CN-122000856-A - Dynamic operation optimization method and system of source network charge storage integrated overcharging system

CN122000856ACN 122000856 ACN122000856 ACN 122000856ACN-122000856-A

Abstract

The invention discloses a dynamic operation optimization method and a system of a source network charge storage integrated overcharging system, and relates to the technical field of intelligent power grids. The method comprises the steps of periodically collecting system multisource real-time operation data, forming a standardized system state data set after synchronous verification processing, generating an ultra-short-term prediction curve of photovoltaic power generation and charging load based on the data set and weather forecast data, constructing a mixed integer linear programming model containing multiple constraints based on the ultra-short-term prediction curve with minimum total operation cost as a target, solving the model to obtain an optimal power distribution plan of each unit in a future period, immediately issuing a real-time reference power instruction to each execution device, automatically starting a complete flow of new data collection to instruction issuing after a preset optimization period, and performing rolling optimization by utilizing updated system state data, so that closed-loop dynamic optimization control is realized, and the economy and the grid mutual capacity of the system are improved.

Inventors

DENG WANLI
FENG GUIQING
ZHANG XINYONG
HUANG XIAOHUI
HUANG GANG

Assignees

深能源(深圳)创新技术有限公司
南京赫曦电气有限公司

Dates

Publication Date: 20260508
Application Date: 20251128

Claims (10)

1. The dynamic operation optimization method of the source network charge storage integrated overcharging system is characterized by comprising the following steps of: s1, periodically acquiring multi-source real-time operation data of a super-charging system, and performing time synchronization and validity check processing on the multi-source real-time operation data to form a standardized system state data set; S2, generating a power generation power prediction curve of the photovoltaic unit and a power demand prediction curve of a charging load in a future preset time window based on a standardized system state data set and weather forecast data; s3, constructing a mixed integer linear programming model containing power balance constraint, equipment operation constraint and user demand constraint based on a standardized system state data set, a generated power prediction curve and a power demand prediction curve by taking the total operation cost of a minimized system as a target; S4, solving a mixed integer linear programming model to obtain an optimal power distribution plan of each controllable unit in a preset time window in the future, and sending a real-time reference power instruction corresponding to the current moment in the power distribution plan to a power grid interaction interface, an energy storage converter and a bidirectional charging pile; and S5, after a preset optimization period, returning to the execution steps S1 to S4, and starting a new round of optimization flow by using the updated system state data to realize closed-loop rolling optimization control.
2. The method for optimizing dynamic operation of a source network charge storage integrated overcharge system of claim 1, wherein S1 comprises: real-time operation parameters and state quantities of a power supply side, a power grid side and a load side are respectively acquired from a photovoltaic inverter, an energy storage system, a grid-connected point, a charging pile and an in-station load loop through corresponding industrial communication protocols; detecting and eliminating abnormal values of the collected original data stream, and filling the missing data segment by using an interpolation method; Performing time stamp alignment on the cleaned data by taking a high-precision clock source as a reference, and performing aggregation at intervals of a preset optimization period to generate a system state instantaneous value or average value with a unified time tag; The aggregated data is organized according to a preset structure to form a standard data frame containing time stamps, unit powers, key state quantities and external signals, and the standard data frame is used as a standardized system state data set.
3. The method for optimizing dynamic operation of a source network charge storage integrated overcharge system of claim 1, wherein said S2 comprises: Based on the acquired weather forecast data of the future preset period, combining the rated parameters and physical characteristics of the photovoltaic module, and generating a power generation prediction curve of the photovoltaic unit in the future preset time window through deterministic physical model calculation; Predicting a future newly-increased charging load through a probabilistic model based on real-time charging demand and historical charging behavior statistical data of the on-station vehicle in the standardized system state data set; And superposing the deterministic charging demand of the existing vehicles and the probabilistic charging demand of the newly-added vehicles in the future, and carrying out statistical analysis on the superposition result by utilizing a Monte Carlo simulation method to generate a power demand prediction curve of the total charging load in a preset time window in the future.
4. The method for optimizing dynamic operation of a source network charge storage integrated overcharge system of claim 1, wherein said S3 comprises: defining continuous decision variables related to power grid interaction power, energy storage system charging and discharging power and charging and discharging power of each bidirectional charging pile, and defining binary decision variables related to the energy storage system and the charging and discharging states of the bidirectional charging piles; Constructing an objective function aiming at minimizing the total operation cost of the system, wherein the total operation cost comprises the electricity purchasing cost of the power grid, the electricity selling income to the power grid, the ageing cost of the energy storage equipment and the punishment cost which does not meet the charging requirement; Establishing a constraint condition set of a system, wherein the constraint condition set comprises a system power balance constraint based on power prediction, a physical operation constraint of an energy storage system and a bidirectional charging pile and a completeness constraint for guaranteeing the charging requirement of an electric automobile user; And constructing a mixed integer linear programming model by integrating the decision variables, the objective function and the constraint condition set.
5. The method for optimizing dynamic operation of a source network charge storage integrated overcharge system of claim 1, wherein said S4 comprises: Converting the mixed integer linear programming model into a standard input format of a mathematical programming solver, setting solving parameters, and calling the solver to carry out numerical solution so as to obtain an optimal solution set of decision variables; under the condition of successful solution, extracting a decision variable value corresponding to the first time interval in the optimization window from the optimal solution set; mapping the extracted decision variable value into a real-time reference power instruction of the power grid interactive interface, the energy storage converter and each bidirectional charging pile; And respectively issuing real-time reference power instructions to corresponding equipment controllers to execute a power distribution plan.
6. The method for optimizing dynamic operation of a source network charge storage integrated overcharge system according to claim 1, wherein said S5 comprises: after the real-time reference power instruction of the current period is issued, waiting for a preset optimization period, and automatically triggering a new round of optimization flow; when a new round of optimization flow is started, rolling and updating a prediction curve based on the latest collected system state data, and updating initial conditions of a mixed integer linear programming model; And (3) forward rolling the optimization window for one preset optimization period, and repeatedly executing S1 to S4 by taking the updated prediction curve, the model initial condition and the optimization window as inputs to form closed loop feedback control so as to realize dynamic operation optimization of the system.
7. The method for optimizing dynamic operation of a source-network-charge-storage integrated overcharging system according to claim 6, wherein the rolling updating of the prediction curve comprises rolling the generated power prediction curve and the power demand prediction curve forward along the time axis for one preset optimization period and discarding outdated data segments.
8. The method for optimizing dynamic operation of a source network charge storage integrated overcharge system according to claim 1, wherein the issuing and execution of the real-time reference power instruction are realized by a central controller deployed in a station through an industrial Ethernet and by adopting ModbusTCP or MQTT communication protocol to carry out real-time communication with a power grid interaction interface, an energy storage converter and a bidirectional charge pile.
9. The method for optimizing dynamic operation of a source network charge storage integrated overcharging system according to claim 1, further comprising displaying a power generation power prediction curve, a power demand prediction curve and a real-time reference power command in real time through a graphical interface for monitoring the operation state of the system.
10. A dynamic operation optimization system of a source-network-charge-storage integrated overcharge system, for implementing a dynamic operation optimization method of a source-network-charge-storage integrated overcharge system as set forth in any one of claims 1 to 9, comprising: The preprocessing module is used for periodically acquiring multi-source real-time operation data of the super-charging system, and performing time synchronization and validity check processing on the multi-source real-time operation data to form a standardized system state data set; The ultra-short-term prediction module is used for generating a power generation power prediction curve of the photovoltaic unit and a power demand prediction curve of the charging load in a future preset time window based on the standardized system state data set and the weather forecast data; The optimization modeling module is used for constructing a mixed integer linear programming model containing power balance constraint, equipment operation constraint and user demand constraint based on a standardized system state data set, a generated power prediction curve and a power demand prediction curve by taking the total operation cost of a minimized system as a target; The optimization control module is used for solving the mixed integer linear programming model to obtain an optimal power distribution plan of each controllable unit in a preset time window in the future, and sending a real-time reference power instruction corresponding to the current moment in the power distribution plan to the power grid interaction interface, the energy storage converter and the bidirectional charging pile; and the rolling optimization module returns to execute the steps S1 to S4 after a preset optimization period, and starts a new round of optimization flow with updated system state data to realize closed-loop rolling optimization control.

Description

Dynamic operation optimization method and system of source network charge storage integrated overcharging system Technical Field The invention relates to the technical field of smart power grids, in particular to a dynamic operation optimization method and a system of a source network charge storage integrated overcharging system. Background With the deep advancement of global energy transformation strategies and the establishment of "two carbon" targets, the electric automobile industry presents an explosive growth situation. As a key infrastructure for popularization of electric automobiles, the construction requirements of high-power super-charging stations are increasingly urgent. However, the super-charging station load has the characteristics of strong randomness, high volatility, high power level and the like, and the large-scale access of the super-charging station load forms a serious challenge for the stable operation of the regional power distribution network, and the super-charging station load is mainly characterized by the problems of aggravating the peak load of the power grid, causing voltage fluctuation, increasing line loss and the like. In order to improve the capacity of a power grid for absorbing renewable energy sources and relieve capacity expansion pressure, a super-charging station is combined with a distributed photovoltaic and energy storage system, and a source network and charge storage integrated system is constructed, so that the method has become an important development direction. Under the background, how to perform collaborative optimization management on the distributed power supply, the energy storage unit and the flexible charging load in the system is a core key for realizing economical, efficient and power grid friendly operation. In the prior art, various researches have been carried out on the operation optimization of the super-charging station. Some schemes focus on guiding users to charge orderly through electricity price strategies so as to stabilize load curves, and other schemes use an energy storage system to cut peaks and fill valleys. However, the existing technical solutions generally face some challenges, such as fluctuation of photovoltaic output, uncertainty of charging requirements of electric vehicles, and coordination and scheduling of diversified controllable resources, such as energy storage, V2G, and the like. These factors often result in overall optimization of the system and limited real-time control capabilities. How to dynamically respond to the power grid demand and simultaneously consider the economical efficiency of system operation and the satisfaction of the charging demand of users has become a problem to be solved in the optimization management of the super-charging station. Therefore, a dynamic operation optimization method capable of deeply integrating each element of source network load storage and performing online rolling optimization is needed in the field, so that the comprehensive operation efficiency of the super-charging station and the supporting capacity of the power grid are comprehensively improved. Disclosure of Invention Based on the shortcomings of the prior art, the invention aims to provide a dynamic operation optimization method and a system of a source network charge storage integrated super-charging system, so as to solve the technical problems. In order to achieve the purpose, the invention provides the following technical scheme that the dynamic operation optimization method of the source network charge storage integrated overcharging system comprises the following steps: s1, periodically acquiring multi-source real-time operation data of a super-charging system, and performing time synchronization and validity check processing on the multi-source real-time operation data to form a standardized system state data set; S2, generating a power generation power prediction curve of the photovoltaic unit and a power demand prediction curve of a charging load in a future preset time window based on a standardized system state data set and weather forecast data; s3, constructing a mixed integer linear programming model containing power balance constraint, equipment operation constraint and user demand constraint based on a standardized system state data set, a generated power prediction curve and a power demand prediction curve by taking the total operation cost of a minimized system as a target; S4, solving a mixed integer linear programming model to obtain an optimal power distribution plan of each controllable unit in a preset time window in the future, and sending a real-time reference power instruction corresponding to the current moment in the power distribution plan to a power grid interaction interface, an energy storage converter and a bidirectional charging pile; and S5, after a preset optimization period, returning to the execution steps S1 to S4, and starting a new round of optimization flow by using the upda