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Title: Stochastic Optimal Power Flow Based on Data-Driven Distributionally Robust Optimization

Abstract

We propose a data-driven method to solve a stochastic optimal power flow (OPF) problem based on limited information about forecast error distributions. The objective is to determine power schedules for controllable devices in a power network to balance operational cost and conditional value-at-risk (CVaR) of device and network constraint violations. These decisions include scheduled power output adjustments and reserve policies, which specify planned reactions to forecast errors in order to accommodate fluctuating renewable energy sources. Instead of assuming the uncertainties across the networks follow prescribed probability distributions, we assume the distributions are only observable through a finite training dataset. By utilizing the Wasserstein metric to quantify differences between the empirical data-based distribution and the real data-generating distribution, we formulate a distributionally robust optimization OPF problem to search for power schedules and reserve policies that are robust to sampling errors inherent in the dataset. A multi-stage closed-loop control strategy based on model predictive control (MPC) is also discussed. A simpIe numerical example illustrates inherent tradeoffs between operational cost and risk of constraint violation, and we show how our proposed method offers a data-driven framework to balance these objectives.

Authors:
 [1];  [2];  [3];  [4];  [2]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
  2. University of Texas at Dallas
  3. University of Colorado
  4. Tsinghua University
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
OSTI Identifier:
1474836
Report Number(s):
NREL/CP-5D00-72489
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the 2018 Annual American Control Conference (ACC), 27-29 June 2018, Milwaukee, Wisconsin
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; robustness; stochastic processes; probability distribution; cost function; schedules; training

Citation Formats

Dall-Anese, Emiliano, Guo, Yi, Baker, Kyri, Hu, Zechun, and Summers, Tyler. Stochastic Optimal Power Flow Based on Data-Driven Distributionally Robust Optimization. United States: N. p., 2018. Web. doi:10.23919/ACC.2018.8431542.
Dall-Anese, Emiliano, Guo, Yi, Baker, Kyri, Hu, Zechun, & Summers, Tyler. Stochastic Optimal Power Flow Based on Data-Driven Distributionally Robust Optimization. United States. https://doi.org/10.23919/ACC.2018.8431542
Dall-Anese, Emiliano, Guo, Yi, Baker, Kyri, Hu, Zechun, and Summers, Tyler. 2018. "Stochastic Optimal Power Flow Based on Data-Driven Distributionally Robust Optimization". United States. https://doi.org/10.23919/ACC.2018.8431542.
@article{osti_1474836,
title = {Stochastic Optimal Power Flow Based on Data-Driven Distributionally Robust Optimization},
author = {Dall-Anese, Emiliano and Guo, Yi and Baker, Kyri and Hu, Zechun and Summers, Tyler},
abstractNote = {We propose a data-driven method to solve a stochastic optimal power flow (OPF) problem based on limited information about forecast error distributions. The objective is to determine power schedules for controllable devices in a power network to balance operational cost and conditional value-at-risk (CVaR) of device and network constraint violations. These decisions include scheduled power output adjustments and reserve policies, which specify planned reactions to forecast errors in order to accommodate fluctuating renewable energy sources. Instead of assuming the uncertainties across the networks follow prescribed probability distributions, we assume the distributions are only observable through a finite training dataset. By utilizing the Wasserstein metric to quantify differences between the empirical data-based distribution and the real data-generating distribution, we formulate a distributionally robust optimization OPF problem to search for power schedules and reserve policies that are robust to sampling errors inherent in the dataset. A multi-stage closed-loop control strategy based on model predictive control (MPC) is also discussed. A simpIe numerical example illustrates inherent tradeoffs between operational cost and risk of constraint violation, and we show how our proposed method offers a data-driven framework to balance these objectives.},
doi = {10.23919/ACC.2018.8431542},
url = {https://www.osti.gov/biblio/1474836}, journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {8}
}

Conference:
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