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Title: Optimum aggregation of geographically distributed flexible resources in strategic smart-grid/microgrid locations

Abstract

This paper determines optimum aggregation areas for a given distribution network considering spatial distribution of loads and costs of aggregation. An elitist genetic algorithm combined with a hierarchical clustering and a Thevenin network reduction is implemented to compute strategic locations and aggregate demand within each area. The aggregation reduces large distribution networks having thousands of nodes to an equivalent network with few aggregated loads, thereby significantly reducing the computational burden. Furthermore, it not only helps distribution system operators in making faster operational decisions by understanding during which time of the day will be in need of flexibility, from which specific area, and in which amount, but also enables the flexibilities stemming from small distributed resources to be traded in various power/energy markets. A combination of central and local aggregation scheme where a central aggregator enables market participation, while local aggregators materialize the accepted bids, is implemented to realize this concept. The effectiveness of the proposed method is evaluated by comparing network performances with and without aggregation. Finally, for a given network configuration, steady-state performance of aggregated network is significantly accurate (≈ ±1.5% error) compared to very high errors associated with forecast of individual consumer demand.

Authors:
 [1];  [1];  [2];  [3];  [1];  [1]
+ Show Author Affiliations
  1. Idaho National Lab. (INL), Idaho Falls, ID (United States)
  2. Aalborg Univ. (Denmark)
  3. Energinet.dk, Fredericia (Denmark)
Publication Date:
Research Org.:
Idaho National Lab. (INL), Idaho Falls, ID (United States)
Sponsoring Org.:
USDOE Office of Nuclear Energy (NE)
OSTI Identifier:
1375244
Report Number(s):
INL/JOU-16-40290
Journal ID: ISSN 0142-0615; PII: S0142061516325601
Grant/Contract Number:  
AC07-05ID14517
Resource Type:
Accepted Manuscript
Journal Name:
International Journal of Electrical Power and Energy Systems
Additional Journal Information:
Journal Volume: 92; Journal ID: ISSN 0142-0615
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; 30 DIRECT ENERGY CONVERSION; 25 ENERGY STORAGE; demand aggregation; electric vehicles; hierarchical clustering; network reduction; smart grid

Citation Formats

Bhattarai, Bishnu P., Myers, Kurt S., Bak-Jensen, Brigitte, de Cerio Mendaza, Iker Diaz, Turk, Robert J., and Gentle, Jake P. Optimum aggregation of geographically distributed flexible resources in strategic smart-grid/microgrid locations. United States: N. p., 2017. Web. doi:10.1016/j.ijepes.2017.05.005.
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Bhattarai, Bishnu P., Myers, Kurt S., Bak-Jensen, Brigitte, de Cerio Mendaza, Iker Diaz, Turk, Robert J., & Gentle, Jake P. Optimum aggregation of geographically distributed flexible resources in strategic smart-grid/microgrid locations. United States. https://doi.org/10.1016/j.ijepes.2017.05.005
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Bhattarai, Bishnu P., Myers, Kurt S., Bak-Jensen, Brigitte, de Cerio Mendaza, Iker Diaz, Turk, Robert J., and Gentle, Jake P. Wed . "Optimum aggregation of geographically distributed flexible resources in strategic smart-grid/microgrid locations". United States. https://doi.org/10.1016/j.ijepes.2017.05.005. https://www.osti.gov/servlets/purl/1375244.
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@article{osti_1375244,
title = {Optimum aggregation of geographically distributed flexible resources in strategic smart-grid/microgrid locations},
author = {Bhattarai, Bishnu P. and Myers, Kurt S. and Bak-Jensen, Brigitte and de Cerio Mendaza, Iker Diaz and Turk, Robert J. and Gentle, Jake P.},
abstractNote = {This paper determines optimum aggregation areas for a given distribution network considering spatial distribution of loads and costs of aggregation. An elitist genetic algorithm combined with a hierarchical clustering and a Thevenin network reduction is implemented to compute strategic locations and aggregate demand within each area. The aggregation reduces large distribution networks having thousands of nodes to an equivalent network with few aggregated loads, thereby significantly reducing the computational burden. Furthermore, it not only helps distribution system operators in making faster operational decisions by understanding during which time of the day will be in need of flexibility, from which specific area, and in which amount, but also enables the flexibilities stemming from small distributed resources to be traded in various power/energy markets. A combination of central and local aggregation scheme where a central aggregator enables market participation, while local aggregators materialize the accepted bids, is implemented to realize this concept. The effectiveness of the proposed method is evaluated by comparing network performances with and without aggregation. Finally, for a given network configuration, steady-state performance of aggregated network is significantly accurate (≈ ±1.5% error) compared to very high errors associated with forecast of individual consumer demand.},
doi = {10.1016/j.ijepes.2017.05.005},
journal = {International Journal of Electrical Power and Energy Systems},
number = ,
volume = 92,
place = {United States},
year = {Wed May 17 00:00:00 EDT 2017},
month = {Wed May 17 00:00:00 EDT 2017}
}
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Publisher's Version of Record
https://doi.org/10.1016/j.ijepes.2017.05.005
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Works referenced in this record:

A market-oriented hierarchical framework for residential demand response
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Smart grid energy storage controller for frequency regulation and peak shaving, using a vanadium redox flow battery
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Optimal siting and sizing of demand response in a transmission constrained system with high wind penetration
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Two-step algorithm for the optimization of vehicle fleet in electricity distribution company
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  • 2014 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)
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Hierarchical control architecture for demand response in smart grids
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Residential power scheduling for demand response in smart grid
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Risk-constrained framework for residential storage space heating load management
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Aggregation Model-Based Optimization for Electric Vehicle Charging Strategy
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Adaptive control for evaluation of flexibility benefits in microgrid systems
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    Works referencing / citing this record:

    Calculating Operational Patterns for Electric Vehicle Charging on a Real Distribution Network Based on Renewables’ Production
    journal, September 2018

    • Lazarou, Stavros; Vita, Vasiliki; Christodoulou, Christos
    • Energies, Vol. 11, Issue 9
    • DOI: 10.3390/en11092400

    On the Determination of Meshed Distribution Networks Operational Points after Reinforcement
    journal, August 2019

    • Vita, Vasiliki; Lazarou, Stavros; Christodoulou, Christos A.
    • Applied Sciences, Vol. 9, Issue 17
    • DOI: 10.3390/app9173501

    Protection Schemes of Meshed Distribution Networks for Smart Grids and Electric Vehicles
    journal, November 2018

    • Lazarou, Stavros; Vita, Vasiliki; Ekonomou, Lambros
    • Energies, Vol. 11, Issue 11
    • DOI: 10.3390/en11113106
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