Stochastic optimal planning scheme of a zero-carbon multi-energy system (ZC-MES) considering the uncertainties of individual energy demand and renewable resources: An integrated chance-constrained and decomposition algorithm (CC-DA) approach
出版年份 2021 全文链接
标题
Stochastic optimal planning scheme of a zero-carbon multi-energy system (ZC-MES) considering the uncertainties of individual energy demand and renewable resources: An integrated chance-constrained and decomposition algorithm (CC-DA) approach
作者
关键词
Stochastic optimization, Zero-carbon, Multi-energy system, Renewable energy, Chance-constrained programming, Benders decomposition
出版物
ENERGY
Volume 232, Issue -, Pages 121000
出版商
Elsevier BV
发表日期
2021-05-30
DOI
10.1016/j.energy.2021.121000
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- A novel multi-objective stochastic risk co-optimization model of a zero-carbon multi-energy system (ZCMES) incorporating energy storage aging model and integrated demand response
- (2021) Tobi Michael Alabi et al. ENERGY
- Multi-objective planning for integrated energy systems considering both exergy efficiency and economy
- (2020) Xiao Hu et al. ENERGY
- Optimal Sizing, Scheduling and Building Structure Strategies for a Risk-averse Isolated Hybrid Energy System in Kish Island
- (2020) Reza Ghaffarpour ENERGY AND BUILDINGS
- Probabilistic scheduling of power-to-gas storage system in renewable energy hub integrated with demand response program
- (2020) Zhi Yuan et al. Journal of Energy Storage
- A novel optimal configuration model for a zero-carbon multi-energy system (ZC-MES) integrated with financial constraints
- (2020) Tobi Michael Alabi et al. Sustainable Energy Grids & Networks
- A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series
- (2020) Seçkin Karasu et al. ENERGY
- A new hybrid model for wind speed forecasting combining long short-term memory neural network, decomposition methods and grey wolf optimizer
- (2020) Aytaç Altan et al. APPLIED SOFT COMPUTING
- Stochastic Model Predictive Control Based Scheduling Optimization of Multi-Energy System Considering Hybrid CHPs and EVs
- (2019) Xiaogang Guo et al. Applied Sciences-Basel
- Robust and optimal design of multi-energy systems with seasonal storage through uncertainty analysis
- (2019) Paolo Gabrielli et al. APPLIED ENERGY
- Data-driven decision making in power systems with probabilistic guarantees: Theory and applications of chance-constrained optimization
- (2019) Xinbo Geng et al. ANNUAL REVIEWS IN CONTROL
- Two-stage robust planning-operation co-optimization of energy hub considering precise energy storage economic model
- (2019) Cong Chen et al. APPLIED ENERGY
- Energy storage technologies as techno-economic parameters for master-planning and optimal dispatch in smart multi energy systems
- (2019) Stefano Mazzoni et al. APPLIED ENERGY
- Modeling multimodal energy systems
- (2019) Arash Shahbakhsh et al. AT-Automatisierungstechnik
- Achieving low carbon local energy communities in hot climates by exploiting networks synergies in multi energy systems
- (2019) Gabriele Comodi et al. APPLIED ENERGY
- Capacity planning and optimization of business park-level integrated energy system based on investment constraints
- (2019) Yongli Wang et al. ENERGY
- Thermal management of the waste energy of a stand-alone hybrid PV-wind-battery power system in Hong Kong
- (2019) J. Yan et al. ENERGY CONVERSION AND MANAGEMENT
- A Hybrid Stochastic-Interval Operation Strategy for Multi-Energy Microgrids
- (2019) Yibao Jiang et al. IEEE Transactions on Smart Grid
- Optimal design of multi-energy systems with seasonal storage
- (2018) Paolo Gabrielli et al. APPLIED ENERGY
- Stochastic operation of home energy management systems including battery cycling
- (2018) Carlos Adrian Correa-Florez et al. APPLIED ENERGY
- Risk-based optimal scheduling of reconfigurable smart renewable energy based microgrids
- (2018) M. Hemmati et al. INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
- Energy Systems Integration in Smart Districts: Robust Optimisation of Multi-Energy Flows in Integrated Electricity, Heat and Gas Networks
- (2018) E. A. Martinez Cesena et al. IEEE Transactions on Smart Grid
- Stochastic Optimal Planning of Battery Energy Storage Systems for Isolated Microgrids
- (2018) Hisham Alharbi et al. IEEE Transactions on Sustainable Energy
- Chance-Constrained Optimization for Multi Energy Hub Systems in a Smart City
- (2018) IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
- Coordinated Regional-District Operation of Integrated Energy Systems for Resilience Enhancement in Natural Disasters
- (2018) Mingyu Yan et al. IEEE Transactions on Smart Grid
- Congestion Risk-Averse Stochastic Unit Commitment with Transmission Reserves in Wind-Thermal Power Systems
- (2018) Yu Huang et al. Applied Sciences-Basel
- The Benders decomposition algorithm: A literature review
- (2017) Ragheb Rahmaniani et al. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
- A type-2 fuzzy chance-constrained programming method for planning Shanghai’s energy system
- (2017) C. Suo et al. INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
- Flexibility in multi-energy communities with electrical and thermal storage: A stochastic, robust approach for multi-service demand response
- (2017) Nicholas Good et al. IEEE Transactions on Smart Grid
- Medium-term energy hub management subject to electricity price and wind uncertainty
- (2016) Arsalan Najafi et al. APPLIED ENERGY
- Chance constrained programming using non-Gaussian joint distribution function in design of standalone hybrid renewable energy systems
- (2014) Azadeh Kamjoo et al. ENERGY
- Chance constrained programming approach to process optimization under uncertainty
- (2007) Pu Li et al. COMPUTERS & CHEMICAL ENGINEERING
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