CN-122001003-A - Carbon index constraint-based demand side resource and energy storage cooperative regulation and control method and system

CN122001003ACN 122001003 ACN122001003 ACN 122001003ACN-122001003-A

Abstract

The invention discloses a method and a system for regulating and controlling demand side resources and energy storage in a coordinated manner based on carbon index constraint, wherein the method comprises the steps of collecting real-time load data, renewable energy output data, energy storage equipment state data of a regional power grid and dynamic carbon emission intensity signals issued by a superior power grid; the method comprises the steps of generating a load side carbon emission intensity spectrum with space-time resolution through a source-charge-storage full-link carbon flow tracking algorithm based on dynamic carbon emission intensity signals, establishing a virtual carbon pool taking carbon cost as a weight factor and generating a flexible load regulation priority sequence according to the load side carbon emission intensity spectrum, combining the flexible load regulation priority sequence with a real-time charge-discharge boundary of an energy storage system, solving a multi-time scale collaborative optimization model by taking the system carbon intensity as a core constraint and generating a source network charge storage integrated regulation strategy. By utilizing the embodiment of the invention, the fine metering and active regulation of the carbon emission at the load side can be realized, and the response capability of the system to the dynamic carbon constraint is improved.

Inventors

YAO HUI
LI JUNXIANG
GE LINGFENG
HUANG LIANG
YU RUITING

Assignees

浙江省邮电工程建设有限公司

Dates

Publication Date: 20260508
Application Date: 20251216

Claims (10)

1. A demand side resource and energy storage cooperative regulation and control method based on carbon index constraint is characterized by comprising the following steps: Collecting real-time load data, renewable energy output data, energy storage equipment state data of a regional power grid and dynamic carbon emission intensity signals issued by a superior power grid; generating a load side carbon emission intensity spectrum with space-time resolution through a source-load-storage full-link carbon flow tracking algorithm based on the dynamic carbon emission intensity signal; according to the load side carbon emission intensity spectrum and a demand side resource adjustable potential model, establishing a virtual carbon pool taking carbon cost as a weight factor and generating a flexible load regulation priority sequence; and combining the flexible load regulation priority sequence with the real-time charge and discharge boundary of the energy storage system, solving a multi-time scale collaborative optimization model by taking the carbon intensity of the system as a core constraint, and generating a source network charge storage integrated regulation strategy to realize the real-time controllable carbon emission intensity and the collaborative optimization of the system operation economy.
2. The method of claim 1, wherein the collecting real-time load data, renewable energy output data, energy storage device status data, and dynamic carbon emission intensity signals issued by the upper level power grid comprises: Acquiring power data of load nodes in real time through an Internet of things sensor deployed at each node of a regional power grid, acquiring real-time output data of a renewable energy power station through an energy management system, acquiring charge state and charge-discharge power data of energy storage equipment through a battery management system, and generating an original heterogeneous data set; Performing time stamp alignment and data cleaning on the original heterogeneous data set, unifying data with different sampling frequencies to the same time reference, removing bad data caused by abnormal jump points and communication interruption, and generating a time sequence data set after cleaning alignment; receiving a dynamic carbon emission intensity signal issued by a superior power grid through a power dispatching data network, updating the signal with a minute frequency, correlating the signal with a regional power grid gateway metering point, and generating a dynamic carbon emission intensity time sequence signal; and fusing the cleaned and aligned time sequence data set with the dynamic carbon emission intensity time sequence signal, and packaging according to a standardized data model to generate a standardized multi-source data set for subsequent carbon flow tracking analysis.
3. The method of claim 2, wherein generating a load side carbon emission intensity spectrum with spatiotemporal resolution by a source-load-storage full-link carbon flow tracking algorithm based on the dynamic carbon emission intensity signal comprises: Analyzing a dynamic carbon emission intensity time sequence signal in a standardized multi-source data set, establishing a carbon flow network topology taking a regional power grid gateway as a starting point and a load node as an ending point, and generating a carbon flow network model; based on a carbon flow network model and real-time tide data, calculating electric energy carbon flow distribution from a power generation side to a load side by adopting a proportion sharing principle, and generating a node carbon flow intensity matrix; Establishing a carbon state transition model of the energy storage equipment, tracking carbon emission corresponding to electric energy absorbed in an energy storage charging period, and carrying out carbon flow tracing and distribution in a discharging period to generate an energy storage carbon flow distribution matrix; And (3) integrating the node carbon flow intensity matrix and the energy storage carbon flow distribution matrix, precisely distributing carbon emission to each load node and each time section by utilizing a mixed integer linear programming algorithm, and finally generating a load side carbon emission intensity spectrum with space-time resolution.
4. A method according to claim 3, wherein said creating a virtual carbon pool with carbon cost as a weighting factor and generating a flexible load regulation priority sequence according to the load side carbon emission intensity spectrum in combination with a demand side resource adjustable potential model comprises: Analyzing a load side carbon emission intensity spectrum, calculating marginal carbon emission intensity of each flexible load node under different time sections, converting the marginal carbon emission intensity into unit electric quantity carbon cost, and generating a carbon cost vector; Calling a demand-side resource adjustable potential model, and quantizing the reducible power, the transferable electric quantity and the response time constant of various flexible loads based on historical operation data and load characteristics to generate an adjustable potential mapping table; Taking the carbon cost vector as a core weight factor, combining an adjustable potential mapping table, designing a pricing and settlement mechanism of a virtual carbon pool, and establishing a dynamic carbon price curve related to carbon cost and adjustment value; And comprehensively sequencing according to the dynamic carbon price curve and the regulating cost of each flexible load to generate a flexible load regulating priority sequence which is comprehensively and optimally arranged according to the carbon emission reduction benefit and the economy.
5. The method of claim 4, wherein the combining the flexible load control priority sequence with the real-time charging and discharging boundary of the energy storage system, taking the system carbon intensity as a core constraint without limit crossing, solving a multi-time scale collaborative optimization model and generating a source network load storage integrated control strategy, realizing the real-time controllable carbon emission intensity and the collaborative optimization of the system operation economy, comprises: Integrating a flexible load regulation priority sequence, a real-time charge-discharge power boundary of an energy storage system and a renewable energy output prediction curve, constructing a collaborative optimization model framework comprising three time scales of day before, day in and real time, and generating a multi-time-scale optimization model parameter set; In the optimization model, taking the real-time carbon strength at the gateway of the regional power grid and the upper power grid as a core constraint condition, and embedding an energy storage operation constraint and a load adjustment constraint to generate an optimization problem with carbon constraint; Solving an optimization problem with carbon constraint by adopting a distributed robust optimization algorithm, calculating to obtain the optimal adjustment quantity of each flexible load, the optimal charge-discharge plan of an energy storage system and a renewable energy source absorption scheme under a plurality of time sections in the future, and generating a preliminary cooperative regulation strategy; And carrying out safety check and dynamic adjustment on the preliminary cooperative regulation strategy to form a final executable source network charge storage integrated regulation instruction set, and realizing the cooperative target of controllable carbon emission intensity in the whole process and optimal total system operation cost.
6. Demand side resource and energy storage cooperative regulation and control system based on carbon index constraint, which is characterized by comprising: The acquisition module is used for acquiring real-time load data, renewable energy output data, energy storage equipment state data and dynamic carbon emission intensity signals issued by the upper-level power grid; The generation module is used for generating a load side carbon emission intensity spectrum with space-time resolution through a source-charge-storage full-link carbon flow tracking algorithm based on the dynamic carbon emission intensity signal; the building module is used for building a virtual carbon pool taking carbon cost as a weight factor and generating a flexible load regulation priority sequence according to the load side carbon emission intensity spectrum and combining a demand side resource adjustable potential model; And the regulation and control module is used for combining the flexible load regulation and control priority sequence and the real-time charge and discharge boundary of the energy storage system, solving the multi-time scale collaborative optimization model by taking the system carbon intensity as a core constraint without limit crossing, generating a source network charge and storage integrated regulation and control strategy, and realizing the real-time controllable carbon emission intensity and the collaborative optimization of the system operation economy.
7. The system according to claim 6, wherein the acquisition module is specifically configured to: Acquiring power data of load nodes in real time through an Internet of things sensor deployed at each node of a regional power grid, acquiring real-time output data of a renewable energy power station through an energy management system, acquiring charge state and charge-discharge power data of energy storage equipment through a battery management system, and generating an original heterogeneous data set; Performing time stamp alignment and data cleaning on the original heterogeneous data set, unifying data with different sampling frequencies to the same time reference, removing bad data caused by abnormal jump points and communication interruption, and generating a time sequence data set after cleaning alignment; receiving a dynamic carbon emission intensity signal issued by a superior power grid through a power dispatching data network, updating the signal with a minute frequency, correlating the signal with a regional power grid gateway metering point, and generating a dynamic carbon emission intensity time sequence signal; and fusing the cleaned and aligned time sequence data set with the dynamic carbon emission intensity time sequence signal, and packaging according to a standardized data model to generate a standardized multi-source data set for subsequent carbon flow tracking analysis.
8. The system according to claim 7, wherein the generating module is specifically configured to: Analyzing a dynamic carbon emission intensity time sequence signal in a standardized multi-source data set, establishing a carbon flow network topology taking a regional power grid gateway as a starting point and a load node as an ending point, and generating a carbon flow network model; based on a carbon flow network model and real-time tide data, calculating electric energy carbon flow distribution from a power generation side to a load side by adopting a proportion sharing principle, and generating a node carbon flow intensity matrix; Establishing a carbon state transition model of the energy storage equipment, tracking carbon emission corresponding to electric energy absorbed in an energy storage charging period, and carrying out carbon flow tracing and distribution in a discharging period to generate an energy storage carbon flow distribution matrix; And (3) integrating the node carbon flow intensity matrix and the energy storage carbon flow distribution matrix, precisely distributing carbon emission to each load node and each time section by utilizing a mixed integer linear programming algorithm, and finally generating a load side carbon emission intensity spectrum with space-time resolution.
9. A storage medium having a computer program stored therein, wherein the computer program is arranged to perform the method of any of claims 1-5 when run.
10. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, the processor being arranged to run the computer program to perform the method of any of claims 1-5.

Description

Carbon index constraint-based demand side resource and energy storage cooperative regulation and control method and system Technical Field The invention belongs to the technical field of energy storage, and particularly relates to a method and a system for collaborative regulation and control of demand side resources and energy storage based on carbon index constraint. Background With the continuous deepening of global energy transformation, high-proportion renewable energy grid connection provides a serious challenge for the real-time balance and carbon emission reduction collaborative management of an electric power system. The existing power grid regulation and control method focuses on maintaining power balance and economic dispatch, is insufficient in cooperative utilization of resources on the demand side and energy storage, and generally lacks a refined carbon management means penetrating through the whole links of source-network-load-storage. Traditional approaches typically treat carbon emissions as a system-wide or power-generation-side indicator, and it is difficult to accurately track and regulate the load-side space-time carbon responsibilities, resulting in the demand-side regulatory potential not being fully activated to service grid low-carbon operation. Meanwhile, the current method is difficult to realize real-time, accurate and cooperative control of the carbon emission intensity on the premise of ensuring the system safety in the face of dynamic change of the carbon emission intensity and renewable energy fluctuation. Disclosure of Invention The invention aims to provide a method and a system for collaborative regulation and control of demand side resources and energy storage based on carbon index constraint, which are used for solving the defects in the prior art, realizing fine metering and active regulation and control of load side carbon emission and improving the response capability of the system to dynamic carbon constraint. The embodiment of the application provides a method for collaborative regulation and control of demand side resources and energy storage based on carbon index constraint, which comprises the following steps: Collecting real-time load data, renewable energy output data, energy storage equipment state data of a regional power grid and dynamic carbon emission intensity signals issued by a superior power grid; generating a load side carbon emission intensity spectrum with space-time resolution through a source-load-storage full-link carbon flow tracking algorithm based on the dynamic carbon emission intensity signal; according to the load side carbon emission intensity spectrum and a demand side resource adjustable potential model, establishing a virtual carbon pool taking carbon cost as a weight factor and generating a flexible load regulation priority sequence; and combining the flexible load regulation priority sequence with the real-time charge and discharge boundary of the energy storage system, solving a multi-time scale collaborative optimization model by taking the carbon intensity of the system as a core constraint, and generating a source network charge storage integrated regulation strategy to realize the real-time controllable carbon emission intensity and the collaborative optimization of the system operation economy. Optionally, the collecting real-time load data, renewable energy output data, energy storage device state data of the regional power grid and dynamic carbon emission intensity signals issued by the upper power grid includes: Acquiring power data of load nodes in real time through an Internet of things sensor deployed at each node of a regional power grid, acquiring real-time output data of a renewable energy power station through an energy management system, acquiring charge state and charge-discharge power data of energy storage equipment through a battery management system, and generating an original heterogeneous data set; Performing time stamp alignment and data cleaning on the original heterogeneous data set, unifying data with different sampling frequencies to the same time reference, removing bad data caused by abnormal jump points and communication interruption, and generating a time sequence data set after cleaning alignment; receiving a dynamic carbon emission intensity signal issued by a superior power grid through a power dispatching data network, updating the signal with a minute frequency, correlating the signal with a regional power grid gateway metering point, and generating a dynamic carbon emission intensity time sequence signal; and fusing the cleaned and aligned time sequence data set with the dynamic carbon emission intensity time sequence signal, and packaging according to a standardized data model to generate a standardized multi-source data set for subsequent carbon flow tracking analysis. Optionally, the generating, based on the dynamic carbon emission intensity signal, a load side carbon emission intensity spectr