Robust power management system with generation and demand prediction and critical loads in DC microgrid

[1]  P. A. Michael,et al.  Weather Forecasting for Renewable Energy System: A Review , 2022, Archives of Computational Methods in Engineering.

[2]  E. Akarslan.  Learning Vector Quantization based predictor model selection for hourly load demand forecasting , 2022, Appl. Soft Comput..

[3]  U. A. Khan,et al.  Short Term Hybrid PV/Wind Power Forecasting for Smart Grid Application using Feedforward Neural Network (FNN) Trained by a Novel Atomic Orbital Search (AOS) Optimization Algorithm , 2021, 2021 International Conference on Frontiers of Information Technology (FIT).

[4]  Zheng Qian,et al.  Day-ahead hourly photovoltaic power forecasting using attention-based CNN-LSTM neural network embedded with multiple relevant and target variables prediction pattern , 2021 .

[5]  V. Mohanavel,et al.  Artificial neural network-based output power prediction of grid-connected semitransparent photovoltaic system , 2021, Environmental Science and Pollution Research.

[6]  V. Ulansky,et al.  Generalization of minimax and maximin criteria in a game against nature for the case of a partial a priori uncertainty , 2021, Heliyon.

[7]  J. Catalão,et al.  Day-ahead optimal bidding of microgrids considering uncertainties of price and renewable energy resources , 2021, Energy.

[8]  Susana Relvas,et al.  Hybrid ensemble intelligent model based on wavelet transform, swarm intelligence and artificial neural network for electricity demand forecasting , 2021 .

[9]  Hemanshu R. Pota,et al.  Energy management of community energy storage in grid-connected microgrid under uncertain real-time prices , 2021, Sustainable Cities and Society.

[10]  Jan Ortmann,et al.  Decision-based scenario clustering for decision-making under uncertainty , 2021, Annals of Operations Research.

[11]  Yan Zhang,et al.  Robust model predictive control for optimal energy management of island microgrids with uncertainties , 2018, Energy.

[12]  Mi Yang,et al.  Multi-Source Dynamic Coordinated Control Strategy for DC Microgrid Based on Fuzzy Control , 2018, 2018 2nd IEEE Conference on Energy Internet and Energy System Integration (EI2).

[13]  Mehdi Hosseinzadeh,et al.  Fault-Tolerant Supervisory Controller for a Hybrid AC/DC Micro-Grid , 2018, IEEE Transactions on Smart Grid.

[14]  Jun Hu,et al.  Short-Term Load Forecasting With Deep Residual Networks , 2018, IEEE Transactions on Smart Grid.

[15]  Woo-Sik Yoo,et al.  Daily prediction of solar power generation based on weather forecast information in Korea , 2017 .

[16]  Hassan Bevrani,et al.  Robust Frequency Control in an Islanded Microgrid: H∞ and μ-Synthesis Approaches , 2016, IEEE Trans. Smart Grid.

[17]  Igor Kuzle,et al.  Adaptive control for evaluation of flexibility benefits in microgrid systems , 2015, Energy.

[18]  Takashi Takeda,et al.  Operation method of microgrid using the forecast method by neural network , 2015, 2015 IEEE International Telecommunications Energy Conference (INTELEC).

[19]  Guohong Wu,et al.  Development of a resilient hybrid microgrid with integrated renewable power generations supplying DC and AC loads , 2015, 2015 IEEE International Telecommunications Energy Conference (INTELEC).

[20]  Mehdi Hosseinzadeh,et al.  Power management of an isolated hybrid AC/DC micro-grid with fuzzy control of battery banks , 2015 .

[21]  Felix F. Wu,et al.  Uncertainty management in power system operation , 2015 .

[22]  El Habib Nfaoui,et al.  Multi-agent system based on fuzzy control and prediction using NN for smart microgrid energy management , 2015, 2015 Intelligent Systems and Computer Vision (ISCV).

[23]  Frank L. Lewis,et al.  Optimal, Nonlinear, and Distributed Designs of Droop Controls for DC Microgrids , 2014, IEEE Transactions on Smart Grid.

[24]  Marta Basualdo,et al.  Energy management of a hybrid system based on wind–solar power sources and bioethanol , 2013 .

[25]  Z. Qu,et al.  Cooperative control for self-organizing microgrids and game strategies for optimal dispatch of distributed renewable generations , 2012, Energy Systems.

[26]  Prashant J. Shenoy,et al.  Predicting solar generation from weather forecasts using machine learning , 2011, 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[27]  A Kwasinski,et al.  Quantitative Evaluation of DC Microgrids Availability: Effects of System Architecture and Converter Topology Design Choices , 2011, IEEE Transactions on Power Electronics.

[28]  Majid Oloomi Buygi,et al.  A Scenario-Based Multi-Objective Model for Multi-Stage Transmission Expansion Planning , 2011, IEEE Transactions on Power Systems.

[29]  Jianhui Wang,et al.  Smart Transmission Grid: Vision and Framework , 2010, IEEE Transactions on Smart Grid.

[30]  Wei-Neng Chang,et al.  Design and implementation of a hybrid regenerative power system combining grid-tie and uninterruptible power supply functions , 2010 .

[31]  William A. Dembski,et al.  Bernoulli's principle of insufficient reason and conservation of information in computer search , 2009, 2009 IEEE International Conference on Systems, Man and Cybernetics.

[32]  Zhao Yang Dong,et al.  Flexible Transmission Expansion Planning With Uncertainties in an Electricity Market , 2009, IEEE Transactions on Power Systems.

[33]  S. Barghinia,et al.  Short term load forecasting of Iran national power system using artificial neural network , 2001, 2001 IEEE Porto Power Tech Proceedings (Cat. No.01EX502).

[34]  Masahiro Inuiguchi,et al.  Minimax regret solution to linear programming problems with an interval objective function , 1995 .

[35]  H. Akbari,et al.  Day-ahead optimal scheduling of microgrid with considering demand side management under uncertainty , 2022, Electric Power Systems Research.

[36]  Eklas Hossain,et al.  A Short-Term Load Forecasting Method Using Integrated CNN and LSTM Network , 2021, IEEE Access.

[37]  Amin Khodaei,et al.  AC Versus DC Microgrid Planning , 2017, IEEE Transactions on Smart Grid.

[38]  Pravin Varaiya,et al.  Smart Operation of Smart Grid: Risk-Limiting Dispatch , 2011, Proceedings of the IEEE.

[39]  R. Aumann,et al.  Some Thoughts on the Minimax Principle , 1972 .