Robust Data-Driven and Fully Distributed Volt/VAR Control for Active Distribution Networks With Multiple Virtual Power Plants

Li, Siyun; Wu, Wenchuan; Lin, Yi

doi:10.1109/tsg.2022.3166274

Cited by 24 publications

(9 citation statements)

References 34 publications

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“…Note that the latter GE characterizes the set of Nash equilibria of (8), which is nonempty by the first part of the proof. Now, we can finally invoke [41,Theorem 26.14] to prove convergence of the sequence {u k } k∈N generated by (13) to some u * such that 0 ∈ zer(F(u * ) + N U (u * )), which is a Nash equilibrium of (8), and concludes the proof. □…”

Section: Appendix Proof Of Theoremmentioning

confidence: 93%

“…, γ N ) is a diagonal matrix with the control gains γ i on the main diagonal. The iteration (13) corresponds to a forward-backward splitting algorithm [41, § 26.5], which converges to a zero of ΓF + ΓN U under the conditions of [41,Theorem 26.14]. Namely, cocoercivity of ΓF, maximal monotonicity of ΓN U , and existence of a solution.…”

Section: Appendix Proof Of Theoremmentioning

confidence: 99%

See 1 more Smart Citation

Sampled-Data Online Feedback Equilibrium Seeking: Stability and Tracking

Belgioioso

Liao‐McPherson

Badyn

et al. 2021

2021 60th IEEE Conference on Decision and Control (CDC)

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This paper proposes a general framework for constructing feedback controllers that drive complex dynamical systems to "efficient" steady-state (or slowly varying) operating points. Efficiency is encoded using generalized equations which can model a broad spectrum of useful objectives, such as optimality or equilibria (e.g. Nash, Wardrop, etc.) in noncooperative games. The core idea of the proposed approach is to directly implement iterative solution (or equilibrium seeking) algorithms in closed loop with physical systems. Sufficient conditions for closed-loop stability and robustness are derived; these also serve as the first closed-loop stability results for sampleddata feedback-based optimization. Numerical simulations of smart building automation and game-theoretic robotic swarm coordination support the theoretical results.

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Section: Appendix Proof Of Theoremmentioning

confidence: 93%

Section: Appendix Proof Of Theoremmentioning

confidence: 99%

Sampled-Data Online Feedback Equilibrium Seeking: Stability and Tracking

Belgioioso

Liao‐McPherson

Badyn

et al. 2021

2021 60th IEEE Conference on Decision and Control (CDC)

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show abstract

“…Ref. [25] further applied the robust HO-RLS method to control multiple virtual power plants to realize data-driven distributed voltage control, which improved the control robustness.…”

Section: Setsmentioning

confidence: 99%

Data-Driven Multi-Mode Adaptive Operation of Soft Open Point With Measuring Bad Data

Gao,

Li,

et al. 2024

IEEE Trans. Power Syst.

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“…[24]. The above optimizationbased decision-making models formulates the uncertainties of PV predictions by stochastic [14], [16], robust [15], chanceconstrained [17], and data-driven [18]- [21] methods, which seldom consider the reverse impact of decision-making on the multiple prediction models for learning to enhance decision quality.…”

Section: Introductionmentioning

confidence: 99%

Safety-Aware Semi-End-to-End Coordinated Decision Model for Voltage Regulation in Active Distribution Network

Sang

Long

et al. 2023

IEEE Trans. Smart Grid

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Prediction plays a vital role in the active distribution network voltage regulation under the high penetration of photovoltaics. Current prediction models aim at minimizing individual prediction errors but overlook their collective impacts on downstream decision-making. Hence, this paper proposes a safety-aware semi-end-to-end coordinated decision model to bridge the gap from the downstream voltage regulation to the upstream multiple prediction models in a coordinated differential way. The semi-end-to-end model maps the input features to the optimal var decisions via prediction, decision-making, and decisionevaluating layers. It leverages the neural network and the secondorder cone program (SOCP) to formulate the stochastic PV/load predictions and the var decision-making/evaluating separately. Then the var decision quality is evaluated via the weighted sum of the power loss for economy and the voltage violation penalty for safety, denoted by regulation loss. Based on the regulation loss and prediction errors, this paper proposes the hybrid loss and hybrid stochastic gradient descent algorithm to back-propagate the gradients of the hybrid loss with respect to multiple predictions for enhancing decision quality. Case studies verify the effectiveness of the proposed model with lower power loss for economy and lower voltage violation rate for safety awareness.

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Robust Data-Driven and Fully Distributed Volt/VAR Control for Active Distribution Networks With Multiple Virtual Power Plants

Cited by 24 publications

References 34 publications

Sampled-Data Online Feedback Equilibrium Seeking: Stability and Tracking

Sampled-Data Online Feedback Equilibrium Seeking: Stability and Tracking

Data-Driven Multi-Mode Adaptive Operation of Soft Open Point With Measuring Bad Data

Safety-Aware Semi-End-to-End Coordinated Decision Model for Voltage Regulation in Active Distribution Network

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