A day power and electricity balance analysis method considering multi-partition thermal power balanced scheduling

Abstract

The application provides a daily power and electricity balance analysis method considering multi-partition thermal power balanced scheduling, and belongs to the technical field of power planning and decision-making. The method comprises the following steps: S1, obtaining system parameters, power supply parameters and electric load parameters of each partition of a power system; S2, taking each partition of the power system as a unit, and performing the same type of power aggregation; S3, establishing a capacity balance model, and determining the thermal power opening capacity of each partition; S4, establishing a multi-power supply power distribution model, and determining the power distribution results of each power supply of each partition and the system unbalanced power results. The application can realize the determination of the thermal power opening capacity of each partition, the power generation power of each type of power supply and the system power unbalance amount, and can realize the balanced scheduling of daily power and electricity, for a provincial power system containing internal sections, external tie lines, thermal power, hydropower, pumped storage power, wind power and new energy power and the like, with a day as a cycle, considering time period coupling, and taking the balanced scheduling of internal thermal power as a principle.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power planning decision, and particularly relates to a daily power and electricity balance analysis method considering multi-partition thermal power balanced scheduling. BACKGROUND

[0002] In the power system, power and electricity balance analysis is an important work of power system planning and operation. At present, with the proposal of the "3060 carbon target", the proportion of intermittent and volatile power sources such as wind power and photovoltaic power in the power system is increasing, and the power system balance problem is becoming more and more prominent.

[0003] On the one hand, in order to cope with faults and load fluctuations, the system needs sufficient adjustable capacity to ensure the safety and stability of the power system, but the existence of internal transmission sections divides the power system into multiple partitions, and the support of adjustable capacity between different partitions will be limited by the transmission capacity of the section. Combined with the current situation that the dispatching department of most provincial power systems adopts equal proportion scheduling for the start-up and power generation of each partition, the problem of multi-partition thermal power balanced scheduling is brought about. On the other hand, in order to avoid power surplus during the low net load period, the thermal power needs to be depressed to below the basic load rate, which brings about the problem of deep peaking of thermal power. In addition, pumped storage power stations and other energy storage play an important role in absorbing excess power during the low net load period, and the coordinated cooperation of multiple types of power sources becomes an important problem in power and electricity balance analysis.

[0004] Therefore, a daily power and electricity balance analysis method is needed, which considers the multi-partition thermal power balanced scheduling, deep peaking of thermal power, coordinated cooperation of multiple types of power sources, and multi-period coupling of the provincial power system.

[0005] After searching, no existing technology consistent with or similar to the present application has been found. SUMMARY

[0006] The technical problem to be solved by the present application is to provide a daily power and electricity balance analysis method considering multi-partition thermal power balanced scheduling, which can realize daily power and electricity balance scheduling considering multi-partition thermal power balanced scheduling, deep peaking of thermal power, coordinated cooperation of multiple types of power sources, and multi-period coupling of the power system, in view of the deficiencies of the prior art.

[0007] To solve the above technical problems, the technical solution adopted by the present application is:

[0008] A daily power and electricity balance analysis method considering multi-partition thermal power balanced scheduling, comprising:

[0009] S1, obtaining system parameters, power source parameters and electrical load parameters of each partition of the power system;

[0010] S2, aggregating the same type of power sources in each partition of the power system as a unit;

[0011] S3, establishing a capacity balance model to determine the capacity of each subarea thermal power;

[0012] S4, establishing a multi-source power distribution model to determine the power distribution results of each source in each subarea and the system unbalanced power results.

[0013] Further, in step S1, the power supply parameters are obtained according to the power supply types, and the power supply types include pure condensing thermal power, heat supply thermal power, hydropower and pumped storage.

[0014] Further, in step S2, when the power supply is aggregated:

[0015] The aggregation parameters of the pure condensing thermal power include installed capacity and minimum capacity;

[0016] The aggregation parameters of the heat supply thermal power include the capacity of the thermal power;

[0017] The aggregation parameters of the hydropower include installed capacity and daily power generation;

[0018] The aggregation parameters of the pumped storage include pumping capacity, power generation capacity, maximum energy storage and efficiency.

[0019] Further, the capacity balance model established in step S3 considers the balanced scheduling of multi-subarea thermal power, including a balanced capacity guiding term, a deep peak shaving penalty term, a shortage of adjustable capacity penalty term and a power imbalance penalty term.

[0020] Further, the constraint conditions of the capacity balance model established in step S3 include the operation boundary constraints of various power sources, the internal section power transmission boundary constraints, the system power balance constraints and the system capacity demand constraints.

[0021] Further, the multi-source power distribution model established in step S4 considers the balanced scheduling of multi-subarea thermal power, including a balanced power generation guiding term, a deep peak shaving penalty term and a power imbalance penalty term.

[0022] Further, the constraint conditions of the multi-source power distribution model established in step S4 include the operation boundary constraints of various power sources, the internal section power transmission boundary constraints and the system power balance constraints.

[0023] With large-scale access of new energy such as wind power and photovoltaic power to the power grid, its strong randomness and volatility have a great impact on the safe and stable operation of the power grid, and the power grid peak shaving and frequency modulation problem becomes prominent. At present, the dispatching mode of the power grid is mainly to consider the economic operation of the power grid and the unit operation constraint condition within a certain time scale, which is essentially a load optimization distribution problem to ensure the minimum operation cost of the generator unit. In the existing technology, when establishing the dispatching model, the minimum cost of the generator unit in each period is generally taken as the objective function, and the power load balance constraint, unit output constraint, unit start-stop time constraint and the like are taken as the limiting conditions, and by solving a mathematical programming model, the unit output plan in each period within a certain time scale is obtained. Patent documents CN113852140A, CN112508401A and the like adopt similar modes.

[0024] However, the existing technology mainly considers the dispatching problem of a single section of the power grid. However, the power grid is connected by multiple sections, and adjacent sections may transmit power through the tie line, so the dispatching of a large-scale power grid needs to consider the power transmission of multiple sections and the balance between sections.

[0025] A kind of large-scale new energy power generation grid-connected power system day-by-day simulation method proposed in patent document CN109449988A considers the cross-section flow constraint between subareas when setting the constraint adjustment of the model. This patent document considers the case of large-scale new energy power generation grid connection, but at the current stage, in China, especially in the north, thermal power units account for a large proportion of installed capacity, and this power supply structure determines that thermal power units are still the main force of power grid peak shaving and frequency modulation, so solving the problem of power grid peak shaving and frequency modulation in China still needs to be based on thermal power dispatching.

[0026] Under this condition, the applicant proposes a multi-subarea balanced thermal power dispatching analysis method based on the dispatching demand of provincial and regional scale power grid and considering the access of new energy, hydropower and pumped storage power station.

[0027] Compared with the prior art, the beneficial effects of the present application are as follows:

[0028] The present application is aimed at a provincial-level power system containing internal sections, external tie lines, and power sources such as thermal power, hydropower, pumped storage, wind power, and new energy sources, aggregates the same type of power sources in the internal sections, establishes a capacity balance model considering the balanced dispatching of thermal power in multiple sections, determines the thermal power start-up capacity of each section with the minimum sum of the balanced start-up guide term, the deep peak shaving penalty term, the capacity shortage penalty term, and the power imbalance penalty term as the target, and the operation boundary of each type of power source, the internal section power transmission and reception boundary, the system power balance, and the system capacity demand as the constraints, establishes a multi-power source power distribution model considering the balanced dispatching of thermal power in multiple sections based on the thermal power start-up capacity of each section, and determines the power distribution results of each power source in each section and the system unbalanced power results.

[0029] The present application can realize the determination of the thermal power start-up capacity of each section, the power generation of each type of power source, and the system power imbalance, and thus realize the balanced dispatching of daily power and energy, for the provincial-level power system containing internal sections, external tie lines, and power sources such as thermal power, hydropower, pumped storage, wind power, and new energy sources, with a day as the cycle and considering the time period coupling and the balanced dispatching of thermal power in the internal sections.

[0030] The present application can help the relevant departments of the power system to perform power and energy balance analysis on the provincial-level power system containing internal sections, external tie lines, and power sources such as thermal power, hydropower, pumped storage, wind power, and new energy sources, and assist decision makers in making power planning decisions.

[0031] When the present application is used to assist in dispatching, the peak shaving effect of hydropower and the peak shaving and valley filling effect of pumped storage can be fully exerted, and the hydropower and pumped storage generate power in the net load peak period, and the pumped storage consumes power to pump water in the net load valley period.

[0032] The present application can reasonably coordinate the various types of power sources such as water and fire in the power grid, realize the balanced dispatching of thermal power, improve the stability of the power grid system operation, reduce the power grid operation cost, and promote the consumption of renewable energy. BRIEF DESCRIPTION OF DRAWINGS

[0033] The present application will be further described in detail below with reference to the drawings.

[0034] Figure 1 The flowchart of the present application;

[0035] Figure 2 The schematic diagram of the test system of each section, section, and tie line in Example 2 of the present application;

[0036] Figure 3 The power curve of the external tie line of each section in the test system of Example 2 of the present application;

[0037] Figure 4: The wind power output curve of each subarea of the test system in Embodiment 2 of the present application;

[0038] Figure 5 : The photovoltaic output curve of each subarea of the test system in Embodiment 2 of the present application;

[0039] Figure 6 : The electrical load curve of each subarea of the test system in Embodiment 2 of the present application;

[0040] Figure 7 : The peak shaving effect of hydropower in Embodiment 2 of the present application;

[0041] Figure 8 : The peak shaving and valley filling effect of pumped storage in Embodiment 2 of the present application. DETAILED DESCRIPTION

[0042] For better understanding of the present application, the content of the present application is further clarified below in combination with embodiments and drawings, but the protection content of the present application is not limited to the following embodiments. In the following description, a large number of specific details are given in order to provide a more complete understanding of the present application. However, it is obvious to those skilled in the art that the present application can be implemented without one or more of these details.

[0043] Embodiment 1:

[0044] Reference Figure 1 The purpose of the present embodiment is to provide a daily power and energy balance analysis method considering multi-subarea thermal power balanced scheduling, which comprises the following steps:

[0045] Step S1, obtaining system parameters, power supply parameters and electrical load parameters of each subarea of the power system.

[0046] The current power system is divided into multiple subareas due to the existence of power transmission sections. In this step, the number of subareas in the power system, the section capacity between each subarea, the external tie line of each subarea and other related system parameters are obtained, the related technical parameters of the thermal power, hydropower, pumped storage, wind and photovoltaic power sources connected to each subarea are obtained, and the electrical load of each subarea in each time period is analyzed.

[0047] According to the power information of the external tie line, the power curve graph of the external tie line associated with each subarea is determined.

[0048] Thermal power is classified into pure condensing thermal power (pure condensing thermal power unit) and heat supply thermal power (cogeneration unit). The pure condensing thermal power obtains indexes such as installed capacity, minimum starting capacity and minimum output rate (minimum power output ratio of starting capacity), and the heat supply thermal power obtains indexes such as starting capacity, adjustable output rate (maximum power output ratio of starting capacity) and minimum output rate (minimum power output ratio of starting capacity).

[0049] Water power obtains installed capacity and power generation, etc. Pumped storage obtains pumping capacity, power generation capacity, maximum energy storage and pumping efficiency (ratio of energy storage increment to pumping power consumption) and other indicators.

[0050] New energy power sources such as wind and light have fluctuating output, and the output curves of each are obtained.

[0051] Step S2, as a unit of each sub-area of the power system, the same type of power aggregation is carried out.

[0052] This step uses the method of the same type of power aggregation to aggregate the same type of power as a unit of each sub-area of the power system.

[0053] The aggregation parameters of pure condensing thermal power in each sub-area are obtained by formula (1), wherein is the installed capacity of pure condensing thermal power in sub-area i; is the number of installed units of pure condensing thermal power in sub-area i; is the number of pure condensing thermal power units in sub-area i; is the capacity of the pure condensing thermal power unit numbered is the minimum starting capacity of pure condensing thermal power in sub-area i; is the number of pure condensing thermal power units that must be started in sub-area i; is the capacity of the pure condensing thermal power unit numbered that must be started.

[0054]

[0055] The aggregation parameters of heating thermal power in each sub-area are obtained by formula (2), wherein is the starting capacity of heating thermal power in sub-area i; is the number of starting units of heating thermal power in sub-area i; is the number of starting units of heating thermal power in sub-area i; is the capacity of the heating thermal power unit numbered

[0056] The aggregation parameters of water power in each sub-area are obtained by formula (3), wherein is the installed capacity of water power in sub-area i;

[0057] is the number of installed units of water power in sub-area i; is the number of water power units in sub-area i; is the capacity of the water power unit numbered is the daily power generation of water power in sub-area i; is the daily power generation of the water power unit numbered ​​​​

[0058]

[0059] The aggregate parameters of pumped storage within each partition are obtained by formula (4), wherein Pi, Pj, Ei, Ej, and ηi, ηj are the pumped storage capacity, the power generation capacity, the maximum energy storage, and the efficiency (the ratio of the energy storage increment to the pumped storage power consumption, both the pumped storage and the power generation loss are converted to the pumped storage, and the power generation efficiency is calculated as 1) of the pumped storage power stations in partitions i and j, respectively. N is the number of pumped storage power stations in partition i. N is the number of pumped storage power stations in partition i. Pi, Pj, Ei, Ej, and ηi, ηj are the pumped storage capacity, the power generation capacity, the maximum energy storage, and the efficiency (the ratio of the energy storage increment to the pumped storage power consumption, both the pumped storage and the power generation loss are converted to the pumped storage, and the power generation efficiency is calculated as 1) of the pumped storage power stations in partitions i and j, respectively. Pi, Pj, Ei, Ej, and ηi, ηj are the pumped storage capacity, the power generation capacity, the maximum energy storage, and the efficiency (the ratio of the energy storage increment to the pumped storage power consumption, both the pumped storage and the power generation loss are converted to the pumped storage, and the power generation efficiency is calculated as 1) of the pumped storage power stations in partitions i and j, respectively.

[0060]

[0061] Step S3, a capacity balance model is established to determine the thermal power start-up capacity of each partition.

[0062] The established capacity balance model considers the balanced dispatching of multi-partition thermal power, and is composed of a balanced start-up guiding term, a deep peak regulation penalty term, a shortage of adjustable capacity penalty term, and a power imbalance penalty term. Formula (5) is the objective function thereof, wherein is the balanced start-up guiding term of partition i at time period t; is the deep peak regulation penalty term of partition i at time period t; is the shortage of adjustable capacity penalty term at time period t; is the power imbalance penalty term of partition i at time period t; T is the number of time periods of the analysis day, and I is the set of partitions.

[0063]

[0064] The balanced start-up guiding term is obtained by formula (6), wherein is the balanced start-up guiding coefficient of the pure condensing thermal power of partition i, the value is set artificially, and needs to satisfy x , i y of any partition i are all equal; is the pure condensing thermal power start-up capacity of partition i at time period t, which is to be solved.

[0065]

[0066] The deep peak regulation penalty term is obtained by formula (7), wherein is the deep peak regulation penalty term of partition i at time period t; K is the number of deep peak regulation levels; are the deep peak regulation penalty coefficients of the pure condensing thermal power and the heat supply thermal power, respectively; respectively, are the deep regulation peak power of the pure condensing thermal power and the heat supply thermal power in the kth step of the partition i at the t period; respectively, are the deep regulation peak lower load rates of the pure condensing thermal power and the heat supply thermal power in the kth step, when k = 1, respectively, are the basic regulation peak lower load rates of the pure condensing thermal power and the heat supply thermal power; respectively, are the basic regulation peak lower load rates of the pure condensing thermal power and the heat supply thermal power; respectively, are the power generation powers of the pure condensing thermal power and the heat supply thermal power of the partition i at the t period.

[0067]

[0068] The adjustable capacity deficiency penalty term is obtained by formula (8), wherein is the adjustable capacity deficiency penalty term of the partition i at the t period; M R is the adjustable capacity deficiency penalty coefficient; is the adjustable capacity deficiency amount of the partition i at the t period; is the adjustable capacity deficiency amount of the whole system.

[0069]

[0070] The power imbalance penalty term is obtained by formula (9), wherein is the power imbalance penalty term of the partition i at the t period; M P+ , M P- respectively, are the power surplus and power deficiency penalty coefficients; respectively, are the power surplus power and the power deficiency power of the partition i at the t period.

[0071]

[0072] The constraint conditions of the established capacity balance model include the operation boundary constraints of various power sources such as the pure condensing thermal power, the heat supply thermal power, the hydropower and the pumped storage, the internal section power transmission boundary constraint, the system power balance constraint and the system capacity demand constraint.

[0073] The pure condensing thermal power operation boundary constraint is formula (10), wherein is the minimum output rate of the pure condensing thermal power of the partition i; in the analysis day, the pure condensing thermal power start-up capacities at any two time points t m , t n are equal.

[0074]

[0075] The heat supply thermal power operation boundary constraint is formula (11), wherein is the minimum output rate of the heat supply thermal power of the partition i; is the adjustable output rate of the heat supply thermal power of the partition i.

[0076]

[0077] The hydro operation boundary constraint is formula (12), where is the hydro generation power of zone i at time period t.

[0078]

[0079] The pumped storage operation boundary constraint is formula (13), where is the pumped storage generation power (pumping is negative, generation is positive) of zone i; is the pumped storage energy storage of zone i; the third equation represents the coupling of energy storage between time periods.

[0080]

[0081] The internal section power exchange boundary constraint is formula (14), where is the total external sending capacity of all sections associated with zone i; is the total receiving capacity of all sections associated with zone i; is the total power exchanged between zone i and other zones (external sending is negative, receiving is positive).

[0082]

[0083] The system power balance constraint is formula (15), where are the wind and PV generation power of zone i at time period t, respectively; is the power of external tie-line of zone i at time period t (negative for sending, positive for receiving); is the electrical load of zone i at time period t.

[0084]

[0085] The system capacity requirement constraint is formula (16), where are the adjustable capacity provided by wind, PV, pure condensing thermal, heat-supply thermal, hydro, pumped storage, external tie-line, and internal sections of zone i at time period t, respectively; are the adjustable capacity requirements of load and contingency of zone i at time period t; the first equation represents the adjustable capacity constraint of each zone; the second equation represents the adjustable capacity constraint of the whole system.

[0086]

[0087] The capacity provided by wind power, photovoltaic, pure condensing thermal power, heat supply thermal power, hydropower, pumped storage, external tie line and internal section in the system capacity demand constraint is obtained by formula (17), wherein respectively, the wind power and photovoltaic output reliability of the partition i; the pure condensing thermal power adjustable capacity is calculated according to the starting capacity; the heat supply thermal power adjustable capacity is calculated according to the maximum adjustable capacity; the hydropower adjustable capacity is calculated according to the smaller value between the power generation power corresponding to the residual hydropower generation capacity of the analysis day and the installed capacity; the pumped storage adjustable capacity is calculated according to the smaller value between the power generation power corresponding to the energy storage capacity of the period and the power generation installed capacity; the external tie line adjustable capacity is calculated according to the power received by the external tie line in the period; the internal section adjustable capacity is calculated according to the total power received capacity of all the related sections.

[0088]

[0089] The capacity demand of load and accident in the system capacity demand constraint is obtained by formula (18), wherein is the load reserve rate of the partition i, is the maximum thermal power unit capacity of the partition i, is the power received by the maximum external DC tie line of the partition i in the t period; the adjustable capacity demand of the load is the load heating reserve; the adjustable capacity demand of the accident is calculated according to the larger value between the maximum thermal power unit capacity and the loss power of the maximum external DC tie line in single pole blocking.

[0090]

[0091] The above formula completes the establishment of the capacity balance model, and then sets the balance starting guide coefficient, the deep peak regulation penalty coefficient, the capacity shortage penalty coefficient and the power imbalance penalty coefficient, and calculates the thermal power starting capacity of each partition.

[0092] Step S4, a multi-source power distribution model is established to determine the power distribution results of each power source in each partition and the system unbalanced power results.

[0093] The multi-source power distribution model established considers the balanced dispatching of multi-partition thermal power, which consists of three parts: balanced generation guide item, deep peak regulation penalty item and power imbalance penalty item. Formula (19) is the objective function thereof, wherein is the balanced starting guide item of the partition i in the t period; is the deep peak regulation penalty item of the partition i in the t period; is the power imbalance penalty item of the partition i in the t period.

[0094]

[0095] The deep peak regulation penalty item is obtained by formula (7), and the power imbalance penalty item is obtained by formula (9).

[0096] The balanced generation guiding item is obtained by formula (20), wherein is the balanced generation guiding coefficient of the pure condensing thermal power in the partition i in the time period t, the value of which is set artificially and needs to satisfy formula (20) in any partition i in the given time period t x , i y The are all equal. is the balanced generation guiding coefficient of the heat supply thermal power in the partition i in the time period t, the value of which is set artificially and needs to satisfy formula (20) in any partition i in the given time period t x , i y The are all equal.

[0097]

[0098] The constraint conditions of the established capacity balance model include the operation boundary constraints of various power sources such as pure condensing thermal power, heat supply thermal power, hydropower and pumped storage, the internal section power transmission boundary constraint, and the system power balance constraint.

[0099] The operation boundary constraint of the heat supply thermal power is formula (11), the operation boundary constraint of the hydropower is formula (12), the operation boundary constraint of the pumped storage is formula (13), the internal section power transmission boundary constraint is formula (14), and the system power balance constraint is formula (15).

[0100] The operation boundary constraint of the pure condensing thermal power is formula (21), wherein is the minimum output rate of the pure condensing thermal power in the partition i.

[0101]

[0102] Compared with formula (10), formula (21) deletes the constraint that the starting capacity is greater than the minimum starting capacity and less than the installed capacity, because the starting capacity has been determined through the calculation in step S3, so the constraint is no longer performed.

[0103] On the basis of the starting capacity of the thermal power in each region, the balanced generation guiding coefficient is set, wherein the deep peak regulation penalty coefficient and the power imbalance penalty coefficient are the same as those in step S3, the model is calculated by using the optimization solver, and the power distribution results of each power source in each region and the system imbalance power results are obtained.

[0104] Embodiment 2:

[0105] This embodiment specifically describes the method of embodiment 1 in combination with the data of a test system.

[0106] 1. Step S1 is performed to obtain the system parameters, power source parameters and electrical load parameters of each partition of the power system.

[0107] Firstly, the system parameters of the test partition are acquired, such as Figure 2 As shown in the figure, the test system is divided into three partitions. Partition 1 is associated with partition 2 through internal section 12, the capacity of partition 1 to send power to partition 2 is 2200 MW, and the capacity of partition 2 to send power to partition 1 is 2400 MW; partition 2 is associated with partition 3 through internal section 23, the capacity of partition 2 to send power to partition 3 is 5700 MW, and partition 3 cannot send power to partition 2, that is, the capacity is 0. Partition 2 is associated with two external tie lines, which are tie line 21 and tie line 22, wherein tie line 21 and tie line 22 are DC transmission lines; partition 2 is associated with three external tie lines, which are tie line 31, tie line 32 and tie line 33, wherein tie line 31 is a DC transmission line, and the power curve of the five external tie lines is as shown in Figure 3 .

[0108] Then the power supply parameters of each partition are acquired. The technical parameters of each partition thermal power are shown in Table 1, the technical parameters of each partition hydropower and pumped storage are shown in Table 2, the output curve of each partition wind power is shown in Figure 4 , the output curve of photovoltaic power is shown in Figure 5 , and the credibility of wind power and photovoltaic power is 10%.

[0109] Table 1 Technical parameters of thermal power

[0110]

[0111] Table 2 Technical parameters of hydropower and pumped storage

[0112]

[0113] Subsequently, the electrical load parameters of each partition are acquired. The electrical load parameters are the electrical load curve as shown in Figure 6 .

[0114] 2. Perform step S2 to aggregate the same type of power supply in each partition.

[0115] 3. Perform step S3 to establish a capacity balance model and calculate the thermal power start-up capacity of each partition.

[0116] After the capacity balance model is established, the balanced start-up guide coefficient, the deep peak regulation penalty coefficient, the capacity shortage penalty coefficient and the power imbalance penalty coefficient are set, and the thermal power start-up capacity of each partition is calculated.

[0117] The balanced start-up guide coefficient of the pure condensing thermal power of each partition is shown in Table 3.

[0118] Table 3 Balanced start-up guide coefficient of pure condensing thermal power

[0119] Power grid partition a CON,on ]]> b CON,on ]]> Partition 1 0.0002 1 Partition 2 0.000104076 1 Partition 3 0.000162731 1

[0120] The deep peak regulation is divided into two grades, the first grade of the peak regulation range is 50% to 40% of the pure condensing thermal power and 50% to 40% of the heat supply thermal power; the second grade of the peak regulation range is below 40% of the pure condensing thermal power and below 40% of the heat supply thermal power.

[0121] The penalty coefficient of the deep peak regulation of the first grade is set to 1000, the penalty coefficient of the deep peak regulation of the second grade is set to 5000, the penalty coefficient of the insufficient capacity is set to 10000, and the penalty coefficient of the power imbalance is set to 100000.

[0122] The calculated starting capacity of the pure condensing thermal power of each partition in each period is shown in Table 4. It can be seen that the proportion of the starting capacity of the pure condensing thermal power of each partition to the installed capacity is equal, the model can realize the balanced starting of the thermal power, and at this time, the internal section capacity does not limit the balanced starting between the partitions.

[0123] Table 4 Starting capacity of pure condensing thermal power

[0124] Power grid partition C CON,on (MW) C CON,on / C CON ]]> Partition 1 6830 0.437 Partition 2 13120 0.437 Partition 3 8390 0.437

[0125] 4. Perform step S4 to establish a multi-source power distribution model, and calculate the power distribution results of each power source and the system imbalance power results of each partition.

[0126] After the multi-source power distribution model is established, the balanced power generation guide coefficient is set based on the starting capacity of the thermal power of each partition, the deep peak regulation penalty coefficient and the power imbalance penalty coefficient in the coefficient are the same as those in step S3, the model is calculated by using the optimization solver, and the power distribution results of each power source and the system imbalance power results of each partition are obtained.

[0127] The balanced starting guide coefficient of the pure condensing thermal power of each partition is shown in Table 5.

[0128] Table 5 Balanced power generation guide coefficient of thermal power

[0129] Power grid partition a CON,p ]]> b CON,p ]]> a CHP,p ]]> b CHP,p ]]> Partition 1 0.0002 1 0.0002 10 Partition 2 0.000104076 1 0.000115320 10 Partition 3 0.000162731 1 0.000348118 10

[0130] The calculated running power of the pure condensing thermal power, the heat supply thermal power and the controllable power sources such as hydropower and pumped storage of each partition in each period, the section power and the output rate of the pure condensing thermal power and the heat supply thermal power are shown in Tables 6, 7 and 8. It can be seen that the model can realize the balanced power generation of the thermal power of each partition. The output rate of the pure condensing thermal power of partition 1 in period 24 is higher than that of partitions 2 and 3 because the internal section 12 power transmission capacity is limited, resulting in congestion.

[0131] The role of hydropower and pumped storage in the system is as follows Figure 7 、 Figure 8The water power generation and the pumped storage power generation in the net load peak period, and the pumped storage power consumption in the net load valley period can be seen. The water power generation can play a role of peak shaving, and the pumped storage power generation can play a role of peak shaving and valley filling. In the embodiment of the application, the unbalanced power of each period is 0.

[0132] Finally, it is explained that the above embodiments are only used to illustrate the technical solutions of the application and not to limit, and other modifications or equivalent replacements of the technical solutions of the application made by the ordinary skilled in the art should be covered in the scope of the claims of the application as long as they do not deviate from the spirit and scope of the technical solutions of the application.

[0133] Table 6: Calculation results of partition 1

[0134]

[0135] Table 7: Calculation results of partition 2

[0136]

[0137] Table 8: Calculation results of partition 3

[0138]

Claims

1. A daily power balance analysis method considering multi-regional thermal power plant balanced dispatch, characterized in that: The application relates to a power system capacity balance method and device. S1, acquiring system parameters, power supply parameters and electric load parameters of each subarea of a power system; The system parameters include cross-section capacity among subareas, external tie-line parameters of each subarea and power information of the external tie-line; S2, taking each subarea of the power system as a unit, performing aggregation of the same type of power supply, collecting parameters of each type of power supply in each subarea, acquiring installed capacity and minimum starting capacity of pure condensing thermal power, starting capacity of heat supply thermal power, installed capacity and daily power generation of hydropower, and pumping capacity, power generation capacity, maximum energy storage and efficiency of pumped storage power stations; S3, establishing a capacity balance model with the minimum sum of balanced starting guide items, deep peak regulation penalty items, adjustable capacity deficiency penalty items and power imbalance penalty items as a target, setting power supply operation boundary constraints according to the aggregation results of the same type of power supply, setting internal cross-section power transmission boundary constraints, system power balance constraints and system capacity demand constraints according to the acquired system parameters, and determining thermal power starting capacity of each subarea after calculation; S4, establishing a multi-power supply power distribution model with the minimum sum of balanced power generation guide items, deep peak regulation penalty items and power imbalance penalty items as a target, setting power supply operation boundary constraints according to the aggregation results of the same type of power supply, setting internal cross-section power transmission boundary constraints and system power balance constraints according to the acquired system parameters, taking the determined thermal power starting capacity of each subarea as a basis, and determining power distribution results of each power supply and system imbalance power results of each subarea after calculation. 2.The method of claim 1, wherein the method further comprises: determining the power load of each sub-region in each time interval; and determining the power load of each sub-region in each time interval based on the power load of each sub-region in each time interval. The power supply parameters are acquired according to power supply types in step S1, and the power supply types include pure condensing thermal power, heat supply thermal power, hydropower and pumped storage.

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