A Risk-Managed Steady-State Analysis to Assess the Impact of Power Grid Uncertainties

Electricity systems are experiencing increased effects of randomness and variability due to emerging stochastic assets. The increased effects introduce new uncertainties into power systems that can impact system operability and reliability. Existing steady-state methods for assessing system-level operability and reliability are primarily deterministic, therefore, ill-suited to capture randomness and variability. This work introduces a probabilistic steady-state analysis inspired by statistical worst-case circuit analysis to evaluate the risk of operational violations due to stochastic resources. Compared to parallelized Monte Carlo analyses (MCS), we have seen up to 24x improvement in runtime speed using our approach without significant loss of probabilistic accuracy for a Texas7k low-wind day test system.Comment: Submitted for publication to IEEE Transactions in Power Systems and pending revie

Halo - A Personal IoT Air Monitor Powered by Harvested Energy

Urban air pollution leads to widespread respiratory illness and millions of deaths annually. PM2.5, particulate matter with a diameter less than 2.5 micrometers, is the product of many common combustion reactions and poses a particularly serious health risk. Its small size allows it to penetrate deep into the lungs and enter the bloodstream. Existing air quality monitors are aimed at scientific research, di↵erentiating between pollutants and providing high accuracy in measurement. These devices are prohibitively expensive and cannot easily be carried around. Due to the highly localized nature of air pollution, and in order to allow individuals and institutions to easily monitor their real-time exposure to PM2.5, we propose Halo, an air quality monitor costing less than $100. Halo is powered by a 500 mW solar panel and equipped with a 1500 mAh Lithium-Ion battery in order to handle 150 mW peak power consumption and operate continuously for over 24 hours without power input. The device is small enough to be clipped to a backpack or bag for easy portability, and it can be used in personal or public settings. Using an IR emitter and detector, Halo measures reflected IR light to determine the particulate concentration in the air with an error less than 10%. It uses Bluetooth Low Energy (BLE) to communicate these values to a user’s phone. From the phone, air data can be time-stamped, stored in a cloud database, and visualized in an app for easy monitoring of pollution trends and pollution exposure. Additionally, the cloud database allows for the aggregation of data from multiple devices to create crowdsourced pollution maps. These maps can be used to pinpoint areas with particularly bad air quality in order to try to make changes to these areas or to help users to know to avoid these areas in possible

A Steady-State Risk Analysis and Mitigation Framework for Power Systems

Uncertainty in electricity systems is rising partly due to the electricity generation resource mix transforming from primarily fossil-fuel dependent to increasingly reliant on variable renewable energy (VRE) resources. The increasing uncertainty in electricity systems due to the changing resource mix could introduce risks to the reliable planning and operation of electricity systems without new methods and tools.  One area of concern is day-ahead operations. During day-ahead operations, operators schedule generation, procure ancillary services, and set reserve requirements 24 hours before operation to meet the next day’s load demand and ensure grid performance metrics remain within system operating limits. Commercial systems-level steady-state analysis and optimization tools used in day-ahead operations do not use uncertainty analysis and optimization methods. Uncertainty analysis and optimization methods could help assess and mitigate reliability risks to electricity systems introduced by VRE resources.  This thesis develops a steady-state uncertainty analysis method, an optimization formulation, and a framework for deploying the approaches. The method, formulation, and framework evaluate the risk of grid performance metrics violating system operating limits in day-ahead electricity systems operations caused by VRE resources.  The steady-state uncertainty analysis method, known as Risk-Managed Steady-State Analysis (RMSA), can assess system operating limit violation risks to power grids due to the worst-case power uncertainty of VRE generators. Results for hundreds of scenarios show that RMSA estimates worst-case bus voltage magnitude and line flows without significant loss of probabilistic accuracy and provides runtime speedups of up to 21x when compared to parallelized Monte Carlo analyses using 32 CPU cores.  The steady-state uncertainty optimization method, Risk-Managed Steady-State Optimization (RMSO), is a nonlinear optimization that uses outputs provided by RMSA to change the electricity generation dispatched on a grid and prevent system operating limit violations. Results for hundreds of scenarios demonstrate that an RMSO implementation found a feasible solution for 100% of scenarios assessed without significantly changing the generation dispatched on the grid.  The framework and software package for deploying both methods is the Solver for Uncertainty in Power and Energy Resources (SUPER). An analysis of a 2030 synthetic New York transmission system model shows that by using SUPER, it is possible to identify and mitigate the system operating limit violation risks to the New York system. In sum, the methods and framework created could empower grid operators to perform system-level steady-state analyses and optimizations that mitigate uncertainty. </p