# Two-Stage Energy Management Strategies of Sustainable Wind-PV-Hydrogen-Storage Microgrid Based on Receding Horizon Optimization

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## Abstract

**:**

## 1. Introduction

- (1)
- A two-stage energy management model based on receding horizon optimization is proposed to tackle the uncertainties and randomness of renewable energies and loads, as well as to minimize the operation cost.
- (2)
- The day-ahead optimization is performed to minimize the overall operation cost, while the intra-day optimization model is carried out to trace the day-ahead schemes and minimize the deviations of the intra-day and the day-ahead operation strategies.
- (3)
- The roles of battery storage in reducing operation cost and improving the performance of the energy management model have been explored and demonstrated.

## 2. The Sustainable Wind-PV-Hydrogen-Storage Microgrid

#### 2.1. The Wind Turbine Model

#### 2.2. The PV Model

^{2}; ${P}_{rSTC}$ is the rated power of each PV panel at standard test conditions (cell temperature is 25 °C, irradiance is 1000 W/m

^{2}); ${I}_{t}$ and ${T}_{t}$ are irradiance and cell temperature (which approximates to the ambient temperature) at time slot t.

#### 2.3. The Battery Storage Model

#### 2.4. The Power-to-Hydrogen Subsystem Model

#### 2.4.1. The Model of Electrolyzer

#### 2.4.2. The Model of Hydrogen Compressor

#### 2.4.3. The Model of Hydrogen Storage Tank

## 3. The Two-Stage Energy Management Model

#### 3.1. The Day-Ahead Optimization Model

#### 3.2. The Intra-Day Optimization Model

## 4. Numerical Analysis

#### 4.1. Basic Parameter Settings

#### 4.2. The Analysis and Discussions of the Simulation Results

#### 4.2.1. The Day-Ahead Simulation Results

#### 4.2.2. The Intra-Day Simulation Results

#### 4.2.3. The Simulation Results of WPHS Microgrid without Battery Storage

**Remark**

**1:**

## 5. Conclusions

- (1)
- The proposed two-stage optimization is effective in managing the operation of the micro and eliminating the uncertainties and fluctuations of WT, PV and loads. The day-ahead optimization can effectively coordinate the operations of the WT, PV, battery storage and power-to-hydrogen subsystems, and realize the high-efficiency operations. The intra-day optimization model is able to improve the operation stability of the WPHS microgrid and eliminate the adverse influence of the fluctuations of WT, PV, power and hydrogen demands.
- (2)
- The proposed two-stage energy management model is robust and effective in coordinating the operation of the sustainable WHP microgrid, and intra-day receding horizon optimization strategies can effectively trace the day-ahead schemes. In addition, the battery storage can reduce the operation cost dramatically by 12.85%, as well as alleviate the fluctuations of the exchanged power with the power grid, and the maximum deviation of the exchanged power between the day-ahead and intra-day strategies is reduced by 12.77% when the battery storage is considered.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Conflicts of Interest

## Abbreviations

PV | Photovoltaic | WT | Wind turbine |

WPHS | Wind-PV-hydrogen-storage | WPH | Wind-PV-hydrogen |

Parameters and variables of wind turbine model | |||

${P}_{WT}^{t}$ | Outpower of WT at time slot t | ${P}_{RWT}$ | Rated power of WT |

${v}_{t}$ | Wind speed at time slot t | ${v}_{in}$ | Cut-in wind speed |

${v}_{out}$ | Cut-out wind speed | ${v}_{r}$ | Rated wind speed of wind turbine |

Parameters and variables of PV model | |||

${P}_{PV}^{t}$ | Outpower of PV array | ${N}_{PV}$ | Number of PV panes |

${I}_{STC}$ | Standard irradiance | ${P}_{rSTC}$ | Rated power of each PV panel at standard test conditions |

${I}_{t}$ | Irradiance at time slot t | ${T}_{t}$ | Temperature at time slot t |

Parameters and variables of battery storage model | |||

${E}_{bat}^{t}$ | Energy stored in the batteries at time slot t | ${E}_{bat}^{\mathrm{min}}$ | Minimum capacity of battery storages |

${E}_{bat}^{\mathrm{max}}$ | Maximum capacity of battery storages | ${P}_{bat,c}^{t}$ | Charging power at time slot t |

${P}_{bat,d}^{t}$ | Discharging power at time slot t | ${P}_{bat,c}^{\mathrm{max}}$ | Maximum charging power |

${P}_{bat,d}^{\mathrm{max}}$ | Maximum discharging power | ${u}_{bat}^{t}$ | Binary variable |

Parametersand variables of power-to-hydrogen system | |||

${\eta}_{{\mathrm{H}}_{2}}$ | Hydrogen production rate | ${P}_{\mathrm{el}}^{\mathrm{max}}$ | Maximum power of electrolyzer |

${m}_{{\mathrm{H}}_{2}}^{t}$ | Hydrogen mass-produced at time slot t | ${P}_{\mathrm{el}}^{t}$ | Power consumed by electrolyzer at time slot t |

${C}_{{H}_{2}}$ | Specific heat of hydrogen at constant pressure | ${T}_{\mathrm{in}}$ | Inlet hydrogen temperature |

${\eta}_{\mathrm{com}}$ | Efficiency of compressor | ${P}_{\mathrm{out}}/{P}_{\mathrm{in}}$ | Compression ratio of hydrogen |

${P}_{com}^{\mathrm{max}}$ | Maximum power of compressor | ${m}_{com}^{t}$ | Hydrogen flow rate through compressor at time |

$\kappa $ | Isentropic exponent of hydrogen | ${M}_{{\mathrm{H}}_{2}}^{t}$ | Stored hydrogen mass in the hydrogen tank at time slot t |

${L}_{{H}_{2}}^{t}$ | Hydrogen load at time slot t | ${C}_{\mathrm{t}an\mathrm{k}}^{R}$ | Capacity of hydrogen tank |

${\gamma}^{\mathrm{min}}$ | Minimum ratio of the rated capacity of hydrogen tank | ${\gamma}^{\mathrm{max}}$ | Maximum ratio of the rated capacity of hydrogen tank |

Variables of the two-stage energy management model | |||

${C}_{DAC}$ | Day-ahead comprehensive operation cost | ${C}_{PV}$ | Operational and maintenance costs of PV |

${C}_{WT}$ | Operational and maintenance costs of WT | ${C}_{bat}$ | Degradation costs of battery storage |

${C}_{el}$ | Degradation costs of electrolyzer | ${C}_{e}$ | Net energy cost |

${\lambda}_{PV}$ | Maintenance cost coefficient of PV | ${\lambda}_{WT}$ | Maintenance cost coefficient of WT |

${\lambda}_{bat}$ | Degradation cost coefficient of battery storage | ${\lambda}_{el}$ | Degradation cost coefficient of electrolyzer |

${P}_{b}^{t}$ | Buying power from the power grid at time slot t | ${P}_{s}^{t}$ | Selling power to the power grid at time slot t |

${P}_{load}^{t}$ | Predicted power load at time slot t | ${\chi}_{bs}^{t}$ | Binary variable |

${P}_{{\mathrm{H}}_{2},{f}_{s}}^{t,0}$ | Hydrogen production at time slot $t$ | ${P}_{\mathrm{el},{f}_{s}}^{t,0}$ | Power consumed by electrolyzer device at time slot $t$ |

## Appendix A

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Time Slots | Buying Price | Selling Price |
---|---|---|

01:00–07:00, 23:00–24:00 | 0.3376 | 0.4 |

12:00–14:00, 19:00–22:00 | 0.8654 | 0.4 |

08:00–11:00, 15:00–18:00 | 0.5980 | 0.4 |

${\eta}_{{\mathrm{H}}_{2}}$ | 0.0192 | ${\eta}_{com}$ | 0.7 | ${S}_{B}^{\mathrm{max}}$ | 5700 kWh | $\Delta {P}_{grid}^{\mathrm{max}}$ | 200 kW |

${P}_{\mathrm{el}}^{\mathrm{max}}$ | 5000 kW | κ | 1.4 | ${S}_{B}^{\mathrm{min}}$ | 600 kWh | $\Delta {P}_{el}^{\mathrm{max}}$ | 100 kW |

${P}_{grid}^{\mathrm{max}}$ | 6000 kW | ${m}_{{\mathrm{H}}_{2}}^{\mathrm{min}}$ | 0 kg | ${S}_{B}^{t=0}$ | 600 kWh | $\Delta {P}_{com}^{\mathrm{max}}$ | 10 kW |

${R}_{{\mathrm{H}}_{2}}$ | 14.304 | ${m}_{{\mathrm{H}}_{2}}^{\mathrm{max}}$ | 1000 kg | ${P}_{\mathrm{bat},\mathrm{c}}^{\mathrm{max}}$ | 2100 kW | $\Delta {P}_{bat}^{\mathrm{max}}$ | 200 kW |

T_{in} | 40 °C | ${P}_{com}^{\mathrm{max}}$ | 500 kW | ${P}_{\mathrm{bat},\mathrm{d}}^{\mathrm{max}}$ | 2400 kW |

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**MDPI and ACS Style**

Wang, J.; Li, D.; Lv, X.; Meng, X.; Zhang, J.; Ma, T.; Pei, W.; Xiao, H. Two-Stage Energy Management Strategies of Sustainable Wind-PV-Hydrogen-Storage Microgrid Based on Receding Horizon Optimization. *Energies* **2022**, *15*, 2861.
https://doi.org/10.3390/en15082861

**AMA Style**

Wang J, Li D, Lv X, Meng X, Zhang J, Ma T, Pei W, Xiao H. Two-Stage Energy Management Strategies of Sustainable Wind-PV-Hydrogen-Storage Microgrid Based on Receding Horizon Optimization. *Energies*. 2022; 15(8):2861.
https://doi.org/10.3390/en15082861

**Chicago/Turabian Style**

Wang, Jiarui, Dexin Li, Xiangyu Lv, Xiangdong Meng, Jiajun Zhang, Tengfei Ma, Wei Pei, and Hao Xiao. 2022. "Two-Stage Energy Management Strategies of Sustainable Wind-PV-Hydrogen-Storage Microgrid Based on Receding Horizon Optimization" *Energies* 15, no. 8: 2861.
https://doi.org/10.3390/en15082861