Tianjin University

State Grid Corporation of China

State Grid Tianjin Electric Power Company

Electric Power Science & Research Institute of State Grid Tianjin Electric Power Company

Yunfei Mu

Hongjie Jia

Xiaodan Yu

Xiandong Xu

Xiaolong Jin

Xiaohong Dong

Xudong Wang

Yi Ding

Wei Wei

Xianxu Huo

Yan Qi

Lei Wu

Xuejun Shang

Zhijun Zhang

Qian Xiao

Wenbiao Lu

Yansong Zhao

Yu Jin

Huiqiao Liu

Tianxiang Li

Yidi Zhu

Bofeng Luo

Shuo Liang

Kecheng He

Xiaoyu Wang

A collaborative optimization method for an Electric and Heating Networks (EHN) and a building user includes: establishing a coordinated optimization scheduling framework of the EHN and a building in a park; based on the coordinated optimization scheduling framework, constructing an optimal thermal power flow scheduling model of the EHN, and constructing a building model to simulate thermal characteristics and energy consumption of the building; based on the optimal thermal power flow scheduling model and the building model, taking the lowest operation cost of EHN and the lowest heat consumption of building user as a goal, constructing a collaborative optimization model of the EHN and the building user; outputting a collaborative optimization scheme of the optimal thermal power flow scheduling of the EHN and the heat consumption of the building user through the collaborative optimization model of the EHN and the building user.

Collaborative optimization method, system, equipment and storage medium for electric and heating networks and building user

A method and device for supporting weak networks in an AC/DC distribution network are provided. The method includes the following steps: constructing a topology of the AC/DC distribution network, where the topology is configured to support the weak networks and isolate faults actively, and the topology is a multi-terminal ring structure; establishing a distributed optimization method for the AC/DC distribution network based on the topology; obtaining power fluctuations of the AC/DC distribution network, where the AC/DC distribution network can switch a droop control mode automatically according to the power fluctuations; and obtaining frequency fluctuations of the AC/DC distribution network, where the AC/DC distribution network performs energy dispatching according to the frequency fluctuations to match the droop control mode of the AC/DC distribution network.

Method and device for supporting weak networks in AC/DC distribution network

The invention pertains to optimal scheduling and trading technology for microgrid systems, specifically focusing on a real-time peer-to-peer energy trading method involving time-varying virtual energy storage. This method predicts environmental information within a specified time domain and incorporates historical transaction data into virtual energy storage modeling and real-time energy trading. During the supply and demand energy extraction phase, it quantitatively extracts supply and demand energy and the marginal cost for trading using an autonomous energy management model for prosumers. In the transaction price optimization phase, it optimizes the transaction price based on historical data to maximize prosumers' income. The distributed transaction decision optimization method, utilizing a continuous double auction, enhances transaction matching decisions to maximize prosumers' income and accommodate multi-transaction preference levels. This approach leverages the complementary potential of energy resources to balance supply and demand within the system.

Real-time peer-to-peer energy trading method considering time-varying virtual energy storage

A bilevel coordinated optimization method for fixed and mobile charging facilities on highways, includes: constructing an optimization model framework which includes an upper-layer coordinated location optimization model and a lower-layer coordinated capacity optimization model, where the upper-layer coordinated location optimization model is used to optimize locations of charging stations and determine locations and time points of charging demands that require truck mobile charger (TMC) deployment, while the lower-layer coordinated capacity optimization model is used to optimize TMC and fixed charger (FC) capacities at candidate sites, improving an utilization rate of FCs; and performing equivalent linearization on a nonlinear problem using a big-M method and converting the problem into a mixed-integer linear programming model, and implementing a data exchange process between upper and lower layers using analytical target cascading. The present disclosure enhances the utilization and flexibility of charging facilities on highways.

Bilevel coordinated optimization method for fixed and mobile charging facilities on highways

Provided is a source-load cooperation access method for a power distribution region. The method includes: establishing a timing feature model of a distributed generator and a timing feature model of a load respectively, acquiring a timing feature of the distributed generator in an access power distribution region by using maximum likelihood estimation and acquiring a timing feature of a user which accesses the power distribution region by using classification and regression trees; and inputting the timing feature of the distributed generator and the timing feature of the user which access the power distribution region into a combination optimization model, and determining a source-load access feeder through optimization.

Source-load cooperation access method for a power distribution region, terminal and storage medium

A planning method of EV fast charging stations on the expressway, comprising the following steps: Step 1: forecasting the spatial and temporal distribution of EV charging load, that is to determine the time and location of each EV needing charging on the expressway; Step 2: based on the forecast result achieved by Step 1, determining the locations of the fast charging stations on the expressway by the nearest neighbor clustering algorithm; Step 3: according to the spatial and temporal forecast result of the EV charging load and the locations of the fast charging stations, determining the number c of the chargers in each fast charging station by queuing theory. Due to battery characteristics, traditional gas stations do not completely match the fast charging stations. In the planning method, the locations and times for charging of the EVs are considered to determine the capacities and locations of the fast charging stations, which can meet the charging needs of the EVs more than the traditional gas station, and thus promote the development of EVs.

Planning method of electric vehicle fast charging stations on the expressway

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Tianjin, China

Yunfei Mu