Modern smart grid systems exploit a two-way interaction paradigm between the utility and the electricity user and promote the role of prosumer, as a new user type, able to generate and sell energy, or consume energy. Within such a setting, the prosumers and their interactions with the microgrid system become of high significance for its efficient operation. In this article, to model the corresponding interactions, we introduce a labor economics-based framework by exploiting the principles of contract theory, that jointly achieves the satisfaction of the various interacting system entities, i.e., the microgrid operator (MGO) and the prosumers. The MGO offers personalized rewards to the sellers and buyers, to incentivize them to sell and purchase energy, respectively. To provide a stable and efficient operation point, while aiming at jointly satisfying the profit and requirements of the involved competing parties, optimal personalized contracts, i.e., rewards and amount of sold/purchased energy, are determined, by formulating and solving contract-theoretic optimization problems between the MGO and the sellers or buyers. The analysis is provided for both cases of complete and incomplete information availability regarding the prosumers’ types. Detailed numerical results are presented to demonstrate the operation characteristics of the proposed framework under diverse scenarios.
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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
- NSF-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
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/GreenTech48523.2021.00079
