Source-Load-Storage Coupled Scheduling Method and System Considering Source and Load Characteristics

Abstract

The present invention discloses a source-load-storage coupling scheduling method and system considering source and load characteristics, which relates to the technical field of power system optimal scheduling. The method includes Step 1: obtaining the time series data of load, heating load, and wind power in a predicted future period; Step 2: clustering based on the characteristics of the time series data of load, heating load, and wind power to determine the high-energy-consuming load and the heat storage tank call period; Step 3: calculating the equivalent load except for the cogeneration electric power, prefabricating the tie line for the equivalent load through a clustering algorithm, and then determining the generator unit combination through the tie line prefabrication; Step 4: constructing a source-load-storage coupling scheduling model with the minimum total power generation cost as the objective based on the high-energy-consuming load, the heat storage tank call period, and the generator unit combination; Step 5: scheduling the source, load, and storage in a future period according to the source-load-storage coupling scheduling model. The present invention effectively reduces the tie line exchange power, reduces the curtailment of new energy and the deep peak shaving power of thermal power units, and reduces the grid operation cost.

Description

Technical Field

[0001] The present invention relates to the technical field of power system optimal scheduling, and in particular to a source-load-storage coupling scheduling method and system considering source-load characteristics. Background Art

[0002] Wind power has characteristics such as intermittency, volatility, randomness, and reverse peak regulation. With the large-scale access of wind power to the power grid, the problem of power grid peak regulation has become increasingly prominent, bringing challenges to the power grid dispatching operation and restricting the transmission and consumption of wind power. For example, during the winter heating season, due to the peak-valley asynchrony characteristics between wind power and load, as well as between heating load and total load, it is difficult to regulate the peak of the system and the electricity cost is relatively high. In existing solutions, high-energy-consuming loads or heat storage devices, or the cooperation of high-energy-consuming loads and heat storage devices can be used to improve the system peak regulation ability. However, after introducing high-energy-consuming loads and heat storage devices, the calling cost of high-energy-consuming loads and the calling cost of heat storage devices are correspondingly increased, and at the same time, the tie-line exchange cost also increases. Therefore, the effect of reducing the electricity cost after introducing high-energy-consuming loads or heat storage devices is not obvious, and even the electricity cost may increase. Therefore, it is necessary to design a source-load-storage coupling scheduling method to ensure the maximum reduction of electricity cost. Summary of the Invention

[0003] The purpose of the present invention is to overcome the defect that the effect of reducing the electricity cost is not obvious after introducing high-energy-consuming loads and heat storage devices for peak regulation in the existing power system, and even the electricity cost may increase. The present invention provides a source-load-storage coupling scheduling method and system considering source-load characteristics, which determines the calling time periods of high-energy-consuming loads and heat storage tanks through a clustering method, effectively reduces the calling cost of high-energy-consuming loads and the calling cost of heat storage devices, and at the same time uses the clustering method for tie-line power pre-setting to reduce the tie-line exchange cost.

[0004] The purpose of the present invention is achieved through the following technical solutions:

[0005] A source-load-storage coupling scheduling method considering source-load characteristics includes the following steps:

[0006] Step 1, obtaining the time series data of the total load, heating load, and wind power in a predicted future preset time;

[0007] Step 2, clustering based on the characteristics of the time series data of the load, heating load, and wind power to determine the calling time periods of high-energy-consuming loads and heat storage tanks;

[0008] Step 3, calculating the equivalent load except for the electric power of combined heat and power (CHP), pre-setting the tie-line for the equivalent load through a clustering algorithm, and then determining the generator unit combination through the tie-line pre-setting;

[0009] Step 4: Based on the high-energy-consuming load, the call periods of the heat storage tanks, and the generator unit combinations, a source-load-storage coupling scheduling model is constructed with the goal of minimizing the total power generation cost;

[0010] Step 5: According to the source-load-storage coupling scheduling model, the source, load, and storage are scheduled for a preset future time.

[0011] In this solution, by performing clustering analysis on the time-series data of the total load, heating load, and wind power, the call periods of the high-energy-consuming load and the heat storage tanks can be objectively determined according to the data characteristics, avoiding the subjectivity and errors that may be brought by artificial period division. At the same time, it can more accurately identify the optimal call periods of the high-energy-consuming load and the heat storage tanks, so as to more flexibly adjust the use of the load and energy storage during the scheduling process, improving the system's response speed and regulation ability.

[0012] Interconnection line prefabrication refers to the way of active power exchange between different regions or different power grids in a power system through interconnection lines. This exchange is an important link in power dispatching. It helps to balance the supply and demand relationships between different regions or power grids, optimize resource allocation, and ensure the stable operation of the power system. In the existing power dispatching, the determination of the interconnection line exchange power is usually based on empirical rules, historical data, or simple prediction models. Since the conventional load changes are relatively stable, complex clustering algorithms are not required to determine the interconnection line exchange power. However, these methods often do not fully consider the complexity and variability of the source-load characteristics. When new energy, especially wind power, is added to the power dispatching, the source-load characteristics are more complex and variable than when there is no new energy added. The conventional methods for determining the interconnection line exchange power are difficult to meet the actual requirements and often cannot achieve the optimal dispatching effect. In this solution, by performing clustering analysis on the equivalent load, the same source-load characteristics can be more accurately found. Under the condition of meeting the requirements, the interconnection line exchange power mode with the lowest power generation can be generated, optimizing the output allocation of the generator units and reducing the interconnection line exchange cost. The principle of the interconnection line exchange power is to receive power at the maximum power during the peak period and send out power at the maximum power during the valley period.

[0013] In this solution, the unit commitment refers to economically and reasonably arranging the next-day generation plan curve of units under the conditions of meeting load balance, system constraints, unit characteristic constraints, etc., and determining the unit start-stop plan with the goal of minimizing the total generation cost. High energy-consuming loads such as the power of electrolytic aluminum can be adjusted within the range of ±10%, and the quality of electrolytic aluminum is basically unaffected or only slightly affected. When the power supply is lost in winter, the electrolytic cells can remain unfrozen for 2 hours. Therefore, the characteristics of high energy-consuming loads can be used to improve the peak-valley characteristics of the system load. The high energy-consuming loads are divided according to the call period. The high energy-consuming loads are moved from the peak-load - wind-power valley period to the valley-load - wind-power peak period for production, thereby reducing the maximum load power, increasing the minimum load power, and reducing the system peak-valley difference. Thermal energy storage has a relatively low cost compared to electrochemical energy storage. The cost of electrochemical energy storage is about 3000 yuan / kWh, while the cost of thermal energy storage is about 300 yuan / kWh. During the equivalent load valley period, heat is stored, and during the equivalent load peak period, heat is released, which can improve the peak-valley characteristics and reduce the peak-valley difference; in addition, the electricity cost is lower during the valley period and higher during the peak period, which can reduce the heating cost.

[0014] In this solution, the reason for calculating the equivalent load excluding the cogeneration electric power is that the cogeneration system generates both electric energy and thermal energy simultaneously, and its electric power output is a part of the total system load. To accurately evaluate the other load demands of the system excluding CHP, it is necessary to calculate the equivalent load excluding the CHP electric power. This helps to more clearly understand the load situation of the system when not considering the contribution of the CHP electric power, so as to formulate more effective scheduling strategies.

[0015] Preferably, in step 2, the specific determination of the call period of the high energy-consuming load is as follows:

[0016] The time series data of the total load and wind power are divided into peak periods, normal periods, and valley periods, and the number of clustering clusters is set to 3;

[0017] The time series data of the total load and wind power are subjected to the K-means clustering algorithm: randomly select 3 data points as the initial clustering centers, and assign each data point to the cluster where the nearest clustering center is located; recalculate the clustering centers of each cluster, and then assign each data point until the clustering centers no longer change or reach the preset number of iterations;

[0018] Analyze the time series data after clustering is completed to determine whether the power of the high energy-consuming load corresponding to the time series data needs to increase, decrease, or remain unchanged, and whether the high energy-consuming load is in the stage of waiting to increase or waiting to decrease.

[0019] Preferably, when the high energy-consuming load is called, a call capacity constraint and a call time constraint are also imposed on the high energy-consuming load. The call capacity constraint of the high energy-consuming load is:

[0020] ,

[0021] Wherein: and respectively represent the upper and lower limits of the adjustable capacity of the high-energy-consuming load, is a Boolean variable indicating the situation of calling the high-energy-consuming load. +1 indicates increasing the high-energy-consuming load, -1 indicates decreasing the high-energy-consuming load, and 0 indicates not calling the high-energy-consuming load;

[0022] The time constraint for calling the high-energy-consuming load is:

[0023] ,

[0024] Wherein: and respectively represent the upper and lower limits of the time for increasing the high-energy-consuming load, and respectively represent the upper and lower limits of the time for decreasing the high-energy-consuming load.

[0025] Preferably, in step 2, the specific determination of the calling period of the heat storage tank is as follows:

[0026] Divide the time series data of the total load and the heating load into peak periods, normal periods, and valley periods, and set the number of clustering clusters to 3;

[0027] Execute the K-means clustering algorithm on the time series data of the total load and the wind power: randomly select 3 data points as the initial clustering centers, and assign each data point to the cluster where the nearest clustering center is located; recalculate the clustering centers of each cluster, and then assign each data point until the clustering centers no longer change or reach the preset number of iterations;

[0028] Analyze the time series data after clustering to determine whether the heat storage tank corresponding to the time series data needs to release heat, store heat, or remain unchanged, and whether the heat storage tank is in the stage of waiting to release heat or waiting to store heat.

[0029] Preferably, when calling the heat storage tank, heat power constraint and capacity constraint are also imposed on the heat storage tank. The heat power constraint of the heat storage tank is:

[0030] ,

[0031] Wherein: is the heat storage or heat release power of the heat storage tank, and respectively are the maximum and minimum heat storage or heat release powers of the heat storage tank. The heat storage power is recorded as negative, and the heat release power is recorded as positive;

[0032] The capacity constraint of the heat storage tank is ,

[0033] Wherein: is the capacity of the heat storage tank, and are the maximum and minimum heat storage capacities of the heat storage tank respectively.

[0034] Preferably, in step 3, the tie line is prefabricated for the equivalent load through a clustering algorithm, specifically:

[0035] Collect the time series data of historical load, heating load and wind power;

[0036] Extract the characteristic data related to the tie line exchange power from the time series data of historical load, heating load and wind power; the characteristic data includes load peak and valley periods, wind power output peak and valley periods, heating load peak and valley periods, etc.;

[0037] Apply the K-means clustering method to the extracted characteristic data to cluster the processed data, and divide the characteristic data into K clusters. The characteristic data within each cluster has similar source-load characteristics;

[0038] Analyze the source-load characteristics of each cluster and determine the tie line exchange power pattern corresponding to the source-load characteristics;

[0039] Perform tie line prefabrication according to the source-load characteristics and the corresponding tie line exchange power pattern.

[0040] Preferably, the source-load-storage coupling scheduling model is specifically:

[0041] ,

[0042] In the formula: is the curtailment cost of wind power, is the cost of thermal power units, is the cost of combined heat and power, is the cost of calling the heat storage tank, is the cost of calling high-energy-consuming loads;

[0043] ,

[0044] ,

[0045] ,

[0046] ,

[0047] ,

[0048] In the formula, is the wind power, is the new energy accommodation space, is the minimum technical output of thermal power units or the minimum output of combined heat and power, is the wind power operation time, is the penalty cost for wind power curtailment; represents the th thermal power unit's power, is the number of thermal power units, is the operating time of the thermal power unit, , , are the quadratic, linear, and constant coefficients of the thermal power unit cost respectively; represents the th combined heat and power (CHP) unit's electricity / heat power, is the number of CHP units, is the operating time of the CHP, , , are the quadratic, linear, and constant coefficients of the CHP cost respectively; is the power of the heat storage tank called, is the unit cost of the heat storage tank called, is the time of the heat storage tank called; is the power of the high-energy load called, is the unit cost of the high-energy load called, is the time of the high-energy load called.

[0049] Preferably, the constraint conditions of the source-load-storage coupling scheduling model include system power balance constraint, upper and lower limits constraint of thermal power unit output, and ramp rate constraint of thermal power unit. The system power balance constraint includes electric power balance constraint, actual wind power generation power constraint, and heat power balance constraint. The electric power balance constraint is:

[0050] , where is the actual wind power generation power constraint, is the total load power constraint, is the call capacity constraint of the high-energy load, is a Boolean variable,

[0051] The actual wind power generation power constraint is:

[0052] ,

[0053] The heat power balance constraint is:

[0054] , where is the heat storage or heat release power of the heat storage tank, is the heat power balance constraint;

[0055] The upper and lower limits constraint of thermal power unit output is:

[0056] ,

[0057] The ramp constraint of the thermal power unit is:

[0058] , where is the maximum ramp rate of the thermal power unit.

[0059] A source-load-storage coupling scheduling system considering source-load characteristics includes:

[0060] A prediction module for predicting the time series data of the total load, heating load, and wind power in a future preset time;

[0061] A data analysis module for determining the high-energy-consuming load and the charging and discharging periods of the heat storage tank and performing tie-line pre-layout, and then determining the generator unit combination through the tie-line pre-layout;

[0062] A source-load-storage coupling scheduling module including a source-load-storage coupling scheduling model for scheduling the source-load-storage in a future preset time;

[0063] When the system is running, it executes a source-load-storage coupling scheduling method considering source-load characteristics.

[0064] Preferably, the source-load-storage coupling scheduling module includes an electric-electric coupling unit and a thermal-electric coupling unit. The electric-electric coupling unit is used for scheduling between the high-energy-consuming load and the load, and the thermal-electric coupling unit is used for scheduling between the cogeneration unit, the heat storage tank, and the heating load.

[0065] The beneficial effects of the present invention are as follows: The source-load-storage coupling scheduling method and system of the present invention considering source-load characteristics can reasonably plan the calling time periods of the thermal-electric coupling unit and the electric-electric coupling unit, providing a guiding role for calling the high-energy-consuming load and the heat storage tank; the solution of the present invention can also effectively reduce the tie-line exchange power, which is beneficial to scheduling balance; in addition, it can effectively reduce the number of thermal power units in operation and reduce the deep peak shaving of thermal power units. At the same time, through the solution of the present invention, the cogeneration revenue can be increased and the heat loss can be effectively reduced.

[0066] The solution of the present invention can objectively determine the calling periods of the high-energy-consuming load and the heat storage tank according to the data characteristics by performing clustering analysis on the time series data of the total load, heating load, and wind power. This avoids the subjectivity and errors that may be brought about by artificial division of periods. At the same time, it can more accurately identify the optimal calling periods of the high-energy-consuming load and the heat storage tank, so as to more flexibly adjust the use of the load and energy storage during the scheduling process, improving the response speed and regulation ability of the system. By performing clustering analysis on the equivalent load, the same source-load characteristics can be more accurately found, and the power exchange mode of the tie line with the lowest power can be generated under the condition of meeting the demand, optimizing the output allocation of the generating units and reducing the tie line exchange cost.

[0067] The present invention effectively reduces the tie line power exchange, reduces the curtailment of new energy and the deep peak shaving power of thermal power units, reduces the grid operation cost, and can increase the income of high-energy-consuming and cogeneration enterprises. Brief Description of the Drawings

[0068] Figure 1 is a flow chart of the present invention;

[0069] Figure 2 is a coupling characteristic diagram of the heating load and the total load of the present invention;

[0070] Figure 3 is a coupling characteristic diagram of the wind power and the total load of the present invention;

[0071] Figure 4 is a schematic diagram of the division of the calling of the high-energy-consuming load of the present invention;

[0072] Figure 5 is a schematic diagram of the division of the calling of the heat storage tank of the present invention;

[0073] Figure 6 is a schematic diagram of the peak-valley periods of the present invention;

[0074] Figure 7 is a schematic diagram of the division of the calling period of the present invention;

[0075] Figure 8 is a schematic diagram of the calling of the CHP unit and the heat storage tank of the present invention;

[0076] Figure 9 is a schematic diagram of the calling of the tie line and the high-energy-consuming load of the present invention;

[0077] Figure 10 is a schematic diagram of the state of the heat storage tank and the calling power of the heat storage tank and the high-energy-consuming load of the present invention;

[0078] Figure 11 is a schematic diagram of the thermal power unit and the tie line power of the present invention;

[0079] Figure 12It is a schematic diagram of the original scheduling method of the present invention;

[0080] Figure 13 It is a schematic diagram of the actual power of the original scheduling method of the present invention. Detailed implementation manners

[0081] Example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more complete and comprehensive, and will fully convey the concept of the example embodiments to those skilled in the art.

[0082] In addition, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of the embodiments of this application. However, those skilled in the art will realize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. may be employed. In other cases, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of this application.

[0083] The flowcharts shown in the accompanying drawings are merely illustrative and not necessarily include all the contents and operations / steps, nor are they necessarily executed in the described order. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to the actual situation.

[0084] Embodiment:

[0085] A source-load-storage coupling scheduling method considering source-load characteristics, as Figure 1 shown, includes the following steps:

[0086] Step 1, obtain the time series data of the total load, heating load, and wind power in the predicted future preset time;

[0087] Step 2, perform clustering based on the characteristics of the time series data of the load, heating load, and wind power to determine the high-energy-consuming load and the charging and discharging periods of the heat storage tank;

[0088] Step 3, calculate the equivalent load except for the electric power of the combined heat and power (CHP), perform tie-line prefabrication on the equivalent load through a clustering algorithm, and then determine the generator unit combination through tie-line prefabrication;

[0089] Step 4, based on the high-energy-consuming load and the charging and discharging periods of the heat storage tank and the generator unit combination, construct a source-load-storage coupling scheduling model with the goal of minimizing the total power generation cost;

[0090] Step 5: Schedule the power sources, loads, and energy storage according to the source-load-storage coupling scheduling model for a preset future time period.

[0091] In this solution, the unit commitment refers to economically and reasonably arranging the next-day power generation plan curve of the units under the conditions of meeting load balance, system constraints, unit characteristic constraints, etc., and determining the unit start-stop plan with the goal of minimizing the total power generation cost. High-energy-consuming loads such as electrolytic aluminum power can be adjusted within the range of ±10%, with little or only slight impact on the quality of electrolytic aluminum. When the power supply is lost in winter, the electrolytic cells can remain unfrozen for 2 hours. Therefore, the characteristics of high-energy-consuming loads can be used to improve the peak-valley characteristics of the system load. According to the call period, the high-energy-consuming load is moved from the load peak-wind power valley period to the load valley-wind power peak period for production, thereby reducing the maximum load power, increasing the minimum load power, and reducing the system peak-valley difference. Thermal energy storage has a relatively low cost compared to electrochemical energy storage. The cost of electrochemical energy storage is about 3000 yuan / kWh, while the cost of thermal energy storage is about 300 yuan / kWh. During the equivalent load valley period, heat is stored, and during the equivalent load peak period, heat is released, which can improve the peak-valley characteristics and reduce the peak-valley difference. In addition, the electricity cost is lower during the valley period and higher during the peak period, which can reduce the heating cost.

[0092] In this solution, the reason for calculating the equivalent load excluding the cogeneration electric power is that the cogeneration system simultaneously generates electric energy and thermal energy, and its electric power output is a part of the total system load. To accurately evaluate the other load demands of the system excluding CHP, it is necessary to calculate the equivalent load excluding the CHP electric power. This helps to more clearly understand the load situation of the system when not considering the contribution of CHP electric power, thereby formulating more effective scheduling strategies.

[0093] As Figure 2 shown, there is an anti-peak shaving characteristic between the heating load and the total load. The heating load is in the peak period from 3 to 8 o'clock while the total load is in the valley period, and the heating load is in the valley period from 13 to 17 o'clock while the total load is in the peak period. Therefore, if the heat storage tank stores heat during the valley period of the heating load and the peak period of the total load, and releases heat during the peak period of the heating load and the valley period of the total load, the coordination between the heating load and the total load can be increased. As Figure 3As shown, there is an anti-peaking characteristic between wind power and the total load. From 1 to 8 o'clock, wind power is in the peak period while the total load is in the trough period. From 12 to 17 o'clock, wind power is in the trough period while the total load is in the peak period. Therefore, it is necessary to consider other resources to reduce the peak-valley difference of the system. In this solution, by performing cluster analysis on the time series data of the total load, heating load, and wind power, the calling periods of the high-energy-consuming load and the heat storage tank can be objectively determined according to the data characteristics, avoiding the subjectivity and errors that may be brought by artificial division of periods. At the same time, it can more accurately identify the optimal calling periods of the high-energy-consuming load and the heat storage tank, so as to more flexibly adjust the use of load and energy storage during the scheduling process, improving the response speed and regulation ability of the system. The time series is divided into three periods, namely the peak period, the normal period, and the trough period. Then, according to the two groups of time series, it is divided into changing periods: (increasing high-energy-consuming load) load trough - wind power peak period, (decreasing high-energy-consuming load) load peak - wind power trough period; unchanging periods: load peak - wind power peak period, load trough - wind power trough period, load flat - wind power normal period; periods to be changed: (increasing high-energy-consuming load) load peak - wind power normal period, load flat - wind power trough period, (decreasing high-energy-consuming load) load flat - wind power peak period, load trough - wind power normal period. The calling division schematic diagram is as shown in Figure 4 As shown, finally, the high-energy-consuming load is called according to the derivative distance and the constraint conditions of the high-energy-consuming load. The heat storage tank is divided into periods as shown in Figure 5 As shown.

[0094] Interconnection line prefabrication refers to the way of active power exchange between different regions or different power grids in the power system through interconnection lines. This exchange is an important link in power dispatching. It helps to balance the supply and demand relationship between different regions or power grids, optimize resource allocation, and ensure the stable operation of the power system. In this solution, by performing cluster analysis on the equivalent load, the same source-load characteristics can be more accurately found. Under the condition of meeting the demand, the interconnection line exchange power mode with the lowest power generation can be generated, optimizing the output distribution of the generating units and reducing the interconnection line exchange cost. The principle of the interconnection line exchange power is to receive power at the maximum power during the peak period and send out power at the maximum power during the trough period.

[0095] In step 2 described above, the specific determination of the calling period of the high-energy-consuming load is as follows:

[0096] The time series data of the total load and wind power are divided into the peak period, the normal period, and the trough period, and the number of clustering clusters is set to 3;

[0097] Perform the K-means clustering algorithm on the time-series data of the total load and the wind power: randomly select 3 data points as the initial clustering centers, assign each data point to the cluster where the nearest clustering center is located; recalculate the clustering centers of each cluster, and then assign each data point until the clustering centers no longer change or reach the preset number of iterations;

[0098] Analyze the time-series data after clustering is completed to determine whether the power of the high-energy-consuming load corresponding to the time-series data needs to increase, decrease, or remain unchanged, and whether the high-energy-consuming load is in the stage of waiting to increase or waiting to decrease.

[0099] When calling the high-energy-consuming load, capacity constraints and time constraints for calling the high-energy-consuming load are also imposed. The capacity constraint for calling the high-energy-consuming load is:

[0100] ,

[0101] In the formula: and respectively represent the upper and lower limits of the available capacity of the high-energy-consuming load. is a Boolean variable indicating the situation of calling the high-energy-consuming load, +1 indicates increasing the high-energy-consuming load, -1 indicates decreasing the high-energy-consuming load, and 0 indicates not calling the high-energy-consuming load;

[0102] The time constraint for calling the high-energy-consuming load is:

[0103] ,

[0104] In the formula: and respectively represent the upper and lower limits of the time for increasing the high-energy-consuming load. and respectively represent the upper and lower limits of the time for decreasing the high-energy-consuming load.

[0105] In step 2 mentioned above, the specific time period for calling the heat storage tank is determined as follows:

[0106] Divide the time-series data of the total load and the heating load into peak periods, normal periods, and valley periods, and set the number of clustering clusters to 3;

[0107] Perform the K-means clustering algorithm on the time-series data of the total load and the wind power: randomly select 3 data points as the initial clustering centers, assign each data point to the cluster where the nearest clustering center is located; recalculate the clustering centers of each cluster, and then assign each data point until the clustering centers no longer change or reach the preset number of iterations;

[0108] Analyze the time-series data after clustering is completed to determine whether the heat storage tank corresponding to the time-series data needs to release heat, store heat, or remain unchanged, and whether the heat storage tank is in the stage of waiting to release heat or waiting to store heat.

[0109] When the heat storage tank is called, thermal power constraint and capacity constraint are also imposed on the heat storage tank. The thermal power constraint of the heat storage tank is:

[0110] ,

[0111] In the formula: is the heat storage or heat release power of the heat storage tank, and are the maximum and minimum heat storage or heat release powers of the heat storage tank respectively. The heat storage power is recorded as negative, and the heat release power is recorded as positive;

[0112] The capacity constraint of the heat storage tank is ,

[0113] In the formula: is the capacity of the heat storage tank, and are the maximum and minimum heat storage capacities of the heat storage tank respectively.

[0114] The heat storage tank is linked with the combined heat and power generation during the call. To maintain the power generation hours of the thermal power unit, the CHP is put into operation during the heating period, and part of the CHP is taken out of operation during the non-heating period. The CHP during the heating period meets the heat balance requirements, but due to the constraint of the "heat determines power" principle, the output cannot be further reduced, resulting in a large minimum technical output of the system and wind abandonment.

[0115] CHP (back pressure type) electric power and thermal power relationship:

[0116] ,

[0117] In the formula: is the CHP heat-electricity ratio, is a constant.

[0118] The CHP power constraint is:

[0119] ,

[0120] In the formula: and are the maximum and minimum electric (thermal) powers of the CHP respectively.

[0121] CHP up and down ramp electric (thermal) constraint is:

[0122] ,

[0123] In the formula: and They are the maximum and minimum electrical (thermal) powers of CHP during upward ramping, respectively. and They are the maximum and minimum electrical (thermal) powers of CHP during downward ramping, respectively.

[0124] According to the divided call periods, CHP increases its power during the periods of increasing CHP power. The excess electrical power is sent to the power grid, and the excess thermal energy is stored in the heat storage tank. During the periods of decreasing CHP power, CHP reduces its power. The reduced electrical power can increase the accommodation space for wind power, and the reduced thermal energy is released through the heat storage tank to make up for it.

[0125] In step 3 described above, the tie-line prefabrication for the equivalent load is carried out through the clustering algorithm, specifically as follows:

[0126] Collect the time-series data of historical load, heating load, and wind power. These data are recorded hourly to ensure the integrity and continuity of the data.

[0127] Extract the characteristic data related to the tie-line exchange power from the time-series data of historical load, heating load, and wind power. The characteristic data includes load peak-valley periods, wind power output peak-valley periods, heating load peak-valley periods, etc.

[0128] Apply the K-means clustering method to the extracted characteristic data to cluster the processed data. The characteristic data is divided into K clusters, and the characteristic data within each cluster has similar source-load characteristics.

[0129] Analyze the source-load characteristics of each cluster and determine the tie-line exchange power pattern corresponding to the source-load characteristics.

[0130] According to the source-load characteristics and the corresponding tie-line exchange power pattern, carry out tie-line prefabrication.

[0131] In this scheme, the specific steps of the K-means clustering method are as follows:

[0132] Set the number of clustering clusters. Initially set K = 3, indicating that the data is divided into three periods: peak, flat peak, and valley.

[0133] Randomly select K data points from the characteristic data as the initial clustering centers.

[0134] Calculate the clustering of each data point to each clustering center and assign it to the cluster where the nearest clustering center is located.

[0135] For each cluster, recalculate its clustering center.

[0136] Repeat the above assignment and recalculation steps until the clustering center no longer changes or reaches the preset number of iterations.

[0137] The interconnection line power exchange mode is as follows: for clusters where both the total load and wind power output are at peak times, it may be necessary to receive more electricity from other areas through interconnection lines to meet local demand; and for clusters where both the total load and wind power output are at low times, it may be necessary to send excess electricity to other areas through interconnection lines.

[0138] The tie line is prefabricated according to the source load characteristics and the corresponding tie line exchange power mode. Specifically, the tie line exchange power plan for each period can be pre-set according to the source load forecast value of each period and the tie line exchange power mode in the historical data.

[0139] For example, during peak load and wind power output periods, a higher incoming power value can be preset; and during low load and wind power output periods, a higher outgoing power value can be preset. This allows for more flexible adjustment of the interconnection line exchange power during the actual dispatch process to meet the system's supply and demand balance and stable operation requirements.

[0140] The source-load-storage coupling scheduling model is specifically:

[0141] ,

[0142] Where: is the cost of wind curtailment, is the cost of thermal power unit, is the CHP cost, To calculate the cost of using the heat storage tank, To call high energy load costs;

[0143] ,

[0144] ,

[0145] ,

[0146] ,

[0147] ,

[0148] In the formula, is the wind power, To create space for new energy consumption, The minimum technical output of the thermal power unit or the minimum output of the combined heat and power generation. is the wind power operation time, Penalty costs for wind power curtailment; Representative The power of the thermal power unit, is the number of thermal power units, is the operating time of the thermal power unit, , , are the second, first, and constant coefficients of the thermal power unit cost respectively; represents the th combined heat and power (CHP) unit's electricity / heat power magnitude, is the number of CHP units, is the operating time of the CHP, , , are the second, first, and constant coefficients of the CHP cost respectively; is the power of the thermal energy storage tank called, is the unit cost of the thermal energy storage tank called, is the time of the thermal energy storage tank called; is the power of the high-energy load called, is the unit cost of the high-energy load called, is the time of the high-energy load called.

[0149] The constraint conditions of the source-load-storage coupling scheduling model described above include system power balance constraints, upper and lower limits of thermal power unit output constraints, and thermal power unit ramp rate constraints. The system power balance constraints include electric power balance constraints, actual wind power generation power constraints, and heat power balance constraints. The electric power balance constraint is:

[0150] , where is the actual wind power generation power constraint, is the total load power constraint, is the high-energy load call capacity constraint, is a Boolean variable,

[0151] The actual wind power generation power constraint described above is:

[0152] ,

[0153] The heat power balance constraint is:

[0154] , where is the heat storage or heat release power of the thermal energy storage tank, is the heat power balance constraint;

[0155] The upper and lower limits of thermal power unit output constraints are:

[0156] ,

[0157] The thermal power unit ramp rate constraint is:

[0158] , where is the maximum ramp rate of the thermal power unit.

[0159] A source-load-storage coupling dispatching system considering source-load characteristics includes:

[0160] A prediction module for predicting the time-series data of the total load, heating load, and wind power in a future preset time;

[0161] A data analysis module for determining the high-energy-consuming load and the calling period of the heat storage tank and performing tie-line prefabrication, and then determining the generator unit combination through tie-line prefabrication;

[0162] A source-load-storage coupling dispatching module includes a source-load-storage coupling dispatching model for dispatching the source-load-storage in a future preset time;

[0163] When the system is running, it executes the source-load-storage coupling dispatching method considering source-load characteristics.

[0164] The source-load-storage coupling dispatching module includes an electric-electric coupling unit and a thermal-electric coupling unit. The electric-electric coupling unit is used for dispatching between the high-energy-consuming load and the total load, and the thermal-electric coupling unit is used for dispatching between the cogeneration unit, the heat storage tank, and the heating load.

[0165] The scheme of this embodiment is verified by simulation with the actual data of a certain area. The parameters of the thermal power unit are shown in Table 1.

[0166] Table 1 Thermal power unit parameters

[0167]

[0168] The CHP parameters are shown in Table 2.

[0169] Table 2 CHP parameters

[0170]

[0171] The heat storage tank parameters are shown in Table 3.

[0172] Table 3 Heat storage tank parameters

[0173]

[0174] The wind power curtailment penalty cost is 500 yuan / MWh, the first-stage depth cost of the thermal power unit is 200 yuan / MWh, the second-stage depth peak shaving cost of the thermal power unit is 400 yuan / MWh, and the peak shaving income of the user side is 100 yuan / MWh.

[0175] Figure 6 is the peak-valley period division, 2 represents the peak period, 1 represents the normal period, and 0 represents the valley period. Figure 7For the division of the call periods of high-energy-consuming loads and heat storage tanks, 2 indicates an increase in high-energy-consuming loads (heat storage in the heat storage tank), 1 indicates a pending increase in high-energy-consuming loads (pending heat storage in the heat storage tank), 0 indicates no change in status, -1 indicates a pending decrease in high-energy-consuming loads (pending heat release from the heat storage tank), and -2 indicates a decrease in high-energy-consuming loads (heat release from the heat storage tank).

[0176] A source-load-storage coupling dispatching method considering source and load characteristics. The total number of units in operation is 7, including 2 units of 600 MW, 4 units of 300 MW, and 1 unit of 200 MW. The CHP thermal power, CHP electric power, and the call power of the heat storage tank are as Figure 8 shown.

[0177] The heat storage power of the heat storage tank is 2500 MWh, the release power is 1800 MWh, and the heat loss is 700 MWh, which is equivalent to filling 1080 MWh of valley during the low-load period and shaving 15000 MWh of peak during the high-load period.

[0178] In addition to the equivalent load power of CHP electric power, the call power of high-energy-consuming loads, and the pre-set power of the tie line are as Figure 9 shown.

[0179] The call of high-energy-consuming loads, the call of the heat storage tank, and the status of the heat storage tank are as Figure 10 shown. The increased power consumption of high-energy-consuming loads during the low-load period is 4200 MWh, and the decreased power consumption during the high-load period is 3000 MWh.

[0180] In the original dispatching method, 10 units are in operation, including 2 units of 600 MW, 4 units of 300 MW, and 4 units of 200 MW. Compared with the source-load-storage coupling dispatching method considering source and load characteristics, the number of units in operation increases by 3. The output of source-load-storage units, the actual exchange power of the tie line, and the deep peak shaving power of thermal power units are as Figure 11 shown. The electricity involved in deep peak shaving is 1210.5 MWh, including 933.2 MWh in the first stage of deep peak shaving and 277.3 MWh in the second stage of deep peak shaving; the electricity sent out through the tie line is 2467 MWh, and the received electricity is 2352.4 MWh.

[0181] The pre-set power of the tie line, the actual exchange power of the tie line, the deep peak shaving power of the unit, and the new energy curtailment power after deep peak shaving in the original dispatching method are as Figure 12 shown. The heat loss is 1864.1 MWh, which is 1164.1 MWh more than the heat loss of the source-load coupling dispatching method.

[0182] The new energy curtailment after deep peak shaving, the deep peak shaving power, the new energy curtailment power after deep peak shaving, the actual exchange of the tie line, and the unit power are as Figure 13 shown.

[0183] After deep peak shaving, the curtailment of new energy is 2570.19 MWh, and the curtailment rate of new energy is 12.57%. The electricity quantity involved in deep peak shaving is 4276.62 MWh, among which the electricity quantity of the first-level deep peak shaving is 2417.63 MW, and the electricity quantity of the second-level deep peak shaving is 1859 MWh.

[0184] The cost comparison of the two dispatching methods is shown in Table 4.

[0185] Table 4 Cost Comparison of Two Dispatching Methods

[0186]

[0187] The dispatching method of this embodiment reduces the cost by 1.908 million yuan compared with the original dispatching method. The exchange power of the tie line of the original dispatching method is much greater than that of the source-load-storage coupling dispatching method, and there may be the same grid characteristics in the active power exchange area (at this time, power also needs to be received or sent out), which is not conducive to the active power balance of dispatching.

[0188] After considering the specification and the disclosed embodiments herein, those skilled in the art will readily conceive of other embodiments of the present application. The present application is intended to cover any variations, uses, or adaptations of the present application, which follow the general principles of the present application and include the common general knowledge or conventional technical means in the technical field not disclosed in the present application.

[0189] It should be understood that the present application is not limited to the exact structures already described and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present application is only limited by the appended claims.

Claims

1. A source-load-storage coupling scheduling method taking into account source-load characteristics, characterized in that: The following steps are involved: Step 1, obtaining the time series data of the total load, heating load and wind power within the predicted future preset time; Step 2, clustering the characteristics of the time series data of total load, heating load and wind power to determine the high energy load and heat storage tank call period; Step 3, calculate the equivalent load except the cogeneration power, and prefabricate the tie line for the equivalent load through the clustering algorithm, and then determine the generator set combination through the tie line prefabrication; Step 4: Based on the high energy load, the heat storage tank call period and the combination of generator sets, a source-load-storage coupling scheduling model is constructed with the goal of minimizing the total power generation cost; Step 5, scheduling the source, load and storage within a preset time in the future according to the source-load-storage coupling scheduling model; In the step 3, the interconnection line is prefabricated for the equivalent load by using a clustering algorithm, specifically: Collect time series data of historical load, heating load and wind power; Extract characteristic data related to the interconnection line exchange power from the time series data of historical load, heating load and wind power; The K-means clustering method is applied to the extracted feature data to cluster the processed data, and the feature data is divided into K clusters. The feature data in each cluster has similar source-load characteristics. Analyze the source-load characteristics of each cluster and determine the interconnection line exchange power mode corresponding to the source-load characteristics; The interconnection line is prefabricated according to the source-load characteristics and the corresponding interconnection line exchange power mode.

2. The source-load-storage coupling scheduling method taking into account source-load characteristics according to claim 1 is characterized in that: In step 2, the high energy load calling period is determined as follows: The time series data of total load and wind power are divided into peak period, normal period and valley period, and the number of clusters is set to 3; The K-means clustering algorithm is used to perform the time series data of total load and wind power: three data points are randomly selected as the initial cluster centers, and each data point is assigned to the cluster where the nearest cluster center is located; Recalculate the cluster center of each cluster and assign each data point again until the cluster center no longer changes or the preset number of iterations is reached; Analyze the clustered time series data to determine whether the power of the high-energy load corresponding to the time series data needs to be increased, decreased, or kept unchanged, and whether the high-energy load is in the stage of being increased or decreased.

3. The source-load-storage coupling scheduling method taking into account source-load characteristics according to claim 2 is characterized in that: When calling high-energy loads, the calling capacity constraints and calling time constraints are also imposed on the high-energy loads. for: ， Where: and They represent the upper and lower limits of the available capacity for high energy loads, It is a Boolean variable, indicating the call of high energy load. +1 indicates increasing the energy load, -1 indicates decreasing the energy load, and 0 indicates not calling the high energy load. The high energy load call time constraint is: ， Where: and Respectively represent the increase of the upper and lower limits of high energy load time, and Respectively represent the upper and lower limits of reducing high energy load time.

4. The source-load-storage coupling scheduling method taking into account source-load characteristics according to claim 1 is characterized in that: In step 2, the heat storage tank call period is determined as follows: The time series data of total load and heating load are divided into peak period, normal period and valley period, and the number of clusters is set to 3; The K-means clustering algorithm is used to perform the time series data of total load and wind power: three data points are randomly selected as the initial cluster centers, and each data point is assigned to the cluster where the nearest cluster center is located; Recalculate the cluster center of each cluster and assign each data point again until the cluster center no longer changes or the preset number of iterations is reached; The clustered time series data is analyzed to determine whether the thermal contact tank corresponding to the time series data needs to release heat, store heat, or remain unchanged, and whether the thermal contact tank is in the stage of waiting to release heat or store heat.

5. The source-load-storage coupling scheduling method taking into account source-load characteristics according to claim 4 is characterized in that: When the heat storage tank is called, the heat storage tank is also subject to thermal power constraints and capacity constraints. The thermal power constraints of the heat storage tank are: ， Where: The heat storage or heat release power of the heat storage tank, and are the maximum and minimum heat storage or heat release powers of the heat storage tank, respectively. The heat storage power is recorded as negative, and the heat release power is recorded as positive; The capacity constraint of the heat storage tank is , Where: is the capacity of the heat storage tank, and are the maximum and minimum heat storage capacities of the heat storage tank respectively.

6. The source-load-storage coupling scheduling method taking into account source-load characteristics according to claim 1 is characterized in that: The source-load-storage coupling scheduling model is specifically: ， Where: is the cost of wind curtailment, is the cost of thermal power unit, is the CHP cost, To calculate the cost of using the heat storage tank, To call high energy load costs; ， ， ， ， ， In the formula, is the wind power, To create space for new energy consumption, The minimum technical output of the thermal power unit or the minimum output of the combined heat and power generation. is the wind power operation time, Penalty costs for wind power curtailment; Representative The power of the thermal power unit, is the number of thermal power units, is the operating time of the thermal power unit, , , They are the quadratic, primary and constant coefficients of the thermal power unit cost respectively; Representative The size of the combined heat and power generation electricity / heat power, is the amount of combined heat and power generation, is the CHP operation time, , , are the quadratic, linear and constant coefficients of CHP cost respectively; To call the heat storage tank power, The unit cost of using the heat storage tank is: To call the heat storage tank time; To call high energy load power, To call high energy load unit cost, To call high energy load time.

7. The source-load-storage coupling scheduling method taking into account source-load characteristics according to claim 6 is characterized in that: The constraints of the source-load-storage coupling scheduling model include system power balance constraints, upper and lower limit constraints on thermal power unit output, and thermal power unit climbing constraints. The system power balance constraints include electric power balance constraints, wind power actual power generation constraints, and thermal power balance constraints. The electric power balance constraints are: , where is the actual wind power generation constraint, is the total load power constraint, Invoke capacity constraints for high energy loads, is a Boolean variable, The actual wind power generation power constraint is: ， The thermal power balance constraint is: , where The heat storage or heat release power of the heat storage tank, is the thermal power balance constraint; The upper and lower limits of the output of the thermal power unit are: ， The thermal power unit climbing constraint for: , where It is the maximum ramp rate of thermal power unit.

8. A source-load-storage coupling dispatching system taking into account source-load characteristics, characterized by comprising: A prediction module, which is used to predict the time series data of total load, heating load and wind power within a preset time in the future; Data analysis is used to determine the high-energy load and heat storage tank call period and to prefabricate the tie line, and then determine the generator set combination through the tie line prefabrication; The source-load-storage coupling scheduling module includes a source-load-storage coupling scheduling model, which is used to schedule the source-load-storage within a preset time in the future; When running, the system executes the source-load-storage coupling scheduling method taking into account source-load characteristics as described in any one of claims 1 to 7.

9. The source-load-storage coupling scheduling system taking into account source-load characteristics according to claim 8 is characterized in that: The source-load-storage coupling scheduling module includes an electric-electric coupling unit and a thermal-electric coupling unit. The electric-electric coupling unit is used for scheduling between high-energy loads and loads, and the thermal-electric coupling unit is used for scheduling between cogeneration units, heat storage tanks and heating loads.

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