Multiple time‐scale economic dispatching strategy for commercial building with virtual energy storage under demand response mechanism

2021 ◽  
Author(s):  
Xiaoou Liu
Keyword(s):  
Energy Storage ◽  
Demand Response ◽  
Time Scale ◽  
Multiple Time ◽  
Multiple Time Scale ◽  
Commercial Building ◽  
Response Mechanism

scholarly journals Research on the optimal strategy of pumped storage power station to provide multiple time-scale reserves

2021 ◽  
Vol 2108 (1) ◽  
pp. 012052
Author(s):  
Xiaomeng Li ◽  
Xiaopeng Yu ◽  
Ze Gao
Keyword(s):  
Energy Storage ◽  
Time Scale ◽  
Optimal Decision ◽  
Energy Market ◽  
Multiple Time ◽  
Multiple Time Scale ◽  
Total Revenue ◽  
Power Station ◽  
Carbon Neutrality ◽  
Pumped Storage

Abstract In the background of peak carbon emissions and carbon neutrality, renewable energy generation (REG) will become the main generation form in the future power system. Simultaneously, the randomness and volatility increase the reserve requirement in the different time scale. Increasing importance has been attached to energy storage in the aspect of reserve, as energy storage has the advantages of power flexibility and relatively low reserve cost. Trading off the benefits of energy storage in the energy market and the reserve market to maximize its benefits is of great significance to the economic operation and investment of energy storage. In this regard, taking the pumped storage power station (PSPS) as an example, this paper establishes an optimal decision-making model for PSPS to participate in the energy market and to provide reserve services. In addition, an optimal decision model for PSPS to provide multiple reserve services is established. The analysis finds that the power reserve capacity provided by PSPS at different time scales have little impact on each other, but their storage capacity requirements are mutually restricted. Case studies show that the total revenue of the PSPS is significantly increased through providing reserve service. The PSPS may even bid all its capacity to provide reserve service when the compensation price reserve reaches a certain level. In addition, the total revenue of PSPS when providing multiple time-scale reserves is higher than that when providing reserve service at single time scale.

Microgrid System Energy Storage Capacity Optimization Considering Multiple Time Scale Uncertainty Coupling

2019 ◽  
Vol 10 (5) ◽  
pp. 5234-5245 ◽  
Author(s):  
Peng Xie ◽  
Zexiang Cai ◽  
Ping Liu ◽  
Xiaohua Li ◽  
Yongjun Zhang ◽  
...  
Keyword(s):  
Energy Storage ◽  
Time Scale ◽  
Storage Capacity ◽  
Multiple Time ◽  
Multiple Time Scale ◽  
Capacity Optimization ◽  
Scale Uncertainty ◽  
Energy Storage Capacity ◽  
System Energy

Decentralized monthly generation scheduling of cascade hydropower plants in multiple time scale markets

2022 ◽  
Vol 135 ◽  
pp. 107420
Author(s):  
Yuhang Ma ◽  
Yuan Huang ◽  
Gang Wu ◽  
Junyong Liu ◽  
Yang Liu ◽  
...  
Keyword(s):  
Time Scale ◽  
Multiple Time ◽  
Multiple Time Scale ◽  
Generation Scheduling ◽  
Hydropower Plants

The plasma transport equations derived by multiple time‐scale expansions. II. An application

1995 ◽  
Vol 2 (4) ◽  
pp. 1206-1216 ◽  
Author(s):  
J. W. Edenstrasser ◽  
M. M. M. Kassab
Keyword(s):  
Time Scale ◽  
Transport Equations ◽  
Plasma Transport ◽  
Multiple Time ◽  
Multiple Time Scale

Predicting the trend of stock index based on feature engineering and CatBoost model

2021 ◽  
pp. 2150027
Author(s):  
Renzhe Xu ◽  
Yudong Chen ◽  
Tenglong Xiao ◽  
Jingli Wang ◽  
Xiong Wang
Keyword(s):  
Time Scale ◽  
Stock Index ◽  
Feature Engineering ◽  
Multiple Time ◽  
Multiple Time Scale ◽  
Back Testing ◽  
Price Series ◽  
Out Of Sample ◽  
Out Of Sample Prediction ◽  
Sharp Ratio

As an important tool to measure the current situation of the whole stock market, the stock index has always been the focus of researchers, especially for its prediction. This paper uses trend types, which are received by clustering price series under multiple time scale, combined with the day-of-the-week effect to construct a categorical feature combination. Based on the historical data of six kinds of Chinese stock indexes, the CatBoost model is used for training and predicting. Experimental results show that the out-of-sample prediction accuracy is 0.55, and the long–short trading strategy can obtain average annualized return of 34.43%, which is a great improvement compared with other classical classification algorithms. Under the rolling back-testing, the model can always obtain stable returns in each period of time from 2012 to 2020. Among them, the SSESC’s long–short strategy has the best performance with an annualized return of 40.85% and a sharp ratio of 1.53. Therefore, the trend information on multiple time-scale features based on feature engineering can be learned by the CatBoost model well, which has a guiding effect on predicting stock index trends.

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