TY - JOUR
T1 - Optimum aggregation of geographically distributed flexible resources in strategic smart-grid/microgrid locations
AU - Bhattarai, Bishnu P.
AU - Myers, Kurt S.
AU - Bak-Jensen, Birgitte
AU - Mendaza, Iker Diaz de Cerio
AU - Turk, Robert J.
AU - Gentle, Jake P.
PY - 2017/11
Y1 - 2017/11
N2 - This study 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. 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.
AB - This study 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. 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.
KW - Demand aggregation
KW - Electric vehicles
KW - Hierarchical clustering
KW - Network reduction
KW - Smart grid
U2 - 10.1016/j.ijepes.2017.05.005
DO - 10.1016/j.ijepes.2017.05.005
M3 - Journal article
SN - 0142-0615
VL - 92
SP - 193
EP - 201
JO - International Journal of Electrical Power & Energy Systems
JF - International Journal of Electrical Power & Energy Systems
ER -