North China Electric Power University

Other

State Grid Electric Vehicle Service Ltd.

Dunnan Liu

Heping Jia

Mingguang Liu

Lingxiang Wang

Wen Jie Wang

Yue Hua Zhang

Min Yan

Ye Yang

Desheng Bai

Xiaofeng Peng

Shu Su

Mengjiao Zou

Guangyu Qin

Yongquan Chen

The invention relates to an optimal allocation method for stored energy coordinating electric vehicles (EVs) to participate in auxiliary service market (ASM), including the following steps: 1. Predict the reported capacity of daily 96 points for EVs to participate in the ASM by least square support vector machine (LSSVM). 2. Fit the daily total load distribution of EVs. 3. Determine the error distribution between the reported capacity and the actual response capacity, and simulate the total daily load capacity of EVs in the future with Monte Carlo method. 4. Calculate the energy storage capacity required by EVs daily participating in ASM. 5. Build the objective function to minimize the scheduling risk of auxiliary service. 6. Solve the energy storage model in step 5 with particle swarm optimization (PSO), and output the configuration results of optimal energy storage capacity and energy storage power. The invention can improve the adjustable capacity of EVs participating in ASM.

Optimal allocation method for stored energy coordinating electric vehicles to participate in ancillary service market

The present invention discloses a wind power consumption method of a virtual power plant with consideration of comprehensive demand responses of electrical loads and heat loads, which comprises: establishing a wind turbine output model, so as to obtain a wind power prediction curve; establishing heat load demand models before/after demand responses and heat supply equipment output models before/after the demand responses, so as to obtain the abandoned wind quantities per moment before/after the demand responses and the total abandoned wind quantities before/after the demand responses; judging that whether consumption is promoted or not according to the total abandoned wind quantities before/after the demand responses; and establishing a storage battery capacity model and judging the charging/discharging state and the charging/discharging capacity of a storage battery.

Wind power consumption method of virtual power plant with consideration of comprehensive demand responses of electrical loads and heat loads

The present invention relates to a load forecasting method based on a multi-energy coupling scene, which comprises the following steps: step 1, establishing a multilevel indicator system of key influencing factors of load demand in a multi-energy mode; step 2, obtaining key influencing factors influencing the total load demand and the total supply of the multi-energy coupling load; step 3, normalizing the data of the key influencing factors extracted in step 2, and initializing population characteristic parameters of adaptive firework algorithm (AFWA); step 4, forecasting the regional total power demand and regional coupling power supply respectively by adopting LSSVM optimized by the AFWA algorithm; and step 5, forecasting the net power load demand on the regional coupling power supply. The present invention improves the calculation efficiency and model stability and also ensures the forecasting accuracy.

Power load forecasting method in multi-energy coupling mode

Growing community of inventors

Beijing, China

Dunnan Liu