A Review on Machine Learning Models in Forecasting of Virtual Power Plant Uncertainties

Doğan, Ahmet; Dogan, Demet Cidem

doi:10.1007/s11831-022-09860-2

Cited by 10 publications

(4 citation statements)

References 112 publications

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“…However, despite the extensive research conducted in this field, there is a notable gap regarding the integration of time series prediction techniques to obtain these uncertainty parameters [72,73]. While time series prediction models have been widely used for forecasting purposes, their potential for directly estimating uncertainty measures has not been fully explored or leveraged in decision-making workflows [74,75]. This represents a good opportunity that has not yet been fully exploited, as time series prediction offers several advantages over traditional approaches.…”

Section: Literature Review and Research Gapmentioning

confidence: 99%

A Stochastic Decision-Making Tool Suite for Distributed Energy Resources Integration in Energy Markets

Cantillo-Luna,

Moreno-Chuquen,

Celeita

et al. 2024

Energies

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Energy markets are crucial for integrating Distributed Energy Resources (DER) into modern power grids. However, this integration presents challenges due to the inherent variability and decentralized nature of DERs, as well as poorly adapted regulatory environments. This paper proposes a medium-term decision-making approach based on a comprehensive suite of computational tools for integrating DERs into Colombian energy markets. The proposed framework consists of modular tools that are aligned with the operation of a Commercial Virtual Power Plant (CVPP). The tools aim to optimize participation in bilateral contracts and short-term energy markets. They use forecasting, uncertainty management, and decision-making modules to create an optimal portfolio of DER assets. The suite’s effectiveness and applicability are demonstrated and analyzed through its implementation with heterogeneous DER assets across various operational scenarios.

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Section: Literature Review and Research Gapmentioning

confidence: 99%

A Stochastic Decision-Making Tool Suite for Distributed Energy Resources Integration in Energy Markets

Cantillo-Luna,

Moreno-Chuquen,

Celeita

et al. 2024

Energies

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“…These scalability issues could be addressed via transfer learning methods, or by clustering similar components and using a common ML model for the purpose of prediction. A comprehensive review of ML models for forecasting in VPPs is presented in [296]. Deeplearning methods have also been used to improve the performance of VPPs in frequency regulation markets [295].…”

Section: Ai and Soft Computing-based Control Of 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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“…This step also includes several sequential steps applied to the data of the denoised estimated cycle capacity, such as detrending, normalization, and formatting [33] in order to improve the performance of the capacity fade forecasting methods.…”

Section: Phase 21 Capacity Denoising and Preprocessingmentioning

confidence: 99%

Real-Time Lithium Battery Aging Prediction Based on Capacity Estimation and Deep Learning Methods

de la Vega,

Riba,

Ortega-Redondo

2023

Batteries

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Lithium-ion batteries are key elements in the development of electrical energy storage solutions. However, due to cycling, environmental, and operating conditions, battery capacity tends to degrade over time. Capacity fade is a common indicator of battery state of health (SOH) because it is an indication of how the capacity has been degraded. However, battery capacity cannot be measured directly, and thus, there is an urgent need to develop methods for estimating battery capacity in real time. By analyzing the historical data of a battery in detail, it is possible to predict the future state of a battery and forecast its remaining useful life. This study developed a real-time, simple, and fast method to estimate the cycle capacity of a battery during the charge cycle using only data from a short period of each charge cycle. This proposal is attractive because it does not require data from the entire charge period since batteries are rarely charged from zero to full. The proposed method allows for simultaneous and accurate real-time prediction of the health and remaining useful life of the battery over its lifetime. The accuracy of the proposed method was tested using experimental data from several lithium-ion batteries with different cathode chemistries under various test conditions.

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A Review on Machine Learning Models in Forecasting of Virtual Power Plant Uncertainties

Cited by 10 publications

References 112 publications

A Stochastic Decision-Making Tool Suite for Distributed Energy Resources Integration in Energy Markets

A Stochastic Decision-Making Tool Suite for Distributed Energy Resources Integration in Energy Markets

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

Real-Time Lithium Battery Aging Prediction Based on Capacity Estimation and Deep Learning Methods

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