Multi-objective generation expansion planning considering uncertainty and modeling with the Pareto uncertainty index
Başlık çevirisi mevcut değil.
- Tez No: 402056
- Danışmanlar: DR. DAVID W. COIT
- Tez Türü: Doktora
- Konular: Endüstri ve Endüstri Mühendisliği, İşletme, Industrial and Industrial Engineering, Business Administration
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 2015
- Dil: İngilizce
- Üniversite: Rutgers, The State University of New Jersey-New Brunswick Campus
- Enstitü: Yurtdışı Enstitü
- Ana Bilim Dalı: Belirtilmemiş.
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: 227
Özet
Özet yok.
Özet (Çeviri)
Many real life optimization problems are multi-objective problems where objectives under consideration usually conflict with each other and they are also stochastic due to inherent uncertainties. The electricity Generation Expansion Planning (GEP) problem is an example of such problems in which the goal is to expand the electric power network with new power plant investments including renewable resources. Decisions are made where and when to build new power plants and which technology to choose for new investments. Objectives can include but are not limited to minimization of the cost and pollutant emissions and maximization of reliability. There are inherent uncertainties in the GEP problem due to climate change, demand increase, fuel prices, technological progress and many other aspects that have to be considered. Some of these uncertainties directly affect the objective functions and some affect the constraint sets in the optimization model. In this study, a new uncertainty metric, the Pareto Uncertainty Index (PUI), is presented. The PUI includes uncertainty as part of the Pareto optimality concept so that the decision or policy maker can observe the uncertainty of Pareto optimal solutions. Using the PUI approach for objective function uncertainties and chance constrained programming or scenarios for constraint set uncertainties, a new multi-objective stochastic genetic algorithm, the Pareto Uncertain Genetic Algorithm (PUGA), is presented in this research, as well. In contrast with the other multi-objective genetic algorithms and classical methods, PUGA can incorporate both the multi-objective and stochastic aspects of problem solving without any transformation. A new post-Pareto pruning approach that reduces the number of Pareto optimal solutions to a smaller practical set is also included in PUGA with the help of the uncertainty information preserved in the PUI. Furthermore, this uncertainty information is used for risk assessments of solutions depending on the risk preferences of decision makers. The PUI and PUGA concepts are demonstrated and tested on several problems including the US Northeast region generation expansion planning (NEGEP) problem.
Benzer Tezler
- Stokastik programlama yaklaşımı ile elektrik üretim endüstrisinin modellenmesi
Electricity generation industry modelling: Stochastic programming approach
HASAN BASRİ ARSLAN
Doktora
Türkçe
2017
EnerjiHacettepe ÜniversitesiNükleer Enerji Mühendisliği Ana Bilim Dalı
DOÇ. DR. ŞULE ERGÜN
- Generation expansion planning considering electricity market
Elektrik piyasası düşünülerek üretim genişletme planlaması
EGEMEN UYAR
Yüksek Lisans
İngilizce
2022
Elektrik ve Elektronik MühendisliğiDokuz Eylül ÜniversitesiElektrik-Elektronik Mühendisliği Ana Bilim Dalı
PROF. DR. ENGİN KARATEPE
- Inequity-averse optimization in disaster preparedness and response
Afete hazırlık ve müdahale konusunda eşitsizlikten bağımsız optimizasyon
MAHDI MOSTAJABDAVEH
Doktora
İngilizce
2019
Endüstri ve Endüstri MühendisliğiKoç ÜniversitesiEndüstri Mühendisliği Ana Bilim Dalı
Prof. Dr. FATMA SİBEL SALMAN
- Rüzgar enerji santralleri entegre edilmiş elektrik şebekelerinde iletim hattı planlaması
Transmission expansion planning in power systems with wind power plants
FARUK UGRANLI
Doktora
Türkçe
2016
Elektrik ve Elektronik MühendisliğiEge ÜniversitesiElektrik-Elektronik Mühendisliği Ana Bilim Dalı
DOÇ. DR. ENGİN KARATEPE
- Advanced evolutionary computation for distributionsystem automation
Dağıtım şebekesi otomasyonu için gelişmiş evrimsel algoritmalar
BAHMAN AHMADI
Yüksek Lisans
İngilizce
2021
Elektrik ve Elektronik Mühendisliğiİstanbul Teknik ÜniversitesiElektrik Mühendisliği Ana Bilim Dalı
PROF. DR. AYDOĞAN ÖZDEMİR
DR. ÖĞR. ÜYESİ OGUZHAN CEYLAN