@article{li_song_hu_lee_wu_rehm_lu_2024, title={Load Profile Inpainting for Missing Load Data Restoration and Baseline Estimation}, volume={15}, ISSN={["1949-3061"]}, url={https://doi.org/10.1109/TSG.2023.3293188}, DOI={10.1109/TSG.2023.3293188}, abstractNote={This paper introduces a Generative Adversarial Nets (GAN) based, Load Profile Inpainting Network (Load-PIN) for restoring missing load data segments and estimating the baseline for a demand response event. The inputs are time series load data before and after the inpainting period together with explanatory variables (e.g., weather data). We propose a Generator structure consisting of a coarse network and a fine-tuning network. The coarse network provides an initial estimation of the data segment in the inpainting period. The fine-tuning network consists of self-attention blocks and gated convolution layers for adjusting the initial estimations. Loss functions are specially designed for the fine-tuning and the discriminator networks to enhance both the point-to-point accuracy and realisticness of the results. We test the Load-PIN on three real-world data sets for two applications: patching missing data and deriving baselines of conservation voltage reduction (CVR) events. We benchmark the performance of Load-PIN with five existing deep-learning methods. Our simulation results show that, compared with the state-of-the-art methods, Load-PIN can handle varying-length missing data events and achieve 15%-30% accuracy improvement.}, number={2}, journal={IEEE TRANSACTIONS ON SMART GRID}, author={Li, Yiyan and Song, Lidong and Hu, Yi and Lee, Hanpyo and Wu, Di and Rehm, P. J. and Lu, Ning}, year={2024}, month={Mar}, pages={2251–2260} }
 @article{hu_li_song_lee_rehm_makdad_miller_lu_2024, title={MultiLoad-GAN: A GAN-Based Synthetic Load Group Generation Method Considering Spatial-Temporal Correlations}, volume={15}, ISSN={["1949-3061"]}, url={https://doi.org/10.1109/TSG.2023.3302192}, DOI={10.1109/TSG.2023.3302192}, abstractNote={This paper presents a deep-learning framework, Multi-load Generative Adversarial Network (MultiLoad-GAN), for generating a group of synthetic load profiles (SLPs) simultaneously. The main contribution of MultiLoad-GAN is the capture of spatial-temporal correlations among a group of loads that are served by the same distribution transformer. This enables the generation of a large amount of correlated SLPs required for microgrid and distribution system studies. The novelty and uniqueness of the MultiLoad-GAN framework are three-fold. First, to the best of our knowledge, this is the first method for generating a group of load profiles bearing realistic spatial-temporal correlations simultaneously. Second, two complementary realisticness metrics for evaluating generated load profiles are developed: computing statistics based on domain knowledge and comparing high-level features via a deep-learning classifier. Third, to tackle data scarcity, a novel iterative data augmentation mechanism is developed to generate training samples for enhancing the training of both the classifier and the MultiLoad-GAN model. Simulation results show that MultiLoad-GAN can generate more realistic load profiles than existing approaches, especially in group level characteristics. With little finetuning, MultiLoad-GAN can be readily extended to generate a group of load or PV profiles for a feeder or a service area.}, number={2}, journal={IEEE TRANSACTIONS ON SMART GRID}, author={Hu, Yi and Li, Yiyan and Song, Lidong and Lee, Han Pyo and Rehm, P. J. and Makdad, Matthew and Miller, Edmond and Lu, Ning}, year={2024}, month={Mar}, pages={2309–2320} }
 @article{lee_dsouza_chen_lu_baran_2023, title={Adopting Dynamic VAR Compensators to Mitigate PV Impacts on Unbalanced Distribution Systems}, volume={11}, ISSN={["2169-3536"]}, url={https://doi.org/10.1109/ACCESS.2023.3315601}, DOI={10.1109/ACCESS.2023.3315601}, abstractNote={The growing integration of distributed energy resources into distribution systems poses challenges for voltage regulation. Dynamic VAR Compensators (DVCs) are a new generation of power electronics-based Volt/VAR compensation devices designed to address voltage issues in distribution systems with a high penetration of renewable generation resources. Currently, the IEEE Std. 1547-based Volt/VAR Curve (VV-C) is widely used as the local control scheme for controlling a DVC. However, the effectiveness of this scheme is not well documented, and there is limited literature on alternative control and placement schemes that can maximize the effective use of a DVC. In this paper, we propose an optimal dispatch and control mechanism to enhance the conventional VV-C based localized DVC control. First, we establish a multi-objective optimization framework to identify the optimal dispatch strategy and suitable placement for the DVC. Next, we introduce two supervisory control strategies to determine the appropriate instances for adjusting the VV-C when the operating condition changes. The outlined scheme comprises two primary stages: time segmentation and VV-C fitting. Within this framework, each time segment aims to produce optimized Q-V trajectories. The proposed method is tested on a modified IEEE 123-bus test system using OpenDSS for a wide range of operating scenarios, including sunny and cloudy days. Simulation results demonstrate that the proposed scheme effectively reduces voltage variations compared to the standard VV-C specified in IEEE Std. 1547.}, journal={IEEE ACCESS}, author={Lee, Han Pyo and Dsouza, Keith and Chen, Ke and Lu, Ning and Baran, Mesut E.}, year={2023}, pages={101514–101524} }
 @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{lee_song_li_lu_wu_rehm_makdad_miller_2023, title={An Iterative Bidirectional Gradient Boosting Algorithm for CVR Baseline Estimation}, ISSN={["1944-9925"]}, DOI={10.1109/PESGM52003.2023.10252215}, abstractNote={This paper presents a novel iterative, bidirectional, gradient boosting (bidirectional-GB) algorithm for estimating the baseline of the Conservation Voltage Reduction (CVR) program. We define the CVR baseline as the load profile during the CVR period if the substation voltage is not lowered. The proposed algorithm consists of two key steps: selection of similar days and iterative bidirectional-GB training. In the first step, preand post-event temperature profiles of the targeted CVR day are used to select similar days from historical non-CVR days. In the second step, the pre-event and post-event similar days are used to train two GBMs iteratively: a forward-GBM and a backwardGBM. After each iteration, the two generated CVR baselines are reconciled and only the first and the last points on the reconciled baseline are kept. The iteration repeats until all CVR baseline points are generated. We tested two gradient boosting methods (i.e., GBM and LighGBM) with two data resolutions (i.e., 15and 30-minute). The results demonstrate that both the accuracy and performance of the algorithm are satisfactory.}, journal={2023 IEEE POWER & ENERGY SOCIETY GENERAL MEETING, PESGM}, author={Lee, Han Pyo and Song, Lidong and Li, Yiyan and Lu, Ning and Wu, Di and Rehm, P. J. and Makdad, Matthew and Miller, Edmond}, 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} }