More Like this

1. Adaptive Control for Energy Exchange with Probabilistic Interval Predictors in Isolated Microgrids

   https://doi.org/10.3390/en14020375  

   Cheng, Jiayu; Duan, Dongliang; Cheng, Xiang; Yang, Liuqing; Cui, Shuguang (January 2021, Energies)

   null (Ed.)

   Stability and reliability are of the most important concern for isolated microgrid systems that have no support from the utility grid. Interval predictions are often applied to ensure the system stability of isolated microgrids as they cover more uncertainties and robust control can be achieved based on more sufficient information. In this paper, we propose a probabilistic microgrid energy exchange method based on the Model Predictive Control (MPC) approach to make better use of the prediction intervals so that the system stability and cost efficiency of isolated microgrids are improved simultaneously. Appropriate scenarios are selected from the predictions according to the evaluation of future trends and system capacity. In the meantime, a two-stage adaptive reserve strategy is adopted to further utilize the potential of interval predictions and maintain the system security adaptively. Reserves are determined at the optimization stage to prepare some extra capacity for the fluctuations in the renewable generation and load demand at the operation stage based on the aggressive and conservative level of the system, which is automatically updated at each step. The optimal dispatch problem is finally formulated using the mixed-integer linear programming model and the MPC is formulated as an optimization problem with a discount factor introduced to adjust the weights. Case studies show that the proposed method could effectively guarantee the stability of the system and improve economic performance. 

   more » « less
2. A Comparative Study of Formulations and Algorithms for Reliability-Based Co-Design Problems

   https://doi.org/10.1115/DETC2019-98307  

   Cui, Tonghui; Allison, James T.; Wang, Pingfeng (August 2019, American Society of Mechanical Engineers)

   Co-design, or integrated physical and control system design, has been demonstrated successfully for several engineering system design optimization applications, primarily in a deterministic manner. An opportunity exists to study non-deterministic co-design strategies, including incorporation of uncertainty-induced failures, into an integrated co-design framework. Reliability-based design optimization (RBDO) is one such method that can be used to increase the likelihood of having a feasible design that satisfies all reliability constraints. While significant recent advancements have been made in co-design and RBDO separately, limited work has been done where reliability-based dynamic system design and control design optimization are considered jointly. In this paper, the co-design problem is integrated with the RBDO framework to yield a system-optimal design and the corresponding control trajectory, which satisfy all reliability constraints in the presence of parameter variations. Different problem formulations and RBDO algorithms are compared through numerical examples. The design of a horizontal-axis wind turbine (HAWT) supported by a lattice tower (with parameter uncertainties) is presented to demonstrate the applicability of the proposed method. 

   more » « less
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. 

   more » « less
4. An Imitation Learning Method with Multi-Virtual Agents for Microgrid Energy Optimization under Interrupted Periods

   https://doi.org/10.1109/PESGM51994.2024.10689197  

   Lin, Yanbin; Ni, Zhen; Tang, Yufei (July 2024, IEEE)

   Existing computer analytic methods for the microgrid system, such as reinforcement learning (RL) methods, suffer from a long-term problem with the empirical assumption of the reward function. To alleviate this limitation, we propose a multi-virtual-agent imitation learning (MAIL) approach to learn the dispatch policy under different power supply interrupted periods. Specifically, we utilize the idea of generative adversarial imitation learning method to do direct policy mapping, instead of learning from manually designed reward functions. Multi-virtual agents are used for exploring the relationship of uncertainties and corresponding actions in different microgrid environments in parallel. With the help of a deep neural network, the proposed MAIL approach can enhance robust ability by minimizing the maximum crossover discriminators to cover more interrupted cases. Case studies show that the proposed MAIL approach can learn the dispatch policies as well as the expert method and outperform other existing RL methods. 

   more » « less
5. Reliability Analysis of a Decentralized Microgrid Control Architecture

   https://doi.org/10.1109/TSG.2018.2843527  

   Bani-Ahmed, Abedalsalam; Rashidi, Mohammad; Nasiri, Adel; Hosseini, Hossein (June 2018, IEEE Transactions on Smart Grid)

   Reliability enhancement of microgrids is challenged by environmental and operational failures. Centrally controlled microgrids are susceptible to failures at high probability due to a single-point-of-failure, e.g. the central controller. True decentralization of microgrid architecture entails elimination of the central controller, attaining a parallel configuration for the system. In this paper, decentralized microgrid control architecture is proposed as a solution for reliability degradation over the time, and analyzes the reliability aspects of centralized and decentralized control architectures for microgrids. Degree of importance of a single controller in centralized and decentralized architectures is determined and validated by Markov Chain Models (MCM). Results confirm that higher reliability is achieved when true decentralization of control architecture is adopted. Challenges of implementing a true decentralized control architecture are discussed. Hardware-In-the-Loop simulation results for microgrid controller failure scenarios for both architectures are presented and discussed. 

   more » « less