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

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.
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  1. Idaho National Lab. (INL), Idaho Falls, ID (United States)
  2. Aalborg Univ. (Denmark)
  3., Fredericia (Denmark)
Publication Date:
Report Number(s):
Journal ID: ISSN 0142-0615; PII: S0142061516325601
Grant/Contract Number:
Accepted Manuscript
Journal Name:
International Journal of Electrical Power and Energy Systems
Additional Journal Information:
Journal Volume: 92; Journal ID: ISSN 0142-0615
Research Org:
Idaho National Lab. (INL), Idaho Falls, ID (United States)
Sponsoring Org:
USDOE Office of Nuclear Energy (NE)
Country of Publication:
United States
24 POWER TRANSMISSION AND DISTRIBUTION; 30 DIRECT ENERGY CONVERSION; 25 ENERGY STORAGE; demand aggregation; electric vehicles; hierarchical clustering; network reduction; smart grid
OSTI Identifier: