scholarly journals Research on a Bi-Level Collaborative Optimization Method for Planning and Operation of Multi-Energy Complementary Systems

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7930
Author(s):  
Changrong Liu ◽  
Hanqing Wang ◽  
Zhiqiang Liu ◽  
Zhiyong Wang ◽  
Sheng Yang
Keyword(s):  
Optimization Theory ◽  
Optimization Method ◽  
Collaborative Optimization ◽  
Energy Utilization ◽  
Energy Structure ◽  
Optimization Strategy ◽  
Multi Objective ◽  
Multi Objective Programming ◽  
Practical Engineering ◽  
Planning And Operation

Multi-energy complementary systems (MCSs) are complex multilevel systems. In the process of system planning, many aspects—such as power planning, investment cost, and environmental impact—should be considered. However, different decision makers tend to have different levels of control objectives, and the multilevel problems of the system need to be solved effectively with comprehensive judgment. Therefore, based on the terminal MCS energy structure model, the optimization method of MCS planning and operation coordination, considering the influence of planning and operation in the system’s life cycle, is studied in this paper. Consequently, the research on the collaborative optimization strategy of MCS construction and operation was carried out based on the bi-level multi-objective optimization theory in this paper. Considering the mutual restraint and correlation between system construction and operation in practical engineering, a bi-level optimization model for collaborative optimization of MCS construction and operation was constructed. To solve the model effectively, the existing non-dominated sorting genetic algorithm III (NSGA-III) was improved by the authors on the basis of previous research, which could enhance the global search ability and convergence speed of the algorithm. To effectively improve and strengthen the reliability of energy supply, and increase the comprehensive energy utilization of the system, the effects of carbon transaction cost and renewable energy penetration were considered in the optimization process. Based on an engineering example, the bi-level model was solved and analyzed. It should be noted that the optimization results of the model were verified to be applicable and effective by comparison with the single-level multi-objective programming optimization. The findings of this paper could provide theoretical reference and practical guidance for the planning and operation of MCSs, making them significant for social application.

scholarly journals Optimization Strategy of Multiarea Interconnected Integrated Energy System Based on Consistency Theory

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yuan Yu ◽  
Tieyan Zhang ◽  
Yan Zhao
Keyword(s):  
Objective Function ◽  
Energy System ◽  
Optimization Method ◽  
Collaborative Optimization ◽  
Energy Utilization ◽  
Optimization Strategy ◽  
Consistency Theory ◽  
Single Region ◽  
Integrated Energy System

A collaborative optimization strategy of an integrated energy system aiming at improving energy efficiency is studied in this paper for the cluster optimization of an integrated energy system (IES). In this paper, an improved discrete consistency method based on the coordination optimization method for IES is proposed. An IES model considering the mixed energy supply of electricity, heat, and gas is constructed in a single region. And then an objective function with the maximum return is established, on the premise of assuming that the prices of electricity, heat, and gas can be used as an economical means to adjust the energy utilization. Finally, the consistency theory is applied to the IES, and the improved discrete consistency algorithm is utilized to optimize the objective function. In the case study, a certain region IES is taken as an example in Northeast China. The case study demonstrates the effectiveness and accuracy of the coordination optimization method for IES.

Multi-Objective Optimization Method for Borehole Trajectory Design of Directional Well

2012 ◽  
Vol 152-154 ◽  
pp. 816-819
Author(s):  
Jian Bing Zhang ◽  
Xin Liu ◽  
Xiang Hong Lv
Keyword(s):  
Optimization Design ◽  
Ideal Point ◽  
Optimization Theory ◽  
Mathematic Model ◽  
Optimization Method ◽  
Drill String ◽  
Trajectory Design ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Multi Objective Programming

To offer those who are engaged in oil development a multi-objective design method of borehole trajectory for a directional well, the author adopted optimization theory to build a multi-objective optimization mathematic model with the shortest trajectory, the lowest drill string torque and the minimum rig hook load as final objectives, and put forward an approach to seek effective solutions to these multi-objective programming problems with ideal point method. The approach proposed in the paper can help satisfy concurrently multiple objectives of drilling design for an oilfield to implement the multi-objective optimization design schemes of borehole trajectory for a directional well, and to reduce the oilfield development costs accordingly.

scholarly journals An enhanced genetic algorithm–based multi-objective design optimization strategy

2018 ◽  
Vol 10 (7) ◽  
pp. 168781401878483 ◽  
Author(s):  
Rong Yuan ◽  
Haiqing Li ◽  
Qingyuan Wang
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Structure Design ◽  
Optimization Strategy ◽  
Turbine Generator ◽  
Design And Optimization ◽  
Wind Turbine Generator ◽  
Multi Objective ◽  
Practical Engineering ◽  
The Given

In this study, an enhanced genetic algorithm is proposed to solve multi-objective design and optimization problems in practical engineering. In the given approach, designers choose available design results from the given samples first. These samples are re-ordered according to their mutual relationships. After that, designers choose an exact ratio of conformity as available field. Furthermore, more weight information can be obtained through finding the minimum value of the norm of unconformity and satisfactory samples. These samples can be used to reflect the preference chosen for Pareto design solutions. A structure design problem of speed increaser used in wind turbine generator systems is solved to show the application of the given design strategy.

Collaborative Optimization Strategy for Multi-Objective Design

2005 ◽  
Author(s):  
Philippe De´pince´ ◽  
Se´bastien Rabeau ◽  
Fouad Bennis
Keyword(s):  
Design Problem ◽  
Multidisciplinary Optimization ◽  
Collaborative Optimization ◽  
Optimization Strategy ◽  
Multi Objective Optimization ◽  
Economic Competition ◽  
Multi Objective ◽  
Design Variables ◽  
Complex Design ◽  
Industrial Markets

The increasing economic competition of all industrial markets and growing complexity of engineering problems lead to a progressive specialization and distribution of expertise, tools and works. On the other hand, engineering products becomes more and more complex and the designer has to face with an increase design variables and design objectives. Besides multi-objective optimization (MOO) and multi-disciplinary design optimization (MDO) are more commonly used as methods to provide optimal solutions for complex design problems. The paper describes an innovative mixing between genetic algorithms (MOGA) and collaborative optimization (CO) as a tool to: 1) increase the convergence rate when a design problem can be broken up regarding design variables, and 2) provide an optimal set of design variables in case of multi-level design problem. This method gives multidisciplinary optimization the advantages AG has brought to multi-objective optimization. The method, tested on test functions, assures high optimization results containing CPU times.

Multidisciplinary design optimization of stiffened panels using collaborative optimization and artificial neural network

2017 ◽  
Vol 232 (20) ◽  
pp. 3595-3611 ◽  
Author(s):  
Hamda Chagraoui ◽  
Mohamed Soula
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimization Problem ◽  
Optimization Method ◽  
Collaborative Optimization ◽  
System Level ◽  
Stiffened Panels ◽  
Multi Objective ◽  
Efficiency And Effectiveness ◽  
Artificial Neural

A new method for solving the multidisciplinary design optimization problems with a minimal computational effort is presented. The proposed methodology is based on the combination of artificial neural network model and Improved Multi-Objective Collaborative Optimization. In the artificial neural network–Improved Multi-Objective Collaborative Optimization scheme, the back-propagation algorithm is used for training the artificial neural network metamodel and the Non-dominated Sorting Genetic Algorithm-II is used to search a Pareto optimality set for the objective functions of stiffened panels. The artificial neural network–Improved Multi-Objective Collaborative Optimization algorithm aims firstly to decompose the global optimization problem hierarchically into optimization design problem at system level and several sub-problems at sub-system level and secondly to replace each optimization problem at the system and subsystem levels by artificial neural network model to limit the computational cost. To highlight the efficiency and effectiveness of the proposed artificial neural network–Improved Multi-Objective Collaborative Optimization method, mathematical and engineering examples are presented. Results obtained from the application of the artificial neural network–Improved Multi-Objective Collaborative Optimization approach to an optimization problem of a stiffened panel are compared with those obtained by traditional optimization without using prediction tools. The new method (artificial neural network–Improved Multi-Objective Collaborative Optimization) was proven to be superior to traditional optimization. These results have confirmed the efficiency and effectiveness of the artificial neural network–Improved Multi-Objective Collaborative Optimization method. In addition, it converges at faster rate than traditional optimization. The traditional optimization method converges within 7918 s, while artificial neural network–Improved Multi-Objective Collaborative Optimization requires only 42 s, clearly, the artificial neural network–Improved Multi-Objective Collaborative Optimization method is much more efficient.

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