Xing Chen
17 Papers
6 Citations
Xing Chen is an academic researcher. The author has contributed to research in topics: Computer science & Engineering. The author has an hindex of 2, co-authored 10 publications.
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Papers
Distributed Shared Energy Storage Double-Layer Optimal Configuration for Source-Grid Co-Optimization
TL;DR: This paper proposes a distributed shared energy storage double-layer optimal allocation method for source-grid co-optimization, maximizing benefits through a distributed operation model and iterative particle swarm algorithm, achieving 100% new energy consumption and 61% reduced net load peak-valley difference.
5
Research on self-balancing transaction scheduling strategy of new energy power in County considering section load rate
TL;DR: In this article , a self balancing transaction scheduling strategy of new energy power within the county considering the section load rate, and a day ahead real-time two-tier transaction joint optimization model is proposed.
2
Load Prediction Model of Integrated Energy System Based on CNN-LSTM
Xing Chen,Meng Yang,Yihan Zhang,Junhui Liu,Shuo Yin +4 more
- 28 Jul 2023
TL;DR: Experimental results show that the prediction accuracy of CNN-LSTM combined model proposed in this paper is more accurate, which can provide reference for the load prediction of the system.
2
Impact of new energy supporting delivery repurchase on transmission and distribution price and investment and operation of power grid companies under the new situation
TL;DR: Based on the cost accounting and pricing mechanism of power transmission and distribution prices for grid enterprises, and the linkage mechanism of grid investment-transmission and distribution price based on system dynamics, the authors briefly analyzes and summarizes the impact of new energy repurchase work on power distribution prices and investment.
2
Construction of a new levelled cost model for energy storage based on LCOE and learning curve
TL;DR: In this paper , the levelized cost of new energy storage based on the whole life cycle perspective is studied, and a new leveled cost estimation model and prediction model for energy storage are constructed based on LCOE and learning curve methods.