Power System Restoration Research Articles

Towards Forming Optimal Communication Network for Effective Power System Restoration

Towards Forming Optimal Communication Network for Effective Power System Restoration

Hybrid Optimal Power System Recovery and Load Pick-Up With DGAllocation In Electrical Power System

The increasing demand for electricity, the expansion of complex power networks, and the operation of power systems near their capacity have heightened the risk of power outages. Consequently, control room operators must develop comprehensive restoration plans to minimize recovery time. Key steps in power system restoration include starting non-black-start units with black-start units and initiating load pick-up. This process requires identifying the shortest route between nodes to expedite repairs. This research presents an expert plan employing the Modified Cuckoo Search algorithm (MCSA) to determine optimal pathways. Additionally, it explores the optimal allocation of distributed generators (DG) to enhance the system's voltage profile and reduce power losses using particle swarm optimization (PSO). The results demonstrate that DG significantly reduces losses during recovery. The study prioritizes the restoration of DGs with strong black start capability, considering network and DG constraints. The effectiveness of the proposed restoration approach is validated through MATLAB simulations on theIEEE 39-bus system.

Generator Start-Up Strategy for Power System Restoration Considering Interdependency With Natural Gas Transmission Network

Generator Start-Up Strategy for Power System Restoration Considering Interdependency With Natural Gas Transmission Network

A MILP-based power system parallel restoration model with the support of mobile energy storage systems

During bulk power system restoration, black-start (BS) units are used to crank non-BS units in such a way that the over system generation capability is maximized. However, traditional power system restoration methods rely on stationary BS units, whose number is limited due to high investment and geographic conditions, impeding a fast system restoration. To enhance restoration efficiency, this paper proposes an integrated power system parallel restoration method considering the support of mobile energy storage systems (MESSs), which can provide cranking power to non-BS units and transform them into MESS-assisted BS units. MESSs are routed and scheduled via transportation network, along with restoration planning in power network by integrating them into a mixed integrated linear programming model. Specifically, to describe the interactive behavior between MESSs and the power system, a mobility model for MESSs is proposed, which indicates the travel path, travel time and connection nodes. Then, an integrated optimization model involving BS unit location, zone partitioning, network reconfiguration and generator start-up is formulated. The proposed method was simulated in the modified IEEE 30-bus transmission system coupled with 11-node transportation system. The results show that, in contrast with a decoupled restoration method, the proposed method increases system generation capability by 6.3%.

Disaster recovery strategies for cyber-physical systems considering the Interdependence

Improving the resilience of Cyber-Physical Systems (CPS) against extreme events is vital. Current research, overly focused on power system restoration, often misses the complex interplay between the physical and cyber aspects of these systems, leading to potential vulnerabilities. This paper first proposes a framework for CPS considering interdependence. The framework accounts for cyber-physical collaborative recovery and the connectivity between cyber nodes and control center in the cyber system. The model is built considering factors such as repair crew, repair time, and network reconstruction, aimed at rapidly restoring the structural and functional resilience of CPS in the shortest possible time. Next, it integrates complex network theory to propose different repair strategies based on structural and functional indicators. Finally, the repair efficiency of the proposed strategies is comprehensively evaluated using structural and functional metrics. Simulation results show that the repair efficiency of CPS is superior to traditional power grids, and moderate reduction in the repair time of cyber nodes and a decrease in network size both contribute to enhancing repair efficiency.

Situation-Aware Load Restoration Considering Uncertainty and Correlation

In order to minimize power supply interruption to customers, power system restoration needs to be performed rapidly after a blackout. With the integration of intermittent renewable energy and flexible loads, power system load patterns are becoming more uncertain. The goal of this paper is to tackle the challenging task of developing a secure yet speedy load restoration planning strategy considering load uncertainty. The key is to leverage the real-time measurements of the restored load blocks to refine the statistical knowledge about the unrestored load blocks derived from historical data, such that the load restoration plans can be less conservative without harming security. Specifically, as the restoration process rolls forward and more loads are restored, we use a Kalman filter to update the probability distribution of the unrestored loads using the newly available measurements from the restored loads. With the updated distribution, a rolling-horizon load restoration problem for load-pickup decision making is formulated with given specified confidence levels under frequency, voltage, and power flow constraints. The effectiveness of the proposed method is evaluated through case studies on the IEEE 39-bus test system and the NPCC 140-bus system. Results demonstrate that the proposed method can restore loads more quickly and securely than conventional methods under load uncertainty.

A Review of the Literature on Multi Agent Based Power Distribution System Restoration

One of the most popular research areas is multi agent systems (MAS), which consists of multiple agents interacting with one another to accomplish a shared goal. MAS has been created with a variety of power system applications in mind. One major use for such is power system restoration. A number of architectures for fault isolation, problem identification, and power system restoration are presented by researchers. The available architectures for power distribution restoration and upcoming developments in MAS-based power system restoration are thoroughly reviewed in this research.

Agglomerative Hierarchical Clustering Methodology to Restore Power System considering Reactive Power Balance and Stability Factor Analysis

Despite there are significant advancements in modern power systems, blackouts remain a potential risk, necessitating efficient restoration strategies. This paper introduces an innovative concept for power system restoration, focusing on balancing active and reactive power while ensuring voltage stability. For instance, this paper employs an agglomerative clustering technique, which partitions the power system into segments with balanced reactive power, facilitating swift restoration postblackout. Central to this methodology is the use of the line stability factor, which assesses the voltage stability of individual lines, identifying the system’s stronger and weaker sections based on voltage stability levels. This paper demonstrates the effectiveness of the proposed methodology through case study analysis, comparing voltage stability levels across agglomerative clusters and their geographical locations. The power system is divided into two stable partitions, considering the number of black-start generators, available reactive power, and voltage stability levels. This partitioning reveals that the clusters formed by the agglomerative method are inherently stable, suggesting enhanced system stability, dependability, and availability during the restoration phase following a blackout. In addition, this paper discusses the potential causes of blackouts, offering insights into their prevention, and finishes with a novel clustering methodology for power systems, considering reactive power and voltage stability. This method facilitates the parallel restoration of the system’s independent partitions, significantly reducing restoration time; it addresses critical challenges and outcomes, underscoring the methodology’s potential to revolutionize blackout recovery processes in modern power systems.

Evolutionary algorithm application to medium voltage power system restoration

The voltage quality is one of the most important issue in power system. The distribution network has to keep up with the growing consumption and increasing reliability requirements. In many cases supply disturbance is caused by faults on medium voltage distribution networks. Throughout system restoration many switching operation are needed, figuring out the optimal ones is a demanding task. In this paper a genetic algorithm based method is proposed, which can reduce the outage time in those areas where supply can be restored while the repairing is carried out on the failed section

Fluent Restoration Modelling Applied to a Real Power System Using Colored Petri Nets

In this article the Colored Petri Nets (CPN) and the Critical Path Method along with the Program Evaluation Review Technique (CPM/PERT) are described and their characteristics compared for application on the real-time fluent restoration of power systems. A CPN based method is proposed and has been found more suitable since it is able to represent the temporal sequencing and the logical dependency between restoration activities. The restoration planning of electrical power systems are based on a worksheet drawn up by experts, which contains a list of procedures for restoration. The restoration process has been improved and a real case study is presented using the CPN modeling applied with success to an operative fluent restoration post-outage in the Brazilian interconnected system

A novel reinforcement learning method for the plan of generator start-up after blackout

With the expansion of the power system and the impact of natural disasters, blackouts are almost inevitable. A scientific restoration plan can effectively shorten the economic and social losses caused by blackouts. As the first step of power system restoration, the generator start-up process impacts the success rate and time consumption. Considering the diversity of constraints in the generator start-up process and the complexity of the topology, this paper proposes a planning method for the generator start-up process based on reinforcement learning. Q-learning is a model-free reinforcement learning algorithm that can cope well with complex environments and diverse constraints. First, the generator start-up problem is modeled as a Markov Decision Process, and the restoration operation and constraints are considered. Q-learning uses an artificial intelligence agent to plan the generator start-up process and guide operators. Second, different from the restoration methods of sequential restarting the generators, this paper presents a novel strategy for restoration by restoring the path of the subsequent generator while the current generator acquires cranking power. Then, the action selection mechanism is optimized by using the Upper Confidence Bound algorithm to address the shortcoming of low exploration effectiveness in the conventional Q-learning. Finally, the simulation results provide evidence of the excellent performance of the proposed method in terms of optimality and convergence.

An ADMM-based power system partitioned black-start and parallel restoration method considering high-penetrated renewable energy

The large-scale blackouts after natural disasters have attracted much more concern these days. When a blackout occurs in a power transmission system, the whole system will be decomposed into several sub-systems and take much more time to recover. As the capacity of renewable energy continues to increase, the difficulties of the black start and restoration for a power transmission system also keep intensifying. To this end, this paper proposes a power transmission system partitioned black-start and restoration method while applying renewable energy sources to support and improve the recovery process. We first establish the power system black-start and restoration model and linearize the power flow constraints and unit generation constraints. Considering the power system can be decomposed into several sub-systems with high-capacity renewable energy sources, we propose a system partitioning method and a two-level parallel restoration model. The model realizes the independent restoration of each sub-system and is solved by the alternating direction multipliers method. Case studies on a modified New England 39-bus power system and a practical power system verify that the proposed method can offer high feasibility and effectiveness for power transmission systems with high-penetrated renewable energy sources when facing a large-scale blackout.

A self-healing restoration of power grid based on two-stage adaptive decision-making strategy to enhance grid resilience

Large-scale power outages can affect the economy of the country and jeopardize the lives of millions of people, so there is a need for a fast, reliable, and self-healing tool to reduce these losses. Hence, the power system restoration performance can be improved by considering both restoring and operating performance, however, current restoration strategies only consider the restoring performance but ignore the operational performance which can enhance the power system resiliency. Given this background, challenging tasks such as optimal restoration planning (ORP) and optimal real-time operation (ORO) are planned to be tackled by developing an adaptive decision-making strategy (ADMS). In order to reduce the effects of emergency power outages on power systems, this study presents the ADMS, a two-stage self-healing restoration approach. Moreover, a novel metric of capacity accessibility (NMCA) is developed to determine the capacity adequacy status during severe events. The proposed ADMS has two steps, each of which is conducted at a certain time, and is referred to as ORP for the first stage and ORO functions for the second stage. The goal of the ORP optimization model is to maximize system-wide power capacity for electricity demand under extreme events by considering practical constraints, including non-black-start generators (NBSGs) constraints, network topology constraints, and power balance constraints. In order to reduce generation costs, the ORO optimization model adjusts the generated output power to enhance one or more operating performance metrics. The first stage problem is formulated using mixed-integer linear programming (MILP), and it is resolved using the branch-and-bound technique. Moreover, the interior point method solves AC optimal power flow for the second stage. Finally, simulation studies are performed on the modified IEEE 118-bus power systems. The outcomes demonstrate the effectiveness of the proposed strategy for planning (ORP) and real-time operation (ORO); the proposed two-stage strategy is capable of providing an optimal solution for a high-performance online restoration decision support system; the proposed strategy can adapt against the system conditions and contingencies.

Fully decentralized multi-agent coordination scheme in smart distribution restoration: Multilevel consensus

Multi-agent systems (MAS) are emerging as a promising approach for smart power system restoration. After a fault has been detected and isolated, the maximum out-of-service loads should be restored in a timely manner. This study aims to develop reliable restoration through autonomous, rapid, and stable communication. For this purpose, a multilevel consensus is suggested to coordinate MAS in a fully decentralized fashion. System dynamics emerge in a distributed manner based on the state vectors of the agent and its proximate neighbors. The information matrix (IM) is used by the agents to accumulate and exchange state vectors encompassing common variable interests. In order to weight the influence of agents on voting, an optimized weighting model (Optw) is proposed. The parameters are bound by Lyapunov's assessment to ensure consensus and then optimized for quick convergence. The proposed multilevel topology supports parallel information discovery in local bunches and provides extremely quick convergence. The suggested one-by-one communication architecture facilitates the exchange of information since each agent only interacts with four of its neighbors. The redundant communication prevents a single point of failure. These achievements make it suitable for application in large-scale power systems. The effectiveness of the affirmation method is revealed through numerical simulation in various scenarios.

Sympathetic Transformer Inrush Detection and Relay Blocking During Power System Restoration

A classic inrush current occurs during normal energization of reactive grid elements like transformers. It can simulate short-circuit currents. Hence inrush currents have to be detected and protection relays need to be blocked for avoiding their overfunction. Conventional detection criteria are the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$2^{\textrm {nd}}$</tex-math></inline-formula> harmonic content and some kind of current waveform analysis. A classic inrush can evolve into a sympathetic inrush event, if the energization of a single transformers drives other transformers into magnetic saturation. The risk of facing a sympathetic inrush current is particularly high during a power system restoration process. Its characteristic waveform differs from the classic inrush. While the classic inrush has unipolar current peaks, the sympathetic inrush is characterized by bipolar peaks. The conventional detection criteria tend to fail in this case. This paper proposes a new approach to detect sympathetic transformer inrush currents. The approach includes the detection of classic inrush currents as well as the distinction against short-circuit currents for ensuring the relay blocking. First it introduces the basic physical mechanism behind the evolution of a classic to a sympathetic inrush. The higher risk of facing a sympathetic inrush during the restoration process is shown by the physical mechanism as well. The proposed detection method is then introduced in detail. Three new differentiation criteria will be applied and merged together to a final decision. The paper closes with practical lab tests of the new approach on simulated sympathetic and measured classic inrush currents. All lab tests showed good results and promise a practical implementation in the grid.

A Predictive Control Method for LCC HVDC Participated Black Start With Commutation Failure Prevention

Line-commutated converter (LCC) based HVDC systems can significantly speed up the restoration of infeed power systems in blackout states. But they are fragile to commutation failure (CF) under the adverse black-start conditions. To improve the adaptability of LCC HVDC systems in such situations, a predictive control method is proposed in this paper. It includes the linear discrete time predictive model, receding horizon regulator, state estimator, and CF prevention (CFPREV) module. The model is based on the quasi-dynamic and transient-state analysis of load disturbances, where the black-start state is forecasted according to voltage sags caused by inrush currents. The regulator operates in real-time to optimize reference inputs for the HVDC controller. In the estimator, process error and measurement noise are modeled and the optimal system state is estimated with a Kalman filter. Moreover, the dc current setting for CFPREV is calculated using the predicted and estimated state variables. The CFPREV module and regulator work in coordination for secure and fast dc power recovery under abnormal conditions. Small-signal stability analysis and EMT simulations are implemented on the CIGRE benchmark model based black start test grid. The results show that the proposed method i) improves dc power response to black start load startups and ii) presents better recovery performance after the fault.

Preface

The 2023 2nd International Conference on Power System and Power Engineering (PSPE 2023) took place via virtual form in Nanchang, China from March 17th to 19th, 2023, with the attendance of 150 people.PSPE 2023 focused on research areas such as Electrical and Energy Circuits and Systems, Power System Security Defense and Restoration Control, Transmission and Distribution System and Equipment, Power Electronics, etc. The main goal was to bring together related scientists, researchers and developers to bridge the gap between research needs and advanced technological solutions. PSPE 2023 fostered research and development in power engineering and its applications. And it provided an opportunity for researchers and related personnels to present their recent theoretical developments, practical applications, and to discuss unsolved problems and potential challenges.The conference was divided into three main sessions. There are focused on keynote speeches, oral and poster presentations, as well as communication and discussion among all participants.We would like to highlight the main developments presented at the Conference and introduce the prominent speakers. The keynote speaker of the Conference Professor Qifang Chen (Beijing Jiaotong University, China) presented a report on The Interactive Strategy of Thermostatically Controlled Loads (Tcls) to Improve Feasibility and Scalability. In the speech he presented a new interactive control method to improve the feasibility and scalability of TCLs in demand response application. Professor Huihui Song (Harbin Institute of Technology (Weihai), China) talked on Preliminary Study on Synchronization Control Mechanism of Grid-Forming Asynchronous Power Sources under Ultra-High Proportion Consumption of New Energy. She analyzed the existing synchronization mechanism of asynchronous power sources, combined the achievements of NSFC, revealed the new stability mechanism of rhythmic synchronization from a new perspective of coupled oscillator synchronization science, and re-described the evolution process of system synchronization stability.Finally, we would like to thank the members of the Technical Program Committee and the Publication Chair for giving an opportunity to share scientific ideas and new developments at the Conference and the University of Agder, Norway for its friendly support. Last but not the least, our special acknowledgement also goes to the editors and staff of the Journal of Physics: Conference Series, for their kindness and assistance in publishing this volume.The Committee of PSPE 2023List of Committee Member are available in this pdf.

Cyber Restoration of Power Systems: Concept and Methodology for Resilient Observability

In order to have a properly functioning cyber–physical power system, the operational data need to be properly measured, transmitted, and processed. In case of a malicious event on the cyber layer of the power system, such as the wide-area monitoring system, cyber components, such as phasor measurement units (PMUs), communication routers, and phasor data concentrators (PDCs) may be compromised, leading to an unobservable power system. This article proposes the concept of cyber restoration of power systems, and an optimal restoration scheme to recover the system observability swiftly after massive interruptions. The cyber restoration problem is formulated as a mixed integer linear programming (MILP) problem considering PMU measurability, communication network connectivity, and PDC processability conditions, as well as cyber restoration resources as constraints. Results in the IEEE 57-bus system validate that the proposed optimization method can provide solutions that recover system observability much faster than heuristic methods, demonstrating the need for systematic cyber restoration planning research and implementation.

Determining optimal generator start-up sequence in bulk power system restoration considering uncertainties: A confidence gap decision theory based robust optimization approach

A reasonable start-up sequence of generators considering renewable energy generation integration can effectively improve the restoration efficiency of the power system concerned. The restoration strategy of generators is time-sensitive, however, the computation procedure of existing methods is not fast enough to meet the requirements of practical applications. In this paper, the generator start-up sequence optimization (GSSO) problem is addressed with the integration of wind-solar hybrid systems (WSSs) considered in bulk power systems. Specifically, a deterministic model for GSSO is formulated to maximize the total generation capability during the restoration procedure, in which fewer auxiliary variables are required to promote the computational efficiency. On this basis, in order to deal with the inherent and significant uncertainties of WSSs, a confidence gap decision theory (CGDT) based approach is proposed combining the advantages of stochastic optimization and the information gap decision theory based method while considering the worst-case realization of unknown probability distributions, so that a chance-constrained robust generator start-up solution is thereby attained. Moreover, the formulated CGDT based model is transformed into a mixed integer linear programming problem. Finally, the IEEE 39-bus power system and a modified version of an actual power system in China are employed to demonstrate the feasibility and efficiency of the proposed method.

Analytical formulation for estimating power low frequency oscillations between two overhead lines operating at different frequencies

This paper derives an analytical formulation to estimate the period and the amplitude of power low frequency oscillations originated by the mutual coupling of two power systems operating at two slightly different frequencies. This is the typical case of the power system restoration tests, in which the restoration backbone involves two overhead lines installed on the same tower. These oscillations have been detected during several real-world power system restoration tests with a non-negligible amplitude. In some cases, the active power exchanged between the two systems could damage the control and protection equipment of the elements involved in the test, leading to the failure of the restoration test itself. For these reasons, the analytical prediction of the period and the magnitude of these oscillations could constitute a useful and reliable tool for the planning and the simulation of the restoration tests.