Two-stage Robust Stochastic Optimal Dispatch of Regional Integrated Energy System Considering Renewable Energy and Load Uncertainty

2021 ◽  
Author(s):  
Xu Zhao ◽  
Xiangyu Kong ◽  
Wenqi Lu
Keyword(s):  
Renewable Energy ◽  
Energy System ◽  
Two Stage ◽  
Load Uncertainty ◽  
Optimal Dispatch ◽  
Integrated Energy System

Multi-objective two-stage adaptive robust planning method for an integrated energy system considering load uncertainty

2021 ◽  
Vol 235 ◽  
pp. 110741
Author(s):  
Rujing Yan ◽  
Jiangjiang Wang ◽  
Shuaikang Lu ◽  
Zherui Ma ◽  
Yuan Zhou ◽  
...  
Keyword(s):  
Energy System ◽  
Planning Method ◽  
Two Stage ◽  
Load Uncertainty ◽  
Multi Objective ◽  
Robust Planning ◽  
Integrated Energy System

scholarly journals Optimal Dispatch of Integrated Energy System Considering Energy Hub Technology and Multi-Agent Interest Balance

Energies ◽  
2019 ◽  
Vol 12 (16) ◽  
pp. 3112
Author(s):  
Zeng ◽  
Jiang ◽  
Liu ◽  
Tan ◽  
He ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Service Providers ◽  
Pareto Frontier ◽  
Optimal Solution ◽  
Energy System ◽  
Optimal Dispatch ◽  
Energy Hub ◽  
Interest Balance ◽  
Integrated Energy System ◽  
Multi Agent

With the gradual liberalization of the energy market, the future integrated energy system will be composed of multiple agents. Therefore, this paper proposes an optimization dispatch method considering energy hub technology and multi-agent interest balance in an integrated energy system. Firstly, an integrated energy system, including equipment for cogeneration, renewable energy, and electric vehicles, is established. Secondly, energy hub technologies, such as demand response, electricity storage, and thermal storage, are comprehensively considered, and the integrated energy system is divided into three agents: Integrated energy service providers, renewable energy owners, and users, respectively. Then, with the goal of balancing the interests of each agent, the model is solved by the non-dominated sorting genetic algorithm-III (NSGA-III) to obtain the Pareto frontier. Since the Pareto frontier is a series of values, the optimal solution of each agent in the Pareto frontier is found by the technical for order preference with a similar to ideal solution (TOPSIS). Ultimately, taking an integrated energy demonstration park in China as a case study, the function of energy hub technology is analyzed by simulation, and the proposed method is verified to be effective and practicable.

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.

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