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|Title:||State-aware stochastic optimal power flow||Authors:||Pareek, Parikshit
Nguyen, Hung D.
|Keywords:||Engineering::Electrical and electronic engineering::Electric power::Production, transmission and distribution||Issue Date:||2021||Source:||Pareek, P. & Nguyen, H. D. (2021). State-aware stochastic optimal power flow. Sustainability, 13(14), 7577-. https://dx.doi.org/10.3390/su13147577||Project:||2019-T1-001-119 (RG 79/19)
|Journal:||Sustainability||Abstract:||The increase in distributed generation (DG) and variable load mandates system operators to perform decision-making considering uncertainties. This paper introduces a novel state-aware stochastic optimal power flow (SA-SOPF) problem formulation. The proposed SA-SOPF has objective to find a day-ahead base-solution that minimizes the generation cost and expectation of deviations in generation and node voltage set-points during real-time operation. We formulate SA-SOPF for a given affine policy and employ Gaussian process learning to obtain a distributionally robust (DR) affine policy for generation and voltage set-point change in real-time. In simulations, the GP-based affine policy has shown distributional robustness over three different uncertainty distributions for IEEE 14-bus system. The results also depict that the proposed SA-OPF formulation can reduce the expectation in voltage and generation deviation more than 60% in real-time operation with an additional day-ahead scheduling cost of 4.68% only for 14-bus system. For, in a 30-bus system, the reduction in generation and voltage deviation, the expectation is achieved to be greater than 90% for 1.195% extra generation cost. These results are strong indicators of possibility of achieving the day-ahead solution which lead to lower real-time deviation with minimal cost increase.||URI:||https://hdl.handle.net/10356/153086||ISSN:||1937-0695||DOI:||10.3390/su13147577||Schools:||School of Electrical and Electronic Engineering||Rights:||© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Journal Articles|
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