministic policy gradient (DDPG) method enables more cost-effective control strategies. The method can lead to a more efficient operation by considering nonlinear physical characteristics of the energy hub components like nonconvex feasible operating regions of combined heat and power (CHP) units, valve-point effects of power-only units, and fuel cell dynamic efficiency. Moreover, to provide great potential for the DDPG agent to learn an optimal policy in an efficient way, a hybrid forecasting model based on convolutional neural networks (CNNs) and bidirectional long short-term memories (BLSTMs) is developed to overcome the risk associated with PV power generation that can be highly intermittent, particularly on cloudy days. The effectiveness and applicability of the proposed scheduling framework in reducing energy costs and emissions while coping with uncertainties are demonstrated by comparing it against conventional robust optimization and stochastic programming approaches as well as state-of-the-art DRL methods in different case studies.

Abstract: A Hybrid AC/DC microgrid (MG) can integrate distributed generation sources and distributed loads on the AC and DC side of the MG by eliminating many unnecessary power conversion devices, which is more flexible and efficient. However, to achieve reliable and economic operation of a hybrid AC/DC MG is challenging due to its complex structure. In this paper, a novel distributed event-triggered hierarchical control strategy is proposed to improve the economic operation of a hybrid AC/DC MG. For the primary control, distributed local controls of AC DGs, DC DGs, and interlinking converters (ICs) are realized by adopting the droop control method. For the secondary control, the distributed economic dispatch, distributed average bus voltage discovery, and distributed proportional power-sharing algorithms are first proposed; then, control objectives of voltage and frequency restoration and economic operation of the hybrid AC/DC MG are realized based upon the developed algorithms. Furthermore, the distributed secondary control is built upon an event-triggered mechanism developed in this paper, which can reduce the communication burden. The simulation results demonstrate the effectiveness of the proposed control strategy.

Abstract: In cyber-physical power systems (CPPSs), additional control as the compensating control plays a very important part in load frequency control (LFC) systems. The additional control in the LFC system is constrained by limited communication resources and cyber-attacks, which may cause performance degradation or destabilize the system. Accordingly, this paper proposes resilient event-triggered LFC for CPPSs with an additional control loop under denial-of-service (DoS) attacks. Firstly, a resilient event-triggered communication scheme is presented to reduce the occupation of communication resources under DoS attacks in the additional control loop. Then, different from existing event-triggered LFC systems, this paper establishes a novel switched LFC system model, where the resilient event generator is integrated into an additional control loop when suffering from DoS attacks. It is the first time that the additional control loop of the LFC system simultaneously considers communication resources and cyber-security. Furthermore, we derive exponential stabilization criteria by applying the Lyapunov stability theory based on the established model. Criteria are derived to obtain the weighting matrix and controller gain simultaneously by applying the linear matrix inequality technique. Finally, a one-area and two multi-area CPPSs with the additional control loop under DoS attacks are used to testify the availability of the proposed resilient event-triggered LFC scheme.

Abstract: With the new feature of multi-energy coupling and the advancement of the energy market, Energy Internet (EI) has higher requirements for the efficiency and applicability of integrated energy response. This paper proposes an indirect multi-energy transaction (IMET) to promote multi-energy collaborative optimization in local energy market (LEM) and improve energy utilization through personalized responses from We-Energies (WEs). Firstly, an indirect customer-to-customer multi-energy transaction is modeled for local multi-energy coupling market which can satisfy privacy, preference and autonomy of users. The efficiency of energy matching can be promoted through the participation of conversion devices. In addition, multi-time scale hybrid trading mechanism is constructed with the consideration of the transmission speed of different energy sources. Meanwhile, energy transaction process is built as a Markov decision process (MDP) with deep reinforcement learning algorithm so that the system modeling error can be successfully avoided. Furthermore, a distributed training structure is utilized to obtain more experience for a wider range of scenarios. The results of numerical simulations demonstrate the performance of the proposed method.

Abstract: As an efficient tool for uncertainty quantification of renewable energy forecasting, prediction intervals (PIs) provide essential prognosis to power system operator. Merely improving the statistical quality of PIs with respect to calibration and sharpness cannot always contribute to the operational value for specific decision-making issue. In this paper, the cost-oriented prediction intervals are firstly proposed to achieve the joint improvement of forecasting quality and decision performance. In order to bridge the gap between forecasting and decision, a novel cost-oriented machine learning (COML) framework is established, which unifies nonparametric renewable power PI construction and decision-making. Formulated as a bilevel programming model, the COML minimizes the operational costs of decision-making process by adaptively adjusting the quantile proportion pair of PIs resulting from extreme learning machine based quantile regression. The hierarchical optimization model of the COML is equivalently simplified as a single level nonlinear programming problem. Then an enhanced branch-and-contract algorithm with innovative bounds contraction strategy is devised to efficiently capture the optimum of the single level problem with bilinear nonconvexity. Numerical experiments based on actual wind farm data simulate the online forecasting and decision process for wind power offering. Comprehensive comparisons verify the substantial superiority of the proposed COML methodology in terms of forecasting quality, operational value, as well as computational efficiency for practical application.

Abstract: Electric distribution grid operations typically rely on both centralized optimization and local non-optimal control techniques. As an alternative, distribution system operational practices can consider distributed optimization techniques that leverage communications among various neighboring agents to achieve optimal operation. With the rapidly increasing integration of distributed energy resources (DERs), distributed optimization algorithms are growing in importance due to their potential advantages in scalability, flexibility, privacy, and robustness relative to centralized optimization. Implementation of distributed optimization offers multiple challenges and also opportunities. This paper provides a comprehensive review of the recent advancements in distributed optimization for electric distribution systems and classifications using key attributes. Problem formulations and distributed optimization algorithms are provided for example use cases, including volt/var control, market clearing process, loss minimization, and conservation voltage reduction. Finally, this paper also presents future research needs for the applicability of distributed optimization algorithms in the distribution system.

Abstract: Electricity theft behaviors have caused great harm to the economic benefits of power companies and the secure operation of power systems, thus electricity theft detection is paid much attention in the actual power supply management. In this work, a two-step electricity theft detection strategy is proposed to identify electricity theft users and predict potentially stolen electricity (PSE) for maximizing economic return. In the first step, a neural network model called convolutional autoencoder (CAE) is proposed for electricity theft identification, and the convolutional layer is adopted in CAE to extract and identify the abnormalities of electricity theft users against the uniformity and periodicity of normal power consumption features. In the second step, the PSE of each identified electricity theft user is predicted by the improved regression algorithm named Tr-XGBoost, which combines the extreme gradient boosting (XGBoost) algorithm and transfer adaptive boosting (TrAdaBoost) training strategy. The propsoed Tr-XGBoost could learn the relationship between the extracted electricity features and the PSE of each electricity theft user, and then the predicted PSE can be used to determine the list of electricity theft users to be inspected for maximizing economic return. Case studies on both the IEEE 33-bus test system and a low-voltage distribution system of a province in China show that the proposed two-step electricity theft detection strategy can improve the accuracy of electricity theft identification, and obtain a larger economic return because of a more accurate result of PSE prediction than other state-of-the-art algorithms.

Abstract: It is well known that grid-feeding converters that synchronize to the grid through a Phase-Locked Loop (PLL) can become unstable after a fault. An often-neglected element that plays an important role in the converter synchronization stability is the PLL frequency limiter. While it slows down the phase change during the fault, the frequency limiter also constrains the error of the PLL input, thus leading to a longer settling time. This letter investigates the mechanism of the converter synchronization stability caused by the frequency limiter and provides a taxonomy to evaluate its impact on the overall system dynamic response.

Abstract: This paper proposes a novel variable-inertia emulation control (VIEC) scheme that enables voltage-source-converter based high-voltage DC (VSC-HVDC) transmission systems to flexibly support AC grid frequency stability like synchronous generators. The VIEC scheme allows us to extract the energy from the augmented DC capacitance for inertia emulation by controlling the DC voltage without affecting the stability of the AC system connected on the remote side. In particular, with the proposed VIEC scheme, the inertia time constant can be flexibly emulated and its value can be automatically adjusted according to the rate of change of grid frequency as well as grid frequency deviations. This leads to more effective inertial support across different timescales. Modal analysis is carried out to investigate the impacts of inertia and DC capacitance, and to obtain the optimal control parameters. The effectiveness and advantages of the proposed VIEC scheme are demonstrated on an IEEE benchmark system in the presence of faults and load changes.

Abstract: The battery energy storage system (BESS) has been envisaged as an effective solution for renewable energy accommodation in power systems. However, the residual capacity and maximum power of large-scale BESS are highly affected by thermally-induced incidents such as battery degradation and Thermal Runaway (TR) propagation. In the prior-art studies, the impacts of thermally-induced incidents on the BESS service performance have not been well modeled, resulting in relatively over-optimistic reliability estimation of power systems. In this paper, the reliability of large-scale grid-connected BESSs as well as its impacts on the overall reliability of power systems are investigated considering the battery degradation and TR propagation. To quantify the time-varying performance of the BESS, a multi-state model is constructed. The proposed model describes the aging process of batteries inside the BESS, incorporating the combined effects of sequential TR and the performance degradation of the surrounding batteries due to heat absorption. Based on the Monte Carlo method, scenarios that reflect the uncertainties of the intermittent wind generation and fluctuating loads are simulated. An optimal scheduling model is deployed, and a solution algorithm is proposed to calculate the scheduling results of the BESS in the real-time performance range subject to its thermal conditions. Case studies are conducted to validate the effectiveness of the proposed model and technique.

Abstract: The increasing penetration of electric vehicles (EVs) results in growing interdependency between power and transportation networks. This paper proposes a hybrid economic-emission dispatch model to coordinate the operation of both networks toward social optima. Specifically, carbon emission from gasoline vehicles (GVs) is quantified by the macroscopic emission model, while the emission from EVs is indirectly characterized by that emitted from fossil power plants. To enhance regulation efficiency and improve social welfare, a differentiated pricing scheme is proposed to independently regulate GV traffic flow, EV traffic flow, and EV charging flow. Logarithmic transformation, combined with an accuracy-aware adaptive piecewise linearization (AAPWL) approximation method, is developed to reformulate the original nonlinear model into a tractable mixed-integer quadratic constrained programming. Numerical results from two test systems demonstrate the relationship between economic dispatch and emission dispatch. Meanwhile, the effectiveness and superiority of the proposed pricing scheme and the AAPWL method are validated.

Abstract: The dawn of quantum computing brings on a revolution in the way combinatorially complex power system problems such as Unit Commitment are solved. The Unit Commitment problem complexity is expected to increase in the future because of the trend toward the increase of penetration of intermittent renewables. Even though quantum computing has proven effective for solving a host of problems, its applications for power systems’ problems have been rather limited. In this paper, a quantum unit commitment is innovatively formulated and the quantum version of the decomposition and coordination alternate direction method of multipliers (ADMM) is established. The above is achieved by devising quantum algorithms and by exploiting the superposition and entanglement of quantum bits (qubits) for solving subproblems, which are then coordinated through ADMM to obtain feasible solutions. The main contributions of this paper include: 1) the innovative development of a quantum model for Unit Commitment; 2) development of decomposition and coordination-supported framework which paves the way for the utilization of limited quantum resources to potentially solve the large-scale discrete optimization problems; 3) devising the novel quantum distributed unit commitment (QDUC) to solve the problem in a larger scale than currently available quantum computers are capable of solving. The QDUC results are compared with those from its classical counterpart, which validate the efficacy of quantum computing.

Abstract: As the penetration of intermittent renewable energy increases in bulk power systems, flexible generation resources, such as quick-start gas units, become important tools for system operators to address the power imbalance problem. To better capture their flexibility, we proposed a two-stage distributionally robust unit commitment framework with both regular and flexible generation resources, in which the unit commitment decisions for flexible generation resources can be adjusted in the second stage to accommodate the renewable energy intermittency. In order to tackle this challenging two-stage distributionally robust mixed-binary model, to which traditional separation algorithms won’t apply, we designed a revised integer L-shaped algorithm with lift-and-project cutting plane techniques. In comparison to the traditional distributionally robust unit commitment, the proposed approach can reduce the system cost through an improved flexible resource quantification in the modeling.