Machine Learning in Power Systems: Is It Time to Trust It?

Chatzivasileiadis, Spyros; Venzke, Andreas; Stiasny, Jochen; Misyris, George S.

doi:10.1109/mpe.2022.3150810

Cited by 26 publications

(7 citation statements)

References 5 publications

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“…The growing penetration of DERs into the power grid calls for novel control methodologies able to address the fragility and lack of robustness that can emerge in power systems with low inertia. As a potential solution to some of these challenges, the framework of VPPs could further benefit from recent advances in control, optimization, and learning algorithms [276], [277]. Model-free and adaptive control techniques can be particularly useful in systems with high levels of uncertainty and might require real-time re-tuning of parameters based on the current operating conditions of the generators and the grid, see for example [49], [278].…”

Section: B Adaptive and Data-enabled Methods In Vppsmentioning

confidence: 99%

Control Systems for Low-Inertia Power Grids: A Survey on Virtual Power Plants

et al. 2023

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Virtual Power Plants (VPPs) have emerged as a modern real-time energy management architecture that seeks to synergistically coordinate an aggregation of renewable and non-renewable generation systems to overcome some of the fundamental limitations of traditional power grids dominated by synchronous machines. In this survey paper, we review the different existing and emerging feedback control mechanisms and architectures used for the real-time operation of VPPs. In contrast to other works that have mostly focused on the optimal dispatch and economical aspects of VPPs in the hourly and daily time scales, in this paper we focus on the dynamic nature of the system during the faster sub-hourly time scales. The virtual (i.e., software-based) component of a VPP, combined with the power plant (i.e., physics-based) components of the power grid, make VPPs prominent examples of cyber-physical systems, where both continuous-time and discrete-time dynamics play critical roles in the stability and transient properties of the system. We elaborate on this interpretation of VPPs as hybrid dynamical systems, and we further discuss open research problems and potential research directions in feedback control systems that could contribute to the safe development and deployment of autonomous VPPs.INDEX TERMS Smart grids, renewable energy, virtual power plant, feedback control, multi-agent hybrid dynamical systems.

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Section: B Adaptive and Data-enabled Methods In Vppsmentioning

confidence: 99%

Control Systems for Low-Inertia Power Grids: A Survey on Virtual Power Plants

et al. 2023

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“…A novel predictive learning model, model predictive control (MPC) learning, is proposed in this paper to predict the future EV charging loads. Different from predictive learning via machine learning for systems where neural networks are used as a black box model [28], MPC learning employs double closed-loop MPC [24] and adaptive control [29]. Thus, it relies on explicit models and can provide performance guarantees.…”

Section: Interaction Of Evs With Ders In the Power Gridmentioning

confidence: 99%

Coordinated Planning of Electric Vehicle Charging Infrastructure and Renewables in Power Grids

Wang

Dehghanian

Zhao

2023

IEEE Open J. Power Energy

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“…At the same time, the diversity of DERs impedes solutions based on comprehensive modelling approaches, while their multitude and their distributed nature makes it difficult to manage them centrally. These challenges motivate data-driven and distributed decision-making approaches, as elaborated in [1] and [2], respectively.…”

Section: A Motivationmentioning

confidence: 99%

Distributed Sequential Optimal Power Flow under Uncertainty in Power Distribution Systems: A Data-driven Approach

Τσαούσογλου¹,

Ellinas²,

Giraldo³

et al. 2022

Preprint

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<p>Modern distribution systems with high penetration of distributed energy resources face multiple sources of uncertainty. This transforms the traditional Optimal Power Flow problem into a problem of sequential decision-making under uncertainty. In this framework, the solution concept takes the form of a \textit{policy}, i.e., a method of making dispatch decisions when presented with a real-time system state. Reasoning over the future uncertainty realization and the optimal online dispatch decisions is especially challenging when the number of resources increases and only a small dataset is available for the system's random variables. In this paper, we present a data-driven distributed policy for making dispatch decisions online and under uncertainty. The proposed policy is guaranteed to satisfy the system's constraints while experimentally shown to achieve a performance close to the optimal-in-hindsight solution, significantly outperforming state-of-the-art policies based on stochastic programming and plain machine-learning approaches.</p>

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Machine Learning in Power Systems: Is It Time to Trust It?

Cited by 26 publications

References 5 publications

Control Systems for Low-Inertia Power Grids: A Survey on Virtual Power Plants

Control Systems for Low-Inertia Power Grids: A Survey on Virtual Power Plants

Coordinated Planning of Electric Vehicle Charging Infrastructure and Renewables in Power Grids

Distributed Sequential Optimal Power Flow under Uncertainty in Power Distribution Systems: A Data-driven Approach

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