## 3 Method
### RL-Based VESS Operation Strategy
For an example, when the MAE target of each WPG sets as 1.5, in case of the individual ESS operation, each ESS size larger than 1 p.u. is required, as shown in Figure 4a. This is economically not viable. However, in the proposed VESS case, 0.2 p.u. of ESS size is required for each WPG with the same target. This makes a business model such as a VESS service with economic benefit by reducing the ESS size. Moreover, by increasing the number of clusters, the ESS size can be reduced, as shown in Figure 5. The cluster size affects the number of the state-action value function that is related to the memory size and the computational complex. Therefore, the VESS service provider can select the ESS size and the number of clusters considering the ESS cost, the memory cost, and the computational complexity, as well as the WPG forecast error management target. ## 5 Conclusions

This study proposed an RL-based VESS operation strategy to manage WPG forecasting uncertainty. The VESS operation model is the first to consider not only its own uncertainty management requirement, but also the requirements of other units. Applying the VESS model, the expected SARSA-based learning policy is suggested to solve the sequential decision-making problem of the VESS operation. Moreover, the k-means data clustering method is employed to enhance the performance of the proposed strategy by reducing uncertainty variance. The simulation results demonstrate that the proposed strategy provides a near-optimal performance, with a less than 2%-point gap to the optimal solution that requires information including the future time. Moreover, the MAE improvement when applying the proposed method has a similar slope to that of the optimal method according to the storage size. This shows that the proposed method obtains a similar operational diversity to that of the optimal method and can achieve near-optimal performance generally. In addition, we evaluated the performance achieved by the VESS operation in terms of multi-user diversity and the effect of the clustering method according to cluster size. Research on VESS operation is at an early stage. This study shows that VESS operation can outperform individual ESS operation. However, the performance enhancement according to the VESS operation differs for each unit. Therefore, the VESS operation considering the performance balance among units will be the subject of further research. Moreover, this study only considers a simple system model. By including power system requirements, the system model can be practically extended further. Finally, the forecast error management of WPG is highly related to the revenue, and the VESS operation

Figure 5: Change in optimal gap of the proposed method with 1, 3, and 5-cluster cases. is cost-efficient, rather than the individual ESS operation. Therefore, this study can be extended to research in the economic aspect, such as a revenue maximization problem considering ESS costs. This work was supported by 2020 Research Grant of Hanseo University. The authors declare no conflict of interest. ## References

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