arXiv subjects

Rida Fatima

Publications and source records attributed to Rida Fatima.

4 recordsLinked to original sources

arXiv2026

Minimizing Grid Interconnection Capacity Requirements for AI Data Centers: A Developer-Side Planning Framework with Onsite Resources and Workload Flexibility

Securing grid interconnection capacity has become a bottleneck for AI data center projects and can take longer than constructing the facilities themselves. This mismatch can delay deployment for years, making early interconnection planning essential. This paper develops ICP-AI, an interconnection capacity planning framework from a data center developer's perspective. The framework minimizes grid import capacity under a prescribed onsite investment budget while jointly sizing photovoltaic (PV) and battery energy storage system (BESS) resources and scheduling deadline constrained workload flexibility. A secondary refinement fixes the minimum grid capacity and selects the minimum-investment PV-BESS portfolio among solutions that achieve that capacity. The framework is evaluated using monthly composite stress profiles across varying temporal assumptions, load shapes, flexible load fractions, and deferral windows. Results show that interconnection capacity reduction depends strongly on the planning environment: at a $100M budget, it is about 6% for the high load factor baseline, exceeds 10% under monthly average solar availability, and reaches 13.3% for a more diurnal load. At a $10M budget, 5% flexible load with a 1 h workload deferral window reduces BESS capacity from 15.30 to 4.87 MWh while increasing capacity reduction from 4.43% to 4.84%. To test sensitivity to temporal compression, the model is also solved over the full 8,760 h chronology, which preserves the main capacity and flexibility trends. Overall, ICP-AI quantifies the interconnection capacity and infrastructure substitution value of workload flexibility, providing an investment-interconnection frontier to support capital allocation and early project planning in constrained grid environments.

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arXiv2026

Optimal Loss Reduction in Distribution Networks Using Conservation Voltage Reduction and Network Topology Reconfiguration

Conservation voltage reduction (CVR) and network topology reconfiguration (NTR) are widely employed to improve distribution system performance; however, existing approaches largely treat them independently, overlooking their coupled impact on load demand, voltage profiles, and power flow distribution, thereby limiting their overall effectiveness. This paper proposes a coordinated optimization framework for day-ahead operational planning of distribution networks, integrating CVR and NTR to enhance overall network efficiency and reduce active power losses in radial distribution networks. The problem is formulated as a mixed-integer conic programming model incorporating AC power flow constraints, voltage-dependent load representation, and radiality constraints. CVR is implemented to achieve load reduction through coordinated voltage control, while NTR redistributes line loading via optimal switching of controllable branches. The proposed framework is validated on the IEEE 33 and 123-bus distribution systems under varying load conditions. Results demonstrate that the coordinated approach consistently outperforms independent strategies, achieving up to 20.6% reduction in active power losses while maintaining voltage compliance and improving branch loading uniformity. These findings confirm that coordinated optimization provides an effective and scalable solution for enhancing efficiency in modern distribution networks.

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arXiv2025

A Reliability-Cost Optimization Framework for EV and DER Integration in Standard and Reconfigurable Distribution Network Topologies

The rapid growth of electric vehicle (EV) adoption poses operational and economic challenges for power distribution systems, including increased line loading levels and network congestions. This may require potential infrastructure reinforcement and expansion. As a fast inexpensive alternative solution, network topology reconfiguration (NTR) offers a practical means to redistribute power flows, reduce operational costs, and defer infrastructure upgrades. This paper presents a linear programming framework to evaluate the impact of varying EV penetration on operational costs under four configurations: standard distribution network (SDN), SDN with NTR (SDNTR), SDN with distributed energy resources (SDN-DER), and SDNTR with DERs (SDNTR-DER). Numerical simulations are conducted on the IEEE 33-bus system. The analysis demonstrates that integrating DERs reduces operational costs, while NTR further enhances system flexibility, enabling higher EV penetration levels without compromising feasibility. The combined SDNTR-DER approach offers the most cost-effective and reliable pathway for accommodating future EV growth while mitigating the need for immediate infrastructure upgrades.

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arXiv2023

Optimal Dynamic Reconfiguration of Distribution Networks

The aim of distribution networks is to meet their local area power demand with maximum reliability. As the electricity consumption tends to increase every year, limited line thermal capacity can lead to network congestion. Continuous development and upgradation of the distribution network is thus required to meet the energy demand, which poses a significant increase in cost. The objective of this research is to analyze distribution network topologies and introduce a topology reconfiguration scheme based on the cost and demand of electricity. Traditional electrical distribution networks are static and inefficient. To make the network active, an optimal dynamic network topology reconfiguration (DNTR) is proposed to control line switching and reconnect some loads to different substations such that the cost of electricity can be minimized. The proposed DNTR strategy was tested on a synthetic radial distribution network with three substations each connecting to an IEEE 13-bus system. Simulation results demonstrated significant cost saving in daily operations of this distribution system.

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