Electricity consumption pattern recognition based on the big data technology to support the peak shifting potential analysis

Zhang, Xin; Li, Donghua; Cheng, Ming; Zhang, Pei

doi:10.1109/appeec.2014.7066049

Cited by 6 publications

(2 citation statements)

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“…The essence of user potential evaluation is to investigate their load characteristics. At present, the criterion‐based and data‐driven approaches are used to evaluate the user load for ESS investment [24]. The former is fast but oversimplified while the latter requires a lot of historical data profiles and computation resources.…”

Section: The Short‐term Arbitrage Potential Evaluation and Multiple T...mentioning

confidence: 99%

Research on potential user identification and optimal planning of the multiple time scale cloud‐based location sharing energy storage

Jiang

Dong

et al. 2023

IET Generation Trans & Dist

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Cloud energy storage is considered a promising application in future power systems. It focuses on optimally leveraging the capacity of centralized large-scale energy storage compared with the requirements of small-scale localized users. In this paper, to satisfy the small-and medium-scale timely energy storage requirement from localized users, the concept of the cloud-based location sharing energy storage is proposed. The modular mobile energy storage system is flexibly configured and deployed at different sites to fulfil the long-term seasonally dynamic transformer capacity increment and short-term daily energy arbitrage based on economic values. To optimize the overall incomes of the energy storage investment, a two-step user potential identification algorithm is proposed to discover the most valuable users at different time scales from the regional power usage profile. Then, the number/capacity optimal planning algorithm is proposed to optimally share the mobile energy storage system among users seasonally and total profits are further increased through daily energy arbitrage at less valuable seasons. Finally, the structure and dispatching strategy of the cloud-based location sharing energy storage management system is illustrated. Case study results prove that the proposed identification algorithm can excavate the most valuable users at different time scales and the optimal planning and operation strategy is able to guarantee the overall income benefits.

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Section: The Short‐term Arbitrage Potential Evaluation and Multiple T...mentioning

confidence: 99%

Research on potential user identification and optimal planning of the multiple time scale cloud‐based location sharing energy storage

Jiang

Dong

et al. 2023

IET Generation Trans & Dist

View full text Add to dashboard Cite

show abstract

“…In Ref. [17], the authors used big data technology to recognize the patterns of electricity consumption by consumers. They further shifted the peak load by studying the consumption patterns.…”

Section: Related Workmentioning

confidence: 99%

A Big Data Approach for Demand Response Management in Smart Grid Using the Prophet Model

2022

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Smart Grids (SG) generate extensive data sets regarding the system variables, viz., and demand and supply. These extremely large data sets are known as big data. Hence, preprocessing of this vast data and integration become critical steps in the load forecasting process. The precise prediction of the load is the primary concern while balancing the demand and supply in SG. Many techniques were devised for load forecasting using machine learning methods such as Deep-learning Models. However, in the case of large data sets, only a few models provide good performance, viz. Autoregressive Integrated Moving Average (ARIMA). However, this approach is complex, as it takes a minimum of 50 observations to make an evaluation. In this paper, the Prophet technique is used in the prediction of future demand response based on the past data, which is in the form of a time series. This technique is valid even if a few values in the time series are not available. Furthermore, the procedure is not affected by fluctuations, trends, and abnormal variations. The automatic model fitting approach is adopted for its effective performance. Further, ARIMA and Prophet model have been used to forecast and the approach is verified using various evaluation metrics. The demand response management was achieved and is being validated with two data sets. The results show the effectiveness of the Prophet model in the demand response management scheme involving large data sets.

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Data-Driven Optimization Model for Power Grid Planning Based on Peak Load Shifting Strategy

Zeng,

Chen,

Feng

et al. 2022

Proceedings of the Sixteenth International Conference on Management Science and Engineering Management – Volume 1

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Electricity consumption pattern recognition based on the big data technology to support the peak shifting potential analysis

Cited by 6 publications

References 1 publication

Research on potential user identification and optimal planning of the multiple time scale cloud‐based location sharing energy storage

Research on potential user identification and optimal planning of the multiple time scale cloud‐based location sharing energy storage

A Big Data Approach for Demand Response Management in Smart Grid Using the Prophet Model

Data-Driven Optimization Model for Power Grid Planning Based on Peak Load Shifting Strategy

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