Evaluating Network Robustness for Electric Vehicle Charging Infrastructure in Qatar: A Dominating Set Approach

الموضوعات

علوم تطبيقية وتكنولوجية

الملخص

This thesis addresses the robust placement of electric vehicle (EV) charging stations as a covering problem that must remain effective under adverse conditions such as component failures or demand surges. A robust coverage model is formulated as the k-Dominating Set Problem (k-DSP), which ensures that at least k charging stations serve each demand node. This built-in redundancy enhances network resilience so that even if a station fails or experiences an unexpected surge in usage, drivers can still find service nearby. Solving the k-DSP to optimality is NP-hard, making direct computation intractable for large networks and motivating the need for advanced solution strategies. To tackle this complexity, the thesis proposes a Matheuristic Fixed Set Search (MFSS) approach, which integrates exact mathematical programming techniques with a learning-based Fixed Set Search (FSS) metaheuristic. The MFSS method combines the strengths of mixed-integer programming with iterative refinement: it uses FSS’s learning mechanism to identify promising subsets of charging station locations (the “fixed sets”). It uses mathematical programming to fill in the remaining decisions. The proposed MFSS algorithm is evaluated on standard benchmark instances, and its performance is compared against a classical Greedy Randomized Adaptive Search Procedure (GRASP) heuristic and the commercial solver CPLEX. Computational results demonstrate that MFSS consistently produces quality-competitive solutions with the best-known results from GRASP and CPLEX while achieving these solutions in a significantly reduced computation time. In addition to benchmark testing, the proposed model is applied to a case study on Qatar’s EV charging infrastructure using real-world geographic data. The analysis evaluates multiple robustness levels (k) under varying coverage radii, demonstrating that higher k values significantly enhance fault tolerance with marginal increases in infrastructure cost. These results confirm the effectiveness of the MFSS approach in producing scalable and resilient solutions suited to Qatar’s national EV deployment strategy. The k-DSP model, coupled with the MFSS algorithm, offers a robust planning framework that ensures uninterrupted service availability during station failures or peak demand conditions.

روابط وملفات

التعريف والنوع

رقم الوثيقة

0444337f-2409-4d6c-9c2e-8243ebccfd9a

رقم العقد

0

نوع الوسائط

Crawler

نوع المحتوى

الرسائل العلمية

صيغة المصدر

رسائل ماجيستير

نوع الملف

pdf text

أسماء الملفات

0444337f-2409-4d6c-9c2e-8243ebccfd9a_1.pdf

بيانات النشر

ألقاب المؤلفين

[{"name_ar":" Abdus Samad","title_ar":"اعداد","title_en":"Preparation"},{"name_ar":" Raka Jovanovic","title_ar":"اشراف","title_en":"Supervision"},{"name_ar":"Roberto Baldacci ","title_ar":"اشراف","title_en":"Supervision"}]

اللغة

English

المصدر والدورية

اسم المصدر

Evaluating Network Robustness for Electric Vehicle Charging Infrastructure in Qatar: A Dominating Set Approach

المحتوى والصفحات

عدد الصفحات

0

كلمات الباحثين

Electric vehicle - Matheuristic Fixed Set Search - Fixed Set Search - Qatar

إشراف وإعداد

الإشراف

Raka Jovanovic, Roberto Baldacci

الإعداد

Abdus Samad

الاقتباسات الببليوغرافية