Smart Inverter Grid Probing for Learning Loads: Part II - Probing Injection Design
This two-part work puts forth the idea of engaging power electronics to probe an electric grid to infer non-metered loads. Probing can be accomplished by commanding inverters to perturb their power injections and record the induced voltage response. Once a probing setup is deemed topologically observable by the tests of Part I, Part II provides a methodology for designing probing injections abiding by inverter and network constraints to improve load estimates. The task is challenging since system estimates depend on both probing injections and unknown loads in an implicit nonlinear fashion. The methodology first constructs a library of candidate probing vectors by sampling over the feasible set of inverter injections. Leveraging a linearized grid model and a robust approach, the candidate probing vectors violating voltage constraints for any anticipated load value are subsequently rejected. Among the qualified candidates, the design finally identifies the probing vectors yielding the most diverse system states. The probing task under noisy phasor and non-phasor data is tackled using a semidefinite-program (SDP) relaxation. Numerical tests using synthetic and real-world data on a benchmark feeder validate the conditions of Part I; the SDP-based solver; the importance of probing design; and the effects of probing duration and noise.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Smart Inverter Grid Probing for Learning Loads: Part I - Identifiability Analysis
Distribution grids currently lack comprehensive real-time metering. Nevertheless, grid operators require precise knowledge of loads and renewable generation to accomplish any feeder optimization task. At the same time, n…
Active Islanding Detection Using Pulse Compression Probing
An islanding detection scheme is developed using pulse compression probing (PCP). A state space system realization is taken from the probing output. The nu-gap metric is applied to compare the measured system to fully in…
GridLearn: Multiagent Reinforcement Learning for Grid-Aware Building Energy Management
Increasing amounts of distributed generation in distribution networks can provide both challenges and opportunities for voltage regulation across the network. Intelligent control of smart inverters and other smart buildi…
energy managementManagementMulti-agent Reinforcement Learningreinforcement-learning+2On Stability, Ancillary Services, Operation, and Security of Smart Inverters
This paper presents some recent trends in the research of grid-interactive inverters. Particularly, this paper focuses on stability, ancillary services, operation, and security of single and multiple inverters in the mod…
Marketingmu-synthesis-based Generalized Robust Framework for Grid-following and Grid-forming Inverters
Grid-following and grid-forming inverters are integral components of microgrids and for integration of renewable energy sources with the grid. For grid following inverters, which need to emulate controllable current sour…