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.
Highlights
Prosumer response (PR) activation, as DR, is considered a key ingredient in a smart energy system [1,2]
The first prosumer is governed by nonlinear dynamics; the second prosumer is governed by linear dynamics:
Materials and Methods The code used to generate the results presented in this paper can be obtained through the following public repository: https://lab.compute.dtu.dk/freba/sindyc-and-mcmc-framework
Summary
Prosumer response (PR) activation, as DR, is considered a key ingredient in a smart energy system [1,2]. Inclusion of prosumers into the system operation leverages flexibility potentials, which may facilitate the integration of higher shares of RES, thereby contributing to a more sustainable grid operation [4,5]. This may lead to a reduction of the cost of system operation [6]. In order to coordinate prosumers during real-time operation in an optimized manner, a control scheme requires knowledge of the PR dynamics Such a control scheme can act pro-actively, contrary to naive flexibility schemes; see for example [7]
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