Abstract: The disclosure provides a multi-energy integrated short-term load forecasting method and system, which relates to the technical field of load forecasting. In the disclosure, after classifying the acquired relevant data of multi-energy integrated short-term load forecasting, the data after sample classification is used to train the multi-energy integrated short-term load forecasting model. The model is composed of multiple layers of temporal convolutional networks having multi-head self-attention mechanism and rotary position embedding. Finally, the trained model is used to carry out the multi-energy integrated short-term load forecasting. The disclosure can fully mine the coupling feature between multi-energy loads, improve the accuracy of multi-energy integrated short-term load forecasting, and further improve the management level and service efficiency of integrated energy demand side.

Abstract: Provided are a method and a system for optimizing charging and discharging behaviors of a BESS based on a SOH, relating to charging and discharging optimization. The number of cycles of the battery pack and corresponding DODs are obtained based on the curve of the SOC of the battery pack. Then, the SOH of the battery pack is obtained. A charging index sequence and a discharging index sequence of battery packs are obtained based on the SOH, the SOC and a charging and discharging state of the battery pack. The optimal number of the charging and discharging battery packs and optimal DODs are determined. Charging and discharging tasks are carried out according to the charging and discharging index sequences of the battery packs based on the optimal number of the charging and discharging battery packs and the optimal DODs.

Abstract: The present invention provides a method, system and storage medium for load dispatch optimization for residential microgrid. The method includes collecting environmental data and time data of residential microgrid in preset future time period; obtaining power load data of residential microgrid in future time period by inputting environmental data and time data into pre-trained load forecasting model; obtaining photovoltaic output power data of residential microgrid in future time period by inputting environmental data and time data into pre-trained photovoltaic output power forecasting model; determining objective function and corresponding constraint condition of residential microgrid in future time period, where optimization objective of objective function is to minimize total cost of residential microgrid; obtaining load dispatch scheme of residential microgrid in future time period by solving objective function with particle swarm algorithm.

Abstract: A method, a system, a storage medium and an electronic device for peer-to-peer electricity trading based on a double-layer blockchain. Provided herein relates to electricity trading. This application includes a bulk grid blockchain and a plurality of microgrid blockchains and puts forward a double-layer blockchain technology. The energy consumption plan or the energy supply plan of the trading subjects shall preferentially perform a first power dispatching-matching and a second power dispatching-matching on the microgrid blockchain, and the unsatisfied energy consumption plan or the remaining energy supply plan is uploaded to large power grid blockchain for a third power dispatching-matching.

Abstract: A method and a system for energy scheduling of shared energy storage considering degradation cost of energy storage. Provided herein relates to energy scheduling for multiple microgrids. The method includes: acquiring energy data of each of microgrids in a multi-microgrid system of shared energy storage; establishing a peer-to-peer trading model between each of the microgrids; establishing a shared energy storage trading model between the multi-microgrid system and the shared energy storage device thereof; establishing a utility grid trading model between the multi-microgrid system and the utility grid thereof; and based on the peer-to-peer trading model, the shared energy storage trading model and the utility grid trading model, setting an objective of minimizing a total operating cost of the multi-microgrid system, and solving an objective function corresponding to the objective to acquire power data of each of the microgrids at each stage.

Abstract: A capacity configuration method and system of energy storage in a microgrid. In this application, the time-series data related to photovoltaic power generation is acquired and processed to obtain the preprocessed time-series data; a time-series generative adversarial network (Time GAN) is trained based on the preprocessed time-series data to perform data enhancement to obtain enhanced time-series data; and based on the enhanced time-series data, a distributionally robust optimization model is used to perform capacity configuration of energy storage.

Abstract: The disclosure provides a method, a system and a storage medium for predicting power load probability density based on deep learning. The method comprises: S101, collecting power load data of a user, meteorological data and air quality data in a preset historical time period, and dividing the collected data into a training set and a test set; S102, determining a deep learning model for predicting power load; S103, inputting the test set into the deep learning model for predicting power load, and obtaining power load prediction data of the user at different quantile points in a third time interval; S104, performing kernel density estimation and obtaining a probability density curve of the power load of the user in the third time interval.

Abstract: The invention provided an electric vehicle charging scheduling method, apparatus and system based on cloud-edge collaboration, a storage medium and an electronic device. In the present invention, a charging request of an electric vehicle user is accepted and processed by an edge computing unit, and a target charging station for a to-be-charged electric vehicle is determined with a minimum traveling cost as a target, so that a data transmission distance is reduced, and the electric vehicle user is timely assisted in selecting the target charging station and completing a charging appointment. After the charging appointment is made, charging data is uploaded to a charging optimization scheduling model pre-trained by a cloud platform for obtaining an electric vehicle charging scheduling strategy, so that powerful cloud platform computing abilities and rapid response advantages of the edge computing unit are fully utilized, the problem of network congestion is avoided, and timeliness is improved.

Abstract: Provided are a method and a system for optimizing charging and discharging behaviors of a BESS based on a SOH, relating to charging and discharging optimization. The number of cycles of the battery pack and corresponding DODs are obtained based on the curve of the SOC of the battery pack. Then, the SOH of the battery pack is obtained. A charging index sequence and a discharging index sequence of battery packs are obtained based on the SOH, the SOC and a charging and discharging state of the battery pack. The optimal number of the charging and discharging battery packs and optimal DODs are determined. Charging and discharging tasks are carried out according to the charging and discharging index sequences of the battery packs based on the optimal number of the charging and discharging battery packs and the optimal DODs.

Abstract: The present invention provides a method, system and storage medium for load dispatch optimization for residential microgrid. The method includes collecting environmental data and time data of residential microgrid in preset future time period; obtaining power load data of residential microgrid in future time period by inputting environmental data and time data into pre-trained load forecasting model; obtaining photovoltaic output power data of residential microgrid in future time period by inputting environmental data and time data into pre-trained photovoltaic output power forecasting model; determining objective function and corresponding constraint condition of residential microgrid in future time period, where optimization objective of objective function is to minimize total cost of residential microgrid; obtaining load dispatch scheme of residential microgrid in future time period by solving objective function with particle swarm algorithm.

Abstract: The disclosure provides a method, a system and a storage medium for predicting power load probability density based on deep learning. The method comprises: S101, collecting power load data of a user, meteorological data and air quality data in a preset historical time period, and dividing the collected data into a training set and a test set; S102, determining a deep learning model for predicting power load; S103, inputting the test set into the deep learning model for predicting power load, and obtaining power load prediction data of the user at different quantile points in a third time interval; S104, performing kernel density estimation and obtaining a probability density curve of the power load of the user in the third time interval.

Abstract: The present invention relates to a method and a system for compressing data from a smart meter. The method comprises: LZ-encoding electricity load data collected by the smart meter whenever the smart meter collects the electricity load data; storing the LZ-encoded electricity load data in a temporary database through a smart grid communication channel; reading the electricity load data from the temporary database every preset second duration, wherein the read electricity load data is electricity load data stored in the temporary database within the second duration before a corresponding reading time point; and LZ-decoding the read electricity load data, SAX-compressing the LZ-decoded electricity load data, and storing the SAX-compressed electricity load data in a data center. The present invention has high compression rate, reduces the transmission burden for communication lines and storage burden for the data center, and improves the efficiency of smart electricity data analysis and mining.

Abstract: The present invention relates to a method and a system for compressing data from a smart meter. The method comprises: LZ-encoding electricity load data collected by the smart meter whenever the smart meter collects the electricity load data; storing the LZ-encoded electricity load data in a temporary database through a smart grid communication channel; reading the electricity load data from the temporary database every preset second duration, wherein the read electricity load data is electricity load data stored in the temporary database within the second duration before a corresponding reading time point; and LZ-decoding the read electricity load data, SAX-compressing the LZ-decoded electricity load data, and storing the SAX-compressed electricity load data in a data center. The present invention has high compression rate, reduces the transmission burden for communication lines and storage burden for the data center, and improves the efficiency of smart electricity data analysis and mining.

Abstract: A charging and discharging scheduling method for electric vehicles in microgrid under time-of-use price includes: determining the system structure of the microgrid and the characters of each unit; establishing the optimal scheduling objective function of the microgrid considering the depreciation cost of the electric vehicle (EV) battery under time-of-use price; determining the constraints of each distributed generator and EV battery, and forming an optimal scheduling model of the microgrid together with the optimal scheduling objective function of the microgrid; determining the amount, starting and ending time, starting and ending charge state, and other basic calculating data of the EV accessing the microgrid under time-of-use price; determining the charge and discharge power of the EV when accessing the grid, by solving the optimal scheduling model of the microgrid with a particle swarm optimization algorithm.

Abstract: A charging and discharging scheduling method for electric vehicles in microgrid under time-of-use price includes: determining the system structure of the microgrid and the characters of each unit; establishing the optimal scheduling objective function of the microgrid considering the depreciation cost of the electric vehicle (EV) battery under time-of-use price; determining the constraints of each distributed generator and EV battery, and forming an optimal scheduling model of the microgrid together with the optimal scheduling objective function of the microgrid; determining the amount, starting and ending time, starting and ending charge state, and other basic calculating data of the EV accessing the microgrid under time-of-use price; determining the charge and discharge power of the EV when accessing the grid, by solving the optimal scheduling model of the microgrid with a particle swarm optimization algorithm.