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Energies
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Optimal Planning, Integration and Control of Renewable-Based Microgrid Systems
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Optimal Planning, Integration and Control of Renewable-Based Microgrid Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 3615

Special Issue Editor

Prof. Dr. Yun-Su Kim

E-Mail Website
Guest Editor
Graduate School of Energy Convergence, Gwangju Institute of Science and Technology, Gwangju, Korea
Interests: distribution network; microgrid; renewable energy; distributed generation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Power systems have encountered various significant issues, mostly related to the integration of renewable energy sources into the power grid. The high penetration level of renewable energy sources in the grid causes a low inertia problem, variation and uncertainty problems, a weak grid problem, and new types of stability problems. We cannot radically change the conventional power system to solve those types of problems but we can divide and conquer those problems on a down-scaled system, a microgrid.

This Special Issue aims to present and disseminate the most recent advances related to the theory, design, modelling, application, control, monitoring, and planning of all types of renewable-based microgrids.

Topics of interest for publication include, but are not limited to:

  • The modelling of a microgrid and its components;
  • The energy management and/or control of a microgrid;
  • The dynamic/static state estimation of a microgrid;
  • A framework and/or standards for a renewable-based microgrid operation;
  • Stability analysis and improvement techniques for a microgrid control;
  • Communication-free microgrid operation schemes;
  • The control and management of an unbalanced microgrid system;
  • The economic assessment of renewable-based microgrids.

Prof. Dr. Yun-Su Kim
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. Energies is an international peer-reviewed open access semimonthly 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

  • microgrid
  • renewable energy
  • energy management
  • control
  • modelling
  • optimization

Published Papers (3 papers)

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Research

22 pages, 5371 KiB  
Article
Robust Collaborative Scheduling Strategy for Multi-Microgrids of Renewable Energy Based on a Non-Cooperative Game and Profit Allocation Mechanism
by Xiedong Gao and Xinyan Zhang
Energies 2024, 17(2), 519; https://doi.org/10.3390/en17020519 - 21 Jan 2024
Viewed by 801
Abstract
The Multiple Microgrid System (MMG) facilitates synergistic complementarity among various energy sources, reduces carbon emissions, and promotes the integration of renewable energy generation. In this context, we propose a two-stage robust cooperative scheduling model for MMGs based on non-cooperative game theory and a [...] Read more.
The Multiple Microgrid System (MMG) facilitates synergistic complementarity among various energy sources, reduces carbon emissions, and promotes the integration of renewable energy generation. In this context, we propose a two-stage robust cooperative scheduling model for MMGs based on non-cooperative game theory and a benefit allocation mechanism. In the first stage, considering electricity price fluctuations and uncertainties in wind and solar power outputs, a robust optimization approach is applied to establish an electric energy management model for MMGs. This model enables point-to-point energy sharing among microgrids. In the second stage, addressing the benefit allocation problem for shared electric energy, we introduce a Cost Reduction Ratio Distribution (CRRD) model based on non-cooperative game theory. The generalized Nash equilibrium is utilized to determine the benefit distribution for shared electric energy. Finally, through case studies, the proposed model is validated, ensuring fair returns for each microgrid. The results indicate that the proposed model optimizes the operational states of individual microgrids, reduces operational costs for each microgrid, and lowers the overall total operational costs of the MMG system. Additionally, an investigation is conducted into the impact of electricity price uncertainty coefficients and confidence levels of wind and solar uncertainties on the operational costs of microgrids. Full article
(This article belongs to the Special Issue Optimal Planning, Integration and Control of Renewable-Based Microgrid Systems)
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20 pages, 783 KiB  
Article
Model-Free Approach to DC Microgrid Optimal Operation under System Uncertainty Based on Reinforcement Learning
by Roni Irnawan, Ahmad Ataka Awwalur Rizqi, Muhammad Yasirroni, Lesnanto Multa Putranto, Husni Rois Ali, Eka Firmansyah and Sarjiya
Energies 2023, 16(14), 5369; https://doi.org/10.3390/en16145369 - 14 Jul 2023
Viewed by 902
Abstract
There has been tremendous interest in the development of DC microgrid systems which consist of interconnected DC renewable energy sources. However, operating a DC microgrid system optimally by minimizing operational cost and ensures stability remains a problem when the system’s model is not [...] Read more.
There has been tremendous interest in the development of DC microgrid systems which consist of interconnected DC renewable energy sources. However, operating a DC microgrid system optimally by minimizing operational cost and ensures stability remains a problem when the system’s model is not available. In this paper, a novel model-free approach to perform operation control of DC microgrids based on reinforcement learning algorithms, specifically Q-learning and Q-network, has been proposed. This approach circumvents the need to know the accurate model of a DC grid by exploiting an interaction with the DC microgrids to learn the best policy, which leads to more optimal operation. The proposed approach has been compared with with mixed-integer quadratic programming (MIQP) as the baseline deterministic model that requires an accurate system model. The result shows that, in a system of three nodes, both Q-learning (74.2707) and Q-network (74.4254) are able to learn to make a control decision that is close to the MIQP (75.0489) solution. With the introduction of both model uncertainty and noisy sensor measurements, the Q-network performs better (72.3714) compared to MIQP (72.1596), whereas Q-learn fails to learn. Full article
(This article belongs to the Special Issue Optimal Planning, Integration and Control of Renewable-Based Microgrid Systems)
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23 pages, 2239 KiB  
Article
Capacity Optimization of Independent Microgrid with Electric Vehicles Based on Improved Pelican Optimization Algorithm
by Jiyong Li, Ran Chen, Chengye Liu, Xiaoshuai Xu and Yasai Wang
Energies 2023, 16(6), 2539; https://doi.org/10.3390/en16062539 - 8 Mar 2023
Cited by 4 | Viewed by 1468
Abstract
In order to reduce the comprehensive power cost of the independent microgrid and to improve environmental protection and power supply reliability, a two-layer power capacity optimization model of a microgrid with electric vehicles (EVs) was established that considered uncertainty and demand response. Based [...] Read more.
In order to reduce the comprehensive power cost of the independent microgrid and to improve environmental protection and power supply reliability, a two-layer power capacity optimization model of a microgrid with electric vehicles (EVs) was established that considered uncertainty and demand response. Based on the load and energy storage characteristics of electric vehicles, the classification of electric vehicles was proposed, and their mathematical models were established. The idea of robust optimization was adopted to construct the uncertain scenario set. Considering the incentive demand response, a two-layer power capacity optimization model of a microgrid was constructed. The improved pelican optimization algorithm (IPOA) was proposed as the two-layer model. In view of the slow convergence rate of the pelican optimization algorithm (POA) and its tendency to fall into the local optimum, methods such as elite reverse learning were proposed to generate the initial population, set disturbance inhibitors, and introduce Lévy flight to improve the initial population of the algorithm and enhance its global search ability. Finally, an independent microgrid was used as an example to verify the effectiveness of the proposed model and the improved algorithm. Considering that the total power capacity optimization cost of the microgrid after addition of electric vehicles was reduced by CNY 139,600, the total power capacity optimization cost of the microgrid after IOPA optimization was reduced by CNY 49,600 compared with that after POA optimization. Full article
(This article belongs to the Special Issue Optimal Planning, Integration and Control of Renewable-Based Microgrid Systems)
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