A Lagrangian Decomposition Approach to Solve Large Scale Multi-Sector Energy System Optimization Problems

2020 ◽  
pp. 249-255
Author(s):  
Andreas Bley ◽  
Angela Pape ◽  
Frank Fischer
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
System Optimization ◽  
Energy System ◽  
Lagrangian Decomposition ◽  
Decomposition Approach

Cutting Plane Methods for Analytical Target Cascading With Augmented Lagrangian Coordination

2013 ◽  
Vol 135 (10) ◽  
Author(s):  
Wenshan Wang ◽  
Vincent Y. Blouin ◽  
Melissa K. Gardenghi ◽  
Georges M. Fadel ◽  
Margaret M. Wiecek ◽  
...  
Keyword(s):  
Large Scale ◽  
Augmented Lagrangian ◽  
Optimization Problems ◽  
Cutting Plane ◽  
Analytical Target Cascading ◽  
Cutting Plane Methods ◽  
Subgradient Optimization ◽  
Decomposition Approach ◽  
Target Cascading ◽  
Augmented Lagrangian Coordination

Analytical target cascading (ATC), a hierarchical, multilevel, multidisciplinary coordination method, has proven to be an effective decomposition approach for large-scale engineering optimization problems. In recent years, augmented Lagrangian relaxation methods have received renewed interest as dual update methods for solving ATC decomposed problems. These problems can be solved using the subgradient optimization algorithm, the application of which includes three schemes for updating dual variables. To address the convergence efficiency disadvantages of the existing dual update schemes, this paper investigates two new schemes, the linear and the proximal cutting plane methods, which are implemented in conjunction with augmented Lagrangian coordination for ATC-decomposed problems. Three nonconvex nonlinear example problems are used to show that these two cutting plane methods can significantly reduce the number of iterations and the number of function evaluations when compared to the traditional subgradient update methods. In addition, these methods are also compared to the method of multipliers and its variants, showing similar performance.

The Study on Electric Power System Based on Swarm Intelligence

2012 ◽  
Vol 442 ◽  
pp. 424-429 ◽  
Author(s):  
Ze Sheng Xu ◽  
Zhi Feng Ma ◽  
Xin Wen Di ◽  
Tao Luo ◽  
Hong Yun Guo ◽  
...  
Keyword(s):  
Power System ◽  
Swarm Intelligence ◽  
Large Scale ◽  
Optimization Problems ◽  
System Optimization ◽  
Electric Power System ◽  
Central Research ◽  
Centralized Control ◽  
Mathematical Basis ◽  
Selection Of

In this paper, we introduce the swarm intelligence computation and its applications in power system. Because swarm intelligence does not need any precondition of centralized control and global model, it is very suitable to solve large scale power system nonlinear optimization problems which are hard to establish effective formalized models and difficult to be solved by traditional methods. In order to apply swarm intelligence better in power system, we propose two central research directions in the future: (1) The mathematical basis of swarm intelligence is unsubstantial and it lacks profound and pervasive theoretical analysis, so we must analysis its convergence and selection of parameters, especially the parameter selection of large scale power system optimization problems. (2) Because swarm intelligence is internally parallel, we should realize it based on the parallel computation theory. This work will also be helpful for the real-time need of power system.

Computationally Efficient Hybrid Method for Transmission Network Expansion Planning

2017 ◽  
Vol 18 (2) ◽  
Author(s):  
Ashu Verma ◽  
Soumya Das ◽  
P. R. Bijwe
Keyword(s):  
Power System ◽  
Large Scale ◽  
Optimization Problems ◽  
Hybrid Approach ◽  
Solution Process ◽  
System Optimization ◽  
Mixed Integer ◽  
Transmission Network ◽  
Network Expansion ◽  
Expansion Planning

Abstract Transmission network expansion planning (TNEP) is an important and computationally very demanding problem in power system. Many computational approaches have been proposed to handle TNEP in the past. The problem is mixed integer, large scale and its complexity increases exponentially with the size of the system. Metaheuristic techniques have gained a lot of importance in last few years to solve the power system optimization problems, due to their ability to handle complex optimization functions and constraints. Many of them have been successfully applied for TNEP. The biggest challenge in these techniques is the requirement of large computational efforts. This paper uses a two-stage solution process to solve the TNEP problems. The first stage uses compensation based method to generate a quick, suboptimal solution. The valuable information contained in this solution is used to generate a set of heuristics aimed at drastically reducing the number of population for fitness evaluations required in the 2nd stage with application of metaheuristic method. The resulting hybrid approach produces very good quality solutions very efficiently. Results for 24 bus and 93 bus test systems have been obtained with the proposed method to ascertain the potential of the method in comparison to earlier approaches.

Fuel and Energy System Control at Large-Scale Damages. II. Optimization Problems

2018 ◽  
Vol 57 (2) ◽  
pp. 208-221 ◽  
Author(s):  
Yu. E. Malashenko ◽  
I. A. Nazarova ◽  
N. M. Novikova
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Energy System ◽  
System Control

scholarly journals Economic Dispatch Using Parameter-Setting-Free Harmony Search

2013 ◽  
Vol 2013 ◽  
pp. 1-5 ◽  
Author(s):  
Zong Woo Geem
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Economic Dispatch ◽  
Harmony Search ◽  
System Optimization ◽  
Energy System ◽  
Harmony Search Algorithm ◽  
Parameter Setting ◽  
User Friendly ◽  
First Time

Economic dispatch is one of the popular energy system optimization problems. Recently, it has been solved by various phenomenon-mimicking metaheuristic algorithms such as genetic algorithm, tabu search, evolutionary programming, particle swarm optimization, harmony search, honey bee mating optimization, and firefly algorithm. However, those phenomenon-mimicking problems require a tedious and troublesome process of algorithm parameter value setting. Without a proper parameter setting, good results cannot be guaranteed. Thus, this study adopts a newly developed parameter-setting-free technique combined with the harmony search algorithm and applies it to the economic dispatch problem for the first time, obtaining good results. Hopefully more researchers in energy system fields will adopt this user-friendly technique in their own problems in the future.

A decomposition approach for large-scale non-separable optimization problems

2021 ◽  
pp. 108168
Author(s):  
Mohamed Meselhi ◽  
Ruhul Sarker ◽  
Daryl Essam ◽  
Saber Elsayed
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Decomposition Approach ◽  
Separable Optimization

scholarly journals A Techno-Economic Perspective on Natural Gas and Its Value Chain

Gases ◽  
2020 ◽  
Vol 1 (1) ◽  
pp. 1-18
Author(s):  
Ruud Egging-Bratseth
Keyword(s):  
Natural Gas ◽  
Mathematical Programming ◽  
Value Chain ◽  
Large Scale ◽  
Optimization Problems ◽  
Research Question ◽  
Energy System ◽  
Business Case ◽  
Different Types ◽  
Functional Forms

We describe the elements and actors in the global natural gas value chains with an emphasis on characteristics relevant for large-scale energy system and market modeling. We give backgrounds on natural gas as a hydrocarbon to provide a rationale and understanding for what functional representations in mathematical programming models aim to represent. Simply taking the most advanced and detailed functional forms for all value chain characteristics and activities will typically result in numerical intractability. One should carefully determine what is needed to address a research question or analyze a business case. Recent advances in mathematical programming do allow solving large models with adequate detail for many types of analysis. We discuss which functional forms and modeling approaches can be appropriate for representing various characteristics in different types of analysis and provide a succinct and general mathematical programming formulation reflecting the optimization problems for different types of actors in the value chain. We provide an implementation for a stylized network using GAMS.

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