Prosumer Response Estimation Using SINDyc in Conjunction with Markov-Chain Monte-Carlo Sampling

Frederik Banis, Henrik Madsen, Niels K. Poulsen and Daniela Guericke
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Frederik Banis: Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU), Lyngby 2800, Denmark
Henrik Madsen: Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU), Lyngby 2800, Denmark
Niels K. Poulsen: Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU), Lyngby 2800, Denmark
Daniela Guericke: Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU), Lyngby 2800, Denmark

Energies, 2020, vol. 13, issue 12, 1-16

Abstract: Smart grid operation schemes can integrate prosumers by offering economic rewards in exchange for the desired response. In order to activate prosumers appropriately, such operation schemes require models of the dynamic uncertain price-response relationships. In this study, we combine the system identification of nonlinear dynamics with control (SINDyc) algorithm with Bayesian inference techniques based on Markov-chain Monte-Carlo sampling. We demonstrate this combination of two algorithms on an exemplary system in order to obtain parsimonious models alongside parameter uncertainty estimates. The precision of the identified models depends on the identification experiment and the parameterization of the algorithms. Such models may characterize the prosumer response and its uncertainty, thereby facilitating the integration of such entities into smart grid operation schemes.

Keywords: system identification; Bayesian inference; Markov-chain Monte-Carlo; smart energy system (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
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