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Hassan Zahid Butt

Publications and source records attributed to Hassan Zahid Butt.

9 recordsLinked to original sources

Coordinated Conservation Voltage Reduction and Network Topology Reconfiguration in Active Distribution Networks

Distribution networks rely on conservation voltage reduction (CVR) and network topology reconfiguration (NTR) to manage demand, distributed energy resources (DERs), and tighter operating margins. Yet these strategies are typically deployed independently, despite coupled effects on feeder voltages, losses, and loading. This paper addresses that gap by developing a day-ahead mixed-integer second order cone programming (MISOCP) framework that jointly optimizes feeder head voltage control and network reconfiguration. The formulation incorporates AC branch flow constraints, ZIP load models, DERs, battery energy storage systems, and multi substation radiality. Using modified IEEE 33 bus and 123 bus systems, four configurations isolate the individual and coordinated effects of CVR and NTR. On the 33-bus system under mixed ZIP loading, NTR reduces active energy loss by 2.50%, while coordinated operation reduces it by 2.91% relative to SDN and produces a 2.46% CVR induced active load reduction relative to SDNTR. Under constant impedance loading, coordinated operation reduces active energy loss by 17.65% relative to SDN, while CVR reduces active load by 14.61% relative to SDNTR. DER portfolio scaling shows a nonmonotonic coordinated load reduction response, strongest at 20% penetration and decreasing at higher levels. On the 123 bus system, NTR reduces losses by 19.3%, while CVR reduces voltage dependent load energy by 2.3 to 2.4%. Under loading stress, SDN becomes infeasible at 120% loading, whereas CEDNTR remains feasible. Overall, the results demonstrate complementary, operating condition dependent benefits shaped by load composition, DER portfolio scaling, feeder topology, voltage margins, and source constraints, highlighting the value of joint optimization.

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Assessing Modeling Fidelity for Long-Term Battery Energy Storage Planning: Operation, Degradation, and Temporal Representation

Long-term battery energy storage system (BESS) planning often relies on simplified degradation, operational, and temporal representations to maintain computational tractability, yet their effects on lifecycle conclusions are not well understood. This paper assesses the modeling fidelity needed for long-term lifecycle evaluation of BESS designs used in planning studies. A 20-year grid-connected microgrid is sized using a degradation-naive planning model, after which the installed portfolio is fixed and evaluated through sequential lifecycle validation. The reference representation combines nonlinear calendar and cycle aging, C-rate-dependent efficiencies, state-of-health-dependent performance, self-discharge, battery replacement, and full 8,760-h chronology. Battery-model hierarchies, targeted ablations, linear degradation surrogates, health-update intervals, temporal reductions, and combined simplifications are compared using lifecycle cost, replacement timing, state of health, and energy adequacy. The reference case produces replacements in years 9 and 18, a $111.25 million lifecycle net present cost, and 24.01 MWh of cumulative energy not served. Omitting calendar aging eliminates both replacements and understates lifecycle cost by 35.1%, whereas a separately calibrated linear surrogate model reproduces both replacement years and limits the cost deviation to 0.2%, although energy not served remains 31.8% below the reference. A peak-informed calibrated 12-day representation preserves replacement timing but reports zero energy not served, while replacing the preserved peak day with the maximum daily-energy-deficit day substantially overstates energy not served because representative-day closure alters the battery state surrounding the critical event. The results show that modeling fidelity is metric-dependent and should be selected according to the lifecycle outcome to be preserved.

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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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Degradation-Aware Lifecycle Planning of Microgrids with Second-Life Batteries through Reliability-Guided Refinement

Second-life batteries (SLBs) can reduce storage investment in microgrids, but their lower initial state of health and round-trip efficiency (RTE) can make degradation-naive sizing unreliable over long horizons. This paper develops DAVIR-MG, a degradation-aware validation and investment refinement framework for microgrid planning. A 20-year mixed-integer linear planning model determines installed capacities of dispatchable generation (DG), photovoltaic (PV) generation, and a battery energy storage system (BESS) using 365 days of hourly data. The portfolio is assessed through sequential lifecycle validation that accounts for PV degradation, battery capacity fade, RTE decline, and replacement. When validation identifies degradation-induced energy not served (ENS) above the prescribed tolerance, technology-specific refinement options are evaluated and compared using validated lifecycle cost. The framework is evaluated across tariff structures, PV degradation rates, new-BESS and SLB cases, degradation-mechanism ablation, technology-specific reliability refinements, Cost-ENS trade-offs, and SLB break-even conditions. Across seven cases, validated net present cost (NPC) exceeded planning NPC by 3.0%-11.6%, while adequacy shortfalls emerged only after year 13. In the selected SLB case, capacity fade alone accounted for 76.4% of cumulative ENS, RTE decline alone for 10.7%, and their interaction for the remaining 12.9%. BESS and DG refinements restored adequacy at nearly identical lifecycle cost but through different technical pathways. Under equal-reliability sizing, the minimum SLB discount required for cost parity increased from 22.9% to 35.7% when future replacement was priced at the new-BESS cost. DAVIR-MG provides a practical, industry-relevant, and computationally manageable framework for identifying degradation-induced adequacy risks and supporting informed lifecycle investment decisions.

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Optimal Assembly of Repurposed Lithium-Ion Battery Packs under Cell Heterogeneity and Screening Uncertainty

The growing supply of retired electric vehicle batteries presents an opportunity for second-life stationary energy storage, but assembling heterogeneous retired cells into reliable packs is challenging due to substantial variation in capacity, DC internal resistance (DCIR), and self-discharge. This paper proposes a robust optimization framework for cell-to-pack assembly of second-life batteries. A topology-screening stage first identifies minimum-cell series-parallel configurations satisfying inverter and energy requirements, reducing the dimensionality of the subsequent assignment problem. For each candidate topology, a mixed-integer linear program selects cells and assigns them along the series string, enforcing power, voltage, and energy requirements as hard constraints while minimizing a normalized, weighted sum of DCIR spread, capacity spread, and self-discharge imbalance. Additionally, measurement uncertainty in capacity and DCIR is modeled as bounded intervals to guarantee feasibility under worst-case parameter deviations. The framework is evaluated on four heterogeneous inventories for a 10 kW/10 kWh stationary backup application. The proposed method satisfies all feasibility requirements in every case, while single-metric sorting heuristics each fail on at least one inventory. Relative to the best single-metric baseline by objective value, it reduces the normalized mismatch objective by 76-87%, demonstrating that jointly optimizing cell matching with application-level feasibility requirements improves heterogeneous second-life pack assembly under screening uncertainty.

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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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Approximating CCCV charging using SOC-dependent tapered charging power constraints in long-term microgrid planning

Traditional long-term microgrid planning models assume constant power charging for battery energy storage systems (BESS), overlooking efficiency losses that occur toward the end of charge due to rising internal resistance. While this issue can be mitigated at the cell level using constant current-constant voltage (CCCV) charging, it is impractical at the pack level in large-scale systems. However, battery management systems and inverter controls can emulate this effect by tapering charging power at high state-of-charge (SOC) levels, trading off charging speed for improved efficiency and reduced thermal stress. Ignoring this behavior in planning models can lead to undersized batteries and potential reliability issues. This paper proposes a tractable and scalable approach to approximate CCCV behavior using SOC-dependent tapered charging power (TCP) constraints. A MATLAB-based proof of concept demonstrates the energy delivery and efficiency benefits of tapering. The method is integrated into a long-term planning framework and evaluated under a synthetic load and solar profile. Results show tapering significantly affects BESS sizing, cost, and reliability under dynamic operating conditions that demand fast charging. These findings highlight tapering as a critical modeling factor for accurately capturing BESS performance in long-term microgrid planning.

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Enhancing Optimal Microgrid Planning with Adaptive BESS Degradation Costs and PV Asset Management: An Iterative Post-Optimization Correction Framework

The transition to renewable energy has positioned photovoltaic (PV) systems and battery energy storage systems (BESS) as essential assets in microgrids, particularly for remote installations. However, traditional planning models often neglect dynamic degradation costs or rely on complex or non-linear approaches, limiting their scalability and practical applicability. This paper introduces a microgrid planning model that integrates adaptive degradation cost modeling to enable accurate, efficient, and scalable long-term resource allocation. The proposed model employs the iterative post-optimization correction (IPOC) framework, solving a sequence of mixed-integer linear programming problems. Each iteration refines BESS degradation costs based on observed depth-of-discharge profiles and incorporates PV degradation costs to ensure realistic asset performance assessments. Sensitivity analysis of PV and BESS capital costs further underscores the model's robustness under varying economic conditions, with the IPOC framework achieving up to ~1% additional cost savings for the given test system compared to static approaches. The results demonstrate that by iteratively adjusting degradation penalties based on actual usage, the methodology optimizes BESS performance, ensures precise resource allocation, resolves issues of under- or overutilization, enhances system reliability, and facilitates scalable, sustainable microgrid planning.

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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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