An inexact two-stage stochastic risk-aversion model for integrated energy system management in Beijing-Tianjin-Hebei, China

2017 ◽  
Vol 9 (4) ◽  
pp. 045902
Author(s):  
Bin Wang ◽  
Xinxin Zhang ◽  
Xin Li ◽  
ZeYi Jiang ◽  
Yulei Xie
Keyword(s):  
Risk Aversion ◽  
Energy System ◽  
System Management ◽  
Two Stage ◽  
Integrated Energy System ◽  
Stochastic Risk

scholarly journals A Two-Stage Offline-to-Online Multiobjective Optimization Strategy for Ship Integrated Energy System Economical/ Environmental Scheduling Problem

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Qing An ◽  
Jun Zhang ◽  
Xin Li ◽  
Xiaobing Mao ◽  
Yulong Feng ◽  
...  
Keyword(s):  
Multiobjective Optimization ◽  
Real World ◽  
Operation Time ◽  
Energy System ◽  
Scheduling Problem ◽  
Storage Space ◽  
Optimization Strategy ◽  
Two Stage ◽  
Integrated Energy System ◽  
And Storage

The economical/environmental scheduling problem (EESP) of the ship integrated energy system (SIES) has high computational complexity, which includes more than one optimization objective, various types of constraints, and frequently fluctuated load demand. Therefore, the intelligent scheduling strategies cannot be applied to the ship energy management system (SEMS) online, which has limited computing power and storage space. Aiming at realizing green computing on SEMS, in this paper a typical SIES-EESP optimization model is built, considering the form of decision vectors, the economical/environmental optimization objectives, and various types of real-world constraints of the SIES. Based on the complexity of SIES-EESPs, a two-stage offline-to-online multiobjective optimization strategy for SIES-EESP is proposed, which transfers part of the energy dispatch online computing task to the offline high-performance computer systems. The specific constraints handling methods are designed to reduce both continuous and discrete constraints violations of SIES-EESPs. Then, an establishment method of energy scheduling scheme-base is proposed. By using the big data offline, the economical/environmental scheduling solutions of a typical year can be obtained and stored with more computing resources and operation time on land. Thereafter, a short-term multiobjective offline-to-online optimization approach by SEMS is considered, with the application of multiobjective evolutionary algorithm (MOEA) and typical schemes corresponding to the actual SIES-EESPs. Simulation results show that the proposed strategy can obtain enough feasible Pareto solutions in a shorter time and get well-distributed Pareto sets with better convergence performance, which can well adapt to the features of real-world SIES-EESPs and save plenty of operation time and storage space for the SEMS.

Inexact stochastic risk-aversion optimal day-ahead dispatch model for electricity system management with wind power under uncertainty

Energy ◽  
2016 ◽  
Vol 109 ◽  
pp. 920-932 ◽  
Author(s):  
Ling Ji ◽  
Guo-He Huang ◽  
Lu-Cheng Huang ◽  
Yu-Lei Xie ◽  
Dong-Xiao Niu
Keyword(s):  
Risk Aversion ◽  
Wind Power ◽  
System Management ◽  
Electricity System ◽  
Stochastic Risk

scholarly journals A Robust Assessment Model of the Solar Electrical-Thermal Energy Comprehensive Accommodation Capability in a District Integrated Energy System

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1363 ◽  
Author(s):  
Wei ◽  
Jia ◽  
Mu ◽  
Wu ◽  
Jia
Keyword(s):  
Thermal Energy ◽  
Evaluation Model ◽  
Supply And Demand ◽  
Second Law Of Thermodynamics ◽  
Energy System ◽  
Solution Procedure ◽  
Assessment Model ◽  
Two Stage ◽  
Cone Model ◽  
Integrated Energy System

As effective utilization of solar resources is a significant way to address the imbalance between energy supply and demand. Therefore, reasonably assessing the accommodation capability of solar energy is important. A two-stage robust evaluation model is proposed for the solar electricity-thermal energy comprehensive accommodation capability in a district integrated energy system. The accommodation capability index is constructed based on the second law of thermodynamics. A robust optimization model was adopted to deal with the uncertainty of solar irradiance. In the solution procedure, the non-convex non-linear power flow model is transformed into a second-order cone model to effectively fit the proposed two-stage robust evaluation model. Finally, a case study verifies the effectiveness of the proposed model and the solution method. The influence of irradiance fluctuation range, gas boiler, and energy storage is discussed in detail.

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