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1. Noiseless Consensus Based Algorithm for Economic Dispatch problem in Grid-connected Microgrids to Enhance Stability among Distributed Generators

   https://doi.org/10.1109/NAPS46351.2019.9000235  

   Singh, Shruti; Gao, David Wenzhong (October 2019, 2019 North American Power Symposium (NAPS))

   null (Ed.)

   Economic Dispatch is an important problem in the operation of microgrids. It aims to minimize the total cost of operation/generation of microgrids while meeting all the defined constraints. Since microgrids consist of distributed generators, it is imperative for these generators to communicate seamlessly with each other without any losses and to ensure secure operation of the microgrid. With use of distributed generators, noise is inherent in the system. Most of the economic dispatch problems do not include noise in their analysis while finding a relevant solution. This paper focuses on including noises as a constraint in grid connected mode microgrids to find a better economic dispatch solution. This will enhance the microgrid's performance and make it a more resilient system. 

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2. Peer-to-Peer Energy Trading in DC Packetized Power Microgrids

   https://doi.org/10.1109/JSAC.2019.2951991  

   Zhang, Haobo; Zhang, Hongliang; Song, Lingyang; Li, Yonghui; Han, Zhu; Poor, H. Vincent (September 2019, IEEE Journal on Selected Areas in Communications)

   As distributed energy resources (DERs) are widely deployed, DC packetized power microgrids have been considered as a promising solution to incorporate DERs effectively and steadily. In this paper, we consider a DC packetized power microgrid, where the energy is dispatched in the form of power packets with the assist of a power router. However, the benefits of the microgrid can only be realized when energy subscribers (ESs) equipped with DERs actively participate in the energy market. Therefore, peer-to-peer (P2P) energy trading is necessary in the DC packetized power microgrid to encourage the usage of DERs. Different from P2P energy trading in AC microgrids, the dispatching capability of the router needs to be considered in DC microgrids, which will complicate the trading problem. To tackle this challenge, we formulate the P2P trading problem as an auction game, in which the demander ESs submit bids to compete for power packets, and a controller decides the energy allocation and power packet scheduling. Analysis of the proposed scheme is provided, and its effectiveness is validated through simulation. 

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3. A microgrid control scheme for islanded operation and re-synchronization utilizing Model Predictive Control

   https://doi.org/10.1016/j.segan.2024.101464  

   Fachini, Fernando; Bogodorova, Tetiana; Vanfretti, Luigi; Boersma, Sjoerd (September 2024, Sustainable Energy, Grids and Networks)

   Enhancing grid resilience is proposed through the integration of distributed energy resources (DERs) with microgrids. Due to the diverse nature of DERs, there is a need to explore the optimal combined operation of these energy sources within the framework of microgrids. As such, this paper presents the design, implementation and validation of a Model Predictive Control (MPC)-based secondary control scheme to tackle two challenges: optimal islanded operation, and optimal re-synchronization of a microgrid. The MPC optimization algorithm dynamically adjusts input signals, termed manipulated variables, for each DER within the microgrid, including a gas turbine, an aggregate photovoltaic (PV) unit, and an electrical battery energy storage (BESS) unit. To attain optimal islanded operation, the secondary-level controller based on Model Predictive Control (MPC) was configured to uphold microgrid functionality promptly following the islanding event. Subsequently, it assumed the task of power balancing within the microgrid and ensuring the reliability of the overall system. For optimal re-synchronization, the MPC-based controller was set to adjust the manipulated variables to synchronize voltage and angle with the point of common coupling of the system. All stages within the microgrid operation were optimally achieved through one MPC-driven control system, where the controller can effectively guide the system to different goals by updating the MPC’s target reference. More importantly, the results show that the MPC-based control scheme is capable of controlling different DERs simultaneously, mitigating potentially harmful transient rotor torques from the re-synchronization as well as maintaining the microgrid within system performance requirements. 

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4. Multiagent-based Nonlinear Generalized Minimum Variance Control for Islanded AC Microgrids

   https://doi.org/10.1109/tpwrs.2023.3239793  

   Habibi, Seyed Iman; Sheikhi, Mohammad Amin; Khalili, Tohid; Abadi, Seyyed Ali; Bidram, Ali; Guerrero, Josep M. (January 2023, IEEE Transactions on Power Systems)

   Voltage regulation, frequency restoration, and reactive/active power sharing are the crucial tasks of the microgrid's secondary control, especially in the islanding operating mode. Because sensors and communication links in a microgrid are subject to noise, it is of paramount value to design a noise-resilient secondary voltage and frequency control. This paper proposes a minimum variance control approach for the secondary control of AC microgrids that can effectively perform noise attenuation, voltage/frequency restoration, and reactive/active power sharing. To this end, the nonlinear generalized minimum variance (NGMV) control approach is introduced to the islanded microgrid's secondary control system. The effectiveness of the proposed control approach is verified by simulating two microgrid test systems in MATLAB. 

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5. An Efficient Distributed Reinforcement Learning for Enhanced Multi-Microgrid Management

   https://doi.org/10.1109/IJCNN55064.2022.9892754  

   Das, Avijit; Ni, Zhen; Wu, Di (September 2022, 2022 International Joint Conference on Neural Networks (IJCNN))

   Economic dispatch in a multi-microgrid (MMG) system involves an increasing number of states from distributed energy resources (DERs) compared to a single microgrid. In these cases, traditional reinforcement learning (RL) approaches may become computationally expensive or less effective in finding the least-cost solution. This paper presents a novel RL approach that employs local learning agents to interact with individual microgrid environments in a distributed manner and a global agent to search for actions to minimize system cost at the MMG system level. The proposed distributed RL framework is more efficient in learning the dispatch policy compared to conventional approaches. Case studies are performed on a 3-microgrid system with different types of DERs. Results substantiate the effectiveness of the proposed approach in comparison with conventional methods in terms of operation costs, computation time, and peak-to-average ratio. 

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