Browsing by Author "Calero, Ivan"

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A Review of Modeling and Applications of Energy Storage Systems in Power Grids
(Institute of Electrical and Electronics Engineers (IEEE), 2022-03-25) Calero, Fabian; Cañizares, Claudio A.; Bhattacharya, Kankar; Anierobi, Chioma; Calero, Ivan; Zambroni de Souza, Matheus F.; Farrokhabadi, Mostafa; Guzman, Noela Sofia; Mendieta, William; Peralta, Dario; Solanki, Bharatkumar V.; Padmanabhan, Nitin; Violante, Walter
As the penetration of variable renewable generation increases in power systems, issues, such as grid stiffness, larger frequency deviations, and grid stability, are becoming more relevant, particularly in view of 100% renewable energy networks, which is the future of smart grids. In this context, energy storage systems (ESSs) are proving to be indispensable for facilitating the integration of renewable energy sources (RESs), are being widely deployed in both microgrids and bulk power systems, and thus will be the hallmark of the clean electrical grids of the future. Hence, this article reviews several energy storage technologies that are rapidly evolving to address the RES integration challenge, particularly compressed air energy storage (CAES), flywheels, batteries, and thermal ESSs, and their modeling and applications in power grids. An overview of these ESSs is provided, focusing on new models and applications in microgrids and distribution and transmission grids for grid operation, markets, stability, and control.
Compressed Air Energy Storage System Modeling for Power System Studies
(Institute of Electrical and Electronics Engineers (IEEE), 2019-02-25) Calero, Ivan; Canizares, Claudio A.; Bhattacharya, Kankar
In this paper, a detailed mathematical model of the diabatic compressed air energy storage (CAES) system and a simplified version are proposed, considering independent generators/motors as interfaces with the grid. The models can be used for power system steady-state and dynamic analyses. The models include those of the compressor, synchronous motor, cavern, turbine, synchronous generator, and associated controls. The configuration and parameters of the proposed models are based on the existing bulk CAES facilities of Huntorf, Germany. The models and performance of the CAES system are first evaluated with step responses, and then examined when providing frequency regulation in a test power system with high penetration of wind generation, comparing them with existing models of CAES systems. The simulation results confirm that the dynamic responses of the detailed and simplified CAES models are similar, and demonstrate that the simultaneous charging and discharging can significantly contribute to reduce the frequency deviation of the system from the variability of the wind farm power.
Duck-Curve Mitigation in Power Grids With High Penetration of PV Generation
(Institute of Electrical and Electronics Engineers (IEEE), 2021-10-25) Calero, Ivan; Canizares, Claudio A.; Bhattacharya, Kankar; Baldick, Ross
Small-scale PV generation has become popular with residential customers in several jurisdictions with high solar radiation, as an alternative to improve their carbon footprint and reduce their electricity bills. However, massive deployment of such distributed generation is creating a particular and undesirable shape in the net demand, which deepens at hours of peak solar PV injections at noon and suddenly rises towards the evening, known as the “duck curve”. Hence, this paper investigates the use of pre-cooling strategies in residential households to mitigate the duck-curve effects. To this aim, appropriate thermal models and simulations of houses are first developed and carried out to demonstrate the technical feasibility of pre-cooling in a house with a typical configuration, based on the Smart Residential Load Simulator (SRLS) developed at the University of Waterloo. Then, an aggregation technique is proposed to evaluate the effects on a large grid of different penetration levels of PV, and pre-cooling approaches to manage the duck-curve in California and Texas, concluding that such techniques are capable of substantially flattening the system net demand curve.
Implementation of Transient Stability Model of Compressed Air Energy Storage Systems
(Institute of Electrical and Electronics Engineers (IEEE), 2020-05-19) Calero, Ivan; Canizares, Claudio A.; Bhattacharya, Kankar
This paper discusses the implementation of a transient stability model of Compressed Air Energy Storage (CAES) systems in a power system analysis package. A block-diagram based model of a two-machine CAES system is proposed, including specific controls for active power, reactive power, and State of Charge (SoC), which consider limits associated with the cavern pressure. As an application, the model is implemented in Powertech's TSAT software connected to the 9-bus WSCC benchmark power system, which is then used to study the impact of a CAES facility in the transient and frequency stability of the system. Several contingencies are simulated comparing the CAES performance to a gas turbine and a base-case without storage, demonstrating that the CAES system improves the system transient stability due to its charging stage, controls, and additional inertia. Finally, the CAES model is used to study the effects of cavern sizes in the frequency of the system. It is shown that CAES systems have certain special characteristics that make them attractive as a storage technology to provide stability and regulation services, besides their energy arbitrage capabilities.
Machine Learning-Based Control of Electric Vehicle Charging for Practical Distribution Systems With Solar Generation
(Institute of Electrical and Electronics Engineers (IEEE), 2023-11-16) Calero, Ivan; Cañizares, Claudio A.; Farrokhabadi, Mostafa; Bhattacharya, Kankar
The adoption of Electric Vehicles (EVs) and solar Photovoltaic (PV) generation by households is rapidly and significantly increasing. Utilities are facing the challenge of efficiently managing EV and PV resources to help mitigate the undesirable effects on grid operation. Existing approaches to solve these issues depend on accurate but hard to predict behavior of EVs and PVs, detailed knowledge of customers, and grid infrastructure, all of which complicate the effective deployment of these resources. Motivated by these practical challenges and in collaboration with industry partners working on addressing these issues, this paper proposes a two-level data-driven smart controller for EV charging in distribution systems. The controller is modeled as a Deep Reinforcement Learning (DRL) agent, which coordinates the charging rates of multiple EVs connected to a realistic residential feeder with high penetration of PV generation. The first level coordinates the aggregated EV load at distribution Medium Voltage (MV) level to provide Demand Response (DR) services; at the Low Voltage (LV) level it aims to maximize the EVs’ state of charge at departure while avoiding the overloading of the MV/LV distribution transformers. The controller is verified through simulations on an actual utility grid facing the aforementioned challenges, demonstrating the effectiveness and practicality of the proposed DRL-based smart charging approach.