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Title: Stochastic Dual Algorithm for Voltage Regulation in Distribution Networks with Discrete Loads: Preprint

Abstract

This paper considers distribution networks with distributed energy resources and discrete-rate loads, and designs an incentive-based algorithm that allows the network operator and the customers to pursue given operational and economic objectives, while concurrently ensuring that voltages are within prescribed limits. Four major challenges include: (1) the non-convexity from discrete decision variables, (2) the non-convexity due to a Stackelberg game structure, (3) unavailable private information from customers, and (4) different update frequency from two types of devices. In this paper, we first make convex relaxation for discrete variables, then reformulate the non-convex structure into a convex optimization problem together with pricing/reward signal design, and propose a distributed stochastic dual algorithm for solving the reformulated problem while restoring feasible power rates for discrete devices. By doing so, we are able to statistically achieve the solution of the reformulated problem without exposure of any private information from customers. Stability of the proposed schemes is analytically established and numerically corroborated.

Authors:
 [1];  [2];  [2];  [2]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
  2. University of Colorado
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Electricity Delivery and Energy Reliability (OE); USDOE Grid Modernization Lab Consortium
OSTI Identifier:
1406986
Report Number(s):
NREL/CP-5D00-68609
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the 8th IEEE International Conference on Smart Grid Communications (SmartGridComm 2017), 23-26 October 2017, Dresden, Germany
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; voltage regulation; convex relaxation; discrete variables; distributed stochastic dual algorithm

Citation Formats

Dall-Anese, Emiliano, Zhou, Xinyang, Liu, Zhiyuan, and Chen, Lijun. Stochastic Dual Algorithm for Voltage Regulation in Distribution Networks with Discrete Loads: Preprint. United States: N. p., 2017. Web.
Dall-Anese, Emiliano, Zhou, Xinyang, Liu, Zhiyuan, & Chen, Lijun. Stochastic Dual Algorithm for Voltage Regulation in Distribution Networks with Discrete Loads: Preprint. United States.
Dall-Anese, Emiliano, Zhou, Xinyang, Liu, Zhiyuan, and Chen, Lijun. Mon . "Stochastic Dual Algorithm for Voltage Regulation in Distribution Networks with Discrete Loads: Preprint". United States. doi:. https://www.osti.gov/servlets/purl/1406986.
@article{osti_1406986,
title = {Stochastic Dual Algorithm for Voltage Regulation in Distribution Networks with Discrete Loads: Preprint},
author = {Dall-Anese, Emiliano and Zhou, Xinyang and Liu, Zhiyuan and Chen, Lijun},
abstractNote = {This paper considers distribution networks with distributed energy resources and discrete-rate loads, and designs an incentive-based algorithm that allows the network operator and the customers to pursue given operational and economic objectives, while concurrently ensuring that voltages are within prescribed limits. Four major challenges include: (1) the non-convexity from discrete decision variables, (2) the non-convexity due to a Stackelberg game structure, (3) unavailable private information from customers, and (4) different update frequency from two types of devices. In this paper, we first make convex relaxation for discrete variables, then reformulate the non-convex structure into a convex optimization problem together with pricing/reward signal design, and propose a distributed stochastic dual algorithm for solving the reformulated problem while restoring feasible power rates for discrete devices. By doing so, we are able to statistically achieve the solution of the reformulated problem without exposure of any private information from customers. Stability of the proposed schemes is analytically established and numerically corroborated.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Oct 30 00:00:00 EDT 2017},
month = {Mon Oct 30 00:00:00 EDT 2017}
}

Conference:
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