A data-based day-ahead scheduling optimization approach for regional integrated energy systems with varying operating conditions

[1]  Z. Tan,et al.  Two-stage distributionally robust optimization model of integrated energy system group considering energy sharing and carbon transfer , 2023, Applied Energy.

[2]  Ezugwu E. Absalom,et al.  K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data , 2022, Inf. Sci..

[3]  H. Jia,et al.  A two-stage scheduling method for integrated community energy system based on a hybrid mechanism and data-driven model , 2022, Applied Energy.

[4]  Dan Wang,et al.  Mechanism analysis and unified calculation model of exergy flow distribution in regional integrated energy system , 2022, Applied Energy.

[5]  Weiye Zheng,et al.  Review and prospect of data-driven techniques for load forecasting in integrated energy systems , 2022, Applied Energy.

[6]  Chenghui Zhang,et al.  A scenario-based two-stage stochastic optimization approach for multi-energy microgrids , 2022, Applied Energy.

[7]  Zhipeng Cui,et al.  Hybrid modeling-based digital twin for performance optimization with flexible operation in the direct air-cooling power unit , 2022, Energy.

[8]  A. Gambarotta,et al.  Smart management of integrated energy systems through co-optimization with long and short horizons , 2022, Energy.

[9]  H. Jia,et al.  A CVaR-based risk assessment method for park-level integrated energy system considering the uncertainties and correlation of energy prices , 2022, Energy.

[10]  Fugui Dong,et al.  Multi-objective planning of regional integrated energy system aiming at exergy efficiency and economy , 2022, Applied Energy.

[11]  H. Jia,et al.  A scenario-based optimal dispatch for joint operation of wind farms and combined heat and power plants considering energy flexibilities in heating networks , 2021, Electric Power Systems Research.

[12]  J. Zhang,et al.  Combined heat and power economic dispatch using an adaptive cuckoo search with differential evolution mutation , 2021, Applied Energy.

[13]  Jing Jiang,et al.  Digital Twins based Day-ahead Integrated Energy System Scheduling under Load and Renewable Energy Uncertainties , 2021, Applied Energy.

[14]  Jiakun Fang,et al.  Decentralized computation method for robust operation of multi-area joint regional-district integrated energy systems with uncertain wind power , 2021 .

[15]  Weihao Hu,et al.  Look-ahead risk-constrained scheduling for an energy hub integrated with renewable energy , 2021 .

[16]  Nhat-Duc Hoang,et al.  Success-history based adaptive differential evolution method for optimizing fuel loading pattern of VVER-1000 reactor , 2021 .

[17]  Mingzhe Li,et al.  Stochastic robust optimal operation of community integrated energy system based on integrated demand response , 2021 .

[18]  Theodoros Evgeniou,et al.  Artificial intelligence to support the integration of variable renewable energy sources to the power system , 2021 .

[19]  Kun Yu,et al.  Stochastic optimal operation model for a distributed integrated energy system based on multiple-scenario simulations , 2021 .

[20]  Wang Xuan,et al.  A multi-energy load prediction model based on deep multi-task learning and ensemble approach for regional integrated energy systems , 2021 .

[21]  B. Shen,et al.  Capacity planning and optimization for integrated energy system in industrial park considering environmental externalities , 2020 .

[22]  Jing Xu,et al.  A data-based approach for benchmark interval determination with varying operating conditions in the coal-fired power unit , 2020, Energy.

[23]  H. Jia,et al.  Multi‐scene upgrade and renovation method of existing park‐level integrated energy system based on comprehensive analysis , 2020, Energy Conversion and Economics.

[24]  Yuwei Chen,et al.  Urban multi-energy network optimization: An enhanced model using a two-stage bound-tightening approach , 2020 .

[25]  Qiuwei Wu,et al.  Day-ahead stochastic scheduling of integrated multi-energy system for flexibility synergy and uncertainty balancing , 2020, Energy.

[26]  Wei Wei,et al.  Adaptive robust energy and reserve co-optimization of integrated electricity and heating system considering wind uncertainty , 2020 .

[27]  Hongyu Lin,et al.  Multi-scenario operation optimization model for park integrated energy system based on multi-energy demand response , 2020 .

[28]  Michal Pluhacek,et al.  Distance based parameter adaptation for Success-History based Differential Evolution , 2019, Swarm Evol. Comput..

[29]  Fuwei Zhang,et al.  Operation optimization of regional integrated energy system based on the modeling of electricity-thermal-natural gas network , 2019, Applied Energy.

[30]  Xiaofeng Guo,et al.  Modeling and forecasting building energy consumption: A review of data-driven techniques , 2019, Sustainable Cities and Society.

[31]  Peng Li,et al.  Model predictive control based robust scheduling of community integrated energy system with operational flexibility , 2019, Applied Energy.

[32]  Yi Wang,et al.  Matrix modeling of energy hub with variable energy efficiencies , 2019, International Journal of Electrical Power & Energy Systems.

[33]  Eric J. Pauwels,et al.  Short-term scenario-based probabilistic load forecasting: A data-driven approach , 2019, Applied Energy.

[34]  Shanlin Yang,et al.  A shape-based clustering method for pattern recognition of residential electricity consumption , 2019, Journal of Cleaner Production.

[35]  Z. X. Jing,et al.  Multi-time scale dynamic analysis of integrated energy systems: An individual-based model , 2019, Applied Energy.

[36]  Dongliang Xie,et al.  Day-Ahead Hierarchical Steady State Optimal Operation for Integrated Energy System Based on Energy Hub , 2018, Energies.

[37]  Fausto Pedro García Márquez,et al.  A survey of artificial neural network in wind energy systems , 2018, Applied Energy.

[38]  Zhong Wang,et al.  A steady-state detection method based on Gaussian discriminant analysis for the on-line gas turbine process , 2018 .

[39]  Mohammad Reza Mohammadi,et al.  Energy hub: From a model to a concept – A review , 2017 .

[40]  Dean R. Giosio,et al.  Physics-Based Hydraulic Turbine Model for System Dynamic Studies , 2017, IEEE Transactions on Power Systems.

[41]  Hongbin Sun,et al.  Integrated energy systems , 2016 .

[42]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[43]  Danling Cheng,et al.  Monte Carlo analysis of Plug-in Hybrid Vehicles and Distributed Energy Resource growth with residential energy storage in Michigan , 2013 .

[44]  Alex S. Fukunaga,et al.  Success-history based parameter adaptation for Differential Evolution , 2013, 2013 IEEE Congress on Evolutionary Computation.

[45]  Antonio Alonso Ayuso,et al.  Introduction to Stochastic Programming , 2009 .

[46]  Jin Hyun Park,et al.  Process monitoring using a Gaussian mixture model via principal component analysis and discriminant analysis , 2004, Comput. Chem. Eng..

[47]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[48]  M. Fowler,et al.  Optimal energy hub development to supply heating, cooling, electricity and freshwater for a coastal urban area taking into account economic and environmental factors , 2022 .

[49]  Yongjun Zhang,et al.  Stochastic optimization in multi-energy hub system operation considering solar energy resource and demand response , 2022, International Journal of Electrical Power & Energy Systems.

[50]  Qingshan Xu,et al.  Day-ahead robust optimal dispatch of integrated energy station considering battery exchange service , 2022, Journal of Energy Storage.

[51]  Ming Liu,et al.  Operation scheduling of a coal-fired CHP station integrated with power-to-heat devices with detail CHP unit models by particle swarm optimization algorithm , 2021 .

[52]  Xiaojun Liu,et al.  Energy scheduling for a three-level integrated energy system based on energy hub models: A hierarchical Stackelberg game approach , 2020 .