Modeling, Optimization and Algorithms for Power Systems

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Dynamical Systems".

Deadline for manuscript submissions: closed (15 December 2023) | Viewed by 412

Special Issue Editor

Dr. Lachlan Andrew
E-Mail Website
Guest Editor
School of Computing and Information Systems, University of Melbourne, Parkville, VIC 3010, Australia
Interests: algorithms for renewable energy integration

Special Issue Information

Dear Colleagues,

Electricity grids around the world are undergoing major changes, including the scale of planned intercontinental connections for exploiting rich renewable energy sources, the scale of distribution networks absorbing increasing amounts of rooftop photovoltaic generation, and microgrids to increase reliability. Coupled with enabling technologies such as power electronics and affordable sensors, this has led to the need and opportunity to move away from worst-case design and operation of networks to more optimal real-time control based on measurement-driven models. However, much work remains in finding the best way to use these new enabling technologies. What network models lead to tractable optimization problems?  What are the trade-offs between the cost of sensors, the accuracy of models, and the cost of optima. This Special Issue is dedicated to these and the many other question around the modeling and optimization of power systems and related algorithms.  

Dr. Lachlan Andrew
Guest Editor

Manuscript Submission Information

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Keywords

  • mathematical models for integration of intermittent renewable energy
  • demand response
  • optimal power flow/unit commitment/economic dispatch
  • optimization formulations of network planning
  • optimization based data-driven models
  • optimal management, or dynamical systems models, of microgrids

Published Papers (1 paper)

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Research

24 pages, 5115 KiB  
Article
Neural Network Algorithm with Reinforcement Learning for Microgrid Techno-Economic Optimization
Mathematics 2024, 12(2), 280; https://doi.org/10.3390/math12020280 - 15 Jan 2024
Viewed by 68
Abstract
Hybrid energy systems (HESs) are gaining prominence as a practical solution for powering remote and rural areas, overcoming limitations of conventional energy generation methods, and offering a blend of technical and economic benefits. This study focuses on optimizing the sizes of an autonomous [...] Read more.
Hybrid energy systems (HESs) are gaining prominence as a practical solution for powering remote and rural areas, overcoming limitations of conventional energy generation methods, and offering a blend of technical and economic benefits. This study focuses on optimizing the sizes of an autonomous microgrid/HES in the Kingdom of Saudi Arabia, incorporating solar photovoltaic energy, wind turbine generators, batteries, and a diesel generator. The innovative reinforcement learning neural network algorithm (RLNNA) is applied to minimize the annualized system cost (ASC) and enhance system reliability, utilizing hourly wind speed, solar irradiance, and load behavior data throughout the year. This study validates RLNNA against five other metaheuristic/soft-computing approaches, demonstrating RLNNA’s superior performance in achieving the lowest ASC at USD 1,219,744. This outperforms SDO and PSO, which yield an ASC of USD 1,222,098.2, and MRFO, resulting in an ASC of USD 1,222,098.4, while maintaining a loss of power supply probability (LPSP) of 0%. RLNNA exhibits faster convergence to the global solution than other algorithms, including PSO, MRFO, and SDO, while MRFO, PSO, and SDO show the ability to converge to the optimal global solution. This study concludes by emphasizing RLNNA’s effectiveness in optimizing HES sizing, contributing valuable insights for off-grid energy systems in remote regions. Full article
(This article belongs to the Special Issue Modeling, Optimization and Algorithms for Power Systems)
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