Advances in Inverter-Based Resource-Rich Renewable Energy Power Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Power Electronics".

Deadline for manuscript submissions: 15 May 2025 | Viewed by 1040

Special Issue Editors


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Guest Editor
College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Interests: AC/DC power systems; HVDC; renewable energy generation

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Guest Editor
Robert W. Galvin Center for Electricity Innovation, Illinois Institute of Technology, Chicago, IL 60616, USA
Interests: power system resilience; cyber-physical systems; renewable energy integration

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Guest Editor
National Renewable Energy Laboratory, Golden, CO 80401, USA
Interests: power system modeling and analysis; distributed energy resources; power system resilience

Special Issue Information

Dear Colleagues,

Achieving fully sustainable energy requires integrating large amounts of renewable energy into modern grids. Different from fossil fuel energy, which is applied to synchronous generators (SGs), renewable energy resources, like wind and solar energy, are commonly connected to the grid through inverters. It is envisioned that SGs will gradually be replaced by inverter-based generators as the penetration of renewable energy continues to grow. Meanwhile, as flexible regulation resources for the grid, the applications of inverter-based resources (IBRs) on both energy storage systems and vehicle-to-grid-enabled electric vehicles have developed tremendously. However, the penetration of ubiquitous IBRs can greatly complicate the dynamic properties and challenge the indispensable real-time source-load energy balance of power systems. To this end, this Special Issue will focus on advanced technologies for operating IBR-rich renewable energy power systems (REPSs).

Topics of interest include, but are not limited to, the following:

Topic 1: Stability analyses and control methods for IBR-rich REPSs;

Topic 2: Resilience-oriented coordinated energy management of IBR-rich REPSs;

Topic 3. Flexible resource aggregation for accommodating increasing IBR-based renewables;

Topic 4: Advanced artificial intelligence (AI) applications for operating IBR-rich REPSs;

Topic 5: Emerging system services and market designs for IBR-rich REPSs.

Dr. Guoteng Wang
Dr. Chongyu Wang
Dr. Qianzhi Zhang
Dr. Weijia Liu
Guest Editors

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Keywords

  • inverter-based resources
  • renewable energy power systems
  • stability
  • resilience
  • flexible resource aggregation
  • advanced artificial intelligence

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Published Papers (1 paper)

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Research

25 pages, 3488 KiB  
Article
Research on the Collaborative Operation of Diversified Energy Storage and Park Clusters: A Method Combining Data Generation and a Distributionally Robust Chance-Constrained Operational Model
by Zhuoya Siqin, Tiantong Qiao, Ruisheng Diao, Xuejie Wang and Guangjun Xu
Electronics 2024, 13(24), 4997; https://doi.org/10.3390/electronics13244997 - 19 Dec 2024
Cited by 1 | Viewed by 693
Abstract
Energy storage is crucial for enhancing the economic efficiency of integrated energy systems. This paper addresses the need for flexible resources due to high renewable energy integration and the complexity of managing multiple resources. We propose a decentralized collaborative multi-stage distributionally robust scheduling [...] Read more.
Energy storage is crucial for enhancing the economic efficiency of integrated energy systems. This paper addresses the need for flexible resources due to high renewable energy integration and the complexity of managing multiple resources. We propose a decentralized collaborative multi-stage distributionally robust scheduling method for electric-thermal systems, incorporating energy storage to mitigate renewable energy fluctuations. Firstly, we model the electric-thermal system with multiple flexible resources. Uncertain parameters of renewables are estimated using conditional generative adversarial networks (CGANs), assuming empirical probability distributions. Secondly, given the distinct operators of electric and thermal systems and information barriers, we develop a data-driven distributionally robust chance-constrained optimization model (DRCCO). This model ensures decentralized collaboration without compromising information security or fairness. Then, we introduce an Alternating Direction Method of Multipliers (ADMM) algorithm with parallel regularization to decouple the model. This approach facilitates rapid solution finding with minimal information exchange. Finally, numerical examples confirm the model’s effectiveness in enhancing system flexibility and ensuring wind power consumption. Full article
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