Park peak-valley power utilization scheduling method based on BMS

Abstract

The invention discloses a BMS-based park peak-valley power consumption scheduling method, and relates to the technical field of power consumption scheduling, and the method comprises the steps: obtaining historical power consumption data of each region in a park, and constructing a historical power consumption data change diagram; determining a peak-valley time period of each region according to the constructed historical power utilization data change diagram; setting a corresponding regional scheduling influence coefficient according to the peak-valley time period of each region; according to the peak-valley time period of each area and the area scheduling influence coefficient, a power consumption scheduling strategy among the areas is generated, and by analyzing a multi-dimensional historical power consumption curve of each area, the peak-valley time period matching degree among different areas is reflected through the area scheduling influence coefficient when the peak-valley time periods of monthly, day and hour are identified. Therefore, the redundant power of the region with the high scheduling influence coefficient is preferentially called to meet the scheduling strategy required by the peak region, and the fineness and efficiency of the whole energy allocation of the park are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of power dispatching technology, specifically a peak-valley power dispatching method for industrial parks based on a BMS (Building Management System). Background Technology

[0002] As the park develops on a large scale, its internal load composition becomes increasingly complex, often including multiple functional areas such as production, office, research and development and living services. These areas have significantly different electricity consumption characteristics: the production area operates continuously and has a stable load but a small peak-to-valley difference.

[0003] In actual park operation, when one area is at its peak electricity demand, another area may be at its off-peak load or have an immediate surplus of distributed photovoltaic power generation. How to formulate a reasonable and effective cross-regional electricity dispatch strategy based on the peak and off-peak electricity demand periods within the park is a problem we need to solve. To this end, we now provide a park peak and off-peak electricity dispatch method based on BMS. Summary of the Invention

[0004] The purpose of this invention is to provide a BMS-based method for peak-valley power dispatching in industrial parks.

[0005] The objective of this invention can be achieved through the following technical solution: a BMS-based peak-valley power dispatching method for industrial parks, comprising:

[0006] Obtain historical electricity consumption data for each area within the park and construct a historical electricity consumption data change map;

[0007] The peak and valley time periods for each region are determined based on the constructed historical electricity consumption data change map;

[0008] Set the corresponding regional scheduling impact coefficient according to the peak and valley time periods of each region;

[0009] Power dispatching strategies between regions are generated based on peak and valley time periods and regional dispatching impact coefficients.

[0010] Furthermore, there are several areas within the park;

[0011] The electricity consumption data of each region over a period of time is summarized as historical electricity consumption data for each region. The historical electricity consumption data includes input current, input voltage, phase angle and corresponding time.

[0012] For each region, a time axis based on historical electricity consumption data is constructed.

[0013] Based on historical electricity consumption data, corresponding current change curves, voltage change curves, and phase angle change curves are generated respectively.

[0014] Based on the time in historical electricity consumption data, different levels of time dimension ranges are set within the time axis to obtain a historical electricity consumption data change map; wherein, the time dimension range includes annual time range, monthly time range, and daily time range.

[0015] Furthermore, the process of determining the peak and valley time periods for each region based on the constructed historical electricity consumption data change map includes:

[0016] The electricity load value for each monthly time period is obtained based on the variation curves of each monthly time period within the annual time range.

[0017] Set the upper limit threshold and the lower limit threshold for monthly load;

[0018] The electricity load values ​​for each monthly time range in the same region are compared with the monthly upper limit threshold and the monthly lower limit threshold, respectively.

[0019] If the electricity load value is less than the monthly load lower limit threshold, the corresponding monthly time range will be recorded as the annual electricity valley period. If the electricity load value is greater than the monthly load upper limit threshold, the corresponding monthly time range will be recorded as the annual electricity peak period.

[0020] Similarly, the monthly electricity consumption off-peak period, monthly electricity consumption peak period, daily electricity consumption off-peak period, and daily electricity consumption peak period can be obtained.

[0021] Furthermore, the process of setting corresponding regional scheduling impact coefficients based on peak and valley time periods for each region includes:

[0022] Select any region as the baseline region, and select another region as the control region;

[0023] The start and end times of the annual peak electricity consumption period in the benchmark region are used as the benchmark annual time period.

[0024] The start and end times of the annual off-peak electricity consumption period in the control area are used as the control annual time period;

[0025] The baseline year time period is compared with the control year time period. If the baseline year time period is the same as the control year time period, the control area is recorded as the scheduling candidate area for the peak electricity consumption period of the corresponding year of the baseline area. Otherwise, the control area is eliminated and other areas are selected as the control area. This process is repeated until all areas are traversed, thereby determining all scheduling candidate areas for the baseline area.

[0026] If no region has the same annual time period as the baseline year, it means that there is no dispatchable candidate region for the corresponding annual peak electricity consumption time period of the baseline region.

[0027] When there are alternative scheduling areas in the benchmark area, then:

[0028] The difference between the electricity load value of the corresponding reference year time period of the scheduling candidate area and the annual load limit threshold is obtained and recorded as the scheduling load of the scheduling candidate area;

[0029] Based on the available dispatchable load and the power load value of the benchmark area, the monthly dispatch priority coefficient of the dispatch candidate area in the benchmark year period of the benchmark area is obtained.

[0030] The obtained scheduling candidate areas are sorted from high to low according to the scheduling priority coefficient of the corresponding base year time period;

[0031] The peak monthly electricity consumption period within the benchmark year for the benchmark region is recorded as the benchmark monthly period, and a corresponding monthly comparison period is obtained.

[0032] By analogy, the daily scheduling priority coefficient of each scheduling candidate area in the base month time period of the base area is obtained;

[0033] The obtained monthly scheduling priority coefficient, daily scheduling priority coefficient, and hourly scheduling priority coefficient are summarized and used as the regional scheduling influence coefficient between the baseline area and the scheduling candidate area.

[0034] Furthermore, the process of generating power dispatch strategies between regions based on peak and valley time periods and regional dispatch impact coefficients includes:

[0035] Based on the current time, a corresponding time tag sequence is generated, which includes months, days, and hours;

[0036] Each region is labeled according to the time-stamp sequence; the region labels include normal labels, baseline region labels, and scheduling candidate region labels.

[0037] Read the region labels corresponding to the time series labels of each region's "hours", and mark the region with the base region label as the base region;

[0038] Obtain the scheduling candidate area corresponding to the baseline area. The area label corresponding to the time series label marked with "hour" is the scheduling candidate area label, which is used as the power dispatch area corresponding to the baseline area.

[0039] Obtain the regional dispatch influence coefficient corresponding to each power dispatch area, sort the regional dispatch influence coefficients from high to low, and generate power dispatch strategies based on the sorting results.

[0040] Furthermore, the power dispatching strategy is as follows:

[0041] The power dispatching area with the highest regional dispatching impact coefficient is selected as the first tier of power dispatching, and the power redundancy corresponding to the first tier of power dispatching is compared with the power demand of the benchmark area.

[0042] If the power redundancy is greater than or equal to the power demand, then the power dispatch area will be used as the final execution result.

[0043] If the power redundancy is less than the power demand, the difference between the power redundancy and the power demand is taken as the new power demand. The second-ranked power dispatch area is then designated as the second tier of power dispatch. The power redundancy of the second tier is compared with the new power demand, and so on, until the power redundancy is greater than or equal to the power demand, or the traversal of all power dispatch areas is completed.

[0044] Furthermore, the power redundancy refers to the difference between the current hourly power load value and the median of the hourly load lower limit threshold and the hourly load upper limit threshold; the power demand refers to the difference between the current hourly power load value and the hourly load upper limit threshold.

[0045] Compared with the prior art, the beneficial effects of the present invention are:

[0046] By analyzing the historical electricity consumption curves of various regions in multiple dimensions, the peak and valley periods of the month, day and hour are identified. The regional dispatch influence coefficient reflects the matching degree of peak and valley periods between different regions. This enables the dispatch strategy of prioritizing the use of redundant power in regions with high dispatch influence coefficients to meet the demand of peak regions, which significantly improves the precision and efficiency of the overall energy allocation in the park. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0048] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0049] like Figure 1 As shown, the BMS-based peak-valley power dispatching method for industrial parks includes:

[0050] Obtain historical electricity consumption data for each area within the park and construct a historical electricity consumption data change map;

[0051] The peak and valley time periods for each region are determined based on the constructed historical electricity consumption data change map;

[0052] Set the corresponding regional scheduling impact coefficient according to the peak and valley time periods of each region;

[0053] Power dispatching strategies between regions are generated based on peak and valley time periods and regional dispatching impact coefficients.

[0054] It should be further explained that, in the specific implementation process, there are several areas within the park;

[0055] The electricity consumption data of each region over a period of time is summarized as historical electricity consumption data for each region. The historical electricity consumption data includes input current, input voltage, phase angle and corresponding time.

[0056] For each region, a time axis based on historical electricity consumption data is constructed.

[0057] Based on historical electricity consumption data, corresponding current change curves, voltage change curves, and phase angle change curves are generated respectively.

[0058] Based on the time in historical electricity consumption data, different levels of time dimension ranges are set within the time axis to obtain a historical electricity consumption data change chart; wherein, the time dimension range includes annual time range, monthly time range, and daily time range; it should be noted that the annual time range includes several monthly time ranges, and the monthly time range includes several daily time ranges.

[0059] It should be further explained that, in the specific implementation process, the process of determining the peak and valley time periods for each region based on the constructed historical electricity consumption data change map includes:

[0060] The variation curves for each month within the annual time range are marked, and the current variation curve, voltage variation curve, and phase angle variation curve are respectively denoted as... , as well as ;

[0061] Then the electricity load value for each monthly time period is obtained, denoted as K(r), where:

[0062] ;

[0063] Where r represents the monthly time range, tr1 represents the start time of the corresponding monthly time range, and tr2 represents the end time of the corresponding monthly time range. The current variation curve represents the current variation over a monthly time range r. The voltage variation curve represents the voltage variation over a monthly time range r. A curve representing the phase angle variation over a monthly time range r;

[0064] Set the upper limit threshold and the lower limit threshold for monthly load;

[0065] The electricity load values ​​for each monthly time range in the same region are compared with the monthly upper limit threshold and the monthly lower limit threshold, respectively.

[0066] If the electricity load value is less than the monthly load lower limit threshold, the corresponding monthly time range will be recorded as the annual electricity valley period. If the electricity load value is greater than the monthly load upper limit threshold, the corresponding monthly time range will be recorded as the annual electricity peak period.

[0067] Similarly, the monthly off-peak electricity consumption period, monthly peak electricity consumption period, daily off-peak electricity consumption period, and daily peak electricity consumption period can be obtained. It should be noted that the methods for obtaining the monthly off-peak electricity consumption period, monthly peak electricity consumption period, daily off-peak electricity consumption period, and daily peak electricity consumption period are similar to those for obtaining the annual off-peak electricity consumption period and annual peak electricity consumption period, with only the load threshold being different. Therefore, the acquisition process will not be described in detail here.

[0068] It should be further explained that, in the specific implementation process, the process of setting the corresponding regional scheduling impact coefficient according to the peak and valley time periods of each region includes:

[0069] Select any region as the baseline region and mark the annual peak electricity consumption period, monthly peak electricity consumption period, and daily peak electricity consumption period of the baseline region.

[0070] Select another region as a control region, and mark the annual, monthly, and daily off-peak electricity consumption periods for the control region.

[0071] The start and end times of the annual peak electricity consumption period in the benchmark region are used as the benchmark annual time period.

[0072] The start and end times of the annual off-peak electricity consumption period in the control area are used as the control annual time period;

[0073] The baseline year time period is compared with the control year time period. If the baseline year time period is the same as the control year time period, the control area is recorded as the scheduling candidate area for the peak electricity consumption period of the corresponding year of the baseline area. Otherwise, the control area is eliminated and other areas are selected as the control area. This process is repeated until all areas are traversed, thereby determining all scheduling candidate areas for the baseline area.

[0074] If no region has the same annual time period as the baseline year, it means that there is no dispatchable candidate region for the corresponding annual peak electricity consumption time period of the baseline region.

[0075] When there are alternative scheduling areas in the benchmark area, then:

[0076] The scheduling candidate areas corresponding to the baseline area are labeled and denoted as i, where i = 1, 2, ..., n;

[0077] Obtain the difference between the electricity load value of the corresponding reference year time period and the annual load limit threshold for the scheduling candidate area labeled i, and denote it as the dispatchable load of that scheduling candidate area, denoted as . ;

[0078] Then, the monthly scheduling priority coefficient of the candidate scheduling area within the base year time period of the base area is obtained, denoted as . ,in:

[0079] ;

[0080] Where e is a natural number, and NS represents the annual load limit threshold. The electricity load value for the corresponding benchmark year period in the benchmark region;

[0081] The obtained scheduling candidate areas are sorted from high to low according to the scheduling priority coefficient of the corresponding base year time period;

[0082] The peak monthly electricity consumption period within the benchmark year for the benchmark region is recorded as the benchmark monthly period, and a corresponding monthly comparison period is obtained.

[0083] Similarly, the daily scheduling priority coefficient for each candidate scheduling area within the base monthly time period in the base area, and the hourly scheduling priority coefficient for each candidate scheduling area within the base daily time period in the base area are obtained. It should be noted that the process of obtaining the daily and hourly scheduling priority coefficients is similar to that of obtaining the monthly scheduling priority coefficients, except that the electricity load values ​​used are daily and hourly, and the corresponding upper and lower load thresholds are different. The process will not be elaborated here.

[0084] The obtained monthly scheduling priority coefficient, daily scheduling priority coefficient, and hourly scheduling priority coefficient are summarized and used as the regional scheduling influence coefficient between the baseline area and the scheduling candidate area.

[0085] It should be further explained that the process of generating power dispatch strategies between regions based on peak and valley time periods and regional dispatch impact coefficients includes:

[0086] Based on the current time, a corresponding time tag sequence is generated, which includes months, days, and hours;

[0087] Region labels are set for each region based on the time-stamped sequence; the region labels include normal labels, baseline region labels, and scheduling candidate region labels; it should be noted that the setting of region labels depends on the historical electricity consumption data of each region.

[0088] Read the region labels corresponding to the time series labels of each region's "hours", and mark the region with the base region label as the base region;

[0089] Obtain the scheduling candidate area corresponding to the baseline area. The area label corresponding to the time series label marked with "hour" is the scheduling candidate area label, which is used as the power dispatch area corresponding to the baseline area.

[0090] Obtain the regional dispatch influence coefficient corresponding to each power dispatch area, sort the regional dispatch influence coefficients from high to low, and generate power dispatch strategies based on the sorting results.

[0091] It should be noted that the specific power dispatch strategy is as follows:

[0092] The power dispatching area with the highest regional dispatching impact coefficient is selected as the first tier of power dispatching. The power redundancy corresponding to the first tier of power dispatching is compared with the power demand of the benchmark area. Here, power redundancy refers to the difference between the current hourly power load value and the median value of the hourly load lower limit threshold and the hourly load upper limit threshold. Power demand refers to the difference between the current hourly power load value and the hourly load upper limit threshold.

[0093] If the power redundancy is greater than or equal to the power demand, then the power dispatch area will be used as the final execution result.

[0094] If the power redundancy is less than the power demand, the difference between the power redundancy and the power demand is taken as the new power demand. The second-ranked power dispatch area is then designated as the second tier of power dispatch. The power redundancy of the second tier is compared with the new power demand, and so on, until the power redundancy is greater than or equal to the power demand, or the traversal of all power dispatch areas is completed.

[0095] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications or equivalent substitutions made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A BMS-based peak-valley power dispatching method for industrial parks, characterized in that, include: Obtain historical electricity consumption data for each area within the park and construct a historical electricity consumption data change map; The peak and valley time periods for each region are determined based on the constructed historical electricity consumption data change map; Set the corresponding regional scheduling impact coefficient according to the peak and valley time periods of each region; Power dispatching strategies between regions are generated based on peak and valley time periods and regional dispatching impact coefficients.

2. The BMS-based peak-valley power dispatching method for industrial parks according to claim 1, characterized in that, The park contains several areas; The electricity consumption data of each region over a period of time is summarized as historical electricity consumption data for each region. The historical electricity consumption data includes input current, input voltage, phase angle and corresponding time. For each region, a time axis based on historical electricity consumption data is constructed. Based on historical electricity consumption data, corresponding current change curves, voltage change curves, and phase angle change curves are generated respectively. Based on the time in historical electricity consumption data, different levels of time dimension ranges are set within the time axis to obtain a historical electricity consumption data change map; wherein, the time dimension range includes annual time range, monthly time range, and daily time range.

3. The BMS-based peak-valley power dispatching method for industrial parks according to claim 2, characterized in that, The process of determining the peak and valley time periods for each region based on the constructed historical electricity consumption data change map includes: The electricity load value for each monthly time period is obtained based on the variation curves of each monthly time period within the annual time range. Set the upper limit threshold and the lower limit threshold for monthly load; The electricity load values ​​for each monthly time range in the same region are compared with the monthly upper limit threshold and the monthly lower limit threshold, respectively. If the electricity load value is less than the monthly load lower limit threshold, the corresponding monthly time range will be recorded as the annual electricity valley period. If the electricity load value is greater than the monthly load upper limit threshold, the corresponding monthly time range will be recorded as the annual electricity peak period. Similarly, the monthly electricity consumption off-peak period, monthly electricity consumption peak period, daily electricity consumption off-peak period, and daily electricity consumption peak period can be obtained.

4. The BMS-based peak-valley power dispatching method for industrial parks according to claim 3, characterized in that, The process of setting the corresponding regional scheduling impact coefficient based on the peak and valley time periods of each region includes: Select any region as the baseline region, and select another region as the control region; The start and end times of the annual peak electricity consumption period in the benchmark region are used as the benchmark annual time period. The start and end times of the annual off-peak electricity consumption period in the control area are used as the control annual time period; The baseline year time period is compared with the control year time period. If the baseline year time period is the same as the control year time period, the control area is recorded as the scheduling candidate area for the peak electricity consumption period of the corresponding year of the baseline area. Otherwise, the control area is eliminated and other areas are selected as the control area. This process is repeated until all areas are traversed, thereby determining all scheduling candidate areas for the baseline area. If no region has the same annual time period as the baseline year, it means that there is no dispatchable candidate region for the corresponding annual peak electricity consumption time period of the baseline region. When there are alternative scheduling areas in the benchmark area, then: The difference between the electricity load value of the corresponding reference year time period of the scheduling candidate area and the annual load limit threshold is obtained and recorded as the scheduling load of the scheduling candidate area; Based on the available dispatchable load and the power load value of the benchmark area, the monthly dispatch priority coefficient of the dispatch candidate area in the benchmark year period of the benchmark area is obtained. The obtained scheduling candidate areas are sorted from high to low according to the scheduling priority coefficient of the corresponding base year time period; The peak monthly electricity consumption period within the benchmark year for the benchmark region is recorded as the benchmark monthly period, and a corresponding monthly comparison period is obtained. By analogy, the daily scheduling priority coefficient of each scheduling candidate area in the base month time period of the base area is obtained; The obtained monthly scheduling priority coefficient, daily scheduling priority coefficient, and hourly scheduling priority coefficient are summarized and used as the regional scheduling influence coefficient between the baseline area and the scheduling candidate area.

5. The BMS-based peak-valley power dispatching method for industrial parks according to claim 4, characterized in that, The process of generating power dispatch strategies between regions based on peak and valley time periods and regional dispatch impact coefficients includes: Based on the current time, a corresponding time tag sequence is generated, which includes months, days, and hours; Each region is labeled according to the time-stamp sequence; the region labels include normal labels, baseline region labels, and scheduling candidate region labels. Read the region labels corresponding to the time series labels of each region "hour", and mark the region with the base region label as the base region; Obtain the scheduling candidate area corresponding to the baseline area. The area label corresponding to the time series label marked with "hour" is the scheduling candidate area label, which is used as the power dispatch area corresponding to the baseline area. Obtain the regional dispatch influence coefficient corresponding to each power dispatch area, sort the regional dispatch influence coefficients from high to low, and generate power dispatch strategies based on the sorting results.

6. The BMS-based peak-valley power dispatching method for industrial parks according to claim 5, characterized in that, The power dispatch strategy is as follows: The power dispatching area with the highest regional dispatching impact coefficient is selected as the first tier of power dispatching, and the power redundancy corresponding to the first tier of power dispatching is compared with the power demand of the benchmark area. If the power redundancy is greater than or equal to the power demand, then the power dispatch area will be used as the final execution result. If the power redundancy is less than the power demand, the difference between the power redundancy and the power demand is taken as the new power demand. The second-ranked power dispatch area is then designated as the second tier of power dispatch. The power redundancy of the second tier is compared with the new power demand, and so on, until the power redundancy is greater than or equal to the power demand, or the traversal of all power dispatch areas is completed.

7. The BMS-based peak-valley power dispatching method for industrial parks according to claim 6, characterized in that, in, Electricity redundancy refers to the difference between the current hourly electricity load value and the median value of the hourly load lower limit threshold and the hourly load upper limit threshold. Electricity demand refers to the difference between the current hourly electricity load value and the hourly load upper limit threshold.

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