Source load storage coupling scheduling method and system considering source load characteristics

Abstract

The invention discloses a source load storage coupling scheduling method and system considering source load characteristics, and relates to the technical field of power system optimization scheduling, and the method comprises the steps: 1, obtaining predicted time sequence data of a load, a heating load and wind power in a period of time in the future; 2, clustering is carried out according to the characteristics of the time sequence data of the load, the heating load and the wind power, and a high-energy load and a heat storage tank calling time period are determined; 3, equivalent loads except the cogeneration electric power are calculated, tie line prefabrication is carried out on the equivalent loads through a clustering algorithm, and then a generator set combination is determined through tie line prefabrication; 4, on the basis of the high-energy load, the heat storage tank calling time period and the generator set combination, a source-load-storage coupling scheduling model is constructed with the purpose of minimizing the total power generation cost; and 5, scheduling the source load storage in a period of time in the future according to the source load storage coupling scheduling model. According to the invention, tie line exchange power is effectively reduced, new energy power limitation and deep peak regulation power of the thermal power generating unit are reduced, and power grid operation cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of power system optimization dispatching, and in particular to a source-load-storage coupling dispatching method and system taking into account source-load characteristics. Background Art

[0002] Wind power has the characteristics of intermittent, 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 dispatching and operation of the power grid and restricting the transmission and consumption of wind power. For example, in the winter heating season, due to the peak-valley asynchrony between wind power and load, and between heating load and total load, the system peak regulation is difficult and the electricity cost is high. In the existing scheme, high energy loads or heat storage devices, or high energy loads and heat storage devices can be used to improve the peak regulation capacity of the system. However, after the introduction of high energy loads and heat storage devices, the high energy load call cost and the heat storage device call cost are increased accordingly, and the interconnection line exchange cost also increases. Therefore, after the introduction of high energy loads or heat storage devices, the electricity cost reduction effect 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 that the electricity cost can be minimized. Summary of the invention

[0003] The purpose of the present invention is to overcome the defect that after the introduction of high energy load and heat storage device for peak load regulation in the power system in the prior art, the electricity cost reduction effect is not obvious, and even the electricity cost increases. A source-load-storage coupling scheduling method and system taking into account the source-load characteristics are provided, and the high energy load and heat storage tank calling time period are determined by a clustering method, which effectively reduces the high energy load calling cost and the heat storage device calling cost. At the same time, the clustering method is used to prefabricate the interconnection line power and reduce the interconnection line exchange cost.

[0004] The purpose of the present invention is achieved through the following technical solutions: The source-load-storage coupling scheduling method taking into account source-load characteristics comprises the following steps: 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 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 combined heat and power (CHP) electric 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.

[0005] In this scheme, by clustering the time series data of total load, heating load and wind power, the call period of high energy load and heat storage tank can be objectively determined according to the data characteristics, avoiding the subjectivity and error that may be caused by artificial division of time periods. At the same time, it can more accurately identify the best call period of high energy load and heat storage tank, so as to more flexibly adjust the use of load and energy storage in the dispatching process, and improve the response speed and regulation ability of the system.

[0006] Tie-line prefabrication refers to the way in which active power is exchanged between different regions or different power grids in the power system through tie-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 the existing power dispatching, the determination of tie-line exchange power is usually based on empirical rules, historical data or simple prediction models, because the conventional load changes are relatively stable, and no complex clustering algorithm is required to determine the tie-line exchange power. However, these methods often do not fully consider the complexity and variability of source-load characteristics. When new energy, especially wind power, is added to power dispatching, the source-load characteristics are more complex and changeable than when no new energy is added. The conventional tie-line exchange power method is difficult to meet actual needs and often cannot achieve the optimal dispatching effect. In this scheme, by clustering the equivalent load, the same source-load characteristics can be found more accurately. Under the condition of meeting the needs, the lowest power tie-line exchange power method can be generated, the output distribution of the generator set can be optimized, and the tie-line exchange cost can be reduced. The principle of tie-line exchange power is to receive the maximum power during the peak period and send the maximum power during the valley period.

[0007] In this scheme, the combination of generator sets refers to the economical and reasonable arrangement of the next day's power generation plan curve of the unit under the conditions of satisfying load balance, system constraints, unit characteristic constraints, etc., and the start-stop plan of the unit is determined with the goal of minimizing the total cost of power generation. High-energy loads such as electrolytic aluminum power can be adjusted within the range of ±10%, and the quality of electrolytic aluminum has little or only a slight impact. When the power supply is lost in winter, it can be kept for 2 hours without condensation. Therefore, the high-energy load characteristics can be used to improve the peak-valley characteristics of the system load. High-energy loads are divided according to the call period, and high-energy loads are 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 peak-valley difference of the system. Thermal storage is relatively low in cost compared to electrochemical energy storage. The cost of electrochemical energy storage is about 3,000 yuan / kwh, while the cost of thermal storage is about 300 yuan / kwh. Storing heat during the trough period of equivalent load and releasing heat during the peak period of equivalent load can improve the peak-valley characteristics and reduce the peak-valley difference. In addition, the electricity price is lower during the trough period and higher during the peak period, which can reduce the heating cost.

[0008] In this solution, the reason for calculating the equivalent load in addition to the CHP power is that the CHP system generates both electricity and heat, and its power output is part of the total system load. In order to accurately assess the system's load requirements other than CHP, it is necessary to calculate the equivalent load in addition to CHP power. This helps to more clearly understand the system's load status when the CHP power contribution is not considered, so as to formulate a more effective scheduling strategy.

[0009] Preferably, 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; the cluster center of each cluster is recalculated, and each data point is assigned 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.

[0010] As a preferred method, when calling a high energy load, the calling capacity constraint and calling time constraint are also imposed on the high energy load. 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.

[0011] Preferably, in step 2, the heat storage tank calling 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; the cluster center of each cluster is recalculated, and each data point is assigned 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.

[0012] As a preferred embodiment, 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.

[0013] Preferably, in 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 characteristic data include load peak and valley time periods, wind power output peak and valley time periods, heating load peak and valley time periods, etc.; 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.

[0014] Preferably, 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.

[0015] Preferably, 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.

[0016] The source-load-storage coupling dispatching system taking into account the source-load characteristics includes: 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; The system executes a source-load-storage coupling scheduling method that takes into account source-load characteristics during operation.

[0017] 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 high-energy loads and loads, and the thermal-electric coupling unit is used for scheduling between cogeneration units, heat storage tanks and heating loads.

[0018] The beneficial effects of the present invention are as follows: the source-load-storage coupling scheduling method and system taking into account the source-load characteristics of the present invention can reasonably plan the call time period of the thermal-electric coupling unit and the electric-electric coupling unit, and provide guidance for calling high-energy loads and heat storage tanks; the scheme of the present invention can also effectively reduce the exchange power of the tie line, which is conducive to scheduling balance; in addition, it can effectively reduce the number of thermal power units started and reduce the deep peak load of thermal power units. At the same time, the scheme of the present invention can increase the income of hot spot cogeneration and effectively reduce heat loss.

[0019] The solution of the present invention can objectively determine the call time period of high energy load and heat storage tank according to data characteristics by clustering analysis of the time series data of total load, heating load and wind power, avoiding the subjectivity and error that may be caused by artificial division of time periods. At the same time, it can more accurately identify the optimal call time period of high energy load and heat storage tank, so as to more flexibly adjust the use of load and energy storage in the scheduling process, and improve the response speed and regulation ability of the system. By clustering analysis of equivalent loads, the same source-load characteristics can be found more accurately, and the lowest power interconnection line exchange power mode can be generated when the demand is met, optimizing the output distribution of the generator set and reducing the interconnection line exchange cost.

[0020] The present invention effectively reduces the exchange power of the interconnection line, reduces the power restriction of new energy and the deep peak-shaving power of thermal power units, reduces the operating cost of the power grid, and can increase the profits of high-load energy and cogeneration enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a flow chart of the present invention; Figure 2 is a coupling characteristic diagram of heating load and total load of the present invention; Figure 3 is a coupling characteristic diagram of wind power and total load of the present invention; Figure 4 It is a schematic diagram of high energy load calling division of the present invention; Figure 5 This is a schematic diagram of the heat storage tank calling division of the present invention; Figure 6 This is a schematic diagram of the peak and valley time periods of the present invention; Figure 7 It is a schematic diagram of the division of calling time periods of the present invention; Figure 8 It is a schematic diagram of the CHP unit and heat storage tank call of the present invention; Fig. 9 It is a schematic diagram of the tie line and high energy load call of the present invention; Fig.10 It is a schematic diagram of the state of the heat storage tank and the power calling of the heat storage tank and high energy load of the present invention; Fig.11 This is a schematic diagram of the thermal power unit and the tie line power of the present invention; Fig.12 It is a schematic diagram of the original scheduling method plan of the present invention; Fig.13 It is a schematic diagram of the actual power of the original scheduling method of the present invention. DETAILED DESCRIPTION

[0022] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of 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 comprehensive and complete and fully convey the concept of the example embodiments to those skilled in the art.

[0023] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, known methods, devices, realizations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0024] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. 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 actual conditions.

[0025] Example: The source-load-storage coupling scheduling method considering the source-load characteristics is as follows: Figure 1 As shown, the following steps are included: 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 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 combined heat and power (CHP) electric 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.

[0026] In this scheme, the combination of generator sets refers to the economical and reasonable arrangement of the next day's power generation plan curve of the unit under the conditions of satisfying load balance, system constraints, unit characteristic constraints, etc., and the start-stop plan of the unit is determined with the goal of minimizing the total cost of power generation. High-energy loads such as electrolytic aluminum power can be adjusted within the range of ±10%, and the quality of electrolytic aluminum has little or only a slight impact. When the power supply is lost in winter, it can be kept for 2 hours without condensation. Therefore, the high-energy load characteristics can be used to improve the peak-valley characteristics of the system load. High-energy loads are divided according to the call period, and high-energy loads are 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 peak-valley difference of the system. Thermal storage is relatively low in cost compared to electrochemical energy storage. The cost of electrochemical energy storage is about 3,000 yuan / kwh, while the cost of thermal storage is about 300 yuan / kwh. Storing heat during the trough period of equivalent load and releasing heat during the peak period of equivalent load can improve the peak-valley characteristics and reduce the peak-valley difference. In addition, the electricity price is lower during the trough period and higher during the peak period, which can reduce the heating cost.

[0027] In this solution, the reason for calculating the equivalent load in addition to the CHP power is that the CHP system generates both electricity and heat, and its power output is part of the total system load. In order to accurately assess the system's load requirements other than CHP, it is necessary to calculate the equivalent load in addition to CHP power. This helps to more clearly understand the system's load status when the CHP power contribution is not considered, so as to formulate a more effective scheduling strategy.

[0028] like Figure 2 As shown in the figure, the heating load and the total load have an anti-peaking characteristic. The heating load is at its peak from 3 to 8 o'clock while the total load is at its valley period. From 13 to 17 o'clock, the heating load is at its valley period while the total load is at its 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 will be increased. Figure 3As shown in the figure, there is an anti-peaking characteristic between wind power and total load. From 1 to 8 o'clock, wind power is in the peak period while the total load is in the valley period. From 12 to 17 o'clock, wind power is in the valley period while the total load is in the peak period. Therefore, other resources need to be considered to reduce the peak-valley difference of the system. Therefore, in this scheme, by clustering the time series data of total load, heating load and wind power, the calling period of high energy load and heat storage tank can be objectively determined according to the data characteristics, avoiding the subjectivity and errors that may be caused by artificial division of time periods. At the same time, it can more accurately identify the optimal calling period of high energy load and heat storage tank, so as to more flexibly adjust the use of load and energy storage in the scheduling process, and improve the response speed and regulation ability of the system. The time series is divided into three time periods, namely peak period, normal period and valley period. Then, according to the two groups of time series, they are divided into changing periods: (increasing energy load) load valley-wind power peak period, (reducing energy load) load peak-wind power valley period; non-changing period: load peak-wind power peak period, load valley-wind power valley period, load level-wind power level period; waiting period: (increasing energy load) load peak-wind power level period, load level-wind power valley period, (reducing energy load) load level-wind power peak period, load valley-wind power level period. The call division diagram is as follows Figure 4 As shown, the high energy load is finally called according to the derivative distance and high energy load constraint conditions. In order to make the CHP power curve and the total load curve have the same trend, the heat storage tank is divided into time periods as follows Figure 5 shown.

[0029] Tie-line prefabrication refers to the way in which active power is exchanged between different regions or different power grids in the power system through tie-lines. This exchange is an important part of 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 scheme, by clustering the equivalent loads, the same source-load characteristics can be found more accurately. When the demand is met, the lowest power tie-line exchange power mode can be generated, the output distribution of the generator sets can be optimized, and the tie-line exchange cost can be reduced. The principle of tie-line power exchange is to receive the maximum power during peak hours and send the maximum power during valley hours.

[0030] 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; the cluster center of each cluster is recalculated, and each data point is assigned 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.

[0031] 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.

[0032] 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; the cluster center of each cluster is recalculated, and each data point is assigned 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.

[0033] 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.

[0034] The heat storage tank is linked with the hot spot cogeneration when it is called. In order to maintain the power generation hours of the thermal power unit, the CHP is put into operation during the heating period, and some CHP is shut down during the non-heating period. During the heating period, the CHP meets the heat balance needs, but due to the constraint of the "heat-to-power" principle, the output cannot be further reduced, resulting in a large minimum technical output of the system and wind abandonment.

[0035] CHP (back pressure) electric power and thermal power relation: , Where: is the CHP thermal power ratio, is a constant.

[0036] The CHP power constraint is: , Where: and They are the maximum and minimum electrical (thermal) power of CHP respectively.

[0037] CHP Up and Downstream The climbing electrical (thermal) constraint is: , Where: and They are the maximum and minimum electric (thermal) power of CHP climbing, and They are the maximum and minimum electric (thermal) power of CHP downhill climbing respectively.

[0038] CHP is divided into calling periods. Increasing CHP power increases CHP power during the period, and the extra electric power is sent to the grid, and the extra heat is stored in the heat storage tank. Reducing CHP power reduces CHP power during the period, and reducing electric power can increase the wind power consumption space, and the reduced heat energy is released through the heat storage tank to compensate.

[0039] 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, which are recorded every hour to ensure data integrity and continuity; Extract characteristic data related to the interconnection line exchange power from the time series data of historical load, heating load and wind power; the characteristic data include load peak and valley time periods, wind power output peak and valley time periods, heating load peak and valley time periods, etc.; 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.

[0040] In this scheme, the specific steps of K-means clustering method are as follows: Set the number of clusters, initially set K=3, which means that the data is divided into three periods: peak, flat and valley; Randomly select K data points from the feature data as the initial cluster centers; Calculate the clustering of each data point to each cluster center and assign it to the cluster where the nearest cluster center is located; For each cluster, recalculate its cluster center; Repeat the above allocation and recalculation steps until the cluster center no longer changes or the preset number of iterations is reached.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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.

[0046] The source-load-storage coupling dispatching system taking into account the source-load characteristics includes: 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 the system is running, it executes a source-load-storage coupling scheduling method that takes into account the source-load characteristics.

[0047] 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 load and total load, and the thermal-electric coupling unit is used for scheduling between cogeneration units, heat storage tanks and heating loads.

[0048] The scheme of this embodiment is verified by simulation using actual data from a certain region, and the parameters of the thermal power unit are shown in Tables 1 and .

[0049] Table 1 Parameters of thermal power units

[0050] The CHP parameters are shown in Table 2.

[0051] Table 2 CHP parameters

[0052] The parameters of the heat storage tank are shown in Table 3.

[0053] Table 3 Heat storage tank parameters

[0054] The penalty cost for wind power curtailment is 500 yuan / MWh, the deep cost of first gear of thermal power units is 200 yuan / MWh, the deep peak-shaving cost of second gear of thermal power units is 400 yuan / MWh, and the user-side participation in peak-shaving benefits is 100 yuan / MWh.

[0055] Figure 6 It is divided into peak and valley time periods, 2 represents the peak period, 1 represents the normal period, and 0 represents the valley period. Figure 7 It is divided into high energy load and heat storage tank calling period, 2 means increasing energy load (heat storage tank storing heat), 1 means energy load to be increased (heat storage tank waiting to store heat), 0 means unchanged state, -1 means energy load to be reduced (heat storage tank waiting to release heat), -2 means reducing energy load (heat storage tank releasing heat).

[0056] The source-load-storage coupling scheduling method taking into account the source-load characteristics has a total of 7 units in operation, including 2 600MW units, 4 300MW units, and 1 200MW unit. The CHP thermal power, cogeneration power, and heat storage tank call power are as follows: Figure 8 shown.

[0057] The heat storage tank stores 2500MWh of power, releases 1800MWh of power, and has a heat loss of 700MWh, which is equivalent to filling 1080MWh of load during low-load periods and shaving 15000MWh of load during peak periods.

[0058] In addition to the equivalent load power of CHP power, high-energy load call power, and interconnection line prefabrication power, Fig. 9 shown.

[0059] High energy load call, heat storage tank call and heat storage tank status Fig.10 As shown in the figure, the power consumption during off-peak hours is increased by 4200MWh, and the power consumption during peak hours is reduced by 3000MWh.

[0060] The original dispatching method started 10 units, including 2 600MW units, 4 300MW units, and 4 200MW units. Compared with the source-load-storage coupling dispatching method that takes into account the source-load characteristics, the number of units started increased by 3. The output of the source-load-storage units, the actual exchange power of the tie line, and the deep peak-shaving power of the thermal power units are shown in Figure 2. Fig.11 The power involved in deep peak regulation is 1210.5MWh, of which the first gear deep peak regulation is 933.2MWh and the second gear deep peak regulation is 277.3MWh; the power sent out by the interconnection line is 2467MWh and the power received is 2352.4MWh.

[0061] The original dispatching method includes the prefabricated power of the tie line, the actual exchange power of the tie line, the deep peak-shaving power of the unit, and the power limit of new energy after deep peak-shaving. Fig.12The heat loss is 1864.1MWh, which is 1164.1MWh more than the heat loss of the source-load coupling scheduling method.

[0062] Renewable energy power restriction after deep peak regulation, deep peak regulation power, renewable energy power restriction after deep peak regulation, actual exchange of tie lines and unit power, etc. Fig.13 shown.

[0063] After deep peak regulation, the power limit of new energy is 2570.19MWh, and the power limit rate of new energy is 12.57%. The power involved in deep peak regulation is 4276.62MWh, of which the first gear deep peak regulation power is 2417.63MW and the second gear deep peak regulation power is 1859MWh.

[0064] The cost comparison of the two scheduling methods is shown in Table 4.

[0065] Table 4 Cost comparison of two scheduling methods

[0066] The dispatching method of this embodiment reduces the cost by RMB 1.908 million compared with the original dispatching method. The interconnection line exchange power of the original dispatching method is much greater than the source-load-storage coupling dispatching method, and the active power exchangeable area may have the same grid characteristics (at this time, it also needs to be collected or sent out), which is not conducive to the dispatching of active power balance.

[0067] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. 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 common knowledge or customary technical means in the art that are not disclosed in the present application.

[0068] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only 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.

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: 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.

7. 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.

8. The source-load-storage coupling scheduling method taking into account source-load characteristics according to claim 7 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.

9. A source-load-storage coupling dispatching system taking into account source-load characteristics, characterized in that it includes: 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 1-8.

10. The source-load-storage coupling scheduling system taking into account source-load characteristics according to claim 9 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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