An efficient data-driven optimal sizing framework for photovoltaics-battery-based electric vehicle charging microgrid

[1]  Carlos Vargas-Salgado,et al.  Energy management model for a standalone hybrid microgrid through a particle Swarm optimization and artificial neural networks approach , 2022, Energy Conversion and Management.

[2]  J. Adabi,et al.  Hierarchical Energy Management System for Home-Energy-Hubs Considering Plug-In Electric Vehicles , 2022, IEEE Transactions on Industry Applications.

[3]  Chong Li,et al.  Optimization and enviro-economic assessment of hybrid sustainable energy systems: The case study of a photovoltaic/biogas/diesel/battery system in Xuzhou, China , 2022, Energy Strategy Reviews.

[4]  S. Hosseini,et al.  Smart peer-to-peer and transactive energy sharing architecture considering incentive-based demand response programming under joint uncertainty and line outage contingency , 2022, Journal of Cleaner Production.

[5]  Seyed Ehsan Ahmadi,et al.  Designing, optimizing and comparing distributed generation technologies as a substitute system for reducing life cycle costs, CO2 emissions, and power losses in residential buildings , 2022, Energy.

[6]  P. B. Andersen,et al.  Electric vehicle charging infrastructure planning for integrated transportation and power distribution networks: A review , 2022, eTransportation.

[7]  Seyed Ehsan Ahmadi,et al.  Decentralized Bi-Level stochastic optimization approach for multi-agent multi-energy networked micro-grids with multi-energy storage technologies , 2022, Energy.

[8]  M. Ouyang,et al.  Lithium-ion batteries under pulsed current operation to stabilize future grids , 2021, Cell Reports Physical Science.

[9]  Y. Ueda,et al.  Greedy energy management strategy and sizing method for a stand-alone microgrid with hydrogen storage , 2021, Journal of Energy Storage.

[10]  Marc Petit,et al.  Plug-in behavior of electric vehicles users: Insights from a large-scale trial and impacts for grid integration studies , 2021 .

[11]  Baseem Khan,et al.  Collaborative advanced machine learning techniques in optimal energy management of hybrid AC/DC IoT-based microgrids , 2021, Ad Hoc Networks.

[12]  Yonghua Song,et al.  Scheduling Thermostatically Controlled Loads to Provide Regulation Capacity Based on a Learning-Based Optimal Power Flow Model , 2021, IEEE Transactions on Sustainable Energy.

[13]  Tong Wu,et al.  Deep Learning to Optimize: Security-Constrained Unit Commitment With Uncertain Wind Power Generation and BESSs , 2021, IEEE Transactions on Sustainable Energy.

[14]  Matti Lehtonen,et al.  Comprehensive Analytical Expressions for Assessing and Maximizing Technical Benefits of Photovoltaics to Distribution Systems , 2021, IEEE Transactions on Smart Grid.

[15]  M. Ouyang,et al.  A Novel Data Augmentation and Swift Optimal Sizing Framework for PV-based EV Charging Microgrid , 2021, 2021 IEEE 4th International Electrical and Energy Conference (CIEEC).

[16]  Iftekhar Ahmad,et al.  Dispatch management of portable charging stations in electric vehicle networks , 2021 .

[17]  Ionel Vechiu,et al.  Two-level hierarchical model predictive control with an optimised cost function for energy management in building microgrids , 2021 .

[18]  Minggao Ouyang,et al.  A model-based continuous differentiable current charging approach for electric vehicles in direct current microgrids , 2021 .

[19]  P. G. Vidal,et al.  Novel optimization algorithm for the power and energy management and component sizing applied to hybrid storage-based photovoltaic household-prosumers for the provision of complementarity services , 2021 .

[20]  Wei Dong,et al.  Adaptive optimal fuzzy logic based energy management in multi-energy microgrid considering operational uncertainties , 2020, Appl. Soft Comput..

[21]  D. Dumur,et al.  Data Science-based Sizing Approach for Renewable Energy Systems , 2020, 2020 IX International Congress of Mechatronics Engineering and Automation (CIIMA).

[22]  M. Ouyang,et al.  A Novel Framework for Optimal Sizing of A DC Microgrid Considering Energy Management and Battery Degradation , 2020, 2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2).

[23]  Michael Stadler,et al.  Robust design of microgrids using a hybrid minimum investment optimization , 2020, Applied Energy.

[24]  S. Mallapaty How China could be carbon neutral by mid-century , 2020, Nature.

[25]  Lars Wederhake,et al.  The influence of electric vehicle charging strategies on the sizing of electrical energy storage systems in charging hub microgrids , 2020 .

[26]  Dirk Uwe Sauer,et al.  Impact of battery degradation models on energy management of a grid-connected DC microgrid , 2020 .

[27]  H. Zhong,et al.  Review of Learning-Assisted Power System Optimization , 2020, CSEE Journal of Power and Energy Systems.

[28]  Fouad Hasan,et al.  Hybrid Learning Aided Inactive Constraints Filtering Algorithm to Enhance AC OPF Solution Time , 2020, IEEE Transactions on Industry Applications.

[29]  Reza Fachrizal,et al.  Smart charging of electric vehicles considering photovoltaic power production and electricity consumption: A review , 2020, eTransportation.

[30]  Zhenhong Lin,et al.  Quantifying the impacts of micro- and mild- hybrid vehicle technologies on fleetwide fuel economy and electrification , 2020 .

[31]  Xuning Feng,et al.  Virtual-battery based droop control and energy storage system size optimization of a DC microgrid for electric vehicle fast charging station , 2020 .

[32]  Sgouris Sgouridis,et al.  Optimal Design of an Islanded Microgrid With Load Shifting Mechanism Between Electrical and Thermal Energy Storage Systems , 2020, IEEE Transactions on Power Systems.

[33]  Francisco Jurado,et al.  Optimal sizing and power schedule in PV household-prosumers for improving PV self-consumption and providing frequency containment reserve , 2020 .

[34]  Zachary K. Pecenak,et al.  Input data reduction for microgrid sizing and energy cost modeling: Representative days and demand charges , 2019, Journal of Renewable and Sustainable Energy.

[35]  M. R. Elkadeem,et al.  Feasibility analysis and techno-economic design of grid-isolated hybrid renewable energy system for electrification of agriculture and irrigation area: A case study in Dongola, Sudan , 2019, Energy Conversion and Management.

[36]  Prashant Nagapurkar,et al.  Techno-economic optimization and social costs assessment of microgrid-conventional grid integration using genetic algorithm and Artificial Neural Networks: A case study for two US cities , 2019, Journal of Cleaner Production.

[37]  Adil Sarwar,et al.  A Comprehensive review on electric vehicles charging infrastructures and their impacts on power-quality of the utility grid , 2019, eTransportation.

[38]  Chee Wei Tan,et al.  Optimal sizing of an autonomous photovoltaic/wind/battery/diesel generator microgrid using grasshopper optimization algorithm , 2019, Solar Energy.

[39]  Yuyan Sun,et al.  A Bi-Level Capacity Optimization of an Isolated Microgrid With Load Demand Management Considering Load and Renewable Generation Uncertainties , 2019, IEEE Access.

[40]  Navid Ghaffarzadeh,et al.  Optimal sizing of battery energy storage systems in off-grid micro grids using convex optimization , 2019, Journal of Energy Storage.

[41]  Jean-Louis Scartezzini,et al.  Machine learning methods to assist energy system optimization , 2019, Applied Energy.

[42]  Qiuye Sun,et al.  Smart energy: From independence to interconnection—A review of AI technology applied in energy systems , 2019, CSEE Journal of Power and Energy Systems.

[43]  Feng Qiu,et al.  Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems , 2019, INFORMS J. Comput..

[44]  Peng Kou,et al.  A model predictive control approach for matching uncertain wind generation with PEV charging demand in a microgrid , 2019, International Journal of Electrical Power & Energy Systems.

[45]  José L. Bernal-Agustín,et al.  Design of an electric vehicle fast-charging station with integration of renewable energy and storage systems , 2019, International Journal of Electrical Power & Energy Systems.

[46]  A. Hawkes,et al.  Projecting the Future Levelized Cost of Electricity Storage Technologies , 2019, Joule.

[47]  Amir Abdel Menaem,et al.  Integration of Renewable Energy Sources into Microgrid Considering Operational and Planning Uncertainties , 2018, Advances in Intelligent Systems and Computing.

[48]  Gonçalo Cardoso,et al.  Battery aging in multi-energy microgrid design using mixed integer linear programming , 2018, Applied Energy.

[49]  Lalit Goel,et al.  A Two-Layer Energy Management System for Microgrids With Hybrid Energy Storage Considering Degradation Costs , 2018, IEEE Transactions on Smart Grid.

[50]  Li He,et al.  Techno-economic potential of a renewable energy-based microgrid system for a sustainable large-scale residential community in Beijing, China , 2018, Renewable and Sustainable Energy Reviews.

[51]  David Fridovich-Keil,et al.  Toward Distributed Energy Services: Decentralizing Optimal Power Flow With Machine Learning , 2018, IEEE Transactions on Smart Grid.

[52]  Ning Zhang,et al.  Data-Driven Probabilistic Net Load Forecasting With High Penetration of Behind-the-Meter PV , 2018, IEEE Transactions on Power Systems.

[53]  Oleg Wasynczuk,et al.  Applicability of available Li-ion battery degradation models for system and control algorithm design , 2018 .

[54]  Alibakhsh Kasaeian,et al.  Optimizing a hybrid wind-PV-battery system using GA-PSO and MOPSO for reducing cost and increasing reliability , 2017, Energy.

[55]  Shantha Gamini Jayasinghe,et al.  A review on recent size optimization methodologies for standalone solar and wind hybrid renewable energy system , 2017 .

[56]  Rahman Saidur,et al.  Application of Artificial Intelligence Methods for Hybrid Energy System Optimization , 2016 .

[57]  Jose M. Yusta,et al.  Stochastic-heuristic methodology for the optimisation of components and control variables of PV-wind-diesel-battery stand-alone systems , 2016 .

[58]  R. P. Saini,et al.  Development of optimal integrated renewable energy model with battery storage for a remote Indian area , 2016 .

[59]  Temitope Raphael Ayodele,et al.  Optimal allocation and sizing of PV/Wind/Split-diesel/Battery hybrid energy system for minimizing life cycle cost, carbon emission and dump energy of remote residential building , 2016 .

[60]  Naoto Yorino,et al.  Optimal Distributed Generation Allocation in Distribution Systems for Loss Minimization , 2016, IEEE Transactions on Power Systems.

[61]  Ana Estanqueiro,et al.  Integrated sizing and scheduling of wind/PV/diesel/battery isolated systems , 2015 .

[62]  Masoud Aliakbar Golkar,et al.  Optimal design of stand-alone microgrid resources based on proposed Monte-Carlo simulation , 2015, 2015 IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA).

[63]  Yasunori Mitani,et al.  ANN based optimized battery energy storage system size and loss analysis for distributed energy storage location in PV-microgrid , 2015, 2015 IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA).

[64]  Sunanda Sinha,et al.  Review of recent trends in optimization techniques for solar photovoltaic–wind based hybrid energy systems , 2015 .

[65]  Vigna Kumaran Ramachandaramurthy,et al.  A review on the state-of-the-art technologies of electric vehicle, its impacts and prospects , 2015 .

[66]  H. Gharavi,et al.  Imperial competitive algorithm optimization of fuzzy multi-objective design of a hybrid green power system with considerations for economics, reliability, and environmental emissions , 2015 .

[67]  Luis Fontan,et al.  A method for optimal sizing energy storage systems for microgrids , 2015 .

[68]  Mohsen Eskandari,et al.  Operational Strategy Optimization in an Optimal Sized Smart Microgrid , 2015, IEEE Transactions on Smart Grid.

[69]  Alireza Askarzadeh,et al.  A novel framework for optimization of a grid independent hybrid renewable energy system: A case study of Iran , 2015 .

[70]  Victor M. Sanchez,et al.  Techno-economical optimization based on swarm intelligence algorithm for a stand-alone wind-photovoltaic-hydrogen power system at south-east region of Mexico , 2014 .

[71]  Rajesh Kumar Nema,et al.  Determination of reliability constrained optimal resource mix for an autonomous hybrid power system using Particle Swarm Optimization , 2014 .

[72]  Mohsen Gitizadeh,et al.  Battery capacity determination with respect to optimized energy dispatch schedule in grid-connected photovoltaic (PV) systems , 2014 .

[73]  Fredrik Lindsten,et al.  Backward Simulation Methods for Monte Carlo Statistical Inference , 2013, Found. Trends Mach. Learn..

[74]  Scott Kennedy,et al.  Reliability of islanded microgrids with stochastic generation and prioritized load , 2009, 2009 IEEE Bucharest PowerTech.

[75]  Marwan M. Mahmoud,et al.  Techno-economic feasibility of energy supply of remote villages in Palestine by PV-systems, diesel generators and electric grid , 2006 .

[76]  Ujjwal Maulik,et al.  Performance Evaluation of Some Clustering Algorithms and Validity Indices , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[77]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[78]  R. L. McGreevy,et al.  Reverse Monte Carlo Simulation: A New Technique for the Determination of Disordered Structures , 1988 .

[79]  W. Kuckshinrichs,et al.  Micro-economic assessment of residential PV and battery systems: The underrated role of financial and fiscal aspects , 2021 .

[80]  Kaveh Dehghanpour,et al.  Load Node – ESS-DG-PV Level II : MG Asset Dispatching Utility Agent MGCC Agent MG Equivalent Aggregate MG Models ( Controllable Loads ) Level I : Distribution System Control Utility Agent , 2018 .

[81]  Y. Mitani,et al.  ANN method for size determination of storage systems in microgrids , 2015 .

[82]  Samuel Nelson Melegari de Souza,et al.  Sizing and simulation of a photovoltaic-wind energy system using batteries, applied for a small rural property located in the south of Brazil , 2014 .

[83]  Yue Yuan,et al.  Sizing of hybrid energy storage system in independent microgrid based on BP neural network , 2013 .

[84]  Lu Zhang,et al.  Optimal sizing study of hybrid wind/PV/diesel power generation unit , 2011 .

[85]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..