Grid Restoration Research Articles

A Hybrid Approach for Enhancing Grid Restoration

A Hybrid Approach for Enhancing Grid Restoration

A resilience‐oriented restoration framework for multi‐area active distribution network following a disaster

AbstractA disaster disrupts energy trading between the distribution network (DN) and autonomous areas in the multi‐area active distribution network (MA‐ADN). Since these areas are self‐interested agents, the energy transaction cost between them should be considered when determining the grid restoration scheme. In this regard, a new three‐level resilience‐oriented restoration (TLROR) framework is proposed to optimally schedule available tie‐lines, and distributed energy resources (DERs) in the MA‐ADN, considering the autonomy and privacy of their ownership. In the first level of the proposed TLROR, an electrical price vector (EPV) is created with the contribution of different areas. In the second level, each area should calculate the amount of imported active and reactive power from the DN through its tie branches and save it in the transactive power list (TPL). Finally, at the third level, the transactive energy market is cleared by the DN operator considering economic issues and operating limits. The proposed method provides energy transactions between independent agents to enhance MA‐ADN resiliency, with the least information sharing, and privacy preserving. The effectiveness of the proposed method is verified using the modified IEEE 33‐bus MA‐ADN and a part of the DN in the Pol‐e Dokhtar in the Lorestan province of Iran.

Long solution times or low solution quality: On trade-offs in choosing a power flow formulation for the Optimal Power Shutoff problem

The Optimal Power Shutoff (OPS) problem is an optimization problem that makes power line de-energization decisions in order to reduce the risk of igniting a wildfire, while minimizing the load shed of customers. This problem, with DC linear power flow equations, has been used in many studies in recent years. However, using linear approximations for power flow when making decisions on the network topology is known to cause challenges with AC feasibility of the resulting network, as studied in the related contexts of optimal transmission switching or grid restoration planning. This paper explores the accuracy of the DC OPS formulation and the ability to recover an AC-feasible power flow solution after de-energization decisions are made. We also extend the OPS problem to include variants with the AC, Second-Order-Cone, and Network-Flow power flow equations, and compare them to the DC approximation with respect to solution quality and time. The results highlight that the DC approximation overestimates the amount of load that can be served, leading to poor de-energization decisions. The AC and SOC-based formulations are better, but prohibitively slow to solve for even modestly sized networks thus demonstrating the need for new solution methods with better trade-offs between computational time and solution quality.

Optimal restoration of power infrastructure following a disaster with environmental hazards

After a disaster, the power grid helps support infrastructure systems that are essential to the recovery effort in addition to the critical services it provides every day. This paper provides an approach for optimal power grid restoration in disaster contexts with environmental hazards. These hazards, such as unstable structures, smoke, chemical hazards, and abnormal radioactivity, may pose acute and accumulating risks to repair workers and impede the restoration process. We therefore formulate a mixed-integer linear program (MILP) that generates an optimal restoration plan with constraints imposed by acute and accumulating environmental hazards. We also develop a heuristic inspired by trends that we observe in optimal restoration strategies, and we compare its performance to that of an optimal restoration strategy. For our case study, we model a stylized disaster that approximates the patterns of a number of disasters including earthquakes, fires, industrial facility explosions, or nuclear reactor incidents. We analyze the performance of the heuristic and optimal restoration strategies on a modified IEEE 123-bus test network. We find that the optimal restoration strategy is able to restore power service more quickly than the heuristic strategy while also exposing repair workers to less acute and cumulative environmental hazards. We also find that as disaster severity increases, the performance difference between the heuristic and optimal restoration strategies grows. Finally, our results show that both the optimal and heuristic algorithms can be useful tools for identifying vulnerable regions of a power grid.

High-reliability uninterruptible power supply manager for critical virtualization cluster

Information technology servers are complex, delicate, and expensive systems; power grid interruption is one of the possible causes that can lead to hardware damage or logical disk structure damage, with severe consequences. Moreover, a power grid drop causes a sudden interruption of the services managed by a server. Uninterruptible power supplies (UPS) provide backup power when the regular power grid source drops. UPSs can only maintain a server for a short time. Therefore, it is necessary to introduce a blackout manager who can correctly shut down the server activities. Moreover, the manager must be able to restore the servers' status when the electrical grid power returns to its normal state. This paper proposes a possible UPS power manager able to manage servers during a prolonged electrical blackout. The UPS power manager identifies the blackout and keeps the servers safe by saving their state and shutting them down properly. Following the power grid restoration, the UPS power manager restarts the servers and restores their state. The proposed approach has been evaluated on a virtualization cluster used for critical activities at the Politecnico di Torino.

Self-Healing Grids: AI Techniques for Automatic Restoration after Outages

The reliability and resilience of power grids are paramount for sustaining modern society's energy demands. However, power outages resulting from natural disasters, equipment failures, or human errors remain persistent challenges. Traditional approaches to power grid restoration, relying heavily on manual intervention, often lead to delays and inefficiencies in restoring services. [1],[2] Recent advancements in artificial intelligence (AI) have spurred the development of self-healing grids capable of autonomously detecting, diagnosing, and restoring power after outages. This paper presents a comprehensive overview of AI techniques employed in self-healing grids and their applications in automatic restoration following outages. The traditional methods of power grid restoration, characterized by manual inspection and decision-making processes, are discussed, highlighting their limitations and challenges. Subsequently, the paper delves into various AI techniques employed in self-healing grids. Machine learning algorithms, such as supervised and unsupervised learning, are utilized for outage detection by analyzing historical data to identify patterns indicative of faults or anomalies. Fault diagnosis is facilitated through the application of Bayesian networks, neural networks, and fuzzy logic systems, enabling operators to accurately identify the root cause of outages and prioritize restoration efforts. Optimization algorithms, including evolutionary algorithms and reinforcement learning, play a crucial role in planning and coordinating restoration efforts to minimize downtime and maximize efficiency. The benefits of self-healing grids, including improved reliability, reduced downtime, and enhanced safety, are discussed alongside the challenges posed by data quality, scalability, and cybersecurity concerns. Finally, the paper outlines future directions, emphasizing advancements in AI techniques, integration with emerging technologies, and the importance of standardization and regulatory frameworks for the future of power grid management. DOI: https://doi.org/10.52783/pst.302

The impact of load placement on grid resonances during grid restoration

As inverter-based generation is being massively deployed in the grid, these type of units have to take over the current roles of conventional generation, including the capability of restoring the grid. In this context, the resonances of the grid during the first steps of a black start can be concerning, given that the grid is extremely lightly loaded. Especially relevant are the low frequency resonances, that may be excited by the harmonic components of the inverter. A typical strategy to avoid or minimize the effect of such resonances relies on connecting load banks. This was fairly feasible with conventional generation, but given the limited ratings of inverters, the amount of load that can be connected at the beginning is very limited. In this paper we consider the energization of a transmission line, and investigate the optimal location of a load along the line in order to maximize the damping in the system. By analysing the spectral properties as a function of the load location, we formally prove that placing the load in the middle of the transmission line maximizes the damping ratio of the first resonance.

Optimal Siting of EV Fleet Charging Station Considering EV Mobility and Microgrid Formation for Enhanced Grid Resilience

Coordinating infrastructure planning for transportation and the power grid is essential for enhanced reliability and resilience during operation and disaster management. This paper presents a two-stage stochastic model to optimize the location of electric vehicle fleet charging stations (FEVCSs) to enhance the resilience of a distribution network. The first stage of this model deals with the decision to place an FEVCS at the most favorable and optimized location, whereas the second stage aims to minimize the weighted sum of the value of lost load in multiple potential scenarios with different faults. Indeed, the second stage is a joint grid restoration scheme with network reconfiguration and microgrid formation using available distributed generators and fleet electric vehicles. The proposed model is tested on a modified IEEE-33 node distribution network and a four-node transportation network. Case studies demonstrate the effectiveness of the proposed model.

Enabling Resiliency Through Outage Management and Data-Driven Real-Time Aggregated DERs

The integration of distributed energy resources (DERs) has revolutionized the power distribution grids, transitioning them into a decentralized mode. While DERs bring numerous advantages, they also introduce the complexity and uncertainties to the distribution system operation, necessitating adaptable operational strategies to enhance system resilience. This paper presents a pioneering approach that exploits the load and DERs in outage managements to enhance the distribution grid restoration. The proposed network equivalent model aggregates DERs and loads, preserving controllability information for effective real-time system outage management and restoration. A data-driven load model that combines an artificial neural network-based short-term load forecasting model and a delay exponential cold load pickup (CLPU) model is developed to compute the reliable safety margin for restoration and pick up the maximum number of loads during the restoration. Furthermore, an optimal restoration strategy is presented, encompassing restored network topology, controllable utility loads, and DERs set-point determination.

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.

A human operator model for simulation-based resilience assessment of power grid restoration operations

Human operations play a vital role for the resilience of power grids. While past research concentrated on performance in cascading failures, this work proposes a system model that focuses on assessing the impact of the performance of operators in grid operations and restorations. The operator model developed for this purpose addresses human performance and variability by accounting for stochastic durations, potential errors and alternative responses, and the probabilities of these outcomes. Discrete event simulation is the assessment framework, with the model implemented in MATLAB Simulink in conjunction with a dynamic power system model. A re-energization cell provided by a Swiss generation system operator is used for a case study demonstration of the simulation model, which produces distributions of the total restoration duration and restoration success rates while identifying the parts of restoration plans that could be modified to enhance restoration performance. The overall outcomes of the case study suggest that the human element can be treated practically in grid simulations in order to produce findings that will increase grid resilience.

A novel partitioning method for the power grid restoration considering the support of multiple LCC-HVDC systems

The restoration of the modern alternating current-direct current (AC–DC) hybrid power grid after a blackout is difficult and complex. By using the partitioning restoration strategy and the support of high voltage direct current (HVDC) system, the restoration speed of the blackout power grid can be greatly accelerated. Aiming at this problem, this paper proposes a partitioning method for the power grid restoration with multiple line commutated converter-based HVDC (LCC-HVDC) systems. Firstly, by introducing the system strength indices to quantify the interaction between AC and LCC-HVDC systems in the restoration process, a partitioning model considering multiple LCC-HVDCs is proposed. Wherein, the transmission power is optimized to make full use of LCC-HVDCs’ support capability. Then, by combining the system strength characteristic and community structure characteristic of the power grid, a partitioning method based on an improved Girvan Newman (GN) algorithm is proposed. Finally, the results are studied based on a modified IEEE 39-bus system and a realistic partial power grid of southwest China. Compared with two typical partitioning methods for the AC power grid restoration, the results show that the proposed method can effectively use the DC support power and speed up the restoration progress.

Secondary control with grid-forming inverters for an island grid restoration approach without communication

If a blackout occurs, self-starting units, like grid-forming inverters, are needed for a black start and a restoration in a low voltage grid. Reliable power supply of the grid by grid-forming inverters can be achieved by storage systems connected to the grid-forming inverters. The use of storage systems as a power source for the grid-forming inverters requires that the storage systems must not be completely discharged or charged, otherwise the functionality of balancing the power of the grid-forming inverters is not guaranteed and a restoration failure would result. This paper presents a concept for grid restoration and maintaining a reliable island operation using a secondary control structure in combination with the implementation of a state machine in smart meters to handle such critical cases of the storage systems. The secondary control can also restore the nominal frequency after load changes without any communication between grid elements. RMS simulations are carried out in DIgSILENT PowerFactory showing the restoration process and island operation in different scenarios.

Adaptive Virtual Synchronous Generator Based on Model Predictive Control with Improved Frequency Stability

With the massive integration of renewable energy into the grid, grid inertia and its stability continue to decrease. To improve inertia and facilitate grid restoration, a control strategy for radial basis function virtual synchronous generators based on model predictive control (MPC-VSG-RBF) is proposed in this paper. In this method, virtual synchronous generator (VSG) control strategy is introduced into the model predictive control (MPC), so that the reference value of the inner loop current can vary with the grid voltage and frequency. Using the radial basis function (RBF) neural network to adjust the VSG virtual inertia online can solve the large fluctuation of frequency and power caused by excessive load fluctuation. The simulation model was built based on MATLAB and compared and analyzed with the MPC control method. The simulation results show that: when the output power of the inverter changes, the model predictive control of the adaptive virtual synchronous generator can increase the inertia and stability of the power grid; by adjusting the moment of inertia, the system damping ratio is improved to effectively suppress the transient process overshoot and oscillation in medium power.

A resilience-based recovery scheme for smart grid restoration following cyberattacks to substations

The recent surge of interest towards deploying cyber systems in power grids exposes the resulted Smart Grids (SGs) to cyberattacks, and consequently resilience degradation. Among different possible remedies, i.e., prevention, mitigation, and recovery, developing a well-crafted recovery scheme following these cyberattacks can noticeably improve the system resilience. However, an appropriate resilience-based optimization problem with a metric to capture the system resilience during each step of recovery is needed to be solved and enhance it by the right decisions. To address such a concern, this paper proposes an innovative recovery framework based on a novel metric that captures the resilience posture of SGs against cyberattacks. The developed metric captures (i) Power-side Resilience (PsR) including load, available reserve capacity, available line capacity, and reliability, of which the last three are traditionally overlooked in resilience literature, and (ii) Cyber-side Resilience (CsR) considering the maximum possible implication of attacks on the power grid. PsR will calculate the physical side resilience based on immediate damage and CsR measure the cyber-side resilience e based on possible damage to the system. The SG is fully recovered from an attack when the proposed scheme brought back all the cyber and physical aspects to the proximity of the pre-disturbance conditions. For practical feasibility, we have incorporated different SG capabilities and limitations including generator Automatic Generation Control (AGC) capability and ramp rate, load variations during the recovery phase, transient stability, and multi-stage attacks. Moreover, an AC multi-island load flow is utilized to check the power balance, voltage boundaries, and transmission lines limitations in each recovery step. The proposed framework is evaluated on the 39-bus New England test system to demonstrate its effectiveness and also applied to the IEEE 30-bus test case to compare the results of the proposed approach with those of existing works. The comparisons show that the proposed approach can enhance the overall system resilience by 22% in comparison to existing works.

Post-storm repair crew dispatch for distribution grid restoration using stochastic Monte Carlo tree search and deep neural networks

Natural disasters such as storms usually bring significant damages to distribution grids. This paper investigates the optimal routing of utility vehicles to restore outages in the distribution grid as fast as possible after a storm. First, the post-storm repair crew dispatch task with multiple utility vehicles is formulated as a sequential stochastic optimization problem. In the formulated optimization model, the belief state of the power grid is updated according to the phone calls from customers and the information collected by utility vehicles. Second, an AlphaZero based utility vehicle routing (AlphaZero-UVR) approach is developed to achieve the real-time dispatching of the repair crews. The proposed AlphaZero-UVR approach combines stochastic Monte-Carlo tree search (MCTS) with deep neural networks to give a lookahead search decisions, which can learn to navigate repair crews without human guidance. Simulation results show that the proposed approach can efficiently navigate crews to repair all outages.

Graph decomposition for constructing blackstart restoration strategies in benchmark cases

This paper describes methods for constructing benchmark cases and solution strategies related to restoring an electric grid model from a blackstart condition. Existing public cases for grid restoration problems are limited in size and scope—this paper delineates the features required to create new synthetic, detailed datasets for this purpose. As part of validating these datasets, benchmark results are included for an electric grid restoration strategy based on a formulation of the restoration problem as a time sequence of ac power flow solutions. The algorithm produces a benchmark restoration sequence for an example 200-bus case, showing how critical loads can be restored within 45 min, the majority of the grid within 6 h, and all loads within 36 h. At the core of the solution strategy is a directed graph decomposition heuristic, as the algorithm builds a bus energization spanning forest. Because these datasets are built synthetically, they can have a high degree of realism in features such as geographic coordinates and modeling complexities, while avoiding concerns of data confidentiality, meaning that the data produced in this work can be made publicly available for the benefit of the research community.

The water energy nexus: Improved emergency grid restoration with DERs

Water networks as critical infrastructures typically feature emergency electricity generators for bridging short power blackouts. We propose to combine these black start capable generators with available distributed energy resources (DERs) in the power grid, often photovoltaic generation, to jointly restore both the electricity and the water grid in the case of emergencies. This is mutually beneficial for both notworks since common grid-following inverters of DERs cannot supply power without a grid-forming nucleus. We model both grids as a coupled graph and formulate a stochastic mixed-integer linear program to determine optimal switch placement and/or optimal switching sequences jointly for both networks. Limited fuel and power availabilities, grid-forming constraints, storages, and an even distribution of available resources are considered. By minimizing the number of switching devices and switching events we target manual operability. The proposed method extends the time that can be bridged until a full restoration of the main power grid is achieved. For a small example, we demonstrate that given enough solar radiation our solution allows us to extend the water supply duration by a factor of two, compared to using the emergency generators only for the water network, while additionally almost half of the electricity demands can be resupplied. Algorithmic scaling is validated with a combination of the IEEE 123-bus test feeder and the D-Town water network.

Adaptive fuzzy directional bat algorithm for the optimal coordination of protection systems based on directional overcurrent relays

Despite bringing several benefits to modern power systems, distributed generation (DG) and microgrids cause a significant impact on the operation of power distribution networks, especially with regard to the protection system. The topological evolution of power distribution networks comprising DG units and the islanding capacity of microgrids have lead to significant changes in the magnitudes of short-circuit currents, consequently affecting the reliability of protection systems in terms of loss of selectivity (false tripping and/or sympathetic tripping), sensitivity (blind protection), and dynamic response (delayed trip). In this new scenario, the adoption of smart protection systems capable of incorporating advanced control and protection mechanisms is of paramount importance for ensuring the automatic reconfiguration and restoration of the power grid when undesirable phenomena like faults occur. Considering this scenario, this work proposes a novel metaheuristic called adaptive fuzzy directional bat algorithm (AFDBA), which can calculate optimal settings for directional overcurrent relays (DOCRs) in any power grid topology without the need for tuning the initial parameters. The technique is applied to three case studies, that is, the 3-bus, 9-bus, and 30-bus test systems. The algorithm performance is compared with that of genetic algorithm (GA), particle swarm optimization (PSO), differential evolution (DE), bat algorithm (BA), and directional bat algorithm (DBA). The results evidence that the proposed approach has high convergence speed, high robustness, and acceptable computational burden, while providing the proper coordination of protection systems.

Fault Detecting and Isolating Schemes in a Low-Voltage DC Microgrid Network from a Remote Village

Fault detection and isolation are important tasks to improve the protection system of low voltage direct current (LVDC) networks. Nowadays, there are challenges related to the protection strategies in the LVDC systems. In this paper, two proposed methods for fault detection and isolation of the faulty segment through the line and bus voltage measurement were discussed. The impacts of grid fault current and the characteristics of protective devices under pre-fault normal, under-fault, and post-fault conditions were also discussed. It was found that within a short time after fault occurrence in the network, this fault was quickly detected and the faulty line segment was efficiently isolated from the grid, where this grid was restored to its normal operating conditions. For analysing the fault occurrence and its isolation, two algorithms with their corresponding MATLAB/SIMULINK platforms were developed. The findings of this paper showed that the proposed methods would be used for microgrid protection by successfully resolving the fault detection and grid restoration problems in the LVDC microgrids, especially in rural villages.