State-Aware Stochastic Optimal Power Flow

Parikshit Pareek and Hung D. Nguyen
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Parikshit Pareek: School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore
Hung D. Nguyen: School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore

Sustainability, 2021, vol. 13, issue 14, 1-16

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.

Keywords: stochastic optimal power flow; machine learning for energy systems; affine recourse policy; Gaussian process; state-aware (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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