### Core team:

# Pooya Rezaei

### OATI

### Software Engineer, OATI

#### Most recent papers:

Cascading power outages propagate locally in an influence graph that is not the actual grid topology.

Paul Hines, Ian Dobson, Pooya Rezaei. IEEE Transactions on Power Systems, 958-967, 32, 2017.[pdf] [journal page]

**Abstract:**

In a cascading power transmission outage, component outages propagate nonlocally; after one component outages, the next failure may be very distant, both topologically and geographically. As a result, simple models of topological contagion do not accurately represent the propagation of cascades in power systems. However, cascading power outages do follow patterns, some of which are useful in understanding and reducing blackout risk. This paper describes a method by which the data from many cascading failure simulations can be transformed into a graph-based model of influences that provides actionable information about the many ways that cascades propagate in a particular system. The resulting “influence graph” model is Markovian, in that component outage probabilities depend only on the outages that occurred in the prior generation. To validate the model, we compare the distribution of cascade sizes resulting from n-2 contingencies in a 2896 branch test case to cascade sizes in the influence graph. The two distributions are remarkably similar. In addition, we derive an equation with which one can quickly identify modifications to the proposed system that will substantially reduce cascade propagation. With this equation, one can quickly identify critical components that can be improved to substantially reduce the risk of large cascading blackouts.

Cascading failures in power systems.

Paul Hines, Pooya Rezaei. Smart Grid Handbook, , , 2016.[pdf] [journal page]

**Abstract:**

Large electric power systems are among the most complex systems created by humankind. One consequence of this complexity is that small unexpected disturbances trigger long chains of cascading component failures that can lead to massive power outages. Because of the many different mechanisms involved and the limited data available from historical cascades, modeling cascading failure is a challenging problem. Furthermore, among all the possible combinations of triggering events a relatively small number lead to large blackouts, which creates difficulties for conventional statistical approaches to risk analysis. In order to facilitate understanding about this complexity, this chapter describes several different approaches to modeling cascading blackouts and subsequently studying the risk of cascades, given a blackout model. Together these results suggest that when combined with good models, risk analysis can provide valuable and actionable insight into cascading failures in power systems.

Estimating cascading failure risk with random chemistry.

Pooya Rezaei, Paul Hines, Margaret (Maggie) Eppstein. IEEE Transactions on Power Systems, 2726-2735, 30, 2015.[pdf] [journal page]

**Abstract:**

The potential for cascading failure in power systems adds substantially to overall reliability risk. Monte Carlo sampling can be used with a power system model to estimate this impact, but doing so is computationally expensive. This paper presents a new approach to estimating the risk of large cascading blackouts triggered by multiple contingencies. The method uses a search algorithm (Random Chemistry) to identify blackout-causing contingencies, and then combines the results with outage probabilities to estimate overall risk. Comparing this approach with Monte Carlo sampling for two test cases (the IEEE RTS-96 and a 2383-bus model of the Polish system) illustrates that the new approach is at least two orders of magnitude faster than Monte Carlo, without introducing measurable bias. Moreover, the approach enables one to compute the sensitivity of overall blackout risk to individual component-failure probabilities in the initiating contingency, allowing one to quickly identify low-cost strategies for reducing risk. By computing the sensitivity of risk to individual initial outage probabilities for the Polish system, we found that reducing three line-outage probabilities by 50% would reduce cascading failure risk by 33%. Finally, we used the method to estimate changes in risk as a function of load. Surprisingly, this calculation illustrates that risk can sometimes decrease as load increases.

Rapid assessment, visualization, and mitigation of cascading failure risk in power systems.

Pooya Rezaei, Margaret (Maggie) Eppstein, Paul Hines. System Sciences (HICSS), 2015 48th Hawaii International Conference on, 2748-2758, , 2015.[pdf] [journal page]

**Abstract:**

This paper describes a new approach, using "Random Chemistry" sampling, to estimate the risk of large cascading blackouts triggered by multiple contingencies. On a 2383 bus test case the new approach finds the expected value of large-blackout sizes (a measure of risk) two orders of magnitude faster than Monte Carlo sampling, without introducing measurable bias. We also derive a method to compute the sensitivity of blackout risk to individual component-failure probabilities, allowing one to quickly identify low-cost strategies for reducing risk. For example, we show how a 1.9% increase in operational costs reduced the overall risk of cascading failure in a 2383-bus test case by 61%. An examination of how risk changes with load yielded a surprising decrease in cascading failure risk at the highest loadings, due to increased locality in generation and less long-distance transmission. Finally, this paper proposes new visualizations of spatio-temporal patterns in cascading failure risk that could provide valuable guidance to system planners and operators.

Estimating cascading failure risk: Comparing Monte Carlo sampling and random chemistry.

Pooya Rezaei, Paul Hines, Margaret (Maggie) Eppstein. PES General Meeting| Conference & Exposition, 2014 IEEE, 1-5, , 2014.[pdf] [journal page]

**Abstract:**

This paper presents a computationally efficient approach to estimate cascading failure risk in power systems. The method uses the previously published Random Chemistry algorithm [1] to find combinations of branch outages that lead to large blackouts, and then estimates risk by computing the expected blackout size based on the probabilities of various contingencies. We compare this method with Monte Carlo simulation, and show that the method is at least an order of magnitude faster than Monte Carlo simulation. Results from the IEEE RTS-96 and the 2383-bus Polish grid are presented in the paper.

Understanding and Managing the Impacts of Electric Vehicles on Electric Power Distribution Systems.

Paul Hines, Jeff Frolik, Jeffrey S. Marshall, Pooya Rezaei, Andrew Seier, et al.. TRC Report 14-010, , , 2014.[pdf] [journal page]

**Abstract:**

While there are a number of useful studies on the greenhouse gas impacts of transportation electrification, only recently have researchers begun to understand the impacts of electricity on electric power infrastructure. Thus, the primary goals of this research project were to understand these impacts in detail and to develop new methods for reducing the impact of transportation electrification on the electricity transmission and distribution infrastructure. In particular, this report focuses on understanding and mitigating the impact of transportation electrification on the medium and low voltage distribution infrastructure, through which electricity is transported from the bulk power grid, through neighborhoods, to individual homes and businesses. This project focused specifically on the impacts of electric vehicles on two key components of power distribution systems: residential service transformers, and underground cables. This project also studied new methods to dynamically adjust the number of electric vehicles that are charging simultaneously, in order to mitigate the risk of electricity infrastructure damage from electric vehicle charging.

Changes in cascading failure risk with generator dispatch method and system load level.

Pooya Rezaei, Paul Hines. T&D Conference and Exposition, 2014 IEEE PES, 1-5, , 2014.[pdf] [journal page]

**Abstract:**

Industry reliability rules increasingly require utilities to study and mitigate cascading failure risk in their system. Motivated by this, this paper describes how cascading failure risk, in terms of expected blackout size, varies with power system load level and pre-contingency dispatch. We used Monte Carlo sampling of random branch outages to generate contingencies, and a model of cascading failure to estimate blackout sizes. The risk associated with different blackout sizes was separately estimated in order to separate small, medium, and large blackout risk. Results from N − 1 secure models of the IEEE RTS case and a 2383 bus case indicate that blackout risk does not always increase with load level monotonically, particularly for large blackout risk. The results also show that risk is highly dependent on the method used for generator dispatch. Minimum cost methods of dispatch can result in larger long distance power transfers, which can increase cascading failure risk.

Packetized Plug-in Electric Vehicle Charge Management.

Pooya Rezaei, Jeff Frolik, Paul Hines. IEEE Transactions on Smart Grid, 642-640, 5, 2014.[pdf] [journal page]

**Abstract:**

Plug-in electric vehicle (PEV) charging could cause significant strain on residential distribution systems, unless technologies and incentives are created to mitigate charging during times of peak residential consumption. This paper describes and evaluates a decentralized and ‘packetized’ approach to PEV charge management, in which PEV charging is requested and approved for time-limited periods. This method, which is adapted from approaches for bandwidth sharing in communication networks, simultaneously ensures that constraints in the distribution network are satisfied, that communication bandwidth requirements are relatively small, and that each vehicle has fair access to the available power capacity. This paper compares the performance of the packetized approach to an optimization method and a first-come, first- served (FCFS) charging scheme in a test case with a constrained 500 kVA distribution feeder and time-of-use residential electricity pricing. The results show substantial advantages for the packetized approach. The algorithm provides all vehicles with equal access to constrained resources and attains near optimal travel cost performance, with low complexity and communication requirements. The proposed method does not require that vehicles report or record driving patterns, and thus provides benefits over optimization approaches by preserving privacy and reducing computation and bandwidth requirements.

Cascading Failure Risk Variation with Generator Dispatch and System Load Level.

Pooya Rezaei, Paul Hines. Preprint, 2013.[pdf] [journal page] [arXiv]

**Abstract:**

Industry reliability rules increasingly require utilities to study and mitigate cascading failure risk in their system. Motivated by this, this paper describes how cascading failure risk, in terms of expected blackout size, varies with power system load level and pre-contingency dispatch. We used Monte Carlo sampling of random branch outages to model contingencies, and a model of cascading failure to estimate blackout sizes. The risk associated with different blackout sizes is separately estimated in order to separate small, medium, and large blackout risk. Results from N-1 secure models of the IEEE RTS case and a 2383 bus case indicate that blackout risk does not always increase with load level, particularly for large blackout risk. The results also show that risk is highly dependent on the method used for generator dispatch. Minimum cost methods of dispatch can result in larger long distance power transfers, which can increase cascading failure risk.

Estimating the Impact of Electric Vehicle Smart Charging on Distribution Transformer Aging.

Alexander Hilshey, Paul Hines, Pooya Rezaei, Jonathan Dowds. IEEE Transactions on Smart Grid, 905-913, 4, 2013.[pdf] [journal page]

**Abstract:**

This paper describes a method for estimating the impact of plug-in electric vehicle (PEV) charging on overhead distribution transformers, based on detailed travel demand data and under several different schemes for mitigating overloads by shifting PEV charging times (smart charging). The paper also presents a new smart charging algorithm that manages PEV charging based on estimated transformer temperatures. We simulated the varied behavior of drivers from the 2009 National Household Transportation Survey, and transformer temperatures based an IEEE standard dynamic thermal model. Results are shown for Monte Carlo simulation of a 25 kVA overhead distribution transformer, with ambient temperature data from hot and cold climate locations, for uncontrolled and several smart-charging scenarios. These results illustrate the substantial impact of ambient temperatures on distribution transformer aging, and indicate that temperature-based smart charging can dramatically reduce both the mean and variance in transformer aging without substantially reducing the frequency with which PEVs obtain a full charge. Finally, the results indicate that simple smart charging schemes, such as delaying charging until after midnight can actually increase, rather than decrease, transformer aging.

Electric vehicle charging: Transformer impacts and smart, decentralized solutions.

Alexander Hilshey, Pooya Rezaei, Paul Hines, Jeff Frolik. Power and Energy Society General Meeting, 2012 IEEE, 1-8, , 2012.[pdf] [journal page]

**Abstract:**

This paper compares distribution transformer aging impacts resulting from plug-in electric vehicles charging under AC Level 1 versus AC Level 2 charging conditions. Additionally, we propose an algorithm for PEV smart charging and evaluate its effectiveness on transformer aging. We use a Monte Carlo simulation of a 25kVA distribution transformer, with ambient temperature data from Burlington, VT and Phoenix, AZ, to calculate transformer aging under both uncoordinated and smart charging conditions. The results indicate more substantial aging as a result of AC Level 2 charging compared to AC Level 1. Smart charging can significantly mitigate these effects. We also present a more decentralized approach to smart charging and compare two distributed automaton-based charge management strategies, which both prevent the transformer from becoming overloaded. These methods give vehicle owners the ability to select among charging priorities in an environment in which the vehicles manage their charging autonomously.

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