Energy Supply Chains (ESCs) are complex systems made up of a number of heterogeneous components/agents interacting with each other, the environment, its hazards and threats. The components/agents are structured in a hierarchical system, within which they operate and cooperate in a balanced transaction environment to realize the maximization of the benefits, under various environmental and safety constraints. ESCs significantly contribute to the sustainment of many industrial areas, such as biomass, oil and gas, chemical processing, sustainable and renewable energy, etc. However, ESCs are challenged by multiple sources of uncertainties and risks. Uncertainties exist in supply and demand, propagate through the interactions over the whole ESC and influence the agents profits and the ESC operations. Due to the uncertainties, the risk of supply failure is difficult to predict. In such situation, ESCs must offer enhanced flexibility, innovative connectivity and communication, to guarantee an orderly and healthy supply management, so as to sustain the operation of the energy industry. The objectives of the Ph.D. work are to develop a modeling framework for ESC process modeling simulation and optimization, which includes: 1. ESC modeling to identify, understand and analyze the complex interactions and for the evaluation of the resilience of ESCs. 2. ESCs efficient production planning optimization under multiple sources of uncertainty. 3. ESCs production planning considering risk of supply failure. 4. Solving the Many-objective Optimization Problem (MaOP) caused by the different agents for efficient production planning of ESCs. With respect to the objective 1, an Agent-based Modeling(ABM) approach is proposed for modeling and simulating ESCs of the oil and gas industry, capturing the peculiarities of its diverse interacting elements, such as plants, refineries, storages, etc. Different disruption scenarios and recovery strategies are considered in the Agent-based ESC model for investigating the relevant factors influencing ESC resilience. With respect to the objectives 2 and 3, a simulation-based Multi-Objective Optimization (MOO) framework for ESC production planning is developed. The ABM simulation is embedded into a Non-dominated Sorting Genetic Algorithm (NSGA-II) is then adopted for identifying the Pareto solutions. For ESCs with uncertainties and changing structures, the ESC total profit is maximized and the disequilibrium among the agents' profits is minimized. Moreover, considering disruption risks, the ESC total profit is maximized and ESC risk under uncertainties is minimized. Furthermore, an improved Cooperative Co-evolutionary Particle Swarm Optimizer (CCPSO) is proposed to solve the Many-objective Optimization Problem (MaOP) in the agent-based ESC model. The variables are decomposed into different species based on agents relationships and allowed to evolve independently during the optimization process. Each species has its own repository to keep a historical record of the nondominated vectors in which the solutions are evaluated and updated by cooperating with other species. The effectiveness of CCPSO is proven by test functions and a case study.
Le catene di approvvigionamento energetico (ESC) sono sistemi complessi costituiti da un numero di componenti / agenti eterogenei che interagiscono tra loro, l'ambiente, i suoi pericoli e minacce. I componenti / agenti sono strutturati in un sistema gerarchico, all'interno del quale operano e cooperano in un ambiente di transazione equilibrato per realizzare la massimizzazione dei benefici, sotto vari vincoli ambientali e di sicurezza. Le CES contribuiscono in modo significativo al sostegno di molte aree industriali, come biomasse, petrolio e gas, lavorazione chimica, energia sostenibile e rinnovabile, ecc. Tuttavia, le ESC sono sfidate da molteplici fonti di incertezze e rischi. Esistono incertezze nella domanda e nell'offerta, si propagano attraverso le interazioni su tutto l'ESC e influenzano i profitti degli agenti e le operazioni dell'ESC. A causa delle incertezze, è difficile prevedere il rischio di una mancata fornitura. In tale situazione, le CES devono offrire maggiore flessibilità, connettività e comunicazione innovative, per garantire una gestione ordinata e sana dell'approvvigionamento, in modo da sostenere il funzionamento dell'industria energetica. Gli obiettivi del dottorato di ricerca il lavoro consiste nello sviluppo di un framework di modellazione per la simulazione e l'ottimizzazione della modellazione del processo ESC, che include: 1. Modellazione ESC per identificare, comprendere e analizzare le interazioni complesse e per la valutazione della resilienza delle ESC. 2. Ottimizzazione efficiente della pianificazione della produzione delle ESC sotto molteplici fonti di incertezza. 3. Pianificazione della produzione dell'ESC considerando il rischio di interruzione della fornitura. 4. Risoluzione del problema di ottimizzazione a molti obiettivi (MaOP) causato dai diversi agenti per una pianificazione efficiente della produzione di ESC. Rispetto all'obiettivo 1, viene proposto un approccio di modellazione basata su agenti (ABM) per modellare e simulare le ESC dell'industria petrolifera e del gas, catturando le peculiarità dei suoi diversi elementi interagenti, come impianti, raffinerie, depositi, ecc. scenari di interruzione e strategie di recupero sono considerati nel modello ESC basato su agente per indagare i fattori rilevanti che influenzano la resilienza ESC. Rispetto agli obiettivi 2 e 3, viene sviluppato un framework MOO (Multi-Objective Optimization) basato sulla simulazione per la pianificazione della produzione di ESC. La simulazione ABM è incorporata in un algoritmo genetico di ordinamento non dominato (NSGA-II) viene quindi adottato per identificare le soluzioni di Pareto. Per gli ESC con incertezze e strutture mutevoli, il profitto totale dell'ESC è massimizzato e lo squilibrio tra i profitti degli agenti è ridotto al minimo. Inoltre, considerando i rischi di interruzione, il profitto totale ESC è massimizzato e il rischio ESC in condizioni di incertezza è ridotto al minimo. Inoltre, viene proposto un ottimizzatore cooperativo co-evolutivo dello sciame di particelle (CCPSO) migliorato per risolvere il problema di ottimizzazione a molti obiettivi (MaOP) nel modello ESC basato su agenti. Le variabili vengono scomposte in specie diverse in base alle relazioni degli agenti e consentono di evolversi in modo indipendente durante il processo di ottimizzazione. Ogni specie ha il proprio archivio per tenere una registrazione storica dei vettori non nominati in cui le soluzioni vengono valutate e aggiornate collaborando con altre specie. L'efficacia di CCPSO è dimostrata da funzioni di test e da un caso di studio.
A computational framework for modeling, simulation and optimization of energy supply chains
CHEN, SHIYU
2020/2021
Abstract
Energy Supply Chains (ESCs) are complex systems made up of a number of heterogeneous components/agents interacting with each other, the environment, its hazards and threats. The components/agents are structured in a hierarchical system, within which they operate and cooperate in a balanced transaction environment to realize the maximization of the benefits, under various environmental and safety constraints. ESCs significantly contribute to the sustainment of many industrial areas, such as biomass, oil and gas, chemical processing, sustainable and renewable energy, etc. However, ESCs are challenged by multiple sources of uncertainties and risks. Uncertainties exist in supply and demand, propagate through the interactions over the whole ESC and influence the agents profits and the ESC operations. Due to the uncertainties, the risk of supply failure is difficult to predict. In such situation, ESCs must offer enhanced flexibility, innovative connectivity and communication, to guarantee an orderly and healthy supply management, so as to sustain the operation of the energy industry. The objectives of the Ph.D. work are to develop a modeling framework for ESC process modeling simulation and optimization, which includes: 1. ESC modeling to identify, understand and analyze the complex interactions and for the evaluation of the resilience of ESCs. 2. ESCs efficient production planning optimization under multiple sources of uncertainty. 3. ESCs production planning considering risk of supply failure. 4. Solving the Many-objective Optimization Problem (MaOP) caused by the different agents for efficient production planning of ESCs. With respect to the objective 1, an Agent-based Modeling(ABM) approach is proposed for modeling and simulating ESCs of the oil and gas industry, capturing the peculiarities of its diverse interacting elements, such as plants, refineries, storages, etc. Different disruption scenarios and recovery strategies are considered in the Agent-based ESC model for investigating the relevant factors influencing ESC resilience. With respect to the objectives 2 and 3, a simulation-based Multi-Objective Optimization (MOO) framework for ESC production planning is developed. The ABM simulation is embedded into a Non-dominated Sorting Genetic Algorithm (NSGA-II) is then adopted for identifying the Pareto solutions. For ESCs with uncertainties and changing structures, the ESC total profit is maximized and the disequilibrium among the agents' profits is minimized. Moreover, considering disruption risks, the ESC total profit is maximized and ESC risk under uncertainties is minimized. Furthermore, an improved Cooperative Co-evolutionary Particle Swarm Optimizer (CCPSO) is proposed to solve the Many-objective Optimization Problem (MaOP) in the agent-based ESC model. The variables are decomposed into different species based on agents relationships and allowed to evolve independently during the optimization process. Each species has its own repository to keep a historical record of the nondominated vectors in which the solutions are evaluated and updated by cooperating with other species. The effectiveness of CCPSO is proven by test functions and a case study.| File | Dimensione | Formato | |
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https://hdl.handle.net/10589/171046