當再生能源滲透率提高時，由於再生能源的間歇性和電力系統慣量減少，導致電力系統頻率響應能力下降。有鑑於此，我國即將開放日前輔助服務市場，提供經濟誘因鼓勵併網型儲能系統提供調頻備轉和即時備轉等輔助服務支持電網運轉。
因為這兩種輔助服務有不同的技術規範、容量價格、效能價格和操作儲能系統造成的劣化損失，適當的將儲能系統壽命分配至這兩種輔助服務可獲得較大的投資效益。一般而言，為獲得較大的儲能系統投資效益，可讓儲能系統參與容量費與效能費較高但電池循環劣化較大的調頻備轉，當儲能系統劣化至某個程度，再參與容量費與效能費較低但電池循環劣化較小的即時備轉。
本研究提出儲能系統參與輔助服務調頻備轉和即時備轉之最佳容量規劃與壽命分配方法，並利用差分進化演算法加以求解。模擬案例探討不同調頻備轉商品規格、儲能系統電池價格、累計電力系統頻率偏移量和即時備轉平均執行週期之規劃結果。模擬結果不僅顯示本研究提出之最佳化方法的有效性，並指出在歷史電力系統頻率中，以sReg參與調頻備轉的投資報酬率最高。然而dReg0.5的投資報酬率對累計頻率偏移量較不靈敏，若未來累計頻率偏移量增加，則以dReg0.5參與調頻備轉的投資報酬率最高。

When the renewable energy penetration increases, the power system frequency response capability decreases due to renewable energy intermittency and reduced power system inertia. Therefore, Taiwan is going to deregulate the ancillary service market in the immediate future, and that provides the economic incentive to install grid-connected energy storage system (ESS) and supply ancillary service which supports the power system operation, such as regulation reserve and spinning reserve.
Owing to different technical specification, capacity payment, effectiveness payment and ESS degradation between these ancillary services, It is possible to obtain higher profit by suitable allocating ESS lifetime for each service. Generally, in order to obtain the higher profit, ESS could be initially employed to supply regulation reserve with higher payment and larger cycle degradation; after certain ESS degradation, the used ESS is employed to supply spinning reserve with lower payment and lesser cycle degradation.
This research proposes the optimal capacity planning and lifetime allocation method of ESS for participation in regulation reserve and spinning reserve, and this optimization problem is solved by differential evolution algorithm. Case studies explore the planning result under different technical specification of regulation reserve, battery price, cumulative frequency deviation, and mean execution period of spinning reserve. Simulation results not only demonstrate the effectiveness of the proposed method, but also indicate that sReg can obtain the highest rate of return within historical frequency data. Nonetheless, the rate of return of dReg0.5 is insensitive to the cumulative frequency deviation. If the cumulative frequency deviation increases, then dReg0.5 can obtain the highest rate of return.

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