# An Improved Multi-Timescale Coordinated Control Strategy for Stand-Alone Microgrid with Hybrid Energy Storage System

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## Abstract

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## 1. Introduction

- (1)
- HESS is not included as an element of coordinated control; thus, applicability is limited.
- (2)
- The SOC balance of HESS in daily dispatch period has not received enough attention.
- (3)
- The multi-timescale framework is relatively simple, with large gap between different timescales.

## 2. Typical Topology of Stand-Alone Microgrid with Hybrid Energy Storage System

- (1)
- The first layer is the optimal schedule layer and the corresponding control unit is EMS (energy management system). The DAO and IDR algorithm in EMS is used to realize coordinated control strategy of a large time scale and provide scheduling curve for MGCC (microgrid central controller).
- (2)
- The second layer is the MG control layer and the corresponding control unit is MGCC. The QRTC and RTCC in MGCC based on logical judgment is used to correct the deviation between the actual operating state and the ideal state of the MG and issue control commands to the device controller.
- (3)
- The third layer is the local control layer and the corresponding control unit is device controllers. Control commands from the MG real-time control layer are executed by DGs/load/HESS.

## 3. Multi-time Scale Coordinated Control

#### 3.1. Multi-time Scale Coordinated Control Framework

- (1)
- Day-ahead optimization model: Based on the short-term load forecasting results, DGs’ hourly output scheduling curve (except PTESS) and load switching scheme are determined by DAO Day-ahead optimization model in the EMS. The execution cycle of this model is 1 day.
- (2)
- Intraday rolling model: Based on the ultra short-term power forecasting results, the DAO results are continuously corrected by the time-limited rolling optimization scheduling. The time scale of the rolling time window is 4h (same as the ultra short-term prediction). The model has an execution cycle of 15 min.
- (3)
- Quasi-real-time coordinated control model: The deviation between the constantly updated intraday rolling scheduling plan and the actual operating conditions of MG is calculated by the real-time collected data and will be corrected quickly. An integrated criterion is introduced to decide the adjustment priority of the distributed generations. The execution cycle of this model is 1 min.
- (4)
- Real-time coordinated control model: Under the condition of ensuring frequency and voltage stability, real-time control commands are determined to follow the command from quasi-real-time coordinated control as much as possible in second time scale and real-time smooth control strategy for HESS based on logic judgment is used to smooth unbalanced power of second time scale. The execution cycle of this model is 5 s.

#### 3.2. Day-Ahead Optimization Model

#### 3.2.1. Decision Variables

**P**of output scheduling curves of controllable DGs and the set

**u**of DG unit commitment and load switching schedule:

#### 3.2.2. Objective Function and Constraints

_{ipload}is the number of important load in the system. ${P}_{\mathrm{ipload},s}(t)$ is the power of the sth important load in the tth time period. ${P}_{\mathrm{ba},\mathrm{n},j}^{\mathrm{cha}}$ and ${P}_{\mathrm{ba},\mathrm{n},j}^{\mathrm{dis}}$ are the rated charging power and discharging power of the jth ETESS. $So{c}_{\mathrm{ba},\mathrm{max},j}$ and $So{c}_{\mathrm{ba},\mathrm{min},j}$ are the maximum and minimum limit of the jth ETESS SOC. $So{c}_{\mathrm{ba},j}(1)$ and $So{c}_{\mathrm{ba},j}({T}_{1})$ are the SOC value of the jth ETESS at the beginning and the ending of a schedule, $\Delta So{c}_{\mathrm{balance}}$ is the permitted deviation of ETESS energy balance in a cycle. ${P}_{\mathrm{de},\mathrm{n},i}$ is the rated power of ith diesel generator. ${\beta}_{\mathrm{de},i,\mathrm{min}}$ is the minimum operating power factor of ith diesel generator. $\Delta {P}_{\mathrm{de},\mathrm{down},i}$ and $\Delta {P}_{\mathrm{de},\mathrm{up},i}$ are the maximum ramp down rate and ramp up rate of the ith diesel generator. ${E}_{\mathrm{ba},\mathrm{n},j}$ is the rated capacity of the jth ETESS. ${\eta}_{\mathrm{ba},\mathrm{dis}}$ is the discharge efficiency of the ETESS. ${R}_{s}(t)$ is the reserve capacity requirement of MG.

#### 3.3. Intraday Rolling Optimization Based on Model Predictive Control

#### 3.3.1. Model Predictive Control Framework

- (1)
- Prediction model: Based on the measured output data of generators and the predictive data of RES and load data in the next time period, this model is used to predict the future output of generators.
- (2)
- Rolling optimization: Repeated online optimization negates the influence caused by uncertain factors such as RE fluctuations.
- (3)
- Feedback control: The measured data feedback is used to correct the actual output state of the generators in the model and ensure that the next optimization calculation is based on the latest measured data.

#### 3.3.2. Decision Variables

#### 3.3.3. Objective Function and Constraints

_{2}is the rolling window length, which is usually set to 4 h.

#### 3.4. Comprehensive Criteria Based Quasi-Real-Time Coordinated Control

- (1)
- When ①③⑤ are satisfied, increase the discharge power of ETESS with high priority. The increment $\Delta {P}_{\mathrm{ba}}^{\mathrm{m}}(t)$ is calculated by SOC error and rated capacity, as described by the following equation:$$\Delta {P}_{\mathrm{ba}}^{\mathrm{m}}(t)=\mathrm{min}({P}_{\mathrm{ba},\mathrm{n}}^{\mathrm{dis}}-{P}_{\mathrm{ba}}^{\mathrm{h}}(t),\frac{{E}_{\mathrm{ba},\mathrm{n}}(So{c}_{\mathrm{ba}}^{\mathrm{m}}(t)-So{c}_{\mathrm{ba}}^{\mathrm{h}}(t)){\eta}_{\mathrm{ba},\mathrm{dis}}}{\Delta {T}_{\mathrm{rest}}},{P}_{\mathrm{ubl}}^{\mathrm{m}}(t))$$
- (2)
- When ①③⑥ are satisfied, increase the output of diesel generators with high priority. The increment $\Delta {P}_{\mathrm{de}}^{\mathrm{m}}(t)$ takes rated power and spinning reserve margin into consideration, as described by the following equation:$$\Delta {P}_{\mathrm{de}}^{\mathrm{m}}(t)=\mathrm{min}({P}_{\mathrm{de},\mathrm{n}}-{R}_{\mathrm{de}}(t)-{P}_{\mathrm{de}}^{\mathrm{h}}(t),{P}_{\mathrm{ubl}}^{\mathrm{m}}(t))$$
- (3)
- When ①④⑤ are satisfied, decrease the charge power of ETESS with high priority. $\Delta {P}_{\mathrm{ba}}^{\mathrm{m}}(t)$ is described by the following equation:$$\Delta {P}_{\mathrm{ba}}^{\mathrm{m}}(t)=\mathrm{min}(-{P}_{\mathrm{ba}}^{\mathrm{h}}(t),{P}_{\mathrm{ubl}}^{\mathrm{m}}(t))$$
- (4)
- When ①④⑥ are satisfied, the strategy is the same as Equation (2).
- (5)
- When ②③⑤ are satisfied, decrease the output of diesel generators with high priority. The increment $\Delta {P}_{\mathrm{de}}^{\mathrm{m}}(t)$ needs to take minimum load limitation into consideration, as described by the following equation:$$\Delta {P}_{\mathrm{de}}^{\mathrm{m}}(t)=\mathrm{max}({\beta}_{\mathrm{de},\mathrm{min}}{P}_{\mathrm{de},\mathrm{n}}-{P}_{\mathrm{de}}^{\mathrm{h}}(t),{P}_{\mathrm{ubl}}^{\mathrm{m}}(t))$$
- (6)
- When ②③⑥ are satisfied, decrease the discharge power of ETESS with high priority. $\Delta {P}_{\mathrm{ba}}^{\mathrm{m}}(t)$ is described by the following equation:$$\Delta {P}_{\mathrm{ba}}^{\mathrm{m}}(t)=-\mathrm{min}({P}_{\mathrm{ba}}^{\mathrm{h}}(t),{P}_{\mathrm{ubl}}^{\mathrm{m}}(t))$$
- (7)
- When ②④⑤ are satisfied, the strategy is the same as Equation (5).
- (8)
- When ②④⑥ are satisfied, increase the charge power of ETESS with high priority. $\Delta {P}_{\mathrm{ba}}^{\mathrm{m}}(t)$ is described by the following equation:$$\Delta {P}_{\mathrm{ba}}^{\mathrm{m}}(t)=\mathrm{max}({P}_{\mathrm{ba},\mathrm{n}}^{\mathrm{cha}}-{P}_{\mathrm{ba}}^{\mathrm{h}}(t),\frac{{E}_{\mathrm{ba},\mathrm{n}}(So{c}_{\mathrm{ba}}^{\mathrm{m}}(t)-So{c}_{\mathrm{ba}}^{\mathrm{h}}(t)){\eta}_{\mathrm{ba},\mathrm{cha}}}{\Delta {T}_{\mathrm{rest}}},{P}_{\mathrm{ubl}}^{\mathrm{m}}(t))$$

#### 3.5. Real-Time Correction Control of Hybrid Energy Storage

## 4. Case Study

#### 4.1. Basic Data

#### 4.2. Analysis of Multi-Timescale Coordinated Control

#### 4.2.1. Analysis of Day-Ahead Optimization

#### 4.2.2. Analysis of Intraday, Quasi-Real-Time and Real-Time Coordinated Control

- (1)
- The essential of seconds-scale real-time coordinated control is to use HESS to compensate the power imbalance in the interval of quasi-real-time coordinated control, so the power curve of DSG in real-time coordinated control is basically overlapped with that in quasi-real-time coordinated control.
- (2)
- The power curve of DSG is more accurate in the shorter timescale. Compared to real-time power curve, the mean error of quasi-real-time curve is 0.19% while the mean error of intraday rolling curve is 11.73% and for day-ahead scheduling, 26.09%.

- (1)
- ETESS needs to compensate the low frequency unbalanced power in real-time timescale, so its real-time operating power curve is slightly different from the quasi-real-time curve.
- (2)
- Though intraday and day-ahead curves differ from the real-time curve greatly, they still reflect the overall change of the energy of ETESS.

#### 4.2.3. Analysis of Daily SOC Balance of ETESS

#### 4.2.4. Analysis of the DSG Unit Commitment Correction Regularization

#### 4.2.5. Analysis of the Necessity of Introducing Quasi-Real-Time Coordinated Control

## 5. Conclusions

- (1)
- Introduce power shortage regularization term in day-ahead optimization model can significantly improve the power supply reliability but at the cost of some economic profit.
- (2)
- Based on ultra-short-term forecasting, which has smaller prediction error, the intraday rolling optimization model can adjust the day-ahead schedule for DGs output power appropriately. When the day-ahead prediction error is large, DG unit commitment and load switching schedule will be correct to the robustness of the MG system.
- (3)
- To keep the ETESS SOC balance at the beginning and end of the day period, it is necessary to add ETESS SOC error regularization term in the intraday rolling optimization model. Besides, the comprehensive criterial proposed in quasi-real-time model can improve the ETESS’s ability to follow the day-ahead schedule and reducing the HESS operations risk at the same time.

- (1)
- To overcome the shortcomings of large error in day-ahead power prediction and increase the credibility of the optimization results, uncertainty optimization theory can be introduced, such as robust optimization, fuzzy programming and stochastic programming.
- (2)
- In large scale stand-alone MG system, it may contain multiple HESSs. How to manage the charge and discharge commands and the SOC of each HESS in quasi-real-time and real-time timescale, is a problem to be solved.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 4.**Short-term and ultra-short-term power prediction results. (

**a**) Prediction results of RES. (

**b**) Prediction results of load.

**Figure 6.**Test results of multi-timescale coordinated control. (

**a**) Results of 0–6h. (

**b**) Results of 6–12h. (

**c**) Results of 12–18h. (

**d**) Results of 18–24h.

**Figure 8.**SOC waveforms of the HESS. (

**a**) Ignore the SOC error regularization in intraday rolling. (

**b**) Adjust ETESS with high priority in quasi-real-time coordinated control and does not use comprehensive criteria. (

**c**) Adjust DSGs with high priority in quasi-real-time coordinated control and does not use comprehensive criteria.

Quasi-Real-Time Power Imbalance | Power of ETESS in Rolling Optimization | Quasi-Real-Time SOC of ETESS |
---|---|---|

① ${P}_{\mathrm{ubl}}^{\mathrm{m}}(t)>0$ | ③ ${P}_{\mathrm{ba}}^{\mathrm{h}}(t)>0$ | ⑤ $So{c}_{\mathrm{ba}}^{\mathrm{m}}(t)>So{c}_{\mathrm{ba}}^{\mathrm{h}}(t)$ |

② ${P}_{\mathrm{ubl}}^{\mathrm{m}}(t)<0$ | ④ ${P}_{\mathrm{ba}}^{\mathrm{h}}(t)\le 0$ | ⑥ $So{c}_{\mathrm{ba}}^{\mathrm{m}}(t)\le So{c}_{\mathrm{ba}}^{\mathrm{h}}(t)$ |

Serial Number of DSG | Rated Power (kW) | Startup Cost (CNY/Per Time) | Shutdown Cost (CNY/Per Time) | Coefficient of Operation Cost (CNY/kWh) | Fuel Cost (CNY/kWh) | Fuel Curve Slope (L/kWh) | Minimum Load Factor (%) |
---|---|---|---|---|---|---|---|

1 | 50 | 2 | 2 | 0.0088 | 7 | 0. | 30 |

2 | 50 | 2 | 2 | 0.0088 | 7 | 0.3 | 30 |

Type | Rated Power (kW) | Coefficient of Operation Cost (CNY/kWh) |
---|---|---|

Wind power | 50 | 0.0296 |

PV power | 50 | 0.0096 |

Type | Rated Power/Capacity (kW/kWh) | Initial Investment Cost (CNY/kWh) | Coefficient of Operation Cost (CNY/kWh) | Energy Per Unit Capacity (kWh) | Permitted SOC Range (%) | SOC Warning Range (%) | Initial SOC (%) |
---|---|---|---|---|---|---|---|

ETESS | 50/200 | 2 | 0.0088 | 250 | [10, 90] | [30, 70] | 50 |

PTESS | 50/5 | 11.4 | 0 | ∞ | [10, 90] | [30, 70] | 50 |

Energy Balance Error in a Cycle (%) | DSG Minimum Operation (Shutdown) Time (h) | Reserve Capacity (kW) | Regularization Parameter of RE Curtailment | Regularization Parameter of Power Shortage | Regularization Parameter of DSG Unit Commitment Correction | Regularization Parameter of SOC Error Correction |
---|---|---|---|---|---|---|

10 | 2 | 10 | 5000 | 10000 | 10000 | 500 |

Adjusting Coefficient for Time Constant of FOLPF | Reference Value for Time Constant of FOLPF |
---|---|

15 | 30 |

**Table 7.**Comparison test for the verification of effects of regularization terms in day-ahead optimization model.

Day-Ahead Optimization Results | Without Regularization Terms | With Regularization Term |
---|---|---|

RE curtailment (%) | 0.00095619 | 0 |

Power shortage (%) | 28.3567 | 0 |

MG operation cost (CNY) | −1049.9027 | −645.8309 |

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## Share and Cite

**MDPI and ACS Style**

Chen, J.; Yang, P.; Peng, J.; Huang, Y.; Chen, Y.; Zeng, Z. An Improved Multi-Timescale Coordinated Control Strategy for Stand-Alone Microgrid with Hybrid Energy Storage System. *Energies* **2018**, *11*, 2150.
https://doi.org/10.3390/en11082150

**AMA Style**

Chen J, Yang P, Peng J, Huang Y, Chen Y, Zeng Z. An Improved Multi-Timescale Coordinated Control Strategy for Stand-Alone Microgrid with Hybrid Energy Storage System. *Energies*. 2018; 11(8):2150.
https://doi.org/10.3390/en11082150

**Chicago/Turabian Style**

Chen, Jingfeng, Ping Yang, Jiajun Peng, Yuqi Huang, Yaosheng Chen, and Zhiji Zeng. 2018. "An Improved Multi-Timescale Coordinated Control Strategy for Stand-Alone Microgrid with Hybrid Energy Storage System" *Energies* 11, no. 8: 2150.
https://doi.org/10.3390/en11082150