Optimization for Energy Systems and Supply Chains: Fundamentals and Applications

Description

To curb the impacts of rising CO2 emissions, the Intergovernmental Panel on Climate Change report states that a net zero target needs to be achieved by the year 2055. Experts argue that this is a critical time to make important and accurate decisions. Thus, it is essential to have the right tools to efficiently plan and deploy future energy systems and supply chains. Mathematical models can provide decision-makers with the tools required to make well-informed decisions relating to development of energy systems and supply chains. This book provides an understanding of the various available energy systems, the basics behind mathematical models, the steps required to develop mathematical models, and examples/case studies where such models are applied. Divided into two parts, one covering basics for beginners and the other featuring contributed chapters offering illustrative examples, this book Shows how mathematical models are applied to solve problems in energy systems and supply chains Provides fundamentals of the working principles of various energy systems and their technologies Offers basics of how to formulate and best practices for developing mathematical models, topics not covered in other titles Features a wide range of case studies Teaches readers to develop their own mathematical models to make decisions on energy systems This book is aimed at chemical, process, mechanical, and energy engineers. Cover Half Title Series Page Title Page Copyright Page Table of Contents Series Preface Preface Foreword Acknowledgments Editors Contributors Part A Fundamentals Chapter 1 Energy Systems and Supply Chains 1.1 Introduction 1.2 Energy Supply Chains 1.3 Energy Systems 1.3.1 Conventional Power Plants 1.3.2 Cogeneration Systems 1.3.3 Trigeneration Systems 1.3.4 Polygeneration Systems 1.3.5 Hybrid and Renewable Energy Systems 1.4 Process Systems Engineering References Chapter 2 Optimization of Energy Systems and Supply Chains 2.1 Introduction 2.2 Methodology of Mathematical Optimization 2.3 Illustrative Example 2.4 Conclusion Chapter 3 Formulating Generalized Mathematical Models 3.1 Introduction 3.2 Recommended Practices 3.3 General Representations and Equations 3.3.1 Conversion 3.3.2 Branching Points 3.3.3 Summing Points 3.3.4 Other Aspects 3.4 Conclusion Further Reading References Part B Applications Chapter 4 Mixed-Integer Linear Programming Model for the Synthesis of Negative-Emission Biochar Systems 4.1 Introduction 4.2 Negative-Emission Biochar Systems 4.3 Methodology 4.4 Case Study 1: Synthesis of Bioenergy Plant with Biochar Production 4.5 Case Study 2: Synthesis of Bioenergy Plant Producing Multi-grade Biochars 4.6 Conclusion 4.7 Further Reading 4.8 Density and Calorific Value of Process Streams References Appendix Chapter 5 A Comprehensive Guidance on Transitioning Toward Sustainable Hydrogen Network from Localized Renewable Energy System: Case Study of South Korea 5.1 Introduction 5.2 Problem Statement 5.3 Methodology 5.3.1 Mathematically Expressed Optimization Model 5.3.2 P-graph Optimization Model 5.3.3 Pareto Frontier and TOPSIS 5.4 Case Study Description 5.5 Results and Discussions 5.5.1 Increase of Hydrogen Demand 5.5.2 Overall Sustainable System Enhancement 5.6 Conclusion Further Reading Acknowledgments References Chapter 6 An Optimization Framework for Polygeneration System Driven by Glycerine Pitch and Diesel 6.1 Introduction 6.2 Methodology 6.2.1 Brayton Cycle 6.2.2 Steam Cycle 6.2.3 Waste Heat Recovery System 6.2.4 Utility Demand 6.2.5 Objective Function 6.3 Case Study 6.4 Results and Discussion 6.4.1 Scenario 1 6.4.2 Scenario 2 6.4.3 Cost Analysis 6.5 Conclusion Nomenclature Variable Parameter Superscript Further Reading References Chapter 7 Multi-Objective Optimization of TEG Dehydration Process to Mitigate BTEX Emission under Feed Composition Uncertainty 7.1 Introduction 7.2 Methodology 7.2.1 Data Generation and Metamodel Development 7.2.2 Robust Multi-objective Optimization (RMOO) 7.3 Results and Discussions 7.3.1 Metamodel Generation 7.3.2 EACO-Based Robust Multi-objective Optimization 7.3.3 Pareto Front Generation 7.3.4 Value of Stochastic Solution (VSS) 7.4 Conclusions Further Reading Acknowledgments References Chapter 8 Constrained Production Planning with Parametric Uncertainties 8.1 Introduction 8.1.1 APP with Uncertainty: A Brief Overview 8.1.2 APP and Robust Optimization Methodologies 8.2 Methodology 8.2.1 Problem Statement 8.2.2 Deterministic Mathematical Formulation 8.2.3 Robust Mathematical Formulation Including Uncertainty 8.3 Case Study: Indian Steel Industry 8.4 Conclusion Acknowledgments Nomenclature Sets Parameters Variables References Chapter 9 Linear Programming Models Based on the Input-Output Framework 9.1 Introduction 9.2 Methodology 9.2.1 Overview of the I-O Framework 9.2.2 LP Models Based on the I-O Framework 9.3 Case Study 1: Energy Allocation during a Shortage 9.4 Case Study 2: Decarbonization via Economic Restructuring 9.5 Conclusion Further Reading References Appendix 9A: LINGO Code for Case Study 1 Appendix 9B: LINGO Code for Case Study 2 Chapter 10 Optimisation of Oil Palm-Based Biodiesel Supply Chain: Upstream Stages 10.1 Introduction 10.2 Methodology 10.2.1 Problem Statement 10.2.2 Material Flow 10.2.3 Plantation Expansion Cost 10.2.4 LUC Emission 10.3 Case Study Scenario 10.4 Results and Discussion 10.5 Conclusion Future Reading References Appendix Chapter 11 Optimal Design of Islanded Distributed Energy Systems Incorporating Renewable Energy for Rural Africa: A Namibian Case Study 11.1 Introduction 11.2 Problem Statement 11.3 Methodology 11.4 Case Study Scenario 11.4.1 Introduction 11.4.2 Findings of Study 11.4.2.1 Demand Profile 11.5 Conclusion References Appendix Chapter 12 Multi-Objective Optimization for Energy Network Planning: Energy Storage and Distribution in Integrated Solar Powered Grids 12.1 Introduction 12.1.1 An Integrated Solar-Powered Electrical Distribution Grid 12.1.2 System Description 12.2 Methodology 12.2.1 Assumptions 12.2.2 Objective Function 12.2.3 Constraints 12.2.4 Solution Approach 12.3 Case Study 12.3.1 Base Run Results 12.3.2 Solar Production Site Breakdown 12.3.3 Battery Storage Breakdown 12.4 Conclusions and Recommendations References Appendix Index