Model-Free Optimal Control of VAR Resources in Distribution Systems: An Extremum Seeking Approach

Arnold, Dan; Negrete-Pincetic, Matías; Sankur, Michael D.; Auslander, David M.; Callaway, Duncan S.

doi:10.1109/tpwrs.2015.2502554

Cited by 80 publications

(35 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, the information required to execute either the centralized or decentralized control scheme includes an accurate network model and knowledge of injections and extractions of real and reactive power at every point in the network. In practice this information will always be imprecise, and real time control strategies to make up for these errors will be necessary; in the future we will explore the interaction between real-time controllers (e.g., [63], [64]) and the scheduling algorithm developed in this paper.…”

Section: Network Model Discrepanciesmentioning

confidence: 99%

Decentralized Charging Control of Electric Vehicles in Residential Distribution Networks

Liu

Phanivong

Shi

et al. 2019

IEEE Trans. Contr. Syst. Technol.

Self Cite

136

110

View full text Add to dashboard Cite

Abstract-Electric vehicle (EV) charging can negatively impact electric distribution networks by exceeding equipment thermal ratings and causing voltages to drop below standard ranges. In this paper, we develop a decentralized EV charging control scheme to achieve "valley-filling" (i.e., flattening demand profile during overnight charging), meanwhile meeting heterogeneous individual charging requirements and satisfying distribution network constraints. The formulated problem is an optimization problem with a non-separable objective function and strongly coupled inequality constraints. We propose a novel shrunkenprimal-dual subgradient (SPDS) algorithm to support the decentralized control scheme, derive conditions guaranteeing its convergence, and verify its efficacy and convergence with a representative distribution network model.

show abstract

Section: Network Model Discrepanciesmentioning

confidence: 99%

Decentralized Charging Control of Electric Vehicles in Residential Distribution Networks

Liu

Phanivong

Shi

et al. 2019

IEEE Trans. Contr. Syst. Technol.

Self Cite

136

110

View full text Add to dashboard Cite

show abstract

“…Line impedances, shunt admittances, as well as active and reactive loads are adopted from the respective dataset. With reference to Figure 3, it is assumed that eighteen PV systems are located at nodes 4,7,10,13,17,20,22,23,26,28,29,30,31,32,33,34,35, and 36, and their generation profile is simulated based on the real solar irradiance data available in [15]. Solar irradiance data have a granularity of 1 second.…”

Section: Example Of Applicationmentioning

confidence: 99%

Optimal Power Flow Pursuit

Dall’Anese

Simonetto

2018

IEEE Trans. Smart Grid

236

139

View full text Add to dashboard Cite

Abstract-This paper considers distribution networks featuring inverter-interfaced distributed energy resources, and develops distributed feedback controllers that continuously drive the inverter output powers to solutions of AC optimal power flow (OPF) problems. Particularly, the controllers update the power setpoints based on voltage measurements as well as given (time-varying) OPF targets, and entail elementary operations implementable onto low-cost microcontrollers that accompany power-electronics interfaces of gateways and inverters. The design of the control framework is based on suitable linear approximations of the AC power-flow equations as well as Lagrangian regularization methods. Convergence and OPF-target tracking capabilities of the controllers are analytically established. Overall, the proposed method allows to bypass traditional hierarchical setups where feedback control and optimization operate at distinct time scales, and to enable real-time optimization of distribution systems.

show abstract

“…This work covers a variety of contexts. For example, researchers have studied frequency regulation [32] [33] and voltage regulation (or volt-VAR control) [34] [35]. A separate line of work has been work on designing incentives to encourage distributed resources to provide services to the power grid [36] [37].…”

Section: B Related Workmentioning

confidence: 99%

Opportunities for Price Manipulation by Aggregators in Electricity Markets

Azizan

Dvijotham

Chen

et al. 2018

IEEE Trans. Smart Grid

View full text Add to dashboard Cite

Abstract-Aggregators are playing an increasingly crucial role in the integration of renewable generation in power systems. However, the intermittent nature of renewable generation makes market interactions of aggregators difficult to monitor and regulate, raising concerns about potential market manipulation by aggregators. In this paper, we study this issue by quantifying the profit an aggregator can obtain through strategic curtailment of generation in an electricity market. We show that, while the problem of maximizing the benefit from curtailment is hard in general, efficient algorithms exist when the topology of the network is radial (acyclic). Further, we highlight that significant increases in profit are possible via strategic curtailment in practical settings.

show abstract

Model-Free Optimal Control of VAR Resources in Distribution Systems: An Extremum Seeking Approach

Cited by 80 publications

References 18 publications

Decentralized Charging Control of Electric Vehicles in Residential Distribution Networks

Decentralized Charging Control of Electric Vehicles in Residential Distribution Networks

Optimal Power Flow Pursuit

Opportunities for Price Manipulation by Aggregators in Electricity Markets

Contact Info

Product

Resources

About