**6. Discussion**

It can also be found from the simulation results that when N-1 contingency occurs, the most dangerous instance is not necessarily when the penetration of renewable energy is the highest, but tends to occur in the midway from zero penetration to highest penetration.

The reason may be that when the penetration of renewable energy at its peak, the ESS will be charged more. If a contingency occurs, it can quickly trip and reduce a lot of energy use. The lowest point of the frequency is related to the amount of power generation that is tripped. When the penetration rate is the highest, the generators are almost always lightly loaded, so the reduced power generation of the contingency is relatively small. Therefore, even if the system inertia at that time is small, the lowest frequency may not be the lowest during this period.

Furthermore, in theory the outcome of stopping the charging of EVs would be similar to cutting off the ESS from charging in this study, probably also increasing the minimum frequency of N-1 1 contingency. The results of this study can be used to set the time price of electric vehicles to charge, so that more EVs can be charged when the power grid is weak.

In the picture., two ESSs are used to charge and discharge, respectively, to improve the safety of the N-1 contingency. In the real world, energy loss will occur due to the round-trip efficiency of the ESS. This situation can be understood as exchanging energy for N-1 contingency resiliency. May be especially suitable for pumped-storage power plants when rainwater is abundant.

#### **7. Conclusions**

This paper proposes a method by which energy arbitrage energy storage can help the N-1 contingency. The frequency regulation ESS and the energy arbitrage ESS are considered in the simulation. PSS®E is used to verify that the energy arbitrage ESS disconnected from charging can increase the minimum frequency when contingency occurs. In this way, the ESS can provide spinning reserve, energy arbitrage, and help N-1 contingency at the same time. This method is also not only suitable for lithium-ion batteries, but for all battery types. It can also be applied to various ESSs, such as flow batteries, pumped storage power, etc. The simulation results show that the proposed method can effectively improve the minimum contingency frequency higher than the set value.

**Author Contributions:** Conceptualization, T.-C.T.; Methodology, J.-Z.J.; Software, J.-Z.J. and P.- Y.C.; Validation, P.-Y.C.; Visualization, T.-C.T.; Supervision, C.-C.K.; Project administration, C.-C.K.; Funding acquisition, C.-C.K. All authors have read and agreed to the published version of the manuscript.

**Funding:** The support of this research by the Ministry of Science and Technology of the Republic of China under Grant No. MOST 111-2622-8-011-006-TE1 & MOST 111-3116-F-006-006—are gratefully acknowledged.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** All data are provided in this manuscript.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

### **Appendix A**

**Figure A1.** N-1 contingency frequency at 9th hour of first ED.

**Figure A2.** Generators active power at the 9th hour of the first ED.

**Figure A3.** N-1 contingency frequency at 9th hour of 3rd ED.

**Figure A4.** Generator power at the 9th hour of the 3rd ED.

#### **References**


**Hao Yu 1, Xiaojuan Yang 2,\*, Honglin Chen 1, Suhua Lou <sup>2</sup> and Yong Lin <sup>1</sup>**


**Abstract:** This paper proposes a method of energy storage capacity planning for improving offshore wind power consumption. Firstly, an optimization model of offshore wind power storage capacity planning is established, which takes into account the annual load development demand, the uncertainty of offshore wind power, various types of power sources and line structure. The model aims at the lowest cost of investment, operation and maintenance of the system, and takes lower than a certain abandoned wind level as the strict constraint to obtain two parameters of power capacity and energy capacity of energy storage on the source side. Secondly, taking a coastal power grid as a typical case, the energy storage capacity planning method is verified. Finally, the key factors affecting offshore wind power consumption are summarized, and the sensitivity analysis is carried out from the point of view of the transmission protocol of the transmission lines outside the province and the capacity allocation of the tie lines in the province. This study will be helpful for the planning and operation of the high-proportion of offshore wind energy power systems.

**Keywords:** offshore wind power; energy storage system; wind power consumption; planning optimization model
