A multi-timescale smart grid energy management system based on adaptive dynamic programming and Multi-NN Fusion prediction method

The complexity of power grids, the intermittent renewable energy generation and the uncertainty of load consumption bring great challenges to modern energy management systems (EMSs). To solve the energy optimization problem in the time-varying smart grid, this paper proposes a multi-timescale EMS ba...

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Vydané v:Knowledge-based systems Ročník 241; s. 108284
Hlavní autori: Yuan, Jun, Zhang, Guidong, Yu, Samson S., Chen, Zhe, Li, Zhong, Zhang, Yun
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Amsterdam Elsevier B.V 06.04.2022
Elsevier Science Ltd
Predmet:
Adaptive dynamic programming
Algorithms
Classification
Clustering
Consumption
Convergence
Dynamic programming
Electric power grids
Electrical loads
Energy
Energy management
Energy management system
Energy management systems
Hardware-in-loop
Intermittent
Management
Multi-neural network fusion prediction algorithm
Neural networks
Optimization
Power
Power consumption
Predictions
Renewable energy
Renewable resources
Smart grid
Time
Uncertainty
Adaptive dynamic programming
Energy management system
Multi-neural network fusion prediction algorithm
Hardware-in-loop
ISSN:0950-7051, 1872-7409
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Popis
Shrnutí:The complexity of power grids, the intermittent renewable energy generation and the uncertainty of load consumption bring great challenges to modern energy management systems (EMSs). To solve the energy optimization problem in the time-varying smart grid, this paper proposes a multi-timescale EMS based on the adaptive dynamic programming (ADP) algorithm and multi-neural-network fusion (MNNF) prediction technology. In detail, according to different power consumption characteristics, this paper uses fuzzy C-means (FCM) clustering algorithm to classify power users into industrial users, commercial users and residential users. Based on the classification results, an MNNF prediction method is proposed that can integrate different influencing factors to predict load consumption and renewable energy generation. Then a multi-timescale ADP optimization algorithm is proposed to maximize the utilization of renewable energy on daily, intra-day and real-time (i.e., three timescales) of energy behavior. The convergence of the multi-timescale ADP algorithm is proved mathematically when the initial value is a random semi-positive definite function. Finally, the proposed ADP with MNNF energy management system is verified on a hardware-in-the-loop (HIL) platform.
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ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2022.108284