Power System Resilience Research Articles

Towards Avoiding Cascading Failures in Transmission Expansion Planning of Modern Active Power Systems Using Hybrid Snake-Sine Cosine Optimization Algorithm

In this paper, a transmission expansion planning (TEP) model is proposed to guarantee the resilience of power systems and mitigate cascading failures’ impacts. The energy storage systems and fault current limiters’ planning models are integrated into the TEP problem to minimize cascading outages and comply with short-circuit current reliability constraints. Most studies in the literature adopt a single strategy to simulate power systems’ cascading failures that may not be enough to guarantee networks’ resilience. This work elaborates on two scenarios for initiating cascading failures to study the impact of various initiating events on the planned system’s strength and the projects required. The TEP problem is formulated as a non-linear, non-convex large-scale problem. To avoid linearization issues and enhance meta-heuristics performance, a hybridization of two meta-heuristic techniques, namely snake optimizer and sine cosine algorithm (SO-SCA), is proposed to solve the problem. Two hybridization strategies are suggested to improve the exploration and exploitation stages. Defining future loads growth is essential for TEP. Hence, a load forecasting technique based on SO-SCA is investigated and compared with some methods reported in the literature. The results obtained proved the efficiency of the proposed approach in predicting load growth. TEP’s calculations were carried out on the Garver and the IEEE 24-bus system. The results demonstrated the superiority of the hybrid SO-SCA in solving the TEP problem. Moreover, the projects required to expand networks differed according to the type of cascading failures’ initiating scenario.

Resiliency Assessment of Power Systems Using Deep Reinforcement Learning.

Evaluating the resiliency of power systems against abnormal operational conditions is crucial for adapting effective actions in planning and operation. This paper introduces the level-of-resilience (LoR) measure to assess power system resiliency in terms of the minimum number of faults needed to produce a system outage (blackout) under sequential topology attacks. Four deep reinforcement learning (DRL)-based agents: deep Q-network (DQN), double DQN, the REINFORCE (Monte-Carlo policy gradient), and REINFORCE with baseline are used to determine the LoR. In this paper, three case studies based on IEEE 6-bus test system are investigated. The results demonstrate that the double DQN network agent achieved the highest success rate, and it was the fastest among the other agents. Thus, it can be an efficient agent for resiliency evaluation.

Using Extreme Wind-Speed Probabilistic Forecasts to Optimize Unit Scheduling Decision

With the increasing proportion of renewable energy in power systems, the use of probabilistic forecasts to provide uncertainty information for future decision analyses is inevitable. In addition, the resilience of power systems to extreme events is also a matter of concern. This paper proposes a decision-making model for unit scheduling, using ensemble numerical weather predictions. In this analysis, if wind speeds exceed the cut-out value of a wind turbine, then it is defined as a predicted event of concern. The economic value to decision-makers of using probabilistic wind-speed forecasts will be analyzed. Based on the analysis results, the optimal probability threshold is determined and is used to decide whether, in the unit scheduling, wind turbines should be shut down in advance. This study uses four typhoon events as test cases to verify the forecast performance of the proposed numerical weather prediction model and determine different optimal probability thresholds for shutting down wind turbines (decision probability thresholds) in different months and seasons, because if the same decision probability threshold is used as throughout the year, it not only fails to achieve maximum economic value but also endangers the safety of power system operation.

A GPU-Based Resilience Enhanced Voltage Optimization Model for Distribution Networks

Improving the survivability of critical loads after extreme events is essential to enhance the resilience of power systems, especially for distribution networks. A distribution network with various operational resources can be separated into several sub-distribution networks without electrical connections. Maintaining the power supply with acceptable power quality to critical loads in such separated distribution networks is a challenging task for the operators of power systems. In this paper, an optimization model is proposed to maximize the ability to supply power to critical loads in distribution networks. Moreover, a GPU was employed to accelerate the proposed model using genetic algorithm. With the acceleration of the GPU platform, the solving time was reduced and the population size can be enlarged to enhance the convergence rate and convergence quality of the algorithm. Finally, case studies were carried out in IEEE 33-bus and 118-bus systems, and the effectiveness of the method was validated by comparing the solution results on GPU and CPU platforms.

An online resilience assessment method for islanded distribution systems considering uncertainty of intermittent RESs and loads

The frequent natural disasters caused by global warming have been threatening the safety of power systems for decades. A resilient power system has the ability to recover quickly following a disaster and has been continuously studied these years. However, with the progress of energy transition, a large quantity of renewable energy sources (RESs) has been integrated into the power distribution systems and the uncertainty has complicated the decision and operation of resilient distribution systems. This paper proposes an online resilience assessment method for islanded distribution systems considering the time-varying uncertainty of RESs and loads, based on only the measured data without any forecasting tools. An offline probabilistic modeling and online conditional probability distribution derivation method is proposed to generate short-term power scenarios of RESs and loads dynamically, and an online resilience assessment framework is established with an optimization-based system response model in different outage scenarios of distribution systems. The case study to a modified IEEE 37-node test feeder validates the advantages of the proposed method in generating accurate short-term scenarios of uncertainties and providing reliable resilience assessment results. The proposed framework can be used as a basic tool for the online operation of resilient distribution systems.

The independent and synergistic impacts of power outages and floods on hospital admissions for multiple diseases

Highly destructive disasters such as floods and power outages (PO) are becoming more commonplace in the U.S. Few studies examine the effects of floods and PO on health, and no studies examine the synergistic effects of PO and floods, which are increasingly co-occurring events. We examined the independent and synergistic impacts of PO and floods on cardiovascular diseases, chronic respiratory diseases, respiratory infections, and food-/water-borne diseases (FWBD) in New York State (NYS) from 2002 to 2018. We obtained hospitalization data from the NYS discharge database, PO data from the NYS Department of Public Service, and floods events from NOAA. Distributed lag nonlinear models were used to evaluate the PO/floods-health association while controlling for time-varying confounders. We identified significant increased health risks associated with both the independent effects from PO and floods, and their synergistic effects. Generally, the Rate Ratios (RRs) for the co-occurrence of PO and floods were the highest, followed by PO alone, and then floods alone, especially when PO coverage is >75th percentile of its distribution (1.72% PO coverage). For PO and floods combined, immediate effects (lag 0) were observed for chronic respiratory diseases (RR:1.58, 95%CI: 1.24, 2.00) and FWBD (RR:3.02, 95%CI: 1.60, 5.69), but delayed effects were found for cardiovascular diseases (lag 3, RR:1.13, 95%CI: 1.03, 1.24) and respiratory infections (lag 6, RR:1.85, 95%CI: 1.35, 2.53). The risk association was slightly stronger among females, whites, older adults, and uninsured people but not statistically significant. Improving power system resiliency could be a very effective way to alleviate the burden on hospitals during co-occurring floods. We conclude that PO and floods have independently and jointly led to increased hospitalization for multiple diseases, and more research is needed to confirm our findings.

The Effect of Decision Analysis on Power System Resilience and Economic Value During a Severe Weather Event

Because of climate changes, natural disasters are becoming more serious. For instance, the intensity of typhoons has been increasing in recent years. Typhoons and other natural disasters have high-impact low-probability characteristics. Thus, procedures for preparing for natural disasters and increasing power system resilience are important issues. This article proposes an all-inclusive process for system operators to make decisions for enhancing power system resilience and economic value during a severe weather event. This process first considers the typhoon track, the fragility curve and the recovery time of transmission lines. After collecting these data, system simulations and a calculated resilience index are implemented according to cases with and without disaster prevention. Next, the probability threshold and calculated economic value index are obtained based on numerical weather prediction wind speeds and the cost-loss ratio. Finally, the two indexes are considered in combination to obtain the highest resilience with the greatest benefit. The proposed process helps system operators make decisions for appropriate preventive actions at the least cost. An actual Taiwan power system and a severe weather event are used as an example to demonstrate the proposed decision analysis. The simulation results indicate the feasibility of the proposed method, which can reduce potential risks caused by extreme weather events with the maximum economic benefits.

Islanding Detection of Grid-Forming Inverters: Mechanism, methods, and challenges

Over the past decades, because of boosted energy demands and the serious concerns of climate change, inverter-based resources (IBRs) have been widely deployed to integrate renewable energy into power systems for the goal of carbon neutrality. Thanks to the full controllability of power electronic devices, IBRs have the capability to implement reliable and flexible power regulation, which makes them technically feasible for enhancing the resilience and energy efficiency of power systems.

Review of Grid-forming Inverters in Support of Power System Operation

The penetration of distributed energy resources in electrical grids has been steadily increasing in an effort to reduce greenhouse gas emissions. Inverters, as interfaces between distributed energy resources and grids, have become critical assets in modern power systems. In recent years, the development and application of grid-forming inverters have gained significant traction due to their capability of supporting power grid operations. A comprehensive review of grid-forming inverters is presented for power system applications. A comparison between grid-forming inverters and grid-following inverters is conducted in terms of their functionalities to highlight the potential of grid-forming inverter technologies in support of power system stability and resiliency. In addition, advanced control strategies integrated into grid-forming inverters under various operation conditions are presented through reviewing the innovations introduced in recent literature and in industrial applications. This paper is intended to provide an updated reference regarding grid-forming inverters for power system applications to researchers and practitioners in the field of power electronics.

Proactive Resilient Day-Ahead Unit Commitment With Cloud Computing Data Centers

To enhance the resilience of power systems toward the temporal and spatial impacts caused by extreme weather events, e.g., hurricanes, the flexibility of cloud data centers (CDCs) is treated as a kind of efficient demand response. Since the workloads of CDCs have the shifting capacity between different locations and time slots, a day-ahead unit commitment problem including data centers is proposed to explore the integrated spatial and temporal flexibility of the workloads to its full extent through task migration. Considering the uncertainty of probability density functions, the line failure rates, workload arrival rates, power loads are integrated into an ambiguity set. The scheduling process of generators and CDCs is modeled as a two-stage distributionally robust optimization problem, which is reformulated as a large-scale deterministic mixed-integer linear programming problem and solved by the multicuts Benders decomposition method. The performance of the proposed scheduling strategy is tested in both the IEEE 24-bus RTS system and the three-area RTS-96 system. The results reveal that the method could mitigate the adverse impacts of hurricanes by enhancing the resilience of power systems and decreasing the dropping workloads of CDCs.

Application Strategies of Model Predictive Control for the Design and Operations of Renewable Energy-Based Microgrid: A Survey

In recent times, Microgrids (MG) have emerged as solution approach to establishing resilient power systems. However, the integration of Renewable Energy Resources (RERs) comes with a high degree of uncertainties due to heavy dependency on weather conditions. Hence, improper modeling of these uncertainties can have adverse effects on the performance of the microgrid operations. Due to this effect, more advanced algorithms need to be explored to create stability in MGs’. The Model Predictive Control (MPC) technique has gained sound recognition due to its flexibility in executing controls and speed of processors. Thus, in this review paper, the superiority of MPC to several techniques used to model uncertainties is presented for both grid-connected and islanded system. It highlights the features, strengths and incompetencies of several modeling methods for MPCs and some of its variants regarding handling of uncertainties in MGs. This survey article will help researchers and model developers to come up with more robust model predictive control algorithms and techniques to cope with the changing nature of modern energy systems, especially with the increasing level of RERs penetration.

Resiliency enhancement of power system against intentional attacks

Natural disasters, man-made attacks, and cyber-attacks are some of the main hazards of power system which can interrupt the process of continuous power delivery to the end users. In this way, man-made attacks characteristics’ are fundamentally different from other hazards due to its adaptive strategy that allows attackers to target the most vulnerable parts of the power system. By choosing appropriate time and place; attackers always have advantages over the defender to penetrate the system and launch malicious actions. Therefore, it is quite necessary to take into account the optimal policy for allocating resources in the defender's strategy. This research proposes a four-level Defence–Defence–Attack–Defence (DDAD) approach to improve the power system resilience against the intentional human attacks. A new defence level, a planning level, is added to the common three-level Defence–Attack–Defence (DAD) model in power system planning state in order to prevent cut off loads and voltage drop beyond standard with lower cost. In the proposed method, load priority is used to classify the vulnerable sections. The proposed model is performed on IEEE-30 bus test system and the results indicate the feasibility of the proposed method. As the results indicate, in proposed method less than 10% budget is needed compared with the previous method while all critical loads will be remained. Furthermore, the new method needs no security guard to protect transmission lines for 30 years.

Evaluating the Potential Contribution of District Heating to the Flexibility of the Future Italian Power System

Flexibility is crucial to enable the penetration of high shares of renewables in the power system while ensuring the security and affordability of the electricity dispatch. In this regard, heat–electricity sector coupling technologies are considered a promising solution for the integration of flexible devices such as thermal storage units and heat pumps. The deployment of these devices would also enable the decarbonization of the heating sector, responsible for around half of the energy consumption in the EU, of which 75% is currently supplied by fossil fuels. This paper investigates in which measure the diffusion of district heating (DH) coupled with thermal energy storage (TES) units can contribute to the overall system flexibility and to the provision of operating reserves for energy systems with high renewable penetration. The deployment of two different DH supply technologies, namely combined heat and power units (CHP) and large-scale heat pumps (P2HT), is modeled and compared in terms of performance. The case study analyzed is the future Italian energy system, which is simulated through the unit commitment and optimal dispatch model Dispa-SET. Results show that DH coupled with heat pumps and CHP units could enable both costs and emissions related to the heat–electricity sector to be reduced by up to 50%. DH systems also proved to be a promising solution to grant the flexibility and resilience of power systems with high shares of renewables by significantly reducing the curtailment of renewables and cost-optimally providing up to 15% of the total upward reserve requirements.

A data-driven approach for microgrid distributed generation planning under uncertainties

The increasing demand for power system decarbonization and resilience raises the necessity of incorporating the renewable distributed generation (DG) into the microgrid planning. The complexity of the microgrid renewable DG planning largely roots from the intermittent wind and solar energy and load variations throughout the planning period. This paper proposes a novel two-stage data-driven adaptive robust distributed generation planning (DDARDGP) framework considering both grid-connected and islanded modes of microgrids, wherein the overall system cost is minimized. By leveraging the spatio-temporal property of historical weather and grid information, a compact uncertainty set is developed based on a data-driven Bayesian nonparametric approach. The problem is further solved by a modified column and constraint generation (CC&G) algorithm. In the study, the effectiveness of the proposed framework is demonstrated using a modified IEEE 33-bus test system. The case study considers the optimal generation sizing, allocation and mixtures. The simulation results confirm that the proposed data-driven uncertainty set adapts well to the increase of data dimensions and solves the over-conservatism issue, leading to 34.14% reduction in uncertainty estimation compared with the traditional budget uncertainty set. Accordingly, the total cost can achieve a $23,185 reduction under the proposed DDARDGP framework.

Power economic dispatch against extreme weather conditions: The price of resilience

With climate change, we have been witnessing more frequent extreme weather events causing increasingly common large-scale power outages. It is essential and urgent to improve power system resilience, which also substantially impacts the resilience of dependent infrastructures, such as water and health systems. This work investigates the enhancement of power grid resilience using proactive network-constrained economic dispatch (NCED) strategies. An extreme weather event is modeled as an attacker interdicting a selected set of transmission lines to cause overloading of remaining lines, which potentially leads to cascading failures. We define a set of resilience metrics, with the first one being a weighted number of overloaded lines immediately after the attack to capture the potential cascading chain effect, the second one predicting the worst-case value of the first metric to provide a forward-looking evaluation, and the last one assessing whether each line can be overloaded in the worst case to supply more granular awareness. We also propose a defender–attacker–defender NCED model solved by a column-and-constraint generation algorithm to optimize the defined metrics. The model can generate strategies that (1) enhances resilience without additional NCED cost; (2) further enhances resilience with a budgeted extra NCED cost; and (3) achieves a moving target defense scheme shifting the grid’s vulnerable part(s). The associated price of resilience is specifically evaluated. Results on standard test systems demonstrate the proposed methods’ effectiveness. Overall, our methods and results provide insights on the establishment of social, economic and environmental resilience by contributing to the resolution of resilience-related power and energy issues.

A resilience-oriented centralised-to-decentralised framework for networked microgrids management

This paper proposes a cyber–physical cooperative mitigation framework to enhance power systems resilience against power outages caused by extreme events, e.g., earthquakes and hurricanes. Extreme events can simultaneously damage the physical-layer electric power infrastructure and the cyber-layer communication facilities. Microgrid (MG) has been widely recognised as an effective physical-layer response to such events, however, the mitigation strategy in the cyber lay is yet to be fully investigated. Therefore, this paper proposes a resilience-oriented centralised-to-decentralised framework to maintain the power supply of critical loads such as hospitals, data centres, etc., under extreme events. For the resilient control, controller-to-controller (C2C) wireless network is utilised to form the emergency regional communication when centralised base station being compromised. Owing to the limited reliable bandwidth that reserved as a backup, the inevitable delays are dynamically minimised and used to guide the design of a discrete-time distributed control algorithm to maintain post-event power supply. The effectiveness of the cooperative cyber–physical mitigation framework is demonstrated through extensive simulations in MATLAB/Simulink.

A Novel Four-Level Approach for Improving Power System Resilience Against Intentional Attacks

This study proposes a new four-level model to enhance power system resilience against human attacks. The model considers human attacks, such as intelligent attackers and power system planners, as defenders. The attackers want to maximize the impacts of actions, while the defender tries to avoid imposed costs and damages due to budget limitations. To this end, a new four-level model defense-defense-attack-defense (DDAD) is proposed, in which a new defense layer is added to the common three-level model defense-attack-defense (DAD). Looking at the model more closely, new power plants and substations are added to the power system to improve its resilience, while the most important power plants and substations are determined. Subsequently, the conventional three-level model was applied. In this research, the defender and attacker have some strategies with their own costs for every power system component, such as power plants and substations, and their own interactions. The power system model includes the load, power plant and substation, and substation priority, which are defined as a combination of the value of the load and network topology. The proposed model was applied to the IEEE-30 bus test system, and the results indicated the effectiveness of the proposed method.

Wildfires across the world: Power system resilience [Guest Editorial

Extreme weather events, earthquakes, floods, and wildfires in the past few years have resulted in major power disruptions around the world, leading to unplanned electricity interruptions lasting from hours to weeks. This issue of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IEEE Power &amp; Energy Magazine</i> focuses on wildfires.

Power System Resilience: A Comprehensive Literature Review

Power System Resilience: A Comprehensive Literature Review

Microgrids and Resilience: A Review

The occurrence of large-scale disturbances is increasing at an alarming rate throughout the world. As a consequence of this trend, a primary concern of today’s power system is to enhance its resilience against low-probability, high-impact events. In this regard, microgrids, as the smart grid’s building blocks, offer promising approaches toward achieving higher levels of distribution system resilience by accommodating and integrating various distributed energy resources. Accordingly, microgrid-based techniques have been the focus of a growing body of research seeking a more resilient power system. These methods mainly rely on the stand-alone operation of microgrids to supply loads locally in case of extreme events. The objective of this paper is to present an updated comprehensive review of the literature on two main categories of microgrid-based resilience enhancement approaches in distribution systems: 1) optimal microgrid formation and 2) optimal microgrid scheduling and energy management. Distinctive from other review papers, this article systematically surveys the research studies under multiple well-sectionalized features, such as various technologies, techniques, models, constraints, and concepts for each of the above two categories. These features include but are not limited to networked microgrids, demand response programs and electric vehicles scheduling, multi-energy microgrids, dynamic optimization schemes, control schemes, communication resilience, hybrid microgrids, and mobile energy resources. Additionally, a comprehensive introduction to resilience definitions and assessment methods, microgrid components, architectures, and control schemes, as well as, sources of uncertainty is provided.