Data-Driven Distribution System Load Modeling for Quasi-Static Time-Series Simulation

Zhu, Xiangqi; Mather, Barry

doi:10.1109/tsg.2019.2940084

Cited by 7 publications

(11 citation statements)

References 17 publications

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“…By leveraging the synthetic load profile generation tool that was developed in [10] and [11], all the aforementioned distribution systems have been equipped with high-resolution, realistic load profiles. Instead of populating each bus with a load profile scaled from the substation load shape based on the transformer rating (i.e.…”

Section: B System Load Modelingmentioning

confidence: 99%

Grid Impact Analysis and Mitigation of En-Route Charging Stations for Heavy-Duty Electric Vehicles

Zhu

Mishra

Mather

et al. 2023

IEEE Open J. Power Energy

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This paper presents a comprehensive grid impact analysis design and corresponding mitigation strategies for heavy-duty electric vehicle (EV) charging stations. The charging load of heavy-duty charging station can reach several megawatts, which could induce adverse impacts on the distribution grid if not effectively mitigated. To analyze the impacts and provide corresponding solutions, we select four representative distribution systems-including both single-feeder cases and a multi-feeder case-and design thorough test metrics for the impact analysis. The charging load profiles used in the analysis are derived from realistic conventional heavy-duty vehicle travel data. Based on the analysis results, charging stations are placed at three different representative locations in each distribution system: best, good, and worst locations. Mitigation strategies using a combination of smart charger functionality, on-site photovoltaic (PV) generation, and on-site energy storage (ES) are proposed and tested. A sizing method is also proposed to find the optimal PV-ES-charger capacity that minimizes the capital cost.

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Section: B System Load Modelingmentioning

confidence: 99%

Grid Impact Analysis and Mitigation of En-Route Charging Stations for Heavy-Duty Electric Vehicles

Zhu

Mishra

Mather

et al. 2023

IEEE Open J. Power Energy

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“…The voltage support and regulation for this system comes from eight devices, including four voltage regulators and four capacitor banks. Instead of simply allocating the substation load onto each load bus with the same load shape inherited from the substation load profile, diversified load profiles are populated to the load buses to guarantee that each load bus has a unique load shape [15]. This helps maintain the load diversity factor of this test system within a reasonable and realistic range, which helps simulate a more realistic environment for the MVB2B converter application testing.…”

Section: A System Setupmentioning

confidence: 99%

Grid Application and Controls Development for Medium-Voltage SiC-Based Grid Interconnects

Singh

Zhu

Mather

et al. 2020

2020 IEEE Energy Conversion Congress and Exposition (ECCE)

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“…Specifically, we compare the distributions of bus voltages and voltage ramps obtained from power flow studies based on the actual and enriched high-resolution load data, respectively. The reason for assessing voltage ramp is that voltage ramp is significant for renewable energy integration [6]. The voltage ramp V is defined as the difference between the current voltage value and the last voltage value.…”

Section: Performing Time-series Power Flow Studiesmentioning

confidence: 99%

“…It is of significance to compare the performance of our approach with other methods presented in previous works. We primarily focus on comparing our approach with an allocationbased methodology introduced in [6]. The performance of these two approaches is shown in Fig.…”

Section: E Performance Comparisonmentioning

confidence: 99%

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Enriching Load Data Using Micro-PMUs and Smart Meters

Dehghanpour

Wang

2020

Preprint

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In modern distribution systems, load uncertainty can be fully captured by micro-PMUs, which can record highresolution data; however, in practice, micro-PMUs are installed at limited locations in distribution networks due to budgetary constraints. In contrast, smart meters are widely deployed but can only measure relatively low-resolution energy consumption, which cannot sufficiently reflect the actual instantaneous load volatility within each sampling interval. In this paper, we have proposed a novel approach for enriching load data for service transformers that only have low-resolution smart meters. The key to our approach is to statistically recover the high-resolution load data, which is masked by the low-resolution data, using trained probabilistic models of service transformers that have both highand low-resolution data sources, i.e, micro-PMUs and smart meters. The overall framework consists of two steps: first, for the transformers with micro-PMUs, a Gaussian Process is leveraged to capture the relationship between the maximum/minimum load and average load within each low-resolution sampling interval of smart meters; a Markov chain model is employed to characterize the transition probability of known high-resolution load. Next, the trained models are used as teachers for the transformers with only smart meters to decompose known low-resolution load data into targeted high-resolution load data. The enriched data can recover instantaneous load uncertainty and significantly enhance distribution system observability and situational awareness. We have verified the proposed approach using real high-and lowresolution load data.

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Data-Driven Distribution System Load Modeling for Quasi-Static Time-Series Simulation

Cited by 7 publications

References 17 publications

Grid Impact Analysis and Mitigation of En-Route Charging Stations for Heavy-Duty Electric Vehicles

Grid Impact Analysis and Mitigation of En-Route Charging Stations for Heavy-Duty Electric Vehicles

Grid Application and Controls Development for Medium-Voltage SiC-Based Grid Interconnects

Enriching Load Data Using Micro-PMUs and Smart Meters

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