arXiv subjects

Masood Parvania

Publications and source records attributed to Masood Parvania.

9 recordsLinked to original sources

arXiv2025

A Review of Community-Centric Power System Resilience: Strategies, Data-Driven Methods, and Techno-Legal Perspectives

This paper presents a comprehensive review of community-centric power system resilience, emphasizing the integration of community-level resilience considerations and techno-legal governance frameworks with engineering-based resilience enhancement strategies and data-driven approaches to address extreme events. Recent large-scale outages have demonstrated that power disruptions can cascade beyond electrical infrastructure and disproportionately affect vulnerable communities, critical services, and interconnected urban systems, highlighting the need for resilience approaches that integrate technical, social, and regulatory dimensions. Within this community-centric perspective, the review first summarizes state-of-the-art strategies for enhancing power system resilience, including network hardening, resource allocation, optimal scheduling, and system reconfiguration techniques, while highlighting the growing role of artificial intelligence (AI) and data-driven analytics in supporting resilience planning and operational decision-making. It then examines the interdependencies between power system resilience and community resilience, addressing socioeconomic and behavioral dimensions, cross-infrastructure interconnections, and the emerging role of resilience hubs. The paper further examines the techno-legal frameworks governing resilient energy systems by comparing the regulatory landscapes of the European Union (EU) and the United States, highlighting key similarities and distinctions that shape resilience planning and implementation. By analyzing state-of-the-art engineering-based, AI-driven, and techno-legal methods for assessing and mitigating the impacts of high-impact, low-probability (HILP) events, the review identifies critical research gaps and outlines promising directions for future investigation.

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arXiv2023

Scalable Grid-Aware Dynamic Matching using Deep Reinforcement Learning

This paper proposes a two-level hierarchical matching framework for Integrated Hybrid Resources (IHRs) with grid constraints. An IHR is a collection of Renewable Energy Sources (RES) and flexible customers within a certain power system zone, endowed with an agent to match. The key idea is to pick the IHR zones so that the power loss effects within the IHRs can be neglected. This simplifies the overall matching problem into independent IHR-level matching problems and an upper-level optimal power flow problem to meet the IHR-level upstream flow requirements while respecting the grid constraints. Within each IHR, the agent employs a scalable Deep Reinforcement Learning algorithm to identify matching solutions such that the customer's service constraints are met. The central agent then solves an optimal power flow problem with the IHRs as the nodes, with their active power flow and reactive power {capacities}, and grid constraints to scalably determine the final flows such that matched power can be delivered to the extent the grid constraints are satisfied. The proposed framework is implemented on a test power distribution system, and multiple case studies are presented to substantiate the welfare efficiency of the proposed solution and the satisfaction of the grid and customers' servicing constraints.

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arXiv2022

Cyber-Resilient Frequency Control of Power Grids with Energy Storage Systems

The integration of synchronous generators and energy storage systems operated through communication networks introduces new challenges and vulnerabilities to the electric grid, where cyber attacks can corrupt sensor measurements or control inputs and interrupt functions such as frequency regulation. This paper proposes a defense methodology for the design of resilient operating constraints imposed on each generation and storage unit in order to prevent any attack sequence from driving the system's frequency to unsafe conditions. The resilient operating constraints are found by using ellipsoidal approximations of the reachable set of the power system, leading to a convex optimization problem with linear matrix inequalities. Numerical results in a single-area power system with synchronous generation and energy storage demonstrate how the resilient constraints provide security guarantees against any type of attack affecting frequency measurements or controller setpoints.

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arXiv2021

Dynamic Matching Markets in Power Grid: Concepts and Solution using Deep Reinforcement Learning

Traditional bulk load flexibility options, such as load shifting and load curtailment, for managing uncertainty in power markets limit the diversity of options and ignore the preferences of the individual loads, thus reducing efficiency and welfare. This paper proposes an alternative to bulk load flexibility options for managing uncertainty in power markets: a reinforcement learning based dynamic matching market. We propose a novel hybrid learning-based model for maximizing social welfare in the dynamic matching market. The key features of our model is a fixed rule-based function and a learnable component that can be trained by data gathered online with no prior knowledge or expert supervision. The output of the learnable component is a probability distribution over the matching decisions for the individual customers. The proposed hybrid model enables the learning algorithm to find an effective matching policy that simultaneously satisfies the customers' servicing preferences. The simulations show that the learning algorithm learns an effective matching policy for different generation-consumption profiles and exhibits better performance compared to standard online matching heuristics such as Match on Arrival, Match to the Highest, and Match to the Earliest Deadline policies.

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arXiv2021

Chaos Engineering for Enhanced Resilience of Cyber-Physical Systems

Cyber-physical systems (CPS) incorporate the complex and large-scale engineered systems behind critical infrastructure operations, such as water distribution networks, energy delivery systems, healthcare services, manufacturing systems, and transportation networks. Industrial CPS in particular need to simultaneously satisfy requirements of available, secure, safe and reliable system operation against diverse threats, in an adaptive and sustainable way. These adverse events can be of accidental or malicious nature and may include natural disasters, hardware or software faults, cyberattacks, or even infrastructure design and implementation faults. They may drastically affect the results of CPS algorithms and mechanisms, and subsequently the operations of industrial control systems (ICS) deployed in those critical infrastructures. Such a demanding combination of properties and threats calls for resilience-enhancement methodologies and techniques, working in real-time operation. However, the analysis of CPS resilience is a difficult task as it involves evaluation of various interdependent layers with heterogeneous computing equipment, physical components, network technologies, and data analytics. In this paper, we apply the principles of chaos engineering (CE) to industrial CPS, in order to demonstrate the benefits of such practices on system resilience. The systemic uncertainty of adverse events can be tamed by applying runtime CE-based analyses to CPS in production, in order to predict environment changes and thus apply mitigation measures limiting the range and severity of the event, and minimizing its blast radius.

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arXiv2020

Spatio-Temporal Visualization of Interdependent Battery Bus Transit and Power Distribution Systems

The high penetration of transportation electrification and its associated charging requirements magnify the interdependency of the transportation and power distribution systems. The emergent interdependency requires that system operators fully understand the status of both systems. To this end, a visualization tool is presented to illustrate the interdependency of battery bus transit and power distribution systems and the associated components. The tool aims at monitoring components from both systems, such as the locations of electric buses, the state of charge of batteries, the price of electricity, voltage, current, and active/reactive power flow. The results showcase the success of the visualization tool in monitoring the bus transit and power distribution components to determine a reliable cost-effective scheme for spatio-temporal charging of electric buses.

cs.HC↗

arXiv2020

Impact of COVID-19 on Mobility and Electric Vehicle Charging Load

The COVID-19 pandemic has depressed overall mobility across the country. The changes seen reflect responses to new COVID-19 cases, local health guidelines, and seasonality, making the relationship between mobility and COVID-19 unique from region to region. This paper presents a data-driven case study of electric vehicle (EV) charging and mobility in the wake of COVID-19. The study shows that the number of EV charging sessions and total energy consumed per day dropped by 40% immediately after the arrival of the first COVID-19 case in Utah. By contrast, the energy consumed per charging session fell by just 8% over the same periods and the distribution of session start and end times remained consistent throughout the year. While EV mobility dropped more dramatically than total vehicle mobility during the first wave of COVID-19 cases, and returned more slowly, both returned to stable levels near their mean values by September 2020, despite a dramatic third wave in new infections.

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arXiv2020

Online Algorithms for Dynamic Matching Markets in Power Distribution Systems

This paper proposes online algorithms for dynamic matching markets in power distribution systems, which at any real-time operation instance decides about matching -- or delaying the supply of -- flexible loads with available renewable generation with the objective of maximizing the social welfare of the exchange in the system. More specifically, two online matching algorithms are proposed for the following generation-load scenarios: (i) when the mean of renewable generation is greater than the mean of the flexible load, and (ii) when the condition (i) is reversed. With the intuition that the performance of such algorithms degrades with increasing randomness of the supply and demand, two properties are proposed for assessing the performance of the algorithms. First property is convergence to optimality (CO) as the underlying randomness of renewable generation and customer loads goes to zero. The second property is deviation from optimality, is measured as a function of the standard deviation of the underlying randomness of renewable generation and customer loads. The algorithm proposed for the first scenario is shown to satisfy CO and a deviation from optimal that varies linearly with the variation in the standard deviation. But the same algorithm is shown to not satisfy CO for the second scenario. We then show that the algorithm proposed for the second scenario satisfies CO and a deviation from optimal that varies linearly with the variation in standard deviation plus an offset.

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arXiv2020

Hydrothermal Scheduling in the Continuous-Time Framework

Continuous-time optimization models have successfully been used to capture the impact of ramping limitations in power systems. In this paper, the continuous-time framework is adapted to model flexible hydropower resources interacting with slow-ramping thermal generators to minimize the hydrothermal system cost of operation. To accurately represent the non-linear hydropower production function with forbidden production zones, binary variables must be used when linearizing the discharge variables and the continuity constraints on individual hydropower units must be relaxed. To demonstrate the performance of the proposed continuous-time hydrothermal model, a small-scale case study of a hydropower area connected to a thermal area through a controllable high-voltage direct current (HVDC) cable is presented. Results show how the flexibility of the hydropower can reduce the need for ramping by thermal units triggered by intermittent renewable power generation. A reduction of 34% of the structural imbalances in the system is achieved by using the continuous-time model.

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