Publications

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In this work, a model predictive dispatch framework is proposed to utilize Energy Storage Systems (ESSs) for voltage regulation in distribution systems. The objective is to utilize ESS resources to assist with voltage regulation while reducing the utilization of legacy devices such as on-load tap changers (OLTCs), capacitor banks, etc. The proposed framework is part of a two-stage solution where a secondary layer computes the ESS dispatch every 5-min based on 1-hr generation and load forecasts while a primary layer would handle the real-time uncertainties. In this paper, the secondary layer to dispatch the ESS is formulated. Simulation results show that dispatching ESSs by providing active and reactive support can minimize the OLTC movement in distribution networks thus increasing the lifetime of legacy mechanical devices.

The lack of inertial response from non-synchronous, inverter-based generation in microgrids makes the power system vulnerable to a large rate of change of frequency (ROCOF) and frequency excursions. Energy storage systems (ESSs) can be utilized to provide fast-frequency support to prevent such large excursions in the system. However, fast-frequency support is a power-intensive application that has a significant impact on the ESS lifetime. In this paper, a framework that allows the ESS operator to provide fast-frequency support as a service is proposed. The framework maintains the desired quality-of-service (limiting the ROCOF and frequency) while taking into account the ESS lifetime and physical limits. The framework utilizes moving horizon estimation (MHE) to estimate the frequency deviation and ROCOF from noisy phase-locked loop (PLL) measurements. These estimates are employed by a model predictive control (MPC) algorithm that computes control actions by solving a finite-horizon, online optimization problem. Additionally, this approach avoids oscillatory behavior induced by delays that are common when using low-pass filters as with traditional derivative-based (virtual inertia) controllers. MATLAB/Simulink simulations on a test system from Cordova, Alaska, show the effectiveness of the MHE-MPC approach to reduce frequency deviations and ROCOF of a low-inertia microgrid.

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In this work, we introduce the concept of virtual transmission using large-scale energy storage systems. We also develop an optimization framework to maximize the monetized benefits of energy storage providing virtual transmission in wholesale markets. These benefits often come from relieving congestion for a transmission line, including both reduction in energy cost for the downstream loads and increase in production revenue for the upstream generators of the congested line. A case study is conducted using ISO-New England data to demonstrate the framework.

The displacement of rotational generation and the consequent reduction in system inertia is expected to have major stability and reliability impacts on modern power systems. Fast-frequency support strategies using energy storage systems (ESSs) can be deployed to maintain the inertial response of the system, but information regarding the inertial response of the system is critical for the effective implementation of such control strategies. In this paper, a moving horizon estimation (MHE)-based approach for online estimation of inertia constant of low inertia microgrids is presented. Based on the frequency measurements obtained in response to a non-intrusive excitation signal from an ESS, the inertia constant was estimated using local measurements from the ESS's phase-locked loop. The proposed MHE formulation was first tested in a linearized power system model, followed by tests in a modified microgrid benchmark from Cordova, Alaska. Even under moderate measurement noise, the technique was able to estimate the inertia constant of the system well within ±20% of the true value. Estimates provided by the proposed method could be utilized for applications such as fast-frequency support, adaptive protection schemes, and planning and procurement of spinning reserves.

The lack of inertial response from non-synchronous, inverter-based generation in microgrids makes the power system vulnerable to a large rate of change of frequency (ROCOF) and frequency excursions. Energy storage systems (ESSs) can be utilized to provide fast-frequency support to prevent such large excursions in the system. However, fast-frequency support is a power-intensive application that has a significant impact on the ESS lifetime. In this paper, a framework that allows the ESS operator to provide fast-frequency support as a service is proposed. The framework maintains the desired quality-of-service (limiting the ROCOF and frequency) while taking into account the ESS lifetime and physical limits. The framework utilizes moving horizon estimation (MHE) to estimate the frequency deviation and ROCOF from noisy phase-locked loop (PLL) measurements. These estimates are employed by a model predictive control (MPC) algorithm that computes control actions by solving a finite-horizon, online optimization problem. Additionally, this approach avoids oscillatory behavior induced by delays that are common when using low-pass filters as with traditional derivative-based (virtual inertia) controllers. MATLAB/Simulink simulations on a test system from Cordova, Alaska, show the effectiveness of the MHE-MPC approach to reduce frequency deviations and ROCOF of a low-inertia microgrid.

Energy storage systems (ESSs) are being deployed widely due to numerous benefits including operational flexibility, high ramping capability, and decreasing costs. This study investigates the economic benefits provided by battery ESSs when they are deployed for market-related applications, considering the battery degradation cost. A comprehensive investment planning framework is presented, which estimates the maximum revenue that the ESS can generate over its lifetime and provides the necessary tools to investors for aiding the decision making process regarding an ESS project. The applications chosen for this study are energy arbitrage and frequency regulation. Lithium-ion batteries are considered due to their wide popularity arising from high efficiency, high energy density, and declining costs. A new degradation cost model based on energy throughput and cycle count is developed for Lithium-ion batteries participating in electricity markets. The lifetime revenue of ESS is calculated considering battery degradation and a cost-benefit analysis is performed to provide investors with an estimate of the net present value, return on investment and payback period. The effect of considering the degradation cost on the estimated revenue is also studied. The proposed approach is demonstrated on the IEEE Reliability Test System and historical data from PJM Interconnection.

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