Vigorously developing flexible resources in power systems will be the key to building a new power system and realizing energy trans-formation.The investment construction cost and operation cost of various flexible res...Vigorously developing flexible resources in power systems will be the key to building a new power system and realizing energy trans-formation.The investment construction cost and operation cost of various flexible resources are different,and the adjustment ability is different in different timescales.Therefore,the optimization of complementary allocation of various resources needs to take into account the economy and adjustment ability of different resources.In this paper,the global K-means load clustering model is pro-posed and the 365-day net load is reduced to eight typical daily net loads by clustering.Secondly,a two-level optimization model of flexible resource complementary allocation considering wind power and photovoltaic consumption is constructed.The flexible resources involved include the flexible transformation of thermal power,hydropower,pumped storage,energy storage,and demand response.The upper-layer model optimizes the capacity allocation of various flexible resources with the minimum investment and construction cost as the goal and the lower layer optimizes the operating output of various units with the minimum operating cost as the goal.The results of the example analysis show that the flexible capacity of thermal power units has nothing to do with the abandonment rate of renewable energy.As the abandonment rate of renewable energy decreases,the optimal capacity of pumped storage,electrochemical energy storage,and hydropower units increases.When the power-abandonment rate of renewable energy is 5%,the optimal allocation capacity of thermal power flexibility transformation,pumped storage,electrochemical energy storage,hydropower unit,and adjustable load in Province A is 5313,17090,5830,72113,and 4250 MW,respectively.Under the condition that the renewable-energy abandonment rate is 0,5%,and 10%respectively,the configured capacity of pumped storage is 20000,17090,and 14847 MW,respectively.展开更多
Photovoltaic(PV) power generation is characterized by randomness and intermittency due to weather changes.Consequently, large-scale PV power connections to the grid can threaten the stable operation of the power syste...Photovoltaic(PV) power generation is characterized by randomness and intermittency due to weather changes.Consequently, large-scale PV power connections to the grid can threaten the stable operation of the power system. An effective method to resolve this problem is to accurately predict PV power. In this study, an innovative short-term hybrid prediction model(i.e., HKSL) of PV power is established. The model combines K-means++, optimal similar day approach,and long short-term memory(LSTM) network. Historical power data and meteorological factors are utilized. This model searches for the best similar day based on the results of classifying weather types. Then, the data of similar day are inputted into the LSTM network to predict PV power. The validity of the hybrid model is verified based on the datasets from a PV power station in Shandong Province, China. Four evaluation indices, mean absolute error, root mean square error(RMSE),normalized RMSE, and mean absolute deviation, are employed to assess the performance of the HKSL model. The RMSE of the proposed model compared with those of Elman, LSTM, HSE(hybrid model combining similar day approach and Elman), HSL(hybrid model combining similar day approach and LSTM), and HKSE(hybrid model combining K-means++,similar day approach, and LSTM) decreases by 66.73%, 70.22%, 65.59%, 70.51%, and 18.40%, respectively. This proves the reliability and excellent performance of the proposed hybrid model in predicting power.展开更多
基金funded by the Science and Technology Project of State Grid Sichuan Electric Power Company(521996230008).
文摘Vigorously developing flexible resources in power systems will be the key to building a new power system and realizing energy trans-formation.The investment construction cost and operation cost of various flexible resources are different,and the adjustment ability is different in different timescales.Therefore,the optimization of complementary allocation of various resources needs to take into account the economy and adjustment ability of different resources.In this paper,the global K-means load clustering model is pro-posed and the 365-day net load is reduced to eight typical daily net loads by clustering.Secondly,a two-level optimization model of flexible resource complementary allocation considering wind power and photovoltaic consumption is constructed.The flexible resources involved include the flexible transformation of thermal power,hydropower,pumped storage,energy storage,and demand response.The upper-layer model optimizes the capacity allocation of various flexible resources with the minimum investment and construction cost as the goal and the lower layer optimizes the operating output of various units with the minimum operating cost as the goal.The results of the example analysis show that the flexible capacity of thermal power units has nothing to do with the abandonment rate of renewable energy.As the abandonment rate of renewable energy decreases,the optimal capacity of pumped storage,electrochemical energy storage,and hydropower units increases.When the power-abandonment rate of renewable energy is 5%,the optimal allocation capacity of thermal power flexibility transformation,pumped storage,electrochemical energy storage,hydropower unit,and adjustable load in Province A is 5313,17090,5830,72113,and 4250 MW,respectively.Under the condition that the renewable-energy abandonment rate is 0,5%,and 10%respectively,the configured capacity of pumped storage is 20000,17090,and 14847 MW,respectively.
基金supported by the No. 4 National Project in 2022 of the Ministry of Emergency Response (2022YJBG04)the International Clean Energy Talent Program (201904100014)。
文摘Photovoltaic(PV) power generation is characterized by randomness and intermittency due to weather changes.Consequently, large-scale PV power connections to the grid can threaten the stable operation of the power system. An effective method to resolve this problem is to accurately predict PV power. In this study, an innovative short-term hybrid prediction model(i.e., HKSL) of PV power is established. The model combines K-means++, optimal similar day approach,and long short-term memory(LSTM) network. Historical power data and meteorological factors are utilized. This model searches for the best similar day based on the results of classifying weather types. Then, the data of similar day are inputted into the LSTM network to predict PV power. The validity of the hybrid model is verified based on the datasets from a PV power station in Shandong Province, China. Four evaluation indices, mean absolute error, root mean square error(RMSE),normalized RMSE, and mean absolute deviation, are employed to assess the performance of the HKSL model. The RMSE of the proposed model compared with those of Elman, LSTM, HSE(hybrid model combining similar day approach and Elman), HSL(hybrid model combining similar day approach and LSTM), and HKSE(hybrid model combining K-means++,similar day approach, and LSTM) decreases by 66.73%, 70.22%, 65.59%, 70.51%, and 18.40%, respectively. This proves the reliability and excellent performance of the proposed hybrid model in predicting power.
