Spring Technical Conference Night 4: Energy Storage and Uncertainties
The SPRING TECH CONFERENCE is ONLINE again this year! Orlando IEEE PES/IAS/PEL is holding our annual Technical Conference and this year we are continuing to do it virtually. Hopefully by Spring 2023 we can return to a hybrid model that includes in person participation and great food!. A Webex link will be sent to registered attendees the day before the meeting.
CEUs Being Offered! For those who would like CEU's good towards your professional license we will offer 2.5 hours (0.25 CEU) per night. Please register for CEU's in the registration section and be sure you connect to the Webex with your name visible. We will be taking roll at the start and finish of the meeting for those asking for CEU credits.
If you are registering for all four nights, please select the single night option for the first three nights, and then on the fourth night select the fourth-night-free option.
Date and Time
Location
Hosts
Registration
- Date: 26 Apr 2022
- Time: 06:00 PM to 08:30 PM
- All times are (GMT-05:00) US/Eastern
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- Orlando, Florida
- United States
- Starts 25 February 2022 03:10 PM
- Ends 26 April 2022 12:00 AM
- All times are (GMT-05:00) US/Eastern
- Admission fee ?
- Menu: Yes, I would like CEUs, No, I do not need CEUs
Speakers
Hamed Haggi
Optimal Hydrogen Systems Scheduling for Improved Utility Operations: Towards Deep Energy Decarbonization
Decreasing costs of renewable energy resources and net-zero emission energy production policy, set by U.S. government, are two preeminent factors that motivates power utilities to deploy more distributed energy resources to decarbonize electricity production. Since energy decarbonization cannot be achieved without high penetration of renewables, utilities should develop and invest in new business models for power system operation, planning, and resilience improvement during the transition. In recent years, hydrogen energy has demonstrated a great potential for large deployment from economic, environmental, and technical viewpoints. Generally, hydrogen energy can be produced, compressed, stored, and consumed using an electrolyzer, a compressor, a storage tank, and a fuel cell (FC). There are various technologies for water electrolysis such as alkaline, proton exchange membrane, and solid oxide electrolysis, in which electricity is used to decouple water into oxygen and hydrogen. The produced hydrogen can then be compressed and stored in a moderate or high-pressure storage tank, in the form of cryogenic liquid or high-pressure gas. The most important applications of hydrogen energy are 1) power generation with stationary/mobile FC units; 2) transportation fuel for light- and heavy-duty FC vehicles; 3) fuel for residential and commercial buildings (e.g. space and water heating); 4) feedstock for ammonia production, etc., 5) assist utilities with technical concerns such as PV smoothing, voltage regulations, etc. This talk will focus on the optimal scheduling of hydrogen systems for improved utility operations in both normal and emergency operation modes. Additionally, the benefits of hydrogen systems in energy decarbonization of power and transportation sectors, cyber-physical resilience improvement, etc. compared to existing battery technologies such as Li-ion and vanadium redox flow batteries will be discussed.
Ren Hu
Data-driven Optimization of Power System Operation under Uncertainty
The multi-period dynamics of energy storage (ES), intermittent renewable generation and uncontrollable power loads, make the optimization of power system operation (PSO) challenging. A multi-period optimal PSO under uncertainty is formulated using the chance-constrained optimization (CCO) modeling paradigm, where the constraints include the nonlinear energy storage and AC power flow models. Based on the emerging scenario optimization method which does not rely on pre-known probability distribution, this paper develops a novel solution method for this challenging CCO problem. The proposed method is computationally effective for mainly two reasons. First, the original AC power flow constraints are approximated by a set of learning-assisted quadratic convex formulations based on a generalized least absolute shrinkage and selection operator (LASSSO). Second, considering the physical patterns of data and driven by the learning-based sampling, the strategic sampling method is developed to significantly reduce the required number of scenarios by different sampling strategies. The simulation results on IEEE standard systems indicate that 1) the proposed strategic sampling significantly improves the computational efficiency of the scenario-based approach for solving the chance-constrained optimal PSO problem, 2) the data-driven convex approximation of power flow can be promising alternatives of nonlinear and nonconvex AC power flow.