Intelligent Algorithms for High-Penetration New Energy

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: closed (15 December 2024) | Viewed by 3384

Special Issue Editors


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Guest Editor
School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 470074, China
Interests: automata theory; mathematical problems of artificial intelligence; complex networks; dynamical systems; fuzzy logic
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Guest Editor
School of Engineering, RMIT University, Melbourne, VIC 3001, Australia
Interests: bilevel optimization; game theory; machine learning

Special Issue Information

Dear Colleagues, 

We are delighted to extend our invitation to you to submit your cutting-edge research in the field of intelligent algorithms for high-penetration new energy to our Special Issue entitled "Intelligent Algorithms for High-Penetration New Energy". 

With the increasing global focus on renewable energy sources, the development of intelligent algorithms holds significant promise for optimizing energy generation, storage, and utilization in high-penetration new energy systems. The advancements in this field have paved the way for the efficient integration of renewable energy into existing power infrastructures. 

This Special Issue aims to foster research activities in the realm of intelligent algorithms specifically tailored for high-penetration new energy systems. We encourage multidisciplinary contributions that showcase innovative algorithms and methodologies, addressing the distinctive characteristics and challenges associated with the integration of renewable energy sources into power networks.

Prof. Dr. Ming-Feng Ge
Dr. Chen Liu
Guest Editors

Manuscript Submission Information

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Keywords

  • intelligent algorithms for high-penetration new energy
  • optimization techniques for renewable energy generation and storage
  • machine learning algorithms for renewable energy integration and control
  • data-driven forecasting models for renewable energy sources
  • intelligent energy management systems for high-penetration new energy
  • hybrid energy systems and their intelligent control
  • fault detection and self-healing algorithms for renewable energy systems
  • cybersecurity and privacy in high-penetration new energy systems

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Published Papers (3 papers)

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Research

18 pages, 4015 KiB  
Article
Differentially Private Clustered Federated Load Prediction Based on the Louvain Algorithm
by Tingzhe Pan, Jue Hou, Xin Jin, Chao Li, Xinlei Cai and Xiaodong Zhou
Algorithms 2025, 18(1), 32; https://doi.org/10.3390/a18010032 - 8 Jan 2025
Viewed by 459
Abstract
Load forecasting plays a fundamental role in the new type of power system. To address the data heterogeneity and security issues encountered in load forecasting for smart grids, this paper proposes a load-forecasting framework suitable for residential energy users, which allows users to [...] Read more.
Load forecasting plays a fundamental role in the new type of power system. To address the data heterogeneity and security issues encountered in load forecasting for smart grids, this paper proposes a load-forecasting framework suitable for residential energy users, which allows users to train personalized forecasting models without sharing load data. First, the similarity of user load patterns is calculated under privacy protection. Second, a complex network is constructed, and a federated user clustering method is developed based on the Louvain algorithm, which divides users into multiple clusters based on load pattern similarity. Finally, a personalized and adaptive differentially private federated learning Long Short-Term Memory (LSTM) model for load forecasting is developed. A case study analysis shows that the proposed method can effectively protect user privacy and improve model prediction accuracy when dealing with heterogeneous data. The framework can train load-forecasting models with a fast convergence rate and better prediction performance than current mainstream federated learning algorithms. Full article
(This article belongs to the Special Issue Intelligent Algorithms for High-Penetration New Energy)
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19 pages, 7034 KiB  
Article
Hierarchical Optimization Framework for Layout Design of Star–Tree Gas-Gathering Pipeline Network in Discrete Spaces
by Yu Lin, Yanhua Qiu, Hao Chen, Jun Zhou, Jiayi He, Penghua Du and Dafan Liu
Algorithms 2024, 17(8), 340; https://doi.org/10.3390/a17080340 - 5 Aug 2024
Viewed by 1111
Abstract
The gas-gathering pipeline network is a critical infrastructure for collecting and conveying natural gas from the extraction site to the processing facility. This paper introduces a design optimization model for a star–tree gas-gathering pipeline network within a discrete space, aimed at determining the [...] Read more.
The gas-gathering pipeline network is a critical infrastructure for collecting and conveying natural gas from the extraction site to the processing facility. This paper introduces a design optimization model for a star–tree gas-gathering pipeline network within a discrete space, aimed at determining the optimal configuration of this infrastructure. The objective is to reduce the investment required to build the network. Key decision variables include the locations of stations, the plant location, the connections between wells and stations, and the interconnections between stations. Several equality and inequality constraints are formulated, primarily addressing the affiliation between wells and stations, the transmission radius, and the capacity of the stations. The design of a star–tree pipeline network represents a complex, non-deterministic polynomial (NP) hard combinatorial optimization problem. To tackle this challenge, a hierarchical optimization framework coupled with an improved genetic algorithm (IGA) is proposed. The efficacy of the genetic algorithm is validated through testing and comparison with other traditional algorithms. Subsequently, the optimization model and solution methodology are applied to the layout design of a pipeline network. The findings reveal that the optimized network configuration reduces investment costs by 16% compared to the original design. Furthermore, when comparing the optimal layout under a star–star topology, it is observed that the investment needed for the star–star topology is 4% higher than that needed for the star–tree topology. Full article
(This article belongs to the Special Issue Intelligent Algorithms for High-Penetration New Energy)
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16 pages, 778 KiB  
Article
Distributed Control of Hydrogen-Based Microgrids for the Demand Side: A Multiagent Self-Triggered MPC-Based Strategy
by Tingzhe Pan, Jue Hou, Xin Jin, Zhenfan Yu, Wei Zhou and Zhijun Wang
Algorithms 2024, 17(6), 251; https://doi.org/10.3390/a17060251 - 7 Jun 2024
Viewed by 1048
Abstract
With the global pursuit of renewable energy and carbon neutrality, hydrogen-based microgrids have also become an important area of research, as ensuring proper design and operation is essential to achieve optimal performance from hybrid systems. This paper proposes a distributed control strategy based [...] Read more.
With the global pursuit of renewable energy and carbon neutrality, hydrogen-based microgrids have also become an important area of research, as ensuring proper design and operation is essential to achieve optimal performance from hybrid systems. This paper proposes a distributed control strategy based on multiagent self-triggered model predictive control (ST-MPC), with the aim of achieving demand-side control of hydrogen-based microgrid systems. This architecture considers a hybrid energy storage system with renewable energy as the main power source, supplemented by fuel cells based on electrolytic hydrogen. The primary objective of this architecture is aiming at the supply and demand balance problem under the supply and demand relationship of microgrid, the service life of hydrogen-based microgrid energy storage equipment can be increased on the basis of realizing demand-side control of hydrogen energy microgrid system. To accomplish this, model predictive controllers are implemented within a self-triggered framework that dynamically adjusts the counting period. The simulation results demonstrate that the ST-MPC architecture significantly reduces the frequency of control action changes while maintaining an acceptable level of set-point tracking. These findings highlight the viability of the proposed solution for microgrids equipped with multiple types of electrochemical storage, which contributes to improved sustainability and efficiency in renewable-based microgrid systems. Full article
(This article belongs to the Special Issue Intelligent Algorithms for High-Penetration New Energy)
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