Abstract:The component aging has become a significant concern worldwide, and the frequent failures pose a serious threat to the reliability of modern power systems. In light of this issue, this paper presents a power system reliability evaluation method based on sequential Monte Carlo simulation (SMCS) to quantify system reliability considering multiple failure modes of components. First, a three-state component reliability model is established to explicitly describe the state transition process of the component subject to both aging failure and random failure modes. In this model, the impact of each failure mode is decoupled and characterized as the combination of two state duration variables, which are separately modeled using specific probability distributions. Subsequently, SMCS is used to integrate the three-state component reliability model for state transition sequence generation and system reliability evaluation. Therefore, various reliability metrics, including the probability of load curtailment (PLC), expected frequency of load curtailment (EFLC), and expected energy not supplied (EENS), can be estimated. To ensure the applicability of the proposed method, Hash table grouping and the maximum feasible load level judgment techniques are jointly adopted to enhance its computational performance. Case studies are conducted on different aging scenarios to illustrate and validate the effectiveness and practicality of the proposed method.

Abstract:Although power grids have become safer with increased situational awareness, major extreme events still pose reliability and resilience challenges, primarily at the distribution level, due to increased vulnerabilities and limited recovery resources. Information and communication technologies (ICTs) have introduced new vulnerabilities that have been widely investigated in previous studies. These vulnerabilities include remote device failures, communication channel disturbances, and cyberattacks. However, only few studies have explored the opportunity offered by communications to improve the resilience of power grids and eliminate the notion that power-telecom interdependencies always pose a threat. This paper proposes a communication-aware restoration approach of smart distribution grids, which leverages power-telecom interdependencies to determine the optimal restoration strategies. The states of grid-energized telecom points are tracked to provide the best restoration actions, which are enabled through the resilience resources of repair, manual switching, remote reconfiguration, and distributed generators. As the telecom network coordinates the allocation of these resilience resources based on their coupling tendencies, different telecom architectures have been introduced to investigate the contribution of private and public ICTs to grid management and restoration operations. System restoration uses the configuration that follows a remote fast response as the input to formulate the problem as mixed-integer linear programming. Results from numerical simulations reveal an enhanced restoration process derived from telecom-aware recovery and the co-optimization of resilience resources. The existing disparity between overhead and underground power line configurations is also quantified.

Abstract:With the progressive exhaustion of fossil energy and growing concerns about climate change, it has been observed that distributed energy resources such as photovoltaic (PV) systems and electric vehicles (EVs) are being increasingly integrated into distribution systems. This underscores the increasing imperative for a thorough analysis to evaluate reliability from the perspectives of distribution systems and EV charging services, taking into account the stochastic nature of PV and EV load demands. This paper presents an approach for the reliability assessment of distribution systems that incorporate PV and EVs considering reliability models for both PV systems and EV battery systems. It also defines new indices to investigate the adequacy and customer-side reliability for EV charging services. The developed methodology utilizes a Monte Carlo simulation-based approach and is showcased using the modified Roy Billinton Test System (RBTS) Bus 4 distribution system. The results illustrate that reliability indices for EV charging services, such as percentage of charging energy not supplied (PCENS), average EV interruption frequency index (AEVIFI) and average EV interruption duration index (AEVIDI), are improved under the proposed approach.

Abstract:With the rapid expansion of urban road networks and the increasing ownership of vehicles in many countries and regions, the greenhouse gas and pollutant emissions from road travels have become a global concern. The introduction of electric vehicles (EVs) with dynamic charging into road systems, which is defined as electric road systems (ERSs), has been widely recognized as a viable solution to address this problem. This paper presents a comprehensive study on the reliability of power supply systems integrated with renewables for ERS (ERS-PSRs), which interface with both road traffic and power networks. First, a brief introduction to the charging modes of EVs demonstrates the coupling of the two networks. A simplified traffic model is then built, based on which the reliability indices of the system considering the influence of the dynamic charging and static charging modes of EVs are proposed. Further, a simplified trip chain based Monte Carlo reliability assessment method of ERS-PSRs is proposed. Case studies based on the IEEE Roy Billinton Test System (RBTS) show that the dynamic charging mode of EVs can not only effectively balance the supply and demand of the power grid at different time (shaving peaks and filling valleys), but also significantly improve the reliability of ERS-PSRs. The case studies also examine the effects of the ratio of EVs with dynamic charging, wind generation penetration rate, additional wind power, and battery energy storage systems (BESSs) on the reliability of ERS-PSRs.

Abstract:In modern microgrids (MGs) with high penetration of distributed energy resources (DERs), system reconfiguration occurs more frequently and becomes a significant issue. Fixed-parameter controllers may not handle these tasks effectively, as they lack the ability to adapt to the dynamic conditions in such environments. This paper proposes an intelligence-driven grid-forming (GFM) converter control method for islanding MGs using a robustness-guided neural network (RNN). To enhance the adaptability of the proposed method, traditional proportional-integral controllers in the GFM primary control loops are entirely replaced by the RNN. The RNN is trained by a robustness-guided strategy to replicate their robust behaviors. All the training stages are purely data-driven methods, which means that no system parameters are required for the controller design. Consequently, the proposed method is an intelligence-driven model-less GFM converter control. Compared with traditional methods, the simulation results in all testing scenarios show the clear benefits of the proposed method. The proposed method reduces overshoots by more than 71.24%, which keeps all damping ratios within the stable region and provides faster stabilization. In comparison to traditional methods, at the highest probability, the proposed method improves damping by over 14.7% and reduces the rates of change of frequency and voltage by over 59.97%. Additionally, the proposed method effectively suppresses the interactions between state variables caused by inverter-based resources, with frequencies ranging from 1.0 Hz to 1.422 Hz. Consequently, these frequencies contribute less than 19.79% to the observed transient responses.

Abstract:In extreme events, microgrid (MG) formation has drawn attention due to its potential to assist in load restoration in the distribution network by utilizing the distributed generations (DGs). However, most of the state-of-the-art studies pay attention to the steady constraints without considering the transient performance during MG formation process. Power fluctuations caused by line switch operations can lead to frequency overruns in low-inertia DG-based systems, thus tripping protective relays. This paper proposes an MG formation method for load restoration in the distribution network with dynamic frequency constraints during the load restoration process. Firstly, considering the frequency constraints, a frequency nadir formula is derived based on the aggregated model. The proposed MG formation method offers two solutions to ensure the frequency safety. One solution is to incorporate the dynamic frequency constraints into the MG formation optimization model to satisfy the frequency requirements if the load restoration amount is preferred. Another alternative solution is to introduce an inertia-adjustable control strategy using virtual synchronous generators (VSGs), which is aimed to improve the frequency nadir during MG formation process. This solution is implemented without changing the MG formation result that is subject to only steady constraints when the load restoration speed is privileged. Theoretical validity is verified through the simulation results. Case study results prove the effectiveness of proposed solutions under various demands in the aspect of frequency improvement.

Abstract:As power systems scale up and uncertainties deepen, traditional centralized optimization approaches impose significant computation burdens on large-scale optimization problems, introducing new challenges for power system scheduling. To address these challenges, this study formulates a distributionally robust optimization (DRO) scheduling model that considers source-load uncertainty and is solved using a novel distributed approach that considers the distribution of tie-line endpoints. The proposed model includes a constraint related to the transmission interface, which consists of several tie-lines between two subsystems and is specifically designed to ensure technical operation security. In addition, we find that tie-line endpoints enhance the speed of distributed computation, leading to the development of a power system partitioning approach that considers the distribution of these endpoints. Further, this study proposes a distributed approach that employs an integrated algorithm of column-and-constraint generation (C&CG) and sub-gradient descent (IACS) to address the proposed model across multiple subsystems. A case study of two IEEE test systems and a practical provincial power system demonstrates that the proposed model effectively ensures system security. Finally, the scalability and effectiveness of the distributed approach in accelerating problem-solving are confirmed.

Abstract:Wide-area damping controllers (WADCs) help in damping poorly damped inter-area oscillations (IAOs) using wide-area measurements. However, the vulnerability of the communication network makes the WADC susceptible to malicious dynamic attacks. Existing cyber-resilient WADC solutions rely on accurate power system models or extensive simulation data for training the machine learning (ML) model, which are difficult to obtain for large-scale power system. This paper proposes a novel non-intrusive hybrid two-stage detection framework that mitigates these limitations by eliminating the need for real-time access to large system data or attack samples for training the ML model. In the first stage, an autoencoder is deployed at the actuator location to detect dynamic attacks with sharp gradient variations, e.g., triangular, saw-tooth, pulse, ramp, and random attack signals. In the second stage, an unscented Kalman filter with unknown input estimation at the control center identifies smoothly varying dynamic attacks by estimating the control signal received by the actuator using synchrophasor measurements. A modified cosine similarity (MCS) metric is proposed to compare and quantify the similarity between the estimated control signal and the control signal sent by the WADC placed at the control center to detect any dynamic attacks. The MCS is designed to differentiate between events and dynamic attacks. The performance of the proposed framework has been validated on a hardware-in-the-loop (HIL) cyber-physical testbed built by using the OPAL-RT simulator and industry-grade hardware.

Abstract:Unregulated naked selling of virtual power plants (VPPs) in day-ahead markets poses inherent risks to grid security and market fairness. This paper proposes a joint electricity-reserve trading model for VPPs as a strategic measure to mitigate the negative impacts of naked selling. This model systematically evaluates the economic advantages and risks of naked selling, utilizing metrics such as user comfort and conditional value at risk (CVaR). Furthermore, a sophisticated combination of a data-driven level-set fuzzy approach and advanced algorithms, including support vector quantile regression (SVQR) and kernel density estimation (KDE), is employed to quantify the uncertainties related to prices and reserve activation precisely. The results of case studies demonstrate that integrating default penalties within the proposed trading model diminishes the overall revenue of VPPs engaging in naked selling, thereby serving as a robust decision for mitigating the adverse effects of the naked selling of VPPs.

Abstract:This paper proposes an advanced voltage source converter (VSC)-driven model for soft open points (SOPs) and battery energy storage systems (BESSs) to actively balance three-phase distribution networks. The proposed model addresses the phase imbalance caused by the increasing integration of renewable energy and distributed generation. Unlike traditional models, which mainly focus on AC capacity constraints, the proposed model explores the complexities of the DC-link. This allows for a thorough examination of the interactions between active and reactive power, as well as the voltage levels on both the AC and DC sides of VSCs. The relationship between pulse width modulation (PWM) control configurations and VSC power outputs is discussed, enhancing control on both sides of the converters. This improvement also facilitates better cross-phase power transfer through SOPs and enhances the overall balance among the three phases. In addition, the proposed model incorporates the cooperative functionality of VSC-driven BESSs to sustain the phase balance. To further optimize the load distribution, phase-specific dispatching (PSD) is introduced, allowing for the flexible allocation of individual loads to distinct phases. Together, these coordinated technical solutions constitute a systematic optimization strategy. An algorithm is developed to harmonize the VSC-driven modeling for SOPs and BESSs with PSD, thereby improving the computational efficiency in managing power flow and phase balance. The results show that the proposed model significantly reduces losses and enhances the phase balance.

Abstract:Electric power grids are evolving into complex cyber-physical power systems (CPPSs) that integrate advanced information and communication technologies (ICTs) but face increasing cyberspace threats and attacks. This study considers CPPS cyberspace security under distributed denial of service (DDoS) attacks and proposes a nonzero-sum game-theoretical model with incomplete information for appropriate allocation of defense resources based on the availability of limited resources. Task time delay is applied to quantify the expected utility as CPPSs have high time requirements and incur massive damage DDoS attacks. Different resource allocation strategies are adopted by attackers and defenders under the three cases of attack-free, failed attack, and successful attack, which lead to a corresponding consumption of resources. A multidimensional node value analysis is designed to introduce physical and cybersecurity indices. Simulation experiments and numerical results demonstrate the effectiveness of the proposed model for the appropriate allocation of defense resources in CPPSs under limited resource availability.

Abstract:This paper proposes a new method for service restoration of distribution network with the support of transportable power sources (TPSs) and repair crews (RCs). Firstly, a coupling model of distribution networks and vehicle routing of TPSs and RCs is proposed, where the TPSs serve as emergency power supply sources, and the RCs are used to repair the faulted lines. Considering the uncertainty of traffic congestion, the probability distribution of the travel time spent on each road is derived based on the Nesterov user equilibrium model, and a two-stage stochastic program is formulated to determine the optimal routings of TPSs and RCs. To efficiently solve the proposed stochastic mixed-integer linear program (MILP), a two-phase scenario reduction method is then developed to scale down the problem size, and an adaptive progressive hedging algorithm is used for an efficient solution. The effectiveness of the proposed methods and algorithms has been illustrated in a modified IEEE 33-bus system.

Abstract:Continuous power supply of urban power networks (UPNs) is quite essential for the public security of a city because the UPN acts as the basis for other infrastructure networks. In recent years, UPN is threatened by extreme weather events. An accurate modeling of load loss risk under extreme weather is quite essential for the preventive action of UPN. Considering the forecast intensity of a typhoon disaster, this paper proposes analytical modeling of disaster-induced load loss for preventive allocation of mobile power sources (MPSs) in UPNs. First, based on the topological structure and fragility model of overhead lines and substations, we establish an analytical load loss model of multi-voltage-level UPN to quantify the spatial distribution of disaster-induced load loss at the substation level. Second, according to the projected load loss distribution, a preventive allocation method of MPS is proposed, which makes the best use of MPS and dispatches the limited power supply to most vulnerable areas in the UPN. Finally, the proposed method is validated by the case study of a practical UPN in China.

Abstract:Improving the restoration efficiency of a distribution system is essential to enhance the ability of power systems to deal with extreme events. The distribution system restoration (DSR) depends on the interaction among the electric network (EN), cyber network (CN), and traffic network (TN). However, the coordination of these three networks and co-dispatching of multiple recovery resources have been mostly neglected. This paper proposes a novel DSR framework, which is formulated as a mixed-integer linear programming (MILP) problem. The failures in cyber lines result in cyber blind areas, which restrict the normal operation of remote-controlled switches. To accelerate the recovery process, multiple recovery resources are utilized including electric maintenance crews (EMCs), cyber maintenance crews (CMCs), and emergency communication vehicles (ECVs). Specifically, CMCs and ECVs restore the cyber function of switches in cooperation, and EMCs repair damaged electric lines. The travel time of these three dispatchable resources is determined by TN. The effectiveness and superiority of the proposed framework are verified on the modified IEEE 33-node and 123-node test systems.

Abstract:The popularity of electric vehicles (EVs) has sparked a greater awareness of carbon emissions and climate impact. Urban mobility expansion and EV adoption have led to an increased infrastructure for electric vehicle charging stations (EVCSs), impacting radial distribution networks (RDNs). To reduce the impact of voltage drop, the increased power loss (PL), lower system interruption costs, and proper allocation and positioning of the EVCSs and capacitors are necessary. This paper focuses on the allocation of EVCS and capacitor installations in RDN by maximizing net present value (NPV), considering the reduction in energy losses and interruption costs. As a part of the analysis considering reliability, several compensation coefficients are used to evaluate failure rates and pinpoint those that will improve NPV. To locate the best nodes for EVCSs and capacitors, the hybrid of grey wolf optimization (GWO) and particle swarm optimization (PSO) (HGWO_PSO) and the hybrid of PSO and Cuckoo search (CS) (HPSO_CS) algorithms are proposed, forming a combination of GWO, PSO, and CS optimizations. The impact of EVCSs on NPV is also investigated in this paper. The effectiveness of the proposed optimization algorithms is validated on an IEEE 33-bus RDN.

Abstract:This paper presents a novel method for accurately estimating the cumulative capacity credit (CCC) of renewable energy (RE) projects. Leveraging data from the main interconnected system (MIS) of Oman for 2028, where a substantial increase in RE generation is anticipated, our novel method is introduced alongside the traditional effective load carrying capability (ELCC) method. To ensure its robustness, we compare CCC results with ELCC calculations using two distinct standards of reliability criteria: loss of load hours (LOLH) at 24 hour/year and 2.4 hour/year. Our method consistently gives accurate results, emphasizing its exceptional accuracy, efficiency, and simplicity. A notable feature of our method is its independence from loss of load probability (LOLP) calculations and the iterative procedures associated with analytic-based reliability methods. Instead, it relies solely on readily available data such as annual hourly load profiles and hourly generation data from integrated RE plants. This innovation is of particular significance to prospective independent power producers (IPPs) in the RE sector, offering them a valuable tool for estimating capacity credits without the need for sensitive generating unit forced outage rate data, often restricted by privacy concerns.

Abstract:In coastal regions of China, offshore wind farm expansion has spurred extensive research to reduce operational costs in power systems with high penetration of wind power. However, frequent extreme weather conditions such as typhoons pose substantial challenges to system stability and security. Previous research has intensively examined the steady-state operations arising from typhoon-induced faults, with a limited emphasis on the transient frequency dynamics inherent to such faults. To address this challenge, this paper proposes a frequency-constrained unit commitment model that can promote energy utilization and improve resilience. The proposed model analyzes uncertainties stemming from transmission line failures and offshore wind generation through typhoon simulations. Two types of power disturbances resulting from typhoon-induced wind farm cutoff and grid islanding events are revealed. In addition, new frequency constraints are defined considering the changes in the topology of the power system. Further, the complex frequency nadir constraints are incorporated into a two-stage stochastic unit commitment model using the piece-wise linearization. Finally, the proposed model is verified by numerical experiments, and the results demonstrate that the proposed model can effectively enhance system resilience under typhoons and improve frequency dynamic characteristics following fault disturbances.

Abstract:This study introduces a mixed-integer second-order conic programming (MISOCP) model for the effective management of load and energy in active distribution networks featuring prosumers. A multi-objective function is devised to concurrently minimize various costs, including prosumer electricity costs, network energy loss costs, load shedding costs, and costs associated with renewable energy resource outages. The methodology involves determining optimal active power adjustment points for photovoltaic (PV) resources and integrated energy storage systems (ESSs) within network buildings, in conjunction with a demand-side management program. To achieve the optimal solution for the proposed MISOCP model, a robust hybrid algorithm is presented, integrating the modified particle swarm optimization (MPSO) algorithm and the genetic algorithm (GA). This algorithm demonstrates a heightened capability for efficiently converging on challenging problems. The proposed model is evaluated using a distribution network comprising 33 buses, a practical distribution network, and a distribution network comprising 118 buses. Through comprehensive simulations in diverse cases, the results highlight the innovative contributions of the model. Specifically, it achieves a noteworthy reduction of 26.2% in energy losses and a 17.72% decrease in voltage deviation. Additionally, the model proves effective in augmenting prosumer electricity sales, showcasing its potential to improve the overall efficiency and sustainability of active distribution networks.