| dc.contributor.author | Yoldaş, Yeliz | |
| dc.date.accessioned | 2022-06-20T09:22:31Z | |
| dc.date.available | 2022-06-20T09:22:31Z | |
| dc.date.issued | 2021 | en_US |
| dc.date.submitted | 2021-09 | |
| dc.identifier.citation | A THESIS SUBMITTED TO THE DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING AND THE GRADUATE SCHOOL OF ENGINEERING AND SCIENCE OF ABDULLAH GUL UNIVERSITY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY | en_US |
| dc.identifier.uri | https://hdl.handle.net/20.500.12573/1290 | |
| dc.description.abstract | This thesis concerns the transformation of aged power systems to modern power systems that include microgrids with renewable energy sources and energy storage systems. The integration of renewable energy sources brings excellent opportunities to provide better reliability and efficiency. The aim of this dissertation is to maintain the supply-demand balance in microgrids while minimizing the cost in real time operation. A microgrid energy management system that can optimize the dispatch of the controllable distributed energy resources in grid-connected mode of a pilot microgrid on a university campus in Malta was developed to achieve this goal. Designing intelligent method for the real-time energy management of the stochastic and dynamic microgrid is the primary goal of this research. Moreover, the detailed mathematical models of the network model and of the technical model are considered for the economic and environmental operation of the microgrid system to solve the optimization problem under more real-world conditions. The objective is to minimize the total daily operation costs, which include the degradation cost of batteries, the cost of energy bought from the main grid, the fuel cost of the diesel generator, and the emission cost. Q-learning algorithm is adopted to solve the sequential decision subproblems. The proposed algorithm decomposes a multi-stage mixed-integer nonlinear programming (MINLP) problem into a series of single-stage problems so that each subproblem can be solved using Bellman’s equation. A predictive control framework is also proposed to provide optimal operation with minimum cost. This method allows the consideration of operational cost values, demand with uncertainty, generation units’ profiles with uncertainty, and constraints related to the network model and technical model. | en_US |
| dc.description.abstract | Bu tez, eskimiş güç sistemlerinin yenilenebilir enerji kaynakları ve enerji depolama sistemleri ile mikro şebekeleri içeren modern güç sistemlerine dönüşümü ile ilgilidir. Yenilenebilir enerji kaynaklarının belirsizliği ve kesintili doğası, elektrik şebekesinin istikrarını ve kalitesini düşürebilir. Bu nedenle, bu tezin amacı, gerçek zamanlı çalışmada minimum maliyetle mikro şebekede arz-talep dengesini sağlamaktır. Bu amaca ulaşmak için Malta'daki bir üniversite kampüsünde pilot bir şebekeye bağlı mikro şebekenin kontrol edilebilir dağıtık enerji kaynaklarının çıkışlarını optimize edebilen enerji yönetim sistemi geliştirilmiştir..
Stokastik ve dinamik mikro şebekenin gerçek zamanlı enerji yönetimi için akıllı sistem tasarlamak, birincil hedefe ulaşmanın en önemli parçasıdır. Ayrıca, optimizasyon problemini daha gerçek dünya koşullarında çözmek için mikro şebeke sisteminin ekonomik ve çevresel çalışması için şebeke modelinin ve teknik modelin ayrıntılı matematiksel modelleri düşünülmüştür. Buradaki optimizasyon problemindeki amaç, bataryanın degradasyon maliyetini, ana şebekeden satın alınan enerjinin maliyetini, dizel jeneratörün yakıt maliyetini ve emisyon maliyetini kapsayan toplam günlük işletme maliyetlerini en aza indirmektir. Sıralı karar alt problemlerini çözmek için Q-öğrenme algoritması kullanılmıştır. Önerilen algoritma, çok aşamalı Tamsayılı Karışık Doğrusal Olmayan Programlama (TKDOP) problemini tek aşamalı probleme serisine ayrıştırır, böylece her bir alt problem Bellman denklemi kullanılarak çözülebilir. Ayrıca, minimum maliyetle optimum çalışmayı sağlamak için bir öngörülü kontrol metot önerilmiştir. Bu yöntem; işletme maliyet değerlerini, değişkenlik gösteren talebi, belirsizlik içeren üretim elemanlarının profillerini ve şebeke & teknik model ile ilgili kısıtlamaların dikkate alınmasını sağlamaktadır. | en_US |
| dc.description.tableofcontents | TABLE OF CONTENTS
1. INTRODUCTION .................................................................................................... 1
1.1 RESEARCH MOTIVATION AND PROBLEM STATEMENT ............................................ 5
1.2 RESEARCH OBJECTIVES AND CONTRIBUTIONS ....................................................... 6
1.3 DISSERTATION OUTLINE ........................................................................................ 7
2. ENHANCING SMART GRID WITH MICROGRIDS: CHALLENGES AND OPPORTUNITIES ................................................................................................... 9
2.1 MICROGRID TO SMART GRID..................................................................................... 9
2.2 ARCHITECTURAL MODEL OF FUTURE SMART GRID ................................................. 11
2.2.1 AC microgrids ................................................................................................ 11
2.2.2 DC microgrids ............................................................................................... 11
2.2.3 Hybrid AC-DC microgrids ............................................................................ 11
2.3 FUNCTIONS OF SMART GRID COMPONENTS ............................................................. 12
2.3.1. Smart device interface components .............................................................. 12
2.3.2 Advanced forecasting ..................................................................................... 19
2.3.4 Control of generation units ............................................................................ 20
2.3.5 Control of storage units ................................................................................. 20
2.3.6 Data transmission and monitoring ................................................................ 21
2.3.7 Power flow and energy management ............................................................. 24
2.3.8 Vehicle to grid (V2G) ..................................................................................... 25
2.4 CHALLENGES AND OPPORTUNITIES ........................................................................ 26
2.4.1 Technical challenges...................................................................................... 26
2.4.2 Regulation challenges .................................................................................... 27
2.4.3 Smart consumer ............................................................................................. 27
2.4.4 Opportunities in microgrid ............................................................................ 28
2.5 CONCLUSION .......................................................................................................... 29
3. OPTIMIZATION-BASED CONTROL STRATEGIES FOR ENERGY MANAGEMENT SYSTEMS IN MICROGRIDS ............................................... 30
3.1 LITERATURE REVIEW.............................................................................................. 30
3.2 MIXED INTEGER NONLINEAR PROGRAMMING ......................................................... 33
3.3 ROLLING HORIZON CONTROL ................................................................................ 34
3.4 REINFORCEMENT LEARNING................................................................................... 35
3.3.1 Optimal value function and optimal policy.................................................... 37
3.3.2 Markov decision process (MDP) ................................................................... 38
3.3.3 Bellman Equations ......................................................................................... 38
3.3.4 Q-learning ...................................................................................................... 39
4. DYNAMIC ROLLING HORIZON CONTROL APPROACH .......................... 41
4.1 INTRODUCTION ...................................................................................................... 41
4.2 MICROGRID MODEL DESCRIPTION ......................................................................... 43
4.2.1 Battery Model ................................................................................................ 44
4.2.2 Diesel Generator (DG) .................................................................................. 45
4.2.3 Main grid ....................................................................................................... 45
4.2.4 AC Power Flow .............................................................................................. 46
4.2.5 Objective Function ......................................................................................... 46
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4.3 ROLLING HORIZON CONTROL APPROACH .............................................................. 47
4.4 SIMULATION ENVIRONMENT & NUMERICAL ANALYSIS ......................................... 49
4.4.1 Simulation Environment ................................................................................ 49
4.4.2 Numerical Analysis ........................................................................................ 51
4.5 CONCLUSION .......................................................................................................... 55
5. OPTIMAL CONTROL OF MICROGRIDS WITH MULTI-STAGE MIXED-INTEGER NONLINEAR PROGRAMMING GUIDED Q-LEARNING ALGORITHM ......................................................................................................... 57
5.1 INTRODUCTION ...................................................................................................... 58
5.2 MICROGRID MODEL DESCRIPTION ......................................................................... 60
5.2.1 Battery Model ................................................................................................ 61
5.2.2 Diesel Generator............................................................................................ 62
5.2.3 Main grid ....................................................................................................... 63
5.2.4 AC Power Flow .............................................................................................. 63
5.2.5 Emission Cost Calculation............................................................................. 64
5.3 MDP MODEL FOR REAL-TIME SCHEDULING OF MICROGRID ................................. 64
5.3.1 State Variables and Decision (Action) Variables .......................................... 65
5.3.2 Objective Function ......................................................................................... 65
5.4 PROPOSED OPTIMIZATION MODEL ......................................................................... 65
5.5 NUMERICAL AND RESULT ANALYSIS ..................................................................... 67
5.5.1 Simulation Environment ................................................................................ 67
5.5.2 Case Studies ................................................................................................... 69
5.6 CONCLUSION .......................................................................................................... 74
6. CONCLUSIONS AND FUTURE PROSPECTS ................................................. 75
6.1 CONCLUSIONS ........................................................................................................ 75
6.2 SOCIETAL IMPACT AND CONTRIBUTION TO GLOBAL SUSTAINABILITY................... 76
6.3 FUTURE PROSPECTS ............................................................................................. 77 | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | Abdullah Gül Üniversitesi Fen Bilimleri Enstitüsü | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | Microgrid | en_US |
| dc.subject | Rolling horizon control | en_US |
| dc.subject | Reinforcement learning | en_US |
| dc.subject | Energy management | en_US |
| dc.title | DEVELOPMENT OF CONTROL STRATEGIES IN SMART MICROGRIDS | en_US |
| dc.type | doctoralThesis | en_US |
| dc.contributor.department | AGÜ, Fen Bilimleri Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı | en_US |
| dc.contributor.authorID | 0000-0002-9821-9339 | en_US |
| dc.relation.publicationcategory | Tez | en_US |
