Abstract
With the expansion of distributed generation systems and demand response programs, the need to fully utilize distribution system capacity has increased. In addition, the potential bidirectional flow of power on distribution networks demands voltage visibility and control at all voltage levels. Distribution system state estimations, however, have traditionally been less prioritized due to the lack of enough measurement points while being the major role player in knowing the real-time system states of active distribution networks. The advent of smart meters at LV loads, on the other hand, is giving relief to this shortcoming. This study explores the potential of bottom up load flow analysis based on customer level Automatic Meter Reading (AMRs) to compute short time forecasts of demands and distribution network system states. A state estimation frame-work, which makes use of available AMR data, is proposed and discussed.
Highlights
Distribution system state estimation provides the realtime system states to a Distribution Management System (DMS) enabling operators to monitor and control the operation of the distribution system
The utilization of the tool in distribution systems has been lagging due to two reasons: the first was the absence of widespread measurement, in distribution networks and the second is the low need for active management of the distribution network
In this load flow analysis no voltage regulation is involved and the results show a cyclic voltage profile following the trend of the power itself
Summary
Distribution system state estimation provides the realtime system states to a Distribution Management System (DMS) enabling operators to monitor and control the operation of the distribution system. With the transformation of distribution networks from passive networks to active networks, following widespread installations of Distributed Generation (DG) and Smart Metering (SM) technologies, the state estimation tool is becoming a core component. This tool has been used widely and for a long time with transmission systems. Load modeling techniques provide real-time estimates of customer load demands.
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