A multi-objective optimization model for urban energy systems in a specific area

2003 ◽  
Author(s):  
H. Sugihara ◽  
J. Komoto ◽  
K. Tsuji
Keyword(s):  
Optimization Model ◽  
Energy Systems ◽  
Specific Area ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Urban Energy

scholarly journals A multi-objective optimization model for the operation of decentralized multi-energy systems

2019 ◽  
Vol 1343 ◽  
pp. 012104
Author(s):  
Yolaine Adihou ◽  
Mohamed Tahar Mabrouk ◽  
Pierrick Haurant ◽  
Bruno Lacarrièere
Keyword(s):  
Optimization Model ◽  
Energy Systems ◽  
Multi Objective Optimization ◽  
Multi Objective

scholarly journals Analysis of energy systems for poly-generation using optimization modelling: the optimization process strategies and the formalization of an innovative multi-objective optimization model

2021 ◽  
Vol 238 ◽  
pp. 05002
Author(s):  
Petronilla Fragiacomo ◽  
Giuseppe Lucarelli ◽  
Gaetano Florio
Keyword(s):  
Optimization Model ◽  
Energy Systems ◽  
Optimization Models ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Constraint Equations ◽  
Multi Objective ◽  
Polluting Emissions ◽  
Economic Nature ◽  
Numeric Modelling

The purpose of this paper is to describe the numerical modelling processes and optimization strategies of energy systems and to formalize an innovative optimization model for poly-generation systems. The model uses a multi-objective optimization approach, by means of three optimization functions: one of an economic nature, one technical and one relating to polluting emissions. The constraint equations of the problem, the boundary conditions and the input values, as well as some dimensionless parameters are described. In addition to a complete approach, the article proposes innovative aspects based on the studies in the literature, both from a technological and economic-regulatory standpoint. For each proposed innovation, an analysis of how this can influence the optimization model is presented and various types of optimization models have been evaluated, depending on the complexity of the physical system and the consequent numeric modelling.

Multi-objective Optimization Model for Fuel Cell-based Poly-generation Energy Systems

Energy ◽  
2021 ◽  
pp. 121823
Author(s):  
Petronilla Fragiacomo ◽  
Giuseppe Lucarelli ◽  
Matteo Genovese ◽  
Gaetano Florio
Keyword(s):  
Fuel Cell ◽  
Optimization Model ◽  
Energy Systems ◽  
Multi Objective Optimization ◽  
Multi Objective

scholarly journals Research on Blank Optimization Design Based on Low-Carbon and Low-Cost Blank Process Route Optimization Model

2021 ◽  
Vol 13 (4) ◽  
pp. 1929
Author(s):  
Yongmao Xiao ◽  
Wei Yan ◽  
Ruping Wang ◽  
Zhigang Jiang ◽  
Ying Liu
Keyword(s):  
Optimization Model ◽  
Optimization Design ◽  
Design Method ◽  
Low Cost ◽  
Route Optimization ◽  
Low Carbon ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Blank Design ◽  
Process Route

The optimization of blank design is the key to the implementation of a green innovation strategy. The process of blank design determines more than 80% of resource consumption and environmental emissions during the blank processing. Unfortunately, the traditional blank design method based on function and quality is not suitable for today’s sustainable development concept. In order to solve this problem, a research method of blank design optimization based on a low-carbon and low-cost process route optimization is proposed. Aiming at the processing characteristics of complex box type blank parts, the concept of the workstep element is proposed to represent the characteristics of machining parts, a low-carbon and low-cost multi-objective optimization model is established, and relevant constraints are set up. In addition, an intelligent generation algorithm of a working step chain is proposed, and combined with a particle swarm optimization algorithm to solve the optimization model. Finally, the feasibility and practicability of the method are verified by taking the processing of the blank of an emulsion box as an example. The data comparison shows that the comprehensive performance of the low-carbon and low-cost multi-objective optimization is the best, which meets the requirements of low-carbon processing, low-cost, and sustainable production.

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