Evaluating Resilience of Electricity Distribution Networks via A Modification of Generalized Benders Decomposition Method

This paper presents a computational approach to evaluate the resilience of electricity Distribution Networks (DNs) to cyber-physical failures. In our model, we consider an attacker who targets multiple DN components to maximize the loss of the DN operator. We consider two types of operator response: (i) Coordinated emergency response; (ii) Uncoordinated autonomous disconnects, which may lead to cascading failures. To evaluate resilience under response (i), we solve a Bilevel Mixed-Integer Second-Order Cone Program which is computationally challenging due to mixed-integer variables in the inner problem and non-convex constraints. Our solution approach is based on the Generalized Benders Decomposition method, which achieves a reasonable tradeoff between computational time and solution accuracy. Our approach involves modifying the Benders cut based on structural insights on power flow over radial DNs. We evaluate DN resilience under response (ii) by sequentially computing autonomous component disconnects due to operating bound violations resulting from the initial attack and the potential cascading failures. Our approach helps estimate the gain in resilience under response (i), relative to (ii)

Vulnerability analysis of electricity distribution networks with large-penetrations of PEVs and DERs

Thesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2016.Cataloged from PDF version of thesis.Includes bibliographical references (pages 95-98).This thesis focuses on the vulnerability assessment of radial electricity distribution networks (DNs) under large-scale integration of Distributed Energy Resources (DERs) and Plug-in Electric Vehicles (PEVs). We formulate a two-player Stackelberg security game involving an attacker (external threat agent) and the defender (network operator). First, the attacker targets a subset of the insecure DER or PEV nodes, and strategically manipulates their set-points by attacking the DER/PEV controller logic at the nodes. Next, the defender responds to the resulting supply-demand mismatch by triggering network control operations, which includes direct load control and control of available non-compromised DERs/PEVs. The attacker's (resp. defender's) objective is to maximize (resp. minimize) the weighted sum of the cost of active and reactive power supply, costs of DER/PEV and load control, and the cost due to loss of voltage regulation. This composite cost captures the key trade-offs that the network operator faces in balancing power supply and quality during a broad range of contingency conditions. The choice of this cost in the security game reflects the attacker's overall goal of comprising the DER/PEV nodes to maximize the minimum composite cost for the network operator. Solving the sequential game with nonlinear power flow constraints is a computationally hard problem. To address this challenge, we introduce two auxiliary sequential game problems each with linear power flow constraints. We prove that the values of these relaxed problems upper and lower bound the value of the original game. Next, we introduce a greedy algorithm that can be utilized to efficiently compute an optimal attack strategy for both auxiliary games. Our main result is that, under a set of assumptions, the set of optimal attacker strategies is identical for these games, and hence we obtain a tractable solution to compute an optimal attack for the original game. Furthermore, the optimal attack strategy exhibits an interesting structural property: the downstream nodes are more critical for limiting costs of reactive power supply and maintaining voltage regulation. This insight is useful for vulnerability assessment of DNs under DER/PEV node compromises. Finally, we also exploit the structure of optimal attack to design a distributed control strategy for defender response.by Devendra Shelar.S.M. in Transportatio

Resilient operations of smart electricity networks under security and reliability failures

This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Thesis: Ph. D. in Computational Science and Engineering, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2019Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 265-276).Blackouts (or cascading failures) in Electricity Networks (ENs) can result in severe consequences for economic activity, human safety and national security. Recent incidents suggest that risk of blackouts due to cyber-security attacks and extreme weather events is steadily increasing in many regions of the world.This thesis develops a systematic approach to evaluate and improve the resilience of ENs by addressing following questions: (a) How to model security and reliability failures and assess their impact on ENs? (b) What strategies EN operators can implement to plan for and quickly respond to such failures and minimize their overall impact? (c) How to leverage the operational flexibility of "smart" ENs to implement these strategies in a structured manner and provide guarantees against worst-case failure scenarios? We focus on three classes of cyber-physical failures: (i) Inefficient or unsafe economic dispatch decisions induced by an external hacker who exploits the vulnerabilities of control center software; (ii) Simultaneous disruption of a large number of customer-side components (loads and/or distributed generators) by a strategic remote adversary; (iii) Correlated failures of power system components caused by storm events (or hurricanes) with high-intensity wind fields.We develop new network models to capture the impact of these failures, while accounting for a broad range of operator response actions. These actions include: partial load control, pre-emptive disconnection of non-critical loads, active and reactive power supply by Distributed Energy Resources (DERs) capable of providing grid-forming services, and formation of microgrid islands. We develop practically relevant operational strategies to improve the ENs' resilience to failure classes (i) and (ii) (resp. (iii)) based on solutions of bilevel mixed integer programming (resp. two-stage stochastic optimization) formulations. Our bilevel mixed integer programming formulations capture the worst-case impacts of attacks on radial distribution networks operating under grid-connected or microgrid configurations.For the case when the operator response can be modeled as continuous decision variables, we provide a greedy heuristic that exploits the radial network structure and provides near-optimal solutions. For the more general case of mixed-binary decision variables, we develop a computationally tractable solution approach based on Benders Decomposition method. This approach can be used to evaluate the value of timely response actions in reducing various losses to the network operator during contingencies induced by attacker-induced failures. We provide some guidelines on improving the network resilience by proactive allocation of contingency resources, and securing network components in a strategic manner. Furthermore, under reasonable assumptions, we show that myopically reconnecting the disrupted components can be eective in restoring the network operation back to nominal condition.Our two-stage stochastic optimization formulation is motivated by the need of a decision-theoretic framework for allocating DERs and other contingency resources in ENs facing the risk of multiple failures due to high-intensity storm events. The stochastic model in this formulation captures the dependence of probabilistic failure rates on the spatio-temporal wind intensities. Importantly, the formulation allows for the formation of microgrid islands (powered by the allocated DERs), and considers joint DER dispatch and component repair decisions over a multi-period restoration time horizon. We present computational results based on the classical sample average approximation method, with Benders Decomposition applied to solve the mixed-binary programs associated with the restoration stage. Finally, we compare the optimal repair decisions with a simpler greedy scheduling strategy that satisfies soft-precedence constraints."Financial support provided by EPRI grant for "Modeling the Impact of ICT Failures on the Resilience of Electric Distribution Systems" (contract ID: 10000621), and NSF project "CPS Frontiers: Collaborative Research: Foundations Of Resilient CybErphysical Systems (FORCES)" (award number: CNS-1239054)"--Page 5by Devendra Shelar.Ph. D. in Computational Science and EngineeringPh.D.inComputationalScienceandEngineering Massachusetts Institute of Technology, Department of Civil and Environmental Engineerin

DER Allocation and Line Repair Scheduling for Storm-induced Failures in Distribution Networks

Electricity distribution networks (DNs) in many regions are increasingly subjected to disruptions caused by tropical storms. Distributed Energy Resources (DERs) can act as temporary supply sources to sustain 'microgrids' resulting from disruptions. In this paper, we investigate the problem of suitable DER allocation to facilitate more efficient repair operations and faster recovery. First, we estimate the failure probabilities of DN components (lines) using a stochastic model of line failures which parametrically depends on the location-specific storm wind field. Next, we formulate a two-stage stochastic mixed integer program, which models the distribution utility's decision to allocate DERs in the DN (pre-storm stage); and accounts for multi-period decisions on optimal dispatch and line repair scheduling (post-storm stage). A key feature of this formulation is that it jointly optimizes electricity dispatch within the individual microgrids and the line repair schedules to minimize the sum of the cost of DER allocation and cost due to lost load. To illustrate our approach, we use the sample average approximation method to solve our problem for a small-size DN under different storm intensities and DER/crew constraints

Phase 3 RCT comparing docetaxel-platinum with docetaxel-platinum-5FU as neoadjuvant chemotherapy in borderline resectable oral cancer

Background: Neoadjuvant chemotherapy (NACT) with TPF (docetaxel, cisplatin, and 5FU) is one of the treatment options in very locally advanced oral cancer with a survival advantage over PF (cisplatin and 5FU). TP (docetaxel and cisplatin) has shown promising results with a lower rate of adverse events but has never been compared to TPF. Methods: In this phase 3 randomized superiority study, adult patients with borderline resectable locally advanced oral cancers were randomized in a 1:1 fashion to either TP or TPF. After the administration of 2 cycles, patients were evaluated in a multidisciplinary clinic and further treatment was planned. The primary endpoint was overall survival (OS) and secondary endpoints were progression-free survival (PFS) and adverse events. Results: 495 patients were randomized in this study, 248 patients in TP arm and 247 in TPF arm. The 5-year OS was 18.5% (95% CI 13.8–23.7) and 23.9% (95% CI 18.1-30.1) in TP and TPF arms, respectively (Hazard ratio 0.778; 95% CI 0.637–0.952; P = 0.015). Following NACT, 43.8% were deemed resectable, but 34.5% underwent surgery. The 5-year OS was 50.7% (95% CI 41.5–59.1) and 5% (95%CI 2.9–8.1), respectively, in the surgically resected versus unresected cohort post NACT (P &lt; 0.0001). Grade 3 or above adverse events were seen in 97 (39.1%) and 179 (72.5%) patients in the TP and TPF arms, respectively (P &lt; 0.0001). Conclusion: NACT with TPF has a survival benefit over TP in borderline resectable oral cancers, with an increase in toxicity which is manageable. Patients who undergo surgery achieve a relatively good, sustained survival.</p