Measurement-Based Optimal DER Dispatch With a Recursively Estimated Sensitivity Model
| Autor: | Severin Nowak, Liwei Wang, Yu Christine Chen |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry 020209 energy Computation Phasor Energy Engineering and Power Technology 02 engineering and technology System model Units of measurement Control theory Distributed generation Scalability 0202 electrical engineering electronic engineering information engineering Sensitivity (control systems) Electrical and Electronic Engineering business Voltage |
| Zdroj: | IEEE Transactions on Power Systems. 35:4792-4802 |
| ISSN: | 1558-0679 0885-8950 |
| DOI: | 10.1109/tpwrs.2020.2998097 |
| Popis: | This paper presents a measurement-based method to determine distributed energy resource (DER) active- and reactive-power setpoints that minimize bus voltage deviations from prescribed reference values, bus active- and reactive-power deviations from desired setpoints, as well as cost of DER outputs. Central to the proposed method is the estimation of a linear sensitivity model from synchronized voltage and power-injection data collected from distribution-level phasor measurement units installed at only a subset of buses in the distribution system. As new measurements become available, the linear sensitivity model is updated via the recursive weighted partial least-squares estimation method. The estimated sensitivity model is then embedded as an equality constraint in a convex quadratic optimization problem, which can be solved via, e.g., the alternating direction method of multipliers. Numerical simulations involving the IEEE 33-bus distribution test system illustrate key benefits of the proposed method, including (i) eliminating the need for an accurate offline system model, (ii) adapting to online network-topology and operating-point changes, and (iii) being robust against delays potentially attributed to communication, computation, and actuation. Additional numerical simulations involving larger test systems demonstrate computational scalability. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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