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1. 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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2. Scenario-based Economic Dispatch with Tunable Risk Levels in High-renewable Power Systems

   https://doi.org/10.1109/TPWRS.2018.2874464  

   Modarresi, M. Sadegh; Xie, Le; Campi, Marco; Garatti, Simone; Care, Algo; Thatte, Anupam; Kumar, P. R. (January 2018, IEEE Transactions on Power Systems)

   This paper introduces an empirical approach to dispatch resources in real-time power system operation with growing levels of uncertainties emerging from intermittent and distributed energy resources in the supply and the demand side. It is shown that by taking empirical data of specific sizes, the dispatch results can lead to a quantifiable and rigorous bound on the risk of violating constraints at the implementation stage. In particular, we formulate the look-ahead real-time economic dispatch problem using the scenario approach. This approach takes empirical data as input and guarantees a tunable probability of violating the constraints according to the input data size. By exploiting the structure of the economic dispatch, we show that in the absence of transmission constraints, the number of samples theory requires does not grow with the size of the problem. In the more general case with consideration of transmission constraints, it is shown that the posterior bound on the risk of dispatch can be quantified and can be much smaller than the risk bound before solving the dispatch. Numerical examples based on a standard test system suggest that the scenario approach can provide a practically attractive solution with theoretically rigorous properties for risk-limiting power system operations. 

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3. 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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4. Experiments on a Real-Time Energy Management System for Islanded Prosumer Microgrids

   https://doi.org/10.3390/electronics8090925  

   Macana, Carlos A.; Pota, Hemanshu R.; Zhu, Quanyan; Guerrero, Josep M.; Vasquez, Juan C. (September 2019, Electronics)

   This paper presents an experimental demonstration of a novel real-time Energy Management System (EMS) for inverter-based microgrids to achieve optimal economic operation using a simple dynamic algorithm without offline optimization process requirements. The dynamic algorithm solves the economic dispatch problem offering an adequate stability performance and an optimal power reference tracking under sudden load and generation changes. Convergence, optimality and frequency regulation properties of the real-time EMS are shown, and the effectiveness and compatibility with inner and primary controllers are validated in experiments, showing better performance on optimal power tracking and frequency regulation than conventional droop control power sharing techniques. 

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5. Feasibility of hybrid in-stream generator–photovoltaic systems for Amazonian off-grid communities

   https://doi.org/10.1093/pnasnexus/pgac077  

   Brown, Erik; Johansen, Igor Cavallini; Bortoleto, Ana Paula; Pokhrel, Yadu; Chaudhari, Suyog; Cak, Anthony; Sulaeman, Samer; Castro-Diaz, Laura; Lopez, Maria Claudia; Mayer, Adam; et al (June 2022, PNAS Nexus)

   Abstract While there have been efforts to supply off-grid energy in the Amazon, these attempts have focused on low upfront costs and deployment rates. These “get-energy-quick” methods have almost solely adopted diesel generators, ignoring the environmental and social risks associated with the known noise and pollution of combustion engines. Alternatively, it is recommended, herein, to supply off-grid needs with renewable, distributed microgrids comprised of photovoltaics (PV) and in-stream generators (ISG). Utilization of a hybrid combination of renewable generators can provide an energetically, environmentally, and financially feasible alternative to typical electrification methods, depending on available solar irradiation and riverine characteristics, that with community engagement allows for a participatory codesign process that takes into consideration people’s needs. A convergent solution development framework that includes designers—a team of social scientists, engineers, and communication specialists—and communities as well as the local industry is examined here, by which the future negative impacts at the human–machine–environment nexus can be minimized by iterative, continuous interaction between these key actors. 

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