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1. Load Sharing Strategy Incorporating Power Limits in Islanded Inverter-Based Microgrids

   https://doi.org/10.1109/EPEC48502.2020.9320069  

   Eyisi, Chiebuka ; Tian, Guanyu ; Vorobev, Petr ; Li, Qifeng ( November 2020 , 2020 IEEE Electric Power and Energy Conference (EPEC))

   null (Ed.)

   Microgrids (MGs) comprising multiple interconnected distributed energy resources (DERs) with coordinated control strategies can operate in both grid-connected and islanded modes. In the grid-connected mode, the frequency and bus voltages are maintained by the utility grid. In the islanded mode, the DERs maintain the frequency and bus voltages in the MG. This paper presents a load demand sharing strategy in an islanded voltage source inverter-based microgrid (VSI-MG). The survivability of the interconnected MG in the presence of a single fully loaded VSI in an islanded VSI-MG is investigated. The concept of virtual synchronous machines (VSM) that enables the modeling of the VSI to emulate the inertia effect of synchronous machines is applied and then a Jacobian-based approach is formulated that takes into account, the capacity of the VSI. Simulation results are presented to verify the effectiveness of the approach. 

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2. 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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3. Cost-Optimized Microgrid Coalitions Using Bayesian Reinforcement Learning

   https://doi.org/10.3390/en14227481  

   Sadeghi, Mohammad ; Mollahasani, Shahram ; Erol-Kantarci, Melike ( November 2021 , Energies)

   Microgrids are empowered by the advances in renewable energy generation, which enable the microgrids to generate the required energy for supplying their loads and trade the surplus energy to other microgrids or the macrogrid. Microgrids need to optimize the scheduling of their demands and energy levels while trading their surplus with others to minimize the overall cost. This can be affected by various factors such as variations in demand, energy generation, and competition among microgrids due to their dynamic nature. Thus, reaching optimal scheduling is challenging due to the uncertainty caused by the generation/consumption of renewable energy and the complexity of interconnected microgrids and their interplay. Previous works mainly rely on modeling-based approaches and the availability of precise information on microgrid dynamics. This paper addresses the energy trading problem among microgrids by minimizing the cost while uncertainty exists in microgrid generation and demand. To this end, a Bayesian coalitional reinforcement learning-based model is introduced to minimize the energy trading cost among microgrids by forming stable coalitions. The results show that the proposed model can minimize the cost up to 23% with respect to the coalitional game theory model. 

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4. Noiseless Consensus based Economic Dispatch Algorithm in conjunction with STATCOM Controller for Reactive Power Compensation in Islanded Microgrids to enhance Voltage and Power Stability

   https://doi.org/10.1109/NAPS46351.2019.8999971  

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

   null (Ed.)

   Economic Dispatch 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 the use of distributed generators, noise is inherent in the system. This paper focuses on including noises as a constraint in an islanded microgrid to find a better economic dispatch solution. It also introduces a STATCOM controller for reactive power management. The controller will help provide stability to the microgrid's voltage, output power and phase angle. This will enhance the microgrid's performance and make it a more resilient system. 

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5. Unfalsified Switching Adaptive Voltage Control for Islanded Microgrids

   https://doi.org/10.1109/TPWRS.2021.3133381  

   Habibi, Seyed Iman ; Bidram, Ali ( December 2021 , IEEE Transactions on Power Systems)

   Microgrids voltage regulation is of particular importance during both grid-connected and islanded modes of operation. Especially, during the islanded mode, when the support from the upstream grid is lost, stable voltage regulation is vital for the reliable operation of critical loads. This paper proposes a robust and data-driven control approach for secondary voltage control of AC microgrids in the presence of uncertainties. To this end, unfalsified adaptive control (UAC) is utilized to select the best stabilizing controller from a set of pre-designed controllers with the minimum knowledge required from the microgrid. Two microgrid test systems are simulated in MATLAB to verify the effectiveness of the proposed method under different scenarios like load change and communication link failure. 

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