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Optimization and Control of Marine Renewable Energy Systems
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Optimization and Control of Marine Renewable Energy Systems

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Ocean Engineering".

Deadline for manuscript submissions: 25 May 2025 | Viewed by 974

Special Issue Editor

Dr. Saptarshi Bhattacharya

E-Mail Website
Guest Editor
Pacific Northwest National Laboratory, Richland, WA, USA
Interests: design and operation of sustainable energy systems; power system operations; control and optimization for cyber physical energy systems; energy markets; energy-efficient buildings
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Marine energy systems (tidal, wave, and ocean thermal energy conversion) present significant potential for sustainable power generation. However, the operational efficiency and reliability of these energy systems depend on optimal performance and appropriate control strategies. Optimization techniques are typically employed to enhance energy capture, minimize operating costs, and ensure system stability under varying conditions. Control strategies, such as model predictive control (MPC) and adaptive control, are often critical in regulating power output and improving system responsiveness to dynamic environments. These strategies aim to maximize energy extraction while mitigating the effects of turbulence, wave irregularities, and mechanical wear. Furthermore, optimization algorithms are used to design efficient energy converters, manage grid integration, and address challenges related to long-term reliability. This Special Issue (SI) will focus on all aspects of optimization and control for marine energy systems, with a view towards building a more renewable-rich grid and a decarbonized global energy sector.

Dr. Saptarshi Bhattacharya
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • convex/non-convex optimization
  • model predictive control
  • learning-based control
  • adaptive control
  • design optimization
  • power system operations
  • grid integration
  • energy maximizing control
  • inverter-based resource modeling and control
  • microgrid control
  • optimization and control for energy resilience

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

19 pages, 4737 KiB  
Article
A Novel Reactive Power Sharing Control Strategy for Shipboard Microgrids Based on Deep Reinforcement Learning
by Wangyang Li, Hong Zhao, Jingwei Zhu and Tiankai Yang
J. Mar. Sci. Eng. 2025, 13(4), 718; https://doi.org/10.3390/jmse13040718 - 3 Apr 2025
Viewed by 294
Abstract
Reactive power sharing in distributed generators (DGs) is one of the key issues in the control technologies of greenship microgrids. Reactive power imbalance in ship microgrids can cause instability and potential equipment damage. In order to improve the poor performance of the traditional [...] Read more.
Reactive power sharing in distributed generators (DGs) is one of the key issues in the control technologies of greenship microgrids. Reactive power imbalance in ship microgrids can cause instability and potential equipment damage. In order to improve the poor performance of the traditional adaptive droop control methods used in microgrids under high-load conditions and influenced by virtual impedance parameters, this paper proposes a novel strategy based on the deep reinforcement learning DQN-VI, in which a deep Q network (DQN) is combined with the virtual impedance (VI) method. Unlike traditional methods which may use static or heuristically adjusted VI parameters, the DQN-VI strategy employs deep reinforcement learning to dynamically optimize these parameters, enhancing the microgrid’s performance under varying conditions. The proposed DQN-VI strategy considers the current situation in greenships, wherein microgrids are generally equipped with cables of different lengths and measuring the impedance of each cable is challenging due to the lack of space. By modeling the control process as a Markov decision process, the observation space, action space, and reward function are designed. In addition, a deep neural network is used to estimate the Q function that describes the relationship between the state and the action. During the training of the DQN agent, the process is optimized step-by-step by observing the state and rewards of the system, thereby effectively improving the performance of the microgrids. The comparative simulation experiments verify the effectiveness and superiority of the proposed strategy. Full article
(This article belongs to the Special Issue Optimization and Control of Marine Renewable Energy Systems)
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20 pages, 3067 KiB  
Article
Improved Deadbeat Predictive Direct Power Control for Three-Phase PWM Rectifier Based on LADRC
by He Ma, Xuliang Yao, Jingfang Wang, Xinghong Luo and Shengqi Huang
J. Mar. Sci. Eng. 2025, 13(3), 402; https://doi.org/10.3390/jmse13030402 - 21 Feb 2025
Viewed by 424
Abstract
In modern marine vessels equipped with electric propulsion systems, rectifiers are commonly used as part of the setup. However, the conventional deadbeat predictive direct power control strategy for three-phase voltage source pulse-width modulation (PWM) rectifiers tends to underperform when subjected to load variations [...] Read more.
In modern marine vessels equipped with electric propulsion systems, rectifiers are commonly used as part of the setup. However, the conventional deadbeat predictive direct power control strategy for three-phase voltage source pulse-width modulation (PWM) rectifiers tends to underperform when subjected to load variations and external disturbances. To address these limitations, this paper proposes an enhanced linear active disturbance rejection control (LADRC), incorporating virtual capacitance and an improved equivalent input disturbance strategy. The integration of virtual capacitance in the LADRC is specifically applied during load transitions. Virtual capacitance is a capacitor element simulated through the control strategy. It enhances voltage stability and dynamic response capability by compensating for voltage fluctuations and power deficits in the system. By providing a virtual active power, this approach substantially improves power tracking performance, reducing the DC voltage drop and settling time by 60% and 74%, respectively. In addition, the proposed strategy is easy to implement and does not add complexity to the LADRC. Moreover, the equivalent input disturbance is refined through virtual capacitance, enabling accurate disturbance estimation. As a result, the active power ripple and current total harmonic distortion under disturbances are reduced by 44% and 40%, respectively. The stability of the proposed strategy is comprehensively analyzed, and experimental results from a prototype system validate its effectiveness and accuracy. Full article
(This article belongs to the Special Issue Optimization and Control of Marine Renewable Energy Systems)
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