A Data-Driven Scheduling Approach for Hydrogen Penetrated Energy System Using LSTM Network
Abstract
:1. Introduction
- (1)
- This paper proposed two LSTM based operational scheduling and control approaches for HPES.
- (2)
- This paper evaluated the performance of the proposed approaches in the intra-day operation of the HPES.
- (3)
- The intra-day operational cost, intra-day equipment operational scheduling and the computational speed of the proposed scheduling and control approaches are analyzed in detail.
2. Materials and Methods
2.1. Hydrogen Penetrated Energy System (HPES)
2.2. HPES Mathematical Modelling
2.3. Data-Driven Approach for HPES Scheduling
2.4. LSTM Based Approach
2.4.1. Introduction of LSTM
2.4.2. LSTM based Control Strategy
3. Results and Discussion
3.1. Data Description and Processing
3.2. LSTM Network Training Process
3.3. Result Analysis
3.3.1. Analysis of Training Results
3.3.2. Verification of Training Results on Intra-Day Basis
- Experiment 1: 295.5 kWh
- Experiment 2: 1444.7 kWh
- MILP: 326.5 kWh
- CVaR: 1,019 kWh
3.3.3. Running Time of Optimization Program
4. Conclusions
Author Contributions
Funding
Conflicts of Interest
Nomenclature
Symbol | Quantity |
Time length of the interval () | |
Confidence level | |
Factor of the CVaR | |
Ratio between Electrical Energy and Hydrogen () | |
Volume Proportion of Hydrogen in the Gas Mixture | |
Operation Efficiency or Energy Efficiency of Device | |
Binary Variable | |
Value at Risk | |
Air Density () | |
Intermediate Variable of Calculating CVaR | |
Difference Matrix of Scheduling Scheme | |
Cost (¥) | |
Unit Cost (¥) | |
Long Term Memory of LSTM | |
Directional Irradiation () | |
Short Term Memory of LSTM | |
PV Efficiency | |
Cooling load () | |
Electrical Load (kW) | |
Heating Load () | |
Hot water load () | |
Lower Heating Value of Gas Mixture () | |
Number of Intraday Rolling Optimization | |
Number of Monte Carlo Simulation | |
Matrix of Scheduling Scheme | |
Electrical Power (kW) | |
Electricity generation power of CHP () | |
Power consumed by electric boiler () | |
Power consumed by electric refrigerator () | |
Power consumed by electrolyzer () | |
Power bought from grid () | |
Power sold to grid () | |
Fuel cell generation power () | |
Power consumed by heat pump () | |
PV generation power () | |
Wind generation power () | |
Heating/Cooling Power (kW) | |
Output power of absorption chiller () | |
Smoke power consumed by absorption chiller () | |
Hot water power consumed by absorption chiller () | |
Smoke power generated by CHP () | |
Hot water power generated by CHP (kW) | |
Hot water power generated by electric boiler (kW) | |
Cooling power generated by electric cooler (kW) | |
Smoke power consumed by heat exchanger (kW) | |
Hot water power generated by heat exchanger () | |
Hot water power generated by fuel cell(SOFC only) () | |
Cooling power generated by heat pump () | |
Heating power generated by heat pump () | |
Electrical Power Ramping Constraint of CHP (kW/h) | |
Cross Sectional Area of Wind Turbine Blade () | |
Time | |
Rolling Period (h) | |
Gas Volume () | |
Gas Flow Rate () | |
Input to LSTM | |
Output from LSTM | |
Network Input of LSTM | |
Gate Controller of LSTM |
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Device | Quantity | Parameter | Value |
---|---|---|---|
Grid | / | 1000 | |
1000 | |||
Electric Boiler | 2 | 0.95 | |
240 | |||
Absorption Chiller | 12 | 0.8 | |
50 | |||
Hydrogen Storage Tank | 5 | 200 | |
20 | |||
20 | |||
Heat Pump | 14 | 3.85 | |
4 | |||
38 | |||
38 | |||
Fuel Cell | 1 | 1 | |
35 | |||
Heat Exchanger | 1 | 0.85 | |
150 | |||
Electric Refrigerator | 6 | 4 | |
20 | |||
CCHP | 1 | 200 | |
80 | |||
600 | |||
60 | |||
100 | |||
Electrolyser | 3 | 5 | |
50 |
Parameter | Experiment 1 | Experiment 2 |
---|---|---|
Dimension of LSTM Layer Output | 100 | 100 |
Loss Function | MAE | MAE |
Number of Iteration | 600 | 450 |
Dimension of Output Layer | 11 | 11 |
Data Set | 365 | 365 |
Learning Rate | 0.001 | 0.001 |
Solver | adam | adam |
Optimization Method | Experiment 1 | Experiment 2 | MILP | CVaR |
---|---|---|---|---|
Time(s) | 0.16 | 0.23 | 0.98 | 586.5 |
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Share and Cite
Zhou, S.; He, D.; Zhang, Z.; Wu, Z.; Gu, W.; Li, J.; Li, Z.; Wu, G. A Data-Driven Scheduling Approach for Hydrogen Penetrated Energy System Using LSTM Network. Sustainability 2019, 11, 6784. https://doi.org/10.3390/su11236784
Zhou S, He D, Zhang Z, Wu Z, Gu W, Li J, Li Z, Wu G. A Data-Driven Scheduling Approach for Hydrogen Penetrated Energy System Using LSTM Network. Sustainability. 2019; 11(23):6784. https://doi.org/10.3390/su11236784
Chicago/Turabian StyleZhou, Suyang, Di He, Zhiyang Zhang, Zhi Wu, Wei Gu, Junjie Li, Zhe Li, and Gaoxiang Wu. 2019. "A Data-Driven Scheduling Approach for Hydrogen Penetrated Energy System Using LSTM Network" Sustainability 11, no. 23: 6784. https://doi.org/10.3390/su11236784
APA StyleZhou, S., He, D., Zhang, Z., Wu, Z., Gu, W., Li, J., Li, Z., & Wu, G. (2019). A Data-Driven Scheduling Approach for Hydrogen Penetrated Energy System Using LSTM Network. Sustainability, 11(23), 6784. https://doi.org/10.3390/su11236784