Weather Data for Energy Analytics: From Modeling Outages and Reliability Indices to Simulating Distributed Photovoltaic Fleets

Black, Jonathan; Hoffman, Alex; Hong, Tao; Roberts, Joseph N.; Wang, Pu

doi:10.1109/mpe.2018.2801442

Cited by 19 publications

(8 citation statements)

References 3 publications

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“…In this way, the uncertainties of both future temperature and the relationship between temperature and load can be comprehensively considered. The scenario generation method was also used to develop a probabilistic view of power distribution system reliability indices [132].…”

Section: Probabilistic Forecastingmentioning

confidence: 99%

“…It can address the challenges of missing data, multivariate count data, and variable selection. How to use data analytics method to model the outages and reliability indices from weather data was discussed in [132]. Apart from the data analytics method for outage management, more works on smart meter data-based outage managements have been adopted to the corresponding communication architectures [175] [176].…”

Section: B Outage Managementmentioning

confidence: 99%

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Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges

Wang

Chen

Hong

et al. 2019

IEEE Trans. Smart Grid

Self Cite

881

370

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The widespread popularity of smart meters enables an immense amount of fine-grained electricity consumption data to be collected. Meanwhile, the deregulation of the power industry, particularly on the delivery side, has continuously been moving forward worldwide. How to employ massive smart meter data to promote and enhance the efficiency and sustainability of the power grid is a pressing issue. To date, substantial works have been conducted on smart meter data analytics. To provide a comprehensive overview of the current research and to identify challenges for future research, this paper conducts an application-oriented review of smart meter data analytics. Following the three stages of analytics, namely, descriptive, predictive and prescriptive analytics, we identify the key application areas as load analysis, load forecasting, and load management. We also review the techniques and methodologies adopted or developed to address each application. In addition, we also discuss some research trends, such as big data issues, novel machine learning technologies, new business models, the transition of energy systems, and data privacy and security.

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Section: Probabilistic Forecastingmentioning

confidence: 99%

Section: B Outage Managementmentioning

confidence: 99%

Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges

Wang

Chen

Hong

et al. 2019

IEEE Trans. Smart Grid

Self Cite

881

370

View full text Add to dashboard Cite

show abstract

“…Many works, both theoretical and experimental, have been devoted to this subject. In the subject literature, very interesting papers [19,20] should be mentioned. Many of the current works focus on issues related to the integration of SM with building integrated photovoltaic in various climatic and environmental conditions [21,22] or to remote monitoring of the operation of a rural network [19].…”

Section: Advanced Metering Infrastructure (Ami) As a Factor Increasing The Reliability Of The LV Gridmentioning

confidence: 99%

Advanced Metering Infrastructure—Towards a Reliable Network

Kornatka¹,

Popławski²

2021

Energies

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In order to ensure continuous energy supply, Distribution System Operators (DSOs) have to monitor and analyze the condition of the power grid, especially checking for random events, such as breakdowns or other disturbances. Still, relatively little information is available on the operation of the Low Voltage (LV) grid. This can be improved thanks to digital tools, offering online processing of data, which ultimately increases effectiveness of the power grid. Among those tools, the use of the Advanced Metering Infrastructure (AMI) is especially conducive for improving reliability. AMI is one of the elements of the system Supervisory Control and Data Acquisition (SCADA) for the LV grid. Exact knowledge of the reliability conditions of a power grid is also indispensable for optimizing investment. AMI is also key in providing operational capacity for carrying out energy balance in virtual power plants (VPPs). This paper deals with methodology of identification and location of faults in the AMI-supervised LV grid and with calculating the System Average Interruption Duration Index (SAIDI) and System Average Interruption Frequency Index (SAIFI) on the basis of the recorded events. The results presented in the paper are based on data obtained from seven MV/LV transformer stations that supply over 2000 customers.

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“…e concentrator summarizes the information uploaded by the various measurement and control points of the whole school and uploads it to the server through the local area network. At the same time, the various commands downloaded from the master control computer are transmitted to the metering control cabinet through Ethernet/GSM, and the master control computer and the acquisition control unit completed communication and data transmission [20].…”

Section: Intelligent Control System For Testing Equipment In Desertmentioning

confidence: 99%

Intelligent Control System in Desert Areas Based on Photovoltaic Microgrid Power Supply

Liu

2021

Mobile Information Systems

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With the advent of the global energy crisis, the use of sustainable green energy has become more and more widespread and the utilization rate of photovoltaic industry in high-altitude desert areas is getting higher and higher. This article mainly studies the intelligent control system in desert area based on photovoltaic microgrid power supply. The system uses shielded twisted pair to transmit signals, and electrostatic interference and electromagnetic induction interference are effectively suppressed. After ensuring that the encoder feedback is correct and the motor movement direction is consistent with the command direction and the safety of the movement stroke, the PID parameters of the motor are tuned in PAMCturningpro. For the performance test of this system, the design and implementation of the test plan should be carried out according to the concurrent support target and response time target proposed in the performance analysis of the system. Concurrent users use the LoadRunner tool to create the customer test machine to simulate the concurrent access and operation of the system. In each iteration process, the test results are recorded in the same way as the benchmark test and finally the system can still achieve the expected performance when the actual concurrency reaches the limit of 5007. The results show that the intelligent control system in this paper has the advantages of low cost, convenient operation, perfect function, high precision, stable and reliable operation, etc. and has good practical value.

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Weather Data for Energy Analytics: From Modeling Outages and Reliability Indices to Simulating Distributed Photovoltaic Fleets

Cited by 19 publications

References 3 publications

Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges

Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges

Advanced Metering Infrastructure—Towards a Reliable Network

Intelligent Control System in Desert Areas Based on Photovoltaic Microgrid Power Supply

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