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Intelligent Control and Optimization Technologies in Sustainable Smart Grids: Networked Microgrids and Distributed Systems

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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 (3 April 2023) | Viewed by 5966

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Dr. Guannan Lou

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Guest Editor

School of Electrical Engineering, Southeast University, Nanjing 210096, China
Interests: renewable energy; microgrid modeling and control; distributed system operation; storage energy; cyber-physical systems

Special Issue Information

Dear Colleagues,

Economic factors, carbon neutrality and the increasing penetration of distributed generations (DGs) are pushing the incumbent distributed system toward a more intelligent and sustainable future. Microgrid clusters are considered to be important carriers that facilitate the utilization of renewable energies and efficient interactions between DGs, grid, storages and loads. In normal operations, microgrid clusters in grid-connected modes are desirable for providing high-quality power to distributed networks and local loads, while in case of fault events, networked microgrids can enter into islanding operations with reconfigurable cyber-physical networks to guarantee the sustainable power supplies of loads. Hence, it is crucial to explore intelligent technologies/algorithms for the coordination operation of network microgrids and distributed systems, including the relevant optimal dispatching, MGs cooperative control, power electronics control, storage configurations, reconfigurable networks, service restoration, etc. Moreover, cyber-physical systems and artificial intelligence are also interesting topics related to sustainable networked microgrids and distributed networks.

Dr. Guannan Lou
Guest Editor

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Keywords

modeling and control of (networked) microgrids
distributed cooperative control/optimization
intelligent control/optimization algorithms
control of power electronics-interfaced DGs
cyber-physical systems in smart grids
energy storage systems
the application of artificial intelligence in smart grids
network reconfigurations

Published Papers (3 papers)

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Research

15 pages, 14166 KiB

Open AccessFeature PaperArticle

Research on Renewable-Energy Accommodation-Capability Evaluation Based on Time-Series Production Simulations

by Dan Zhou, Qi Zhang, Yangqing Dan, Fanghong Guo, Jun Qi, Chenyuan Teng, Wenwei Zhou and Haonan Zhu

Energies 2022, 15(19), 6987; https://doi.org/10.3390/en15196987 - 23 Sep 2022

Cited by 5 | Viewed by 1043

Abstract

In recent years, renewable energy has received extensive attention due to its advantages of sustainability, economy, and environmental protection. However, with the rapid development of renewable energy, the problem of curtailment is becoming increasingly serious. Studying the calculation method and establishing a quantitative evaluation system of renewable energy accommodation capacity are important means to solve this problem. This paper comprehensively considers the factors affecting the accommodation of renewable energy, establishes a accommodation calculation model with the maximum accommodation of renewable energy as the optimization target based on the time series production simulation method, and uses the hybrid particle swarm optimization (PSO) algorithm to solve it. The model is verified with historical data such as load, photovoltaic (PV), and wind power in a certain region throughout the year. The experimental results verify the rationality of the renewable-energy accommodation-capacity model proposed in this paper and the correctness of the theoretical analysis. The calculation results have important reference and guiding significance for the operation and control of power-grid planning and dispatching. Full article

(This article belongs to the Special Issue Intelligent Control and Optimization Technologies in Sustainable Smart Grids: Networked Microgrids and Distributed Systems)

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21 pages, 1219 KiB

Open AccessArticle

Hybrid Deep Reinforcement Learning Considering Discrete-Continuous Action Spaces for Real-Time Energy Management in More Electric Aircraft

by Bing Liu, Bowen Xu, Tong He, Wei Yu and Fanghong Guo

Energies 2022, 15(17), 6323; https://doi.org/10.3390/en15176323 - 30 Aug 2022

Cited by 1 | Viewed by 1474

Abstract

The increasing number and functional complexity of power electronics in more electric aircraft (MEA) power systems have led to a high degree of complexity in modelling and computation, making real-time energy management a formidable challenge, and the discrete-continuous action space of the MEA system under consideration also poses a challenge to existing DRL algorithms. Therefore, this paper proposes an optimisation strategy for real-time energy management based on hybrid deep reinforcement learning (HDRL). An energy management model of the MEA power system is constructed for the analysis of generators, buses, loads and energy storage system (ESS) characteristics, and the problem is described as a multi-objective optimisation problem with integer and continuous variables. The problem is solved by combining a duelling double deep Q network (D3QN) algorithm with a deep deterministic policy gradient (DDPG) algorithm, where the D3QN algorithm deals with the discrete action space and the DDPG algorithm with the continuous action space. These two algorithms are alternately trained and interact with each other to maximize the long-term payoff of MEA. Finally, the simulation results show that the effectiveness of the method is verified under different generator operating conditions. For different time lengths T, the method always obtains smaller objective function values compared to previous DRL algorithms, is several orders of magnitude faster than commercial solvers, and is always less than 0.2 s, despite a slight shortfall in solution accuracy. In addition, the method has been validated on a hardware-in-the-loop simulation platform. Full article

(This article belongs to the Special Issue Intelligent Control and Optimization Technologies in Sustainable Smart Grids: Networked Microgrids and Distributed Systems)

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15 pages, 2526 KiB

Open AccessArticle

Using Bayesian Deep Learning for Electric Vehicle Charging Station Load Forecasting

by Dan Zhou, Zhonghao Guo, Yuzhe Xie, Yuheng Hu, Da Jiang, Yibin Feng and Dong Liu

Energies 2022, 15(17), 6195; https://doi.org/10.3390/en15176195 - 25 Aug 2022

Cited by 18 | Viewed by 2570

Abstract

In recent years, replacing internal combustion engine vehicles with electric vehicles has been a significant option for supporting reducing carbon emissions because of fossil fuel shortage and environmental contamination. However, the rapid growth of electric vehicles (EVs) can bring new and uncertain load conditions to the electric network. Precise load forecasting for EV charging stations becomes vital to reduce the negative influence on the grid. To this end, a novel day-ahead load forecasting method is proposed to forecast loads of EV charging stations with Bayesian deep learning techniques. The proposed methodological framework applies long short-term memory (LSTM) network combined with Bayesian probability theory to capture uncertainty in forecasting. Based on the actual operational data of the EV charging station collected on the Caltech campus, the experiment results show the superior performance of the proposed method compared with other methods, indicating significant potential for practical applications. Full article

(This article belongs to the Special Issue Intelligent Control and Optimization Technologies in Sustainable Smart Grids: Networked Microgrids and Distributed Systems)

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Intelligent Control and Optimization Technologies in Sustainable Smart Grids: Networked Microgrids and Distributed Systems

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