CN-121983997-A - Collaborative optimization control method and system for building and smart grid

CN121983997ACN 121983997 ACN121983997 ACN 121983997ACN-121983997-A

Abstract

The invention discloses a building and smart grid collaborative optimization control method and system, which are used for carrying out peak-valley matching analysis on building operation state data and electricity price information to generate an energy consumption state base line, extracting a peak-valley electricity price period boundary after generating an electricity price response strategy, identifying a trend turning point to establish a peak-valley differentiation scheduling path, reserving a frequency modulation response margin based on equipment collaborative parameters to start an energy storage and heat recovery system to generate peak-valley scheduling execution record, carrying out peak-valley energy dynamic allocation after ice making and energy storage charging according to the actual peak-valley pressure reduction and the peak-valley time charging, monitoring grid load to generate a load distribution curve, determining an optimal scheduling moment through multi-objective optimization of electricity cost and load stability, executing a building and grid collaborative control output collaborative optimization control instruction, and realizing the release of peak-valley arbitrage potential of building energy storage resources and the collaborative stabilization of grid load.

Inventors

ZHANG HUI
DONG ZHENG

Assignees

无锡锐泰节能系统科学有限公司

Dates

Publication Date: 20260505
Application Date: 20260407

Claims (10)

1. The collaborative optimization control method for the building and the smart grid is characterized by comprising the following steps of: collecting operation state data of a building heating and ventilation system and electricity market price information, and carrying out peak-valley energy consumption state matching analysis on the operation state data and the electricity price information to generate an energy consumption state baseline; Performing peak-valley electricity price optimization analysis based on the energy consumption state base line to generate an electricity price response strategy, performing electricity price interval layering on the electricity price response strategy to extract a peak-valley electricity price time period boundary, and identifying trend turning points in advance based on the peak-valley electricity price time period boundary to establish a peak-valley differentiation scheduling path; Performing cooperative response analysis on the peak-valley differential scheduling paths to generate equipment cooperative parameters, reserving a frequency modulation response margin based on the equipment cooperative parameters, and starting an energy storage and heat recovery system to generate peak-period scheduling execution records; Generating a valley period charging time sequence scheme based on actual pressure reduction and inverted valley period charging quantity in the peak period scheduling execution record, starting ice making and energy storage charging by using the valley period charging time sequence scheme to generate valley period energy storage parameters, and carrying out peak valley energy dynamic allocation compensation by using the valley period energy storage parameters to construct collaborative optimization configuration; And carrying out grid load monitoring based on the collaborative optimization configuration to generate a load distribution curve, carrying out multi-objective optimization on the electricity cost and the load stability of the load distribution curve to determine an optimal scheduling time, and executing building and grid collaborative control to output a collaborative optimization control instruction at the optimal scheduling time.
2. The method of claim 1, wherein said performing peak-to-valley energy consumption state matching analysis on said operating state data and said electricity price information to generate an energy consumption state baseline comprises: extracting energy consumption characteristics of the running state data according to peak-valley time intervals to generate energy consumption characteristic distribution; dividing peak-valley intervals of the electricity price information to generate peak-valley electricity price interval labels; performing state contrast matching on the energy consumption characteristic distribution and the peak-valley electricity price interval label to generate an energy consumption deviation mark; and generating an energy consumption state baseline according to the energy consumption deviation mark.
3. The method of claim 1, wherein said enforcing a power rate interval hierarchy to extract peak to valley power rate interval boundaries for the power rate response strategy comprises: performing amplitude deviation analysis on the electricity price response strategy to determine the fluctuation amplitude of the electricity price; Extracting power price change rate characteristics based on the power price fluctuation amplitude to generate a dynamic power price layering threshold; carrying out layered division on the electricity price response strategy through the dynamic electricity price layered threshold value to form electricity price class classification; and performing period boundary calibration processing to extract peak-valley electricity price period boundaries based on the electricity price class classification.
4. The method of claim 1, wherein the establishing a peak-to-valley differentiated scheduling path based on the peak-to-valley electricity price period boundary identifying trend turning points in advance comprises: extracting power price change rate characteristics based on the peak-valley power price period boundary to generate a trend change sequence; Forming a trend confirmation signal and a scheduling switching advance time through continuous homodromous inflection point identification by utilizing the trend change sequence; Performing switching priority sorting based on the scheduling switching advance time and the trend confirm signal to generate a switching trigger condition; And carrying out path integration on the switching triggering conditions to establish peak-valley differential scheduling paths.
5. The method of claim 1, wherein the initiating the energy storage and heat recovery system to generate peak schedule execution records based on the device co-parameter reservation fm response margin comprises: generating available output distribution of each device based on the device cooperation parameters; Performing frequency modulation margin reservation calculation on available output distribution of each device to obtain margin reserved quantity; performing hierarchical starting of the energy storage and heat recovery system according to the margin reserved quantity to determine effective output configuration; And performing execution state record based on the effective output configuration to generate peak period scheduling execution record.
6. The method of claim 1, wherein generating a valley period fill timing scheme based on an actual pressure decrement reverse valley period fill amount in the peak period schedule execution record comprises: extracting actual pressure reduction of each device to generate pressure reduction summary according to the peak period scheduling execution record; performing valley period filling target back-pushing based on the pressure reduction summary to determine total filling amount in the valley period; Filling the total filling amount in the valley period into a filling amount distribution table according to each period in the valley period to generate a period filling amount distribution table; and executing time period priority ordering based on the time period filling allocation table to generate a valley period filling time sequence scheme.
7. The method of claim 1, wherein said performing a multi-objective optimization of power cost and load stability for the load profile to determine an optimal scheduling instant comprises: Performing time sequence differential decomposition on the load distribution curve to generate a load fluctuation decreasing sequence; Positioning a rapid convergence interval of fluctuation from the load fluctuation decreasing sequence to generate a steady-state prediction time window; Performing electricity cost estimation on each moment in the stability trend prediction time window and the load distribution curve to generate a cost stability comprehensive score; and determining the optimal scheduling time according to the cost stability comprehensive score.
8. The method of claim 4, wherein the forming the trend confirm signal and the scheduled switch advance time by successive co-rotating inflection point identification using the trend change sequence comprises: extracting adjacent inflection points continuously and equidirectionally changing based on the trend change sequence to generate an inflection point candidate pair; Forming a trend confirmation signal for the inflection point candidate pair through direction consistency verification; extracting a corresponding trigger time according to the trend confirmation signal to generate an initial switching time; and performing advance compensation processing on the initial switching time to determine a scheduling switching advance time.
9. The method of claim 6, wherein determining a total filling amount of a valley period based on the summary of pressure reduction for a target reverse thrust of valley period filling comprises: Performing peak-valley energy balance verification on the pressure reduction summary to generate energy gap distribution; identifying a critical compensation period based on the energy gap distribution to generate a compensation priority sequence; Performing segmented charging target calibration on the energy gap distribution by using the compensation priority sequence to generate segmented charging quantity; And accumulating and summarizing the segmented filling amounts to determine the total filling amount in the valley period.
10. A building and smart grid collaborative optimization control system, comprising: the data acquisition module is used for acquiring the running state data of the building heating and ventilation system and the electricity market price information, and carrying out peak-valley energy consumption state matching analysis on the running state data and the electricity price information to generate an energy consumption state baseline; The strategy generation module is used for carrying out peak-to-valley electricity price optimization analysis based on the energy consumption state base line to generate an electricity price response strategy, carrying out electricity price interval layering on the electricity price response strategy to extract peak-to-valley electricity price time period boundaries, and identifying trend turning points in advance based on the peak-to-valley electricity price time period boundaries to establish a peak-to-valley differentiation scheduling path; the peak period scheduling module is used for carrying out cooperative response analysis on the peak-valley differential scheduling paths to generate equipment cooperative parameters, reserving frequency modulation response margin based on the equipment cooperative parameters, and starting an energy storage and heat recovery system to generate a peak period scheduling execution record; the valley period energy storage module is used for generating a valley period charging time sequence scheme based on the actual compression decrement reverse-push valley period charging amount in the peak period scheduling execution record, starting ice making and energy storage charging by using the valley period charging time sequence scheme to generate valley period energy storage parameters, and carrying out peak valley energy dynamic allocation compensation by using the valley period energy storage parameters to construct collaborative optimization configuration; The instruction output module is used for monitoring the power grid load based on the collaborative optimization configuration to generate a load distribution curve, performing multi-objective optimization on the electricity cost and the load stability on the load distribution curve to determine the optimal scheduling time, and executing building and power grid collaborative control and outputting collaborative optimization control instructions at the optimal scheduling time.

Description

Collaborative optimization control method and system for building and smart grid Technical Field The invention relates to the technical field of building energy management, in particular to a collaborative optimization control method and system for a building and a smart grid. Background The building is used as an important flexible load resource of the urban power grid, and the electricity consumption peak of the heating ventilation air conditioning system is overlapped with the price peak height of the electric power market, so that the building operation cost is high, and the load concentration pressure of the power grid side area is increased. The existing building energy management mode mainly controls equipment to start and stop by a fixed period strategy, lacks dynamic sensing capability for real-time power price fluctuation, can not be adaptively adjusted along with power price change in operation plans of a refrigeration host and an energy storage device, and lacks joint optimization in charging and discharging plans of various energy storage media such as ice making, heat storage and the like in a building, and each energy storage system independently operates to cause waste of charging resources in valley periods and insufficient and concurrent storage of available energy in peak periods. There is therefore a need for a method to address at least one of the above problems. Disclosure of Invention The invention discloses a collaborative optimization control method and a collaborative optimization control system for a building and a smart grid, which aim at the problems of building heating general peak Gu Cuopei, insufficient energy storage resource joint optimization, delayed equipment response time and the like, and a differential dispatching path is constructed by peak-valley energy consumption state matching and dynamic layering of electricity price intervals, and (3) restraining orderly intervention of the energy storage system by using equipment cooperative parameters, and outputting a building and power grid cooperative control instruction after determining the optimal scheduling time through multi-objective optimization according to a peak period actual pressure reduction reverse valley period charging plan, so as to realize efficient utilization of building energy storage resources and cooperative stabilization of power grid load. The first aspect of the invention provides a collaborative optimization control method for a building and a smart grid, which comprises the following steps: collecting operation state data of a building heating and ventilation system and electricity market price information, and carrying out peak-valley energy consumption state matching analysis on the operation state data and the electricity price information to generate an energy consumption state baseline; Performing peak-valley electricity price optimization analysis based on the energy consumption state base line to generate an electricity price response strategy, performing electricity price interval layering on the electricity price response strategy to extract a peak-valley electricity price time period boundary, and identifying trend turning points in advance based on the peak-valley electricity price time period boundary to establish a peak-valley differentiation scheduling path; Performing cooperative response analysis on the peak-valley differential scheduling paths to generate equipment cooperative parameters, reserving a frequency modulation response margin based on the equipment cooperative parameters, and starting an energy storage and heat recovery system to generate peak-period scheduling execution records; Generating a valley period charging time sequence scheme based on actual pressure reduction and inverted valley period charging quantity in the peak period scheduling execution record, starting ice making and energy storage charging by using the valley period charging time sequence scheme to generate valley period energy storage parameters, and carrying out peak valley energy dynamic allocation compensation by using the valley period energy storage parameters to construct collaborative optimization configuration; And carrying out grid load monitoring based on the collaborative optimization configuration to generate a load distribution curve, carrying out multi-objective optimization on the electricity cost and the load stability of the load distribution curve to determine an optimal scheduling time, and executing building and grid collaborative control to output a collaborative optimization control instruction at the optimal scheduling time. The second aspect of the present invention provides a collaborative optimization control system for a building and a smart grid, comprising: the data acquisition module is used for acquiring the running state data of the building heating and ventilation system and the electricity market price information, and carrying out peak-valley energy consumption st