|
| Titre : |
Energy Optimization in the Energy Internet |
| Type de document : |
document électronique |
| Auteurs : |
Assala Nacef, Auteur ; Djamila Mechta, Directeur de thèse |
| Editeur : |
Sétif:UFA1 |
| Année de publication : |
2026 |
| Importance : |
1 vol (130 f.) |
| Format : |
29 cm |
| Langues : |
Anglais (eng) |
| Catégories : |
Thèses & Mémoires:Informatique
|
| Mots-clés : |
Energy Internet
Smart Grid
Energy Routing
Peer-to-Peer Energy Trading
Efficient path
Producer Subset
Scheduling. |
| Index. décimale : |
004 - Informatique |
| Résumé : |
Energy has always been a fundamental pillar of human civilization and economic prosperity. In
the modern era, global electricity demand is surging, driven by widespread economic development
and the rapid growth of energy-intensive technologies like Artificial Intelligence. To manage this
escalating demand sustainably, greater focus has been placed on integrating Renewable Energy Sources
(RES)—such as solar, wind, and hydropower—to alleviate pressure on finite, polluting fossil fuels.
Consequently, research into effectively harnessing RES has become critically important, with significant
efforts directed toward managing their inherent intermittency and volatility to ensure grid stability and
reliability. Electricity networks are therefore undergoing a transformation from centralized, fossil-fuelbased
grids to decentralized, sustainable infrastructures. Smart Grids (SG) and the Energy Internet
(EI) have emerged to enable two-way communication, distributed generation, and peer-to-peer (P2P)
energy trading. Yet existing P2P trading frameworks often overlook the network’s physical constraints,
leading to inefficiencies, congestion, and unreliable energy transfers. This thesis aims to bridge the
gap between energy routing and P2P energy trading by developing a system that integrates both
the economic and physical dimensions of energy exchange. The objectives are to optimally match
prosumers, maximize producer profits, minimize consumer costs, and ensure efficient, reliable, and
collision-free energy transfers. To achieve these objectives, several models and algorithms are proposed.
First, the trading problem is formulated as a fractional knapsack problem and solved using a greedy
algorithm combined with Dijkstra’s shortest-path algorithm. Second, simulated annealing is applied to
producer subset determination, demonstrating superior convergence and ability to escape local optima
compared to other heuristic optimization approaches proposed in the literature. Third, power loss
is incorporated into path optimization through a modified greedy search algorithm that outperforms
traditional shortest-path methods. Fourth, a quantum genetic algorithm is employed to pair prosumers,
accounting for both costs and physical losses, thereby significantly reducing computation time while
improving efficiency. A dynamic scheduling mechanism is then introduced to mitigate congestion,
prevent collisions, and enhance fairness and reliability. Finally, an adaptive multi-commodity flow
(MCF) framework with Mirror Descent learning is developed to simultaneously address all three routing
challenges in a unified optimization approach validated on a real-world dataset of 300 Australian
households over 2,648 trading hours. The proposed framework demonstrates significant improvements
in cost-effectiveness, transmission efficiency, and system reliability. Results show up to 39.34% cost
reduction depending on infrastructure capacity, transmission losses maintained below 1.2%, 55.9% grid
independence, and sub-millisecond optimization enabling real-time market operation. The adaptive
approach eliminates manual parameter tuning through online learning, automatically identifying binding
constraints and adjusting routing priorities across diverse operating conditions. By unifying economic
and physical aspects of energy transfers at scale, this thesis contributes a novel foundation for sustainable,
decentralized, and intelligent energy markets. |
| Note de contenu : |
Sommaire
Abstract i
Résumé ii
Acknowledgment iv
Table of contents vii
List of figures ix
List of tables xi
List of algorithms xii
Nomenclature xiii
General Introduction 1
I Background 4
1 Foundations of Smart Grids and the Energy Internet 5
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2 Evolution and core principles of smart grids and the Energy Internet . . . . . . . . . . 6
1.3 Background and history . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.4 From smart grids to Energy Internet . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.5 Energy internet naming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.6 EI physical components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.6.1 Renewable energy resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.6.2 Storage devices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.6.3 Electric vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.6.4 The energy router . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.6.5 The energy hub . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.6.6 Smart meter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.6.7 Microgrid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
1.6.8 UAV Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
1.7 EI software components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
1.7.1 Blockchain technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
1.7.2 Energy management software . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
1.7.3 Cybersecurity software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
1.7.4 Artificial intelligence and machine learning . . . . . . . . . . . . . . . . . . . . . 14
1.7.5 Digital twinning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
1.7.6 Peer-to-peer energy trading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
1.7.7 Plug and play interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
1.8 Working principle of EI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
1.9 Energy internet challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
1.10 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2 Classification of Energy Routing Strategies in Energy Internet and Smart Grids 19
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.1.1 Energy routing algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.1.2 Load forecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.1.3 Demand response (DR) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.2 Optimization algorithms used in energy routing . . . . . . . . . . . . . . . . . . . . . . 25
2.2.1 Mathematical optimization framework . . . . . . . . . . . . . . . . . . . . . . . 25
2.2.2 Autonomous routing algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
2.2.3 Heuristic and Bio-inspired metaheuristic optimization algorithms . . . . . . . . 32
2.2.4 Topology-based solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
II Contributions 41
3 FKD-RA: Efficient Energy Routing Protocol in SG and EI Networks Using Fractional
Knapsack and Dijkstra Algorithm 42
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.2 Problematic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.2.1 Fractional knapsack . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.2.2 Network model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.2.3 Control center management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.2.4 Addressing the optimal producer’s subset problem . . . . . . . . . . . . . . . . 43
3.2.5 Finding the shortest path . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.3 Simulation and performance analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.3.1 Use case 1: Optimal path . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.3.2 Use case 2: Multiple sources and multiple energy demands . . . . . . . . . . . . 46
3.3.3 Use case 3: Multiple sources and mono-source . . . . . . . . . . . . . . . . . . . 46
3.3.4 A comparative analysis with the firefly algorithm . . . . . . . . . . . . . . . . . 47
3.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
4 SA-RA: Simulated Annealing for Producer Subset Determination in Smart Grids 49
4.1 Methodology of Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.1.1 Energy System Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.1.2 Simulated Annealing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.1.3 Initial solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.1.4 Initializing Parameter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.1.5 Energy Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.1.6 Perturbation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
4.2 Simulation Results And Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
4.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
5 GQA-RA: A Quantum Genetic-based Routing Protocol for Real-Time Peer-to-Peer
Energy Transactions 57
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
5.2 Energy Internet: Paramount Component and Topology . . . . . . . . . . . . . . . . . . 58
5.2.1 Network Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
5.2.2 Energy router . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
5.3 Energy routing protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
5.3.1 Minimum path loss algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
5.3.2 Producer subset determination using quantum genetic algorithm . . . . . . . . 64
5.3.3 Dynamic Scheduling for Smart Grid Networks . . . . . . . . . . . . . . . . . . . 68
5.4 Simulation and performance analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
5.4.1 Network topologies and simulation parameters . . . . . . . . . . . . . . . . . . 69
5.4.2 Evaluation metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
5.4.3 6-Node network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
5.4.4 30-Node network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
5.4.5 Greedy search comparison analyses . . . . . . . . . . . . . . . . . . . . . . . . . 77
5.5 Complexity and scalability analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
5.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
5.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
6 Adaptive MCF: Adaptive Multi-Objective Optimization for P2P Energy Trading: A
Multi-Commodity Flow Approach with Mirror Descent Learning 82
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
6.2 Network model and system architecture . . . . . . . . . . . . . . . . . . . . . . . . . . 83
6.2.1 Dataset and preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
6.2.2 Household aggregation into energy routers . . . . . . . . . . . . . . . . . . . . . 84
6.2.3 Router-level power aggregation . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
6.2.4 Physical network topology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
6.2.5 Electrical parameters and line modeling . . . . . . . . . . . . . . . . . . . . . . 86
6.2.6 Economic framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
6.2.7 Utility grid modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
6.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
6.3.1 MCF formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
6.3.2 Mirror descent for adaptive weight learning . . . . . . . . . . . . . . . . . . . . 90
6.3.3 Mirror Descent for Weight Adaptation . . . . . . . . . . . . . . . . . . . . . . . 91
6.4 Results and performance analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
6.4.1 Energy flow analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
6.4.2 Economic performance analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
6.4.3 Fairness analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
6.4.4 Adaptive weight learning dynamics . . . . . . . . . . . . . . . . . . . . . . . . . 102
6.4.5 Computational complexity and runtime analysis . . . . . . . . . . . . . . . . . . 103
6.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
6.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
7 Comparative Analysis and Discussion 108
7.1 Energy flow analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
7.2 Cost analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
7.3 Fairness analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
7.4 Computational analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
7.4.1 Theoretical complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
7.4.2 Measured execution times . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
7.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
7.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
General Conclusion 118
List of Publications 120
Bibliography 121 |
| Côte titre : |
DI/0102 |
Energy Optimization in the Energy Internet [document électronique] / Assala Nacef, Auteur ; Djamila Mechta, Directeur de thèse . - [S.l.] : Sétif:UFA1, 2026 . - 1 vol (130 f.) ; 29 cm. Langues : Anglais ( eng)
| Catégories : |
Thèses & Mémoires:Informatique
|
| Mots-clés : |
Energy Internet
Smart Grid
Energy Routing
Peer-to-Peer Energy Trading
Efficient path
Producer Subset
Scheduling. |
| Index. décimale : |
004 - Informatique |
| Résumé : |
Energy has always been a fundamental pillar of human civilization and economic prosperity. In
the modern era, global electricity demand is surging, driven by widespread economic development
and the rapid growth of energy-intensive technologies like Artificial Intelligence. To manage this
escalating demand sustainably, greater focus has been placed on integrating Renewable Energy Sources
(RES)—such as solar, wind, and hydropower—to alleviate pressure on finite, polluting fossil fuels.
Consequently, research into effectively harnessing RES has become critically important, with significant
efforts directed toward managing their inherent intermittency and volatility to ensure grid stability and
reliability. Electricity networks are therefore undergoing a transformation from centralized, fossil-fuelbased
grids to decentralized, sustainable infrastructures. Smart Grids (SG) and the Energy Internet
(EI) have emerged to enable two-way communication, distributed generation, and peer-to-peer (P2P)
energy trading. Yet existing P2P trading frameworks often overlook the network’s physical constraints,
leading to inefficiencies, congestion, and unreliable energy transfers. This thesis aims to bridge the
gap between energy routing and P2P energy trading by developing a system that integrates both
the economic and physical dimensions of energy exchange. The objectives are to optimally match
prosumers, maximize producer profits, minimize consumer costs, and ensure efficient, reliable, and
collision-free energy transfers. To achieve these objectives, several models and algorithms are proposed.
First, the trading problem is formulated as a fractional knapsack problem and solved using a greedy
algorithm combined with Dijkstra’s shortest-path algorithm. Second, simulated annealing is applied to
producer subset determination, demonstrating superior convergence and ability to escape local optima
compared to other heuristic optimization approaches proposed in the literature. Third, power loss
is incorporated into path optimization through a modified greedy search algorithm that outperforms
traditional shortest-path methods. Fourth, a quantum genetic algorithm is employed to pair prosumers,
accounting for both costs and physical losses, thereby significantly reducing computation time while
improving efficiency. A dynamic scheduling mechanism is then introduced to mitigate congestion,
prevent collisions, and enhance fairness and reliability. Finally, an adaptive multi-commodity flow
(MCF) framework with Mirror Descent learning is developed to simultaneously address all three routing
challenges in a unified optimization approach validated on a real-world dataset of 300 Australian
households over 2,648 trading hours. The proposed framework demonstrates significant improvements
in cost-effectiveness, transmission efficiency, and system reliability. Results show up to 39.34% cost
reduction depending on infrastructure capacity, transmission losses maintained below 1.2%, 55.9% grid
independence, and sub-millisecond optimization enabling real-time market operation. The adaptive
approach eliminates manual parameter tuning through online learning, automatically identifying binding
constraints and adjusting routing priorities across diverse operating conditions. By unifying economic
and physical aspects of energy transfers at scale, this thesis contributes a novel foundation for sustainable,
decentralized, and intelligent energy markets. |
| Note de contenu : |
Sommaire
Abstract i
Résumé ii
Acknowledgment iv
Table of contents vii
List of figures ix
List of tables xi
List of algorithms xii
Nomenclature xiii
General Introduction 1
I Background 4
1 Foundations of Smart Grids and the Energy Internet 5
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2 Evolution and core principles of smart grids and the Energy Internet . . . . . . . . . . 6
1.3 Background and history . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.4 From smart grids to Energy Internet . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.5 Energy internet naming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.6 EI physical components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.6.1 Renewable energy resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.6.2 Storage devices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.6.3 Electric vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.6.4 The energy router . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.6.5 The energy hub . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.6.6 Smart meter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.6.7 Microgrid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
1.6.8 UAV Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
1.7 EI software components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
1.7.1 Blockchain technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
1.7.2 Energy management software . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
1.7.3 Cybersecurity software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
1.7.4 Artificial intelligence and machine learning . . . . . . . . . . . . . . . . . . . . . 14
1.7.5 Digital twinning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
1.7.6 Peer-to-peer energy trading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
1.7.7 Plug and play interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
1.8 Working principle of EI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
1.9 Energy internet challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
1.10 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2 Classification of Energy Routing Strategies in Energy Internet and Smart Grids 19
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.1.1 Energy routing algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.1.2 Load forecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.1.3 Demand response (DR) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.2 Optimization algorithms used in energy routing . . . . . . . . . . . . . . . . . . . . . . 25
2.2.1 Mathematical optimization framework . . . . . . . . . . . . . . . . . . . . . . . 25
2.2.2 Autonomous routing algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
2.2.3 Heuristic and Bio-inspired metaheuristic optimization algorithms . . . . . . . . 32
2.2.4 Topology-based solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
II Contributions 41
3 FKD-RA: Efficient Energy Routing Protocol in SG and EI Networks Using Fractional
Knapsack and Dijkstra Algorithm 42
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.2 Problematic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.2.1 Fractional knapsack . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.2.2 Network model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.2.3 Control center management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.2.4 Addressing the optimal producer’s subset problem . . . . . . . . . . . . . . . . 43
3.2.5 Finding the shortest path . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.3 Simulation and performance analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.3.1 Use case 1: Optimal path . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.3.2 Use case 2: Multiple sources and multiple energy demands . . . . . . . . . . . . 46
3.3.3 Use case 3: Multiple sources and mono-source . . . . . . . . . . . . . . . . . . . 46
3.3.4 A comparative analysis with the firefly algorithm . . . . . . . . . . . . . . . . . 47
3.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
4 SA-RA: Simulated Annealing for Producer Subset Determination in Smart Grids 49
4.1 Methodology of Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.1.1 Energy System Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.1.2 Simulated Annealing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.1.3 Initial solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.1.4 Initializing Parameter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.1.5 Energy Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.1.6 Perturbation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
4.2 Simulation Results And Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
4.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
5 GQA-RA: A Quantum Genetic-based Routing Protocol for Real-Time Peer-to-Peer
Energy Transactions 57
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
5.2 Energy Internet: Paramount Component and Topology . . . . . . . . . . . . . . . . . . 58
5.2.1 Network Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
5.2.2 Energy router . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
5.3 Energy routing protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
5.3.1 Minimum path loss algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
5.3.2 Producer subset determination using quantum genetic algorithm . . . . . . . . 64
5.3.3 Dynamic Scheduling for Smart Grid Networks . . . . . . . . . . . . . . . . . . . 68
5.4 Simulation and performance analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
5.4.1 Network topologies and simulation parameters . . . . . . . . . . . . . . . . . . 69
5.4.2 Evaluation metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
5.4.3 6-Node network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
5.4.4 30-Node network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
5.4.5 Greedy search comparison analyses . . . . . . . . . . . . . . . . . . . . . . . . . 77
5.5 Complexity and scalability analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
5.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
5.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
6 Adaptive MCF: Adaptive Multi-Objective Optimization for P2P Energy Trading: A
Multi-Commodity Flow Approach with Mirror Descent Learning 82
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
6.2 Network model and system architecture . . . . . . . . . . . . . . . . . . . . . . . . . . 83
6.2.1 Dataset and preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
6.2.2 Household aggregation into energy routers . . . . . . . . . . . . . . . . . . . . . 84
6.2.3 Router-level power aggregation . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
6.2.4 Physical network topology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
6.2.5 Electrical parameters and line modeling . . . . . . . . . . . . . . . . . . . . . . 86
6.2.6 Economic framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
6.2.7 Utility grid modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
6.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
6.3.1 MCF formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
6.3.2 Mirror descent for adaptive weight learning . . . . . . . . . . . . . . . . . . . . 90
6.3.3 Mirror Descent for Weight Adaptation . . . . . . . . . . . . . . . . . . . . . . . 91
6.4 Results and performance analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
6.4.1 Energy flow analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
6.4.2 Economic performance analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
6.4.3 Fairness analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
6.4.4 Adaptive weight learning dynamics . . . . . . . . . . . . . . . . . . . . . . . . . 102
6.4.5 Computational complexity and runtime analysis . . . . . . . . . . . . . . . . . . 103
6.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
6.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
7 Comparative Analysis and Discussion 108
7.1 Energy flow analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
7.2 Cost analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
7.3 Fairness analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
7.4 Computational analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
7.4.1 Theoretical complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
7.4.2 Measured execution times . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
7.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
7.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
General Conclusion 118
List of Publications 120
Bibliography 121 |
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Energy Optimization in the Energy Internet
