Optimization of distribution networks using evolutionary algorithms

One of the biggest problems that a distribution network (DN) must face is the constant increase in load demand, which eventually will cause the degradation of its optimal operation. To overcome these challenges the distribution network usually is oversized or reinforced, however, although this is a quick and practical solution, it is not necessarily the most economical and efficient one. For this reason, it is desirable to implement an optimization algorithm to improve the network without increasing investment costs. The optimization of a power distribution network is not an easy task, because this is the most extensive part of the entire electrical system. Due to this extension, along with the high complexity of the topology, and some quality parameters that must be respected, the entire design or improvement of a distribution network can be considered as an extremely hard combinatorial, non-convex, and non-linear optimization problem, difficult to solve by conventional methods. For these reasons, we propose a Two-Stage Multiobjective Evolutionary Approach (TS-MOEAP) capable to design and optimize distribution networks, at primary and secondary levels. Due to the complexity of the optimization problem, the approach is implemented in two stages, that can be summarized as follows: Stage-1. Optimal placement and sizing of generation units, as well as optimal branch routing and conductor sizing. For this purpose, an Improved Particle Swarm Optimization technique (IPSO) combined with a greedy algorithm is introduced. Stage-2. Optimal network reconfiguration. For this, an Improved Nondominated Sorting Genetic Algorithm with a Heuristic Mutation Operator (INSGA-HO) is presented, aiming at minimizing the total power loss and investment cost of the system. Finally, to complement the optimization process, the software DER-CAM will be used to find optimal investment solutions for Distributed Energy Resources (DER). Both algorithms are successfully applied to design and optimize real distribution networks that presented several problems, concluding that the combination of these approaches -network reconfiguration with optimal installation of DERs- can converge toward better configurations than other algorithms.Abstract vii List of Figures ix List of Tables xi Abbreviations and Notations xv Chapter 1 Introduction 1.1. Distribution Network Planning 1.2. Distribution Network Structure and Overview 1.3. Quality Issues, Power Losses, and Possible Solutions 1.4. Optimization Challenges 1.5. Objectives and Hypothesis 1.6. A Two-Stage Multiobjective Evolutionary Approach Chapter 2 Evolutionary Algorithms 2.1. What is an Evolutionary Algorithm? 2.2. The Components of Evolutionary Algorithms 2.3. Evolutionary Algorithm Variants 2.4. Multiobjective Evolutionary Algorithms 2.5. Summary Chapter 3 Construction and Evaluation of a DN Model 3.1. Representation and Model Construction 3.2. Evaluation of the Model 3.3. Economic Evaluation 3.4. Summary Chapter 4 Stage-1 of TS-MOEAP (IPSO – PRIM) 4.1. Objective Functions for the Evolutionary Algorithms 4.2. Implementation of Stage-1 (IPSO – PRIM) 4.3. IPSO-PRIM Complete Algorithm 4.4. Simulations and Results for the IPSO-PRIM Algorithm 4.5. Summary Chapter 5 Stage-2 of TS-MOEAP (INSGA – HO) 5.1. Reformulation of the Objective Functions in Terms of Constraints 5.2. Implementation of Stage-2 (INSGA-HO) 5.3. Summary of TS-MOEAP 5.4. Complete Algorithm of TS-MOEAP (INSGA-HO/IPSO) 5.5. Main Contributions of the Proposal Chapter 6 Implementation of TS - MOEAP 6.1. Optimal Design of Off-Grid Electrification Projects with DG 6.2. Optimal Condition Restoration of Secondary Distribution Networks 6.3. Optimal Reconfiguration of Primary Distribution Networks 6.4. Implementation of the IPSO-PRIM Algorithm in Android-Java 6.5. Summary Chapter 7 Network Optimization using DER-CAM 7.1. DER-CAM 7.2. Implementation of DER-CAM for the Case Study MVDN2 7.3. Summary Chapter 8 Conclusions and Future Work Appendix A Solar Radiation for the Case Studies CM1 y CM2 Appendix B Diagram of the Urban Area for the Case Studies LVDN1 and LVDN2 Appendix C Active and Reactive Loads for the Test System LVDN1 Appendix D Java Code for the IPSO Algorithm Published Papers Curriculum Vitae ReferencesDoctor of Philosophy In Engineering Scienc