Hassan Zahid Butt

Hassan Zahid Butt, Xingpeng Li

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.

Rida Fatima, Hassan Zahid Butt, Xingpeng Li

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.

Hassan Zahid Butt, Xingpeng Li

The growing availability of second-life batteries (SLBs) from electric vehicles is reshaping future microgrid design, requiring planning frameworks that explicitly account for reduced capacity and efficiency over time. However, traditional microgrid planning models often neglect degradation effects or rely on highly simplified formulations, leading to unreliable sizing decisions and increased long-term costs. This paper proposes a degradation-aware iterative optimization framework for long-term microgrid planning that incorporates photovoltaic efficiency fading, battery capacity and efficiency degradation, and SLB characteristics. A cumulative multi-year optimization model is first solved to obtain an initial investment and operational strategy under simplified degradation assumptions, ensuring computational tractability. Subsequently, a yearly validation model evaluates degradation impacts on photovoltaic and battery assets, updating efficiencies and available capacity to assess reliability. An iterative refinement process then adjusts resource allocation to eliminate load shedding while minimizing total system cost. Sensitivity analyses on photovoltaic degradation rates, SLB capital costs, and grid tariffs are conducted to evaluate robustness under varying technical and economic conditions. Results demonstrate that neglecting degradation can compromise reliability and increase blackout risk, while SLBs offer meaningful cost-saving opportunities. The proposed framework provides a scalable and practical tool for planning future microgrids in degradation-constrained environments.

Hassan Zahid Butt, Xingpeng Li

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.