@article{hu_shirsat_muthukaruppan_li_zhang_tang_baran_lu_2024, title={Adaptive cold-load pickup considerations in 2-stage microgrid unit commitment for enhancing microgrid resilience}, volume={356}, ISSN={["1872-9118"]}, DOI={10.1016/j.apenergy.2023.122424}, abstractNote={In an extended main grid outage spanning multiple days, load shedding serves as a critical mechanism for islanded microgrids to maintain essential power and energy reserves that are indispensable for fulfilling reliability and resiliency mandates. However, using load shedding for such purposes leads to increasing occurrence of cold load pickup (CLPU) events. This study presents an innovative adaptive CLPU model that introduces a method for determining and incorporating parameters related to CLPU power and energy requirements into a two-stage microgrid unit commitment (MGUC) algorithm. In contrast to the traditional fixed-CLPU-curve approach, this model calculates CLPU duration, power, and energy demands by considering outage durations and ambient temperature variations within the MGUC process. By integrating the adaptive CLPU model into the MGUC problem formulation, it allows for the optimal allocation of energy resources throughout the entire scheduling horizon to fulfill the CLPU requirements when scheduling multiple CLPU events. The performance of the enhanced MGUC algorithm considering CLPU needs is assessed using actual load and photovoltaic (PV) data. Simulation results demonstrate significant improvements in dispatch optimality evaluated by the amount of load served, customer comfort, energy storage operation, and adherence to energy schedules. These enhancements collectively contribute to reliable and resilient microgrid operation.}, journal={APPLIED ENERGY}, author={Hu, Rongxing and Shirsat, Ashwin and Muthukaruppan, Valliappan and Li, Yiyan and Zhang, Si and Tang, Wenyuan and Baran, Mesut and Lu, Ning}, year={2024}, month={Feb} }
 @article{shirsat_muthukaruppan_hu_paduani_xu_song_li_lu_baran_lubkeman_et al._2023, title={A Secure and Adaptive Hierarchical Multi-Timescale Framework for Resilient Load Restoration Using a Community Microgrid}, volume={14}, ISSN={["1949-3037"]}, url={https://doi.org/10.1109/TSTE.2023.3251099}, DOI={10.1109/TSTE.2023.3251099}, abstractNote={Distribution system integrated community microgrids (CMGs) can partake in restoring loads during extended duration outages. At such times, the CMGs are challenged with limited resource availability, absence of robust grid support, and heightened demand-supply uncertainty. This paper proposes a secure and adaptive three-stage hierarchical multi-timescale framework for scheduling and real-time (RT) dispatch of CMGs with hybrid PV systems to address these challenges. The framework enables the CMG to dynamically expand its boundary to support the neighboring grid sections and is adaptive to the changing forecast error impacts. The first stage solves a stochastic extended duration scheduling (EDS) problem to obtain referral plans for optimal resource rationing. The intermediate near-real-time (NRT) scheduling stage updates the EDS schedule closer to the dispatch time using new obtained forecasts, followed by the RT dispatch stage. To make the decisions more secure and robust against forecast errors, a novel concept called delayed recourse is designed. The approach is evaluated via numerical simulations on a modified IEEE 123-bus system and validated using OpenDSS and hardware-in-loop simulations. The results show superior performance in maximizing load supply and continuous secure distribution network operation under different operating scenarios.}, number={2}, journal={IEEE TRANSACTIONS ON SUSTAINABLE ENERGY}, author={Shirsat, Ashwin and Muthukaruppan, Valliappan and Hu, Rongxing and Paduani, Victor Daldegan and Xu, Bei and Song, Lidong and Li, Yiyan and Lu, Ning and Baran, Mesut and Lubkeman, David and et al.}, year={2023}, month={Apr}, pages={1057–1075} }
 @article{kim_ye_lee_hu_lu_wu_rehm_2023, title={An ICA-Based HVAC Load Disaggregation Method Using Smart Meter Data}, ISSN={["2167-9665"]}, DOI={10.1109/ISGT51731.2023.10066402}, abstractNote={This paper presents an independent component analysis (ICA) based unsupervised-learning method for heat, ventilation, and air-conditioning (HVAC) load disaggregation using low-resolution (e.g., 15 minutes) smart meter data. We first demonstrate that electricity consumption profiles on mild-temperature days can be used to estimate the non-HVAC base load on hot days. A residual load profile can then be calculated by subtracting the mild-day load profile from the hot-day load profile. The residual load profiles are processed using ICA for HVAC load extraction. An optimization-based algorithm is proposed for post-adjustment of the ICA results, considering two bounding factors for enhancing the robustness of the ICA algorithm. First, we use the hourly HVAC energy bounds computed based on the relationship between HVAC load and temperature to remove unrealistic HVAC load spikes. Second, we exploit the dependency between the daily nocturnal and diurnal loads extracted from historical meter data to smooth the base load profile. Pecan Street data with sub-metered HVAC data were used to test and validate the proposed methods. Simulation results demonstrated that the proposed method is computationally efficient and robust across multiple customers.}, journal={2023 IEEE POWER & ENERGY SOCIETY INNOVATIVE SMART GRID TECHNOLOGIES CONFERENCE, ISGT}, author={Kim, Hyeonjin and Ye, Kai and Lee, Han Pyo and Hu, Rongxing and Lu, Ning and Wu, Di and Rehm, P. J.}, year={2023} }
 @article{hu_ye_kim_lee_lu_wu_rehm_2023, title={Design Considerations of a Coordinative Demand Charge Mitigation Strategy}, ISSN={["1944-9925"]}, DOI={10.1109/PESGM52003.2023.10252618}, abstractNote={This paper presents a coordinative demand charge mitigation (DCM) strategy for reducing electricity consumption during system peak periods. Available DCM resources include batteries, diesel generators, controllable appliance loads, and conservation voltage reduction. All resources are directly controlled by load serving entities. A mixed integer linear programming-based energy management algorithm is developed to optimally coordinate DCM resources considering the load payback effect. To better capture system peak periods, two different kinds of load forecast are used: the day-ahead load forecast and the peak-hour probability forecast. Five DCM strategies are compared for reconciling the discrepancy between the two forecasting results. The DCM strategies are tested using actual utility data. Simulation results show that the proposed algorithm can effectively mitigate the demand charge while preventing the system peak from being shifted to the payback hours. We also identify the diminishing return effect, which can help load serving entities optimize the size of their DCM resources.}, journal={2023 IEEE POWER & ENERGY SOCIETY GENERAL MEETING, PESGM}, author={Hu, Rongxing and Ye, Kai and Kim, Hyeonjin and Lee, Hanpyo and Lu, Ning and Wu, Di and Rehm, P. J.}, year={2023} }
 @article{hu_li_zhang_shirsat_muthukaruppan_tang_baran_lubkeman_lu_2021, title={A Load Switching Group based Feeder-level Microgrid Energy Management Algorithm for Service Restoration in Power Distribution System}, ISSN={["1944-9925"]}, DOI={10.1109/PESGM46819.2021.9638231}, abstractNote={This paper presents a load switching group based energy management system (LSG-EMS) for operating microgrids on a distribution feeder powered by one or multiple grid-forming distributed energy resources. Loads on a distribution feeder are divided into load switching groups that can be remotely switched on and off. The LSG-EMS algorithm, formulated as a mixed-integer linear programming (MILP) problem, has an objective function of maximizing the served loads while minimizing the total number of switching actions. A new set of topology constraints are developed for allowing multiple microgrids to be formed on the feeder and selecting the optimal supply path. Customer comfort is accounted for by maximizing the supply duration in the customer preferred service period and enforcing a minimum service duration. The proposed method is demonstrated on a modified IEEE 33-bus system using actual customer data. Simulation results show that the LSG-EMS successfully coordinates multiple grid-forming sources by selecting an optimal supply topology that maximizes the supply period of both the critical and noncritical loads while minimizing customer service interruptions in the service restoration process.}, journal={2021 IEEE POWER & ENERGY SOCIETY GENERAL MEETING (PESGM)}, author={Hu, Rongxing and Li, Yiyan and Zhang, Si and Shirsat, Ashwin and Muthukaruppan, Valliappan and Tang, Wenyuan and Baran, Mesut and Lubkeman, David and Lu, Ning}, year={2021} }
 @article{li_zhang_hu_lu_2021, title={A meta-learning based distribution system load forecasting model selection framework}, volume={294}, ISSN={["1872-9118"]}, DOI={10.1016/j.apenergy.2021.116991}, abstractNote={This paper presents a meta-learning based, automatic distribution system load forecasting model selection framework. The framework includes the following processes: feature extraction, candidate model preparation and labeling, offline training, and online model recommendation. Using load forecasting needs and data characteristics as input features, multiple metalearners are used to rank the candidate load forecast models based on their forecasting accuracy. Then, a scoring-voting mechanism is proposed to weights recommendations from each meta-leaner and make the final recommendations. Heterogeneous load forecasting tasks with different temporal and technical requirements at different load aggregation levels are set up to train, validate, and test the performance of the proposed framework. Simulation results demonstrate that the performance of the meta-learning based approach is satisfactory in both seen and unseen forecasting tasks.}, journal={APPLIED ENERGY}, author={Li, Yiyan and Zhang, Si and Hu, Rongxing and Lu, Ning}, year={2021}, month={Jul} }
 @article{shirsat_muthukaruppan_hu_lu_baran_lubkeman_tang_2021, title={Hierarchical Multi-timescale Framework For Operation of Dynamic Community Microgrid}, ISSN={["1944-9925"]}, DOI={10.1109/PESGM46819.2021.9638104}, abstractNote={Distribution system integrated community microgrids (CMGs) can restore loads during extended outages. The CMG is challenged with limited resource availability, absence of a robust grid-support, and demand-supply uncertainty. To address these challenges, this paper proposes a three-stage hierarchical multi-timescale framework for scheduling and real-time (RT) dispatch of CMGs. The CMG's ability to dynamically expand its boundary to support the neighboring grid sections is also considered. The first stage solves a stochastic day-ahead (DA) scheduling problem to obtain referral plans for optimal resource rationing. The intermediate near real-time scheduling stage updates the DA schedule closer to the dispatch time, followed by the RT dispatch stage. The proposed methodology is validated via numerical simulations on a modified IEEE 123-bus system, which shows superior performance in terms of RT load supplied under different forecast error cases, outage duration scenarios, and against the traditionally used two-stage approach.}, journal={2021 IEEE POWER & ENERGY SOCIETY GENERAL MEETING (PESGM)}, author={Shirsat, Ashwin and Muthukaruppan, Valliappan and Hu, Rongxing and Lu, Ning and Baran, Mesut and Lubkeman, David and Tang, Wenyuan}, year={2021} }