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.
Increasing installation of renewable energy resources makes the power system inertia a time-varying quantity. Furthermore, converter-dominated grids have different dynamics compared to conventional grids and therefore estimates of the inertia constant using existing dynamic power system models are unsuitable. In this paper, a novel inertia estimation technique based on convolutional neural networks that use local frequency measurements is proposed. The model uses a non-intrusive excitation signal to perturb the system and measure frequency using a phase-locked loop. The estimated inertia constants, within 10% of actual values, have an accuracy of 97.35% and root mean square error of 0.2309. Furthermore, the model evaluated on unknown frequency measurements during the testing phase estimated the inertia constant with a root mean square error of 0.1763. The proposed model-free approach can estimate the inertia constant with just local frequency measurements and can be applied over traditional inertia estimation methods that do not incorporate the dynamic impact of renewable energy sources.
This paper investigates the design of low-level probing signals for accurate estimation of inertia and damping constants in microgrids. Increasing utilization of renewable energy sources and their different dynamics has created unknowns in time-varying system inertia and damping constants. Thus, it is difficult to know these parameters at any given time in converter-dominated microgrids. This paper describes the design characteristics, considerations, methodology, and accuracy level of different probing signals in determining unknown parameters of a system. The main goal of this paper is to find an effective probing signal with a simple implementation and minimal impacts on power system operation. The test-case model in this paper analyzes nonintrusive excitation signals to perturb a power system model (i.e., square wave, multisine wave, filtered white Gaussian noise, and pseudo-random binary sequence). A moving horizon estimation (MHE)-based approach is then implemented in an energy storage system (ESS) in MATLAB/Simulink for estimation of inertia and damping constants of a system based on frequency measurements from a local phase-locked-loop (PLL). The accuracy of parameter estimates alters depending on the chosen probing signal; when estimating inertia and damping constants using MHE with the different probing signals, square waves yielded the lowest error.
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.
Fast-frequency control strategies have been proposed in the literature to maintain inertial response of electric generation and help with the frequency regulation of the system. However, it is challenging to deploy such strategies when the inertia constant of the system is unknown and time-varying. In this paper, we present a data-driven system identification approach for an energy storage system (ESS) operator to identify the inertial response of the system (and consequently the inertia constant). The method is first tested and validated with a simulated genset model using small changes in the system load as the excitation signal and measuring the corresponding change in frequency. The validated method is then used to experimentally identify the inertia constant of a genset. The inertia constant of the simulated genset model was estimated with an error of less than 5% which provides a reasonable estimate for the ESS operator to properly tune the parameters of a fast-frequency controller.