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Extreme outside temperatures resulting from heat waves, winter storms, and similar weather-related events trigger the Heating Ventilation and Air Conditioning (HVAC) systems, resulting in challenging, and potentially catastrophic, peak loads. As a consequence, such extreme outside temperatures put a strain on power grids and may thus lead to blackouts. To avoid the financial and personal repercussions of peak loads, demand response and power conservation represent promising solutions. Despite numerous efforts, it has been shown that the current state-of-the-art fails to consider (1) the complexity of human behavior when interacting with power conservation systems and (2) realistic home-level power dynamics. As a consequence, this leads to approaches that are (1) ineffective due to poor long-term user engagement and (2) too abstract to be used in real-world settings. In this article, we propose an auction theory-based power conservation framework for HVAC designed to address such individual human component through a three-fold approach:personalized preferencesof power conservation,models of realistic user behavior, andrealistic home-level power dynamics. In our framework, the System Operator sends Load Serving Entities (LSEs) the required power saving to tackle peak loads at the residential distribution feeder. Each LSE then prompts its users to providebids, i.e.,personalized preferencesof thermostat temperature adjustments, along with corresponding financial compensations. We employmodels of realistic user behaviorby means of online surveys to gather user bids and evaluate user interaction with such system.Realistic home-level power dynamicsare implemented by our machine learning-based Power Saving Predictions (PSP) algorithm, calculating the individual power savings in each user’s home resulting from such bids. A machine learning-based PSPs algorithm is executed by the users’ Smart Energy Management System (SEMS). PSP translates temperature adjustments into the corresponding power savings. Then, the SEMS sends bids back to the LSE, which selects the auction winners through an optimization problem called POwer Conservation Optimization (POCO). We prove that POCO is NP-hard, and thus provide two approaches to solve this problem. One approach is an optimal pseudo-polynomial algorithm called DYnamic programming Power Saving (DYPS), while the second is a heuristic polynomial time algorithm called Greedy Ranking AllocatioN (GRAN). EnergyPlus, the high-fidelity and gold-standard energy simulator funded by the U.S. Department of Energy, was used to validate our experiments, as well as to collect data to train PSP. We further evaluate the results of the auctions across several scenarios, showing that, as expected, DYPS finds the optimal solution, while GRAN outperforms recent state-of-the-art approaches.
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Trust-based User Interface Design for Islanded Alternating Current Microgrids
Microgrid systems can provide extensive information using their measurement units to the operators. As microgrid systems become more pervasive, there will be a need to adjust the information an operator requires to provide an optimized user-interface. In this paper, a combinatorial optimization strategy is used to provide an optimal user-interface for the microgrid operator that selects information for display depending on the operator's trust level in the system, and the assigned task. We employ a method based on sensor placement by capturing elements of the interface as different sensors, that find an optimal set of sensors via combinatorial optimization. However, the typical inverter-based microgrid model poses challenges for the combinatorial optimization due to its poor conditioning. To combat the poor conditioning, we decompose the model into its slow and fast dynamics, and focus solely on the slow dynamics, which are more well conditioned. We presume the operator is tasked with monitoring phase angle and active and reactive power control of inverter-based distributed generators. We synthesize user-interface for each of these tasks under a wide range of trust levels, ranging from full trust to no trust. We found that, as expected, more information must be included in the interface when the operator has low trust. Further, this approach exploits the dynamics of the underlying microgrid to minimize information content (to avoid overwhelming the operator). The effectiveness of proposed approach is verified by modeling an inverter-based microgrid in Matlab.
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- Award ID(s):
- 1757207
- PAR ID:
- 10298383
- Date Published:
- Journal Name:
- 2021 IEEE Green Technologies Conference (GreenTech)
- Page Range / eLocation ID:
- 469 to 476
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/GreenTech48523.2021.00079
