Optimal Operation of a Microgrid with Hydrogen Storage Based on Deep Reinforcement Learning
Abstract
:1. Introduction
- A refined model represents the electrolyzer efficiency characteristics based on the linear interpolation method is proposed;
- An optimal operation model for a microgrid with hydrogen storage is proposed. The electrolyzer efficiency characteristics model is incorporated into the optimal operation model;
- The DDPG algorithm is adopted to solve the optimal operation model, which has a continuous action space.
2. Model of the Microgrid System
2.1. Electrolyzer Efficiency
2.2. Economic Dispatch Model of Microgrid
2.2.1. Objective Function
2.2.2. Constraints
- Power balance
- 2.
- Operating power constraints
- 3.
- Energy storage capacity
3. Deep Reinforcement Learning
3.1. Reinforcement Learning
3.2. Deep Deterministic Policy Gradient Algorithm
4. Optimal Operation of Microgrid Based on DDPG
4.1. State Space
4.2. Action Space
4.3. Reward Function
4.4. Process of the Optimal Operation Method
5. Case Studies
5.1. Simulation Environment
5.2. Simulation Results
5.2.1. Simulation Results of Electrolyzer Efficiency Characteristics
5.2.2. Simulation Results of DDPG Algorithm
5.2.3. Performance Evaluation
- Method1: Optimize the operation of microgrids using DDPG algorithm;
- Method2: Optimize the operation of the microgrid using the GA;
- Method3: Optimize the operation of the microgrid using the interior point method.
5.2.4. Generalization Analysis
6. Conclusions
- The electrolyzer efficiency characteristics model using linear interpolation method can describe the operation of electrolyzer more accurately. The proposed optimal operation method for the microgrid considering electrolyzer efficiency characteristics can reduce the PV curtailment and reduce the microgrid operation cost;
- The optimal microgrid operation method based on DDPG algorithm can effectively reduce the operation cost and improve the microgrid efficiency compared with the method based on traditional algorithms, such as the GA and interior point method;
- The optimal microgrid operation method based on DDPG algorithm has a certain generalization and can be used in in different scenarios.
Author Contributions
Funding
Conflicts of Interest
Abbreviations
Abbreviation | Description |
GAMS | General algebraic modeling system |
DDPG | Deep deterministic policy gradient |
PV | Photovoltaic |
BESS | Battery energy storage system |
SOFC | Solid oxide fuel cell |
SOC | State of charge |
GA | Genetic algorithm |
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Parameters | Value |
---|---|
Unit current density j/A·cm−2 | 0~4 |
Operating temperature T/K | 353 |
Universal gas constants R/J·(mol·K)−1 | 8.31446 |
Faraday’s constant F/C·mol−1 | 96,485.3 |
Cathodic charge transfer coefficient αc | 0.71 |
Anode charge transfer coefficient αa | 0.29 |
Cathode exchange current density jco/mA·cm−2 | 24.6 |
Anode exchange current density jao/mA·cm−2 | 24.1 |
Electron transfer number of cathode and anode nc, na | 2 |
Electrolyte resistance/mΩ | 20 |
Cross-sectional area of electrolyzer/cm2 | 16 |
Device | Maximum Power/kW | Minimum Power/kW | Operation and Maintenance Cost (USD/kWh) |
---|---|---|---|
Electrolyzer | 1 | 0 | 0.01262 |
Microturbine | 1 | 0 | / |
Fuel Cell | 1 | 0 | 0.01325 |
BESS | 2.9 | 2.9 | 0.01311 |
Electrolyzer Efficiency | Microgrid Operating Cost/USD |
---|---|
Considering efficiency characteristic | 5.24 |
Constant efficiency 0.5 | 5.20 |
Constant efficiency 0.6 | 5.42 |
Constant efficiency 0.65 | 5.56 |
Constant efficiency 0.7 | 5.94 |
Algorithm | Method 1 | Method 2 | Method 3 |
---|---|---|---|
Operating cost of microgrid/USD | 5.29 | 5.75 | 5.52 |
DDPG | GA | |
---|---|---|
Operating cost of winter/USD | 2.07 | 2.08 |
Operating cost of summer/USD | 5.31 | 5.70 |
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Zhu, Z.; Weng, Z.; Zheng, H. Optimal Operation of a Microgrid with Hydrogen Storage Based on Deep Reinforcement Learning. Electronics 2022, 11, 196. https://doi.org/10.3390/electronics11020196
Zhu Z, Weng Z, Zheng H. Optimal Operation of a Microgrid with Hydrogen Storage Based on Deep Reinforcement Learning. Electronics. 2022; 11(2):196. https://doi.org/10.3390/electronics11020196
Chicago/Turabian StyleZhu, Zhenshan, Zhimin Weng, and Hailin Zheng. 2022. "Optimal Operation of a Microgrid with Hydrogen Storage Based on Deep Reinforcement Learning" Electronics 11, no. 2: 196. https://doi.org/10.3390/electronics11020196
APA StyleZhu, Z., Weng, Z., & Zheng, H. (2022). Optimal Operation of a Microgrid with Hydrogen Storage Based on Deep Reinforcement Learning. Electronics, 11(2), 196. https://doi.org/10.3390/electronics11020196