Under-frequency load shedding （UFLS） is used in the power industry to rescue systems facing extreme disturbances to avoid system collapse. Traditionally, many computations are repeated to seek the proper power syste...Under-frequency load shedding （UFLS） is used in the power industry to rescue systems facing extreme disturbances to avoid system collapse. Traditionally, many computations are repeated to seek the proper power system settings such that the UFLS provides the desired good performance for selected scenarios. An adaptive UFLS method based on the genetic algorithm was developed to automate the finding of optimal parameters to minimize the repetitive trial-error calculations. Simulations demonstrate that the method has better performance than previous schemes and reduces the time and effort of the repetitive simulations.展开更多
Recently,the fast frequency response(FFR)service by large-scale battery energy storage systems(BESSs)has been successfully proved to arrest the frequency excursion during an unexpected power outage.However,adequate fr...Recently,the fast frequency response(FFR)service by large-scale battery energy storage systems(BESSs)has been successfully proved to arrest the frequency excursion during an unexpected power outage.However,adequate frequency response relies on proper evaluation of the contingency reserve of BESSs.The BESS FFR reserve is commonly managed under fixed contracts,ignoring various response characteristics of different BESSs and their coexisting interactions.This paper proposes a new methodology based on dynamic grid response and various BESS response characteristics to optimise the FFR reserves and prevent the frequency from breaching the under-frequency load shedding(UFLS)thresholds.The superiority of the proposed method is demonstrated to manage three large-scale BESSs operating simultaneously in an Australian power grid under high renewable penetration scenarios.Further,the proposed method can identify remaining battery power and energy reserve to be safely utilised for other grid services(e.g.,energy arbitrage).The results can provide valuable insights for integrating FFR into conventional ancillary services and techno-effective management of multiple BESSs.展开更多
文摘Under-frequency load shedding （UFLS） is used in the power industry to rescue systems facing extreme disturbances to avoid system collapse. Traditionally, many computations are repeated to seek the proper power system settings such that the UFLS provides the desired good performance for selected scenarios. An adaptive UFLS method based on the genetic algorithm was developed to automate the finding of optimal parameters to minimize the repetitive trial-error calculations. Simulations demonstrate that the method has better performance than previous schemes and reduces the time and effort of the repetitive simulations.
文摘Recently,the fast frequency response(FFR)service by large-scale battery energy storage systems(BESSs)has been successfully proved to arrest the frequency excursion during an unexpected power outage.However,adequate frequency response relies on proper evaluation of the contingency reserve of BESSs.The BESS FFR reserve is commonly managed under fixed contracts,ignoring various response characteristics of different BESSs and their coexisting interactions.This paper proposes a new methodology based on dynamic grid response and various BESS response characteristics to optimise the FFR reserves and prevent the frequency from breaching the under-frequency load shedding(UFLS)thresholds.The superiority of the proposed method is demonstrated to manage three large-scale BESSs operating simultaneously in an Australian power grid under high renewable penetration scenarios.Further,the proposed method can identify remaining battery power and energy reserve to be safely utilised for other grid services(e.g.,energy arbitrage).The results can provide valuable insights for integrating FFR into conventional ancillary services and techno-effective management of multiple BESSs.
