Distribution System State Estimation With High Penetration of Demand Response Enabled Loads

Journal Article · Tue Jan 05 00:00:00 EST 2021 · IEEE Transactions on Power Systems

DOI:https://doi.org/10.1109/tpwrs.2020.3047269· OSTI ID:1855955

Liu, Jianzhe ^[1]; Singh, Ravindra ^[1]; Pal, Bikash C. ^[2]

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1. Argonne National Lab. (ANL), Lemont, IL (United States)
2. Imperial College London (United Kingdom)

Demand-side operations incentivize utility customers to take part in various grid services. A demand response enabled load (DREL) is a flexible grid asset that schedules electricity consumption in response to a time-of-use (TOU) energy price. Consequently, its energy profile differs from that of a conventional load that is insensitive to price. This difference may cause new challenges for distribution system state estimation (DSSE). It is well known that DSSE often needs to use pseudo-measurements based on historic load profiles to increase system observability. However, historic profiles of conventional loads are not representative of DREL behaviors. Additionally, the inaccuracy impacts DSSE results and other DSSE-dependent operations. In this paper, we propose an online pseudo-measurement generation approach for DSSE with DRELs. We formulate an optimization model to represent DRELs self-adjusting actions. Sampling-based stochastic optimization techniques are proposed to account for uncertainties in DRELs. A set of representative DREL behavior data corresponding to the samples are used to characterize DREL pseudo-measurements. Case studies with modified IEEE 123-bus test system verify the validity of the proposed work.

• Research Organization: Argonne National Laboratory (ANL), Argonne, IL (United States)
• Sponsoring Organization: USDOE Office of Electricity Delivery and Energy Reliability
• Grant/Contract Number: AC02-06CH11357
• OSTI ID: 1855955
• Journal Information: IEEE Transactions on Power Systems, Journal Name: IEEE Transactions on Power Systems Journal Issue: 4 Vol. 36; ISSN 0885-8950
• Publisher: IEEE
• Country of Publication: United States
• Language: English

Related Subjects

24 POWER TRANSMISSION AND DISTRIBUTION
Load modeling
demand response
distribution system state estimation
load management
load modeling
optimization
state estimation
stochastic optimizaiton
stochastic processes
uncertainty