Independent energy storage grading quantification method considering power regulation and network congestion mitigation

Abstract

The embodiment of the invention provides an independent energy storage grading quantification method considering power regulation and network congestion mitigation, and the method comprises the steps: building an independent energy storage scheduling response model facing a power grid power balance demand based on an independent energy storage response multi-time scale power balance effect, and determining a multi-time scale flexibility risk index; based on an independent energy storage response power flow blockage relieving effect, establishing an independent energy storage response power flow blockage relieving model, and determining a network transmission blockage risk index; according to an entropy weight method and an analytic hierarchy process, determining a comprehensive weight of the multi-time scale flexibility risk index and the network transmission congestion risk index; and according to the comprehensive weight of the multi-time scale flexibility risk index and the network transmission congestion risk index, grading the urgent degree of the energy storage demand of the power grid of each city. In this way, reasonable planning of the energy storage layout is facilitated, and the utilization benefit and economical efficiency of the energy storage system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of independent energy storage planning for power systems, and in particular to an independent energy storage grading and quantification method taking into account power regulation and network congestion relief. Background Art

[0002] With the development of society, renewable energy has developed rapidly in the power industry, and the power network has become increasingly complex. With the large-scale access of renewable energy (such as wind energy and solar energy) to the power system, its intermittency and uncertainty have brought new challenges to the power system. Energy storage technology, as a key solution, can convert excess electrical energy into stored energy and release it when needed, providing flexibility and dispatchability. Therefore, energy storage technology is widely used in the process of renewable energy access to the power grid. However, it is still challenging to rationally plan, design and operate energy storage systems in practical applications. This is because the scale, capacity, power characteristics and control strategy of the energy storage system need to be optimized according to specific circumstances. Summary of the invention

[0003] The purpose of the present invention is to provide an independent energy storage classification and quantification method that takes into account power regulation and network congestion relief, evaluates the urgency of energy storage needs in local power grids, and classifies the urgency of energy storage needs in local power grids into different levels, aiming to alleviate network congestion problems and improve the flexibility and stability of the power system.

[0004] According to one aspect of the present disclosure, a method for quantifying independent energy storage grading considering power regulation and network congestion relief is provided, comprising: Based on the multi-time scale power balancing effect of independent energy storage response, an independent energy storage dispatch response model for power grid power balancing needs is established, and multi-time scale flexibility risk indicators are determined; Based on the independent energy storage response flow congestion mitigation function, an independent energy storage response flow congestion mitigation model is established, and the network transmission congestion risk index is determined; Determine the comprehensive weights of the multi-time scale flexibility risk index and the network transmission congestion risk index according to the entropy weight method and the hierarchical analysis method; The urgency of energy storage demand for power grids in various cities is graded according to the comprehensive weight of the multi-time scale flexibility risk index and the network transmission congestion risk index.

[0005] In some possible implementations of the first aspect, the independent energy storage response to the multi-time scale power balancing effect is used to establish an independent energy storage dispatch response model for power grid power balancing needs, and determine a multi-time scale flexibility risk index, including: According to the characteristics of independent energy storage at different time scales and the different needs of the power system, a multi-time scale independent energy storage operation model is established to generate independent energy storage operation constraints; Based on the multi-time-scale independent energy storage operation model and the independent energy storage operation constraints, the multi-time-scale flexibility risk index is determined from the short time scale, the medium time scale and the long time scale respectively.

[0006] In some implementations of the first aspect, determining the multi-time scale flexibility risk indicator from a short time scale, a medium time scale, and a long time scale respectively includes: Calculate upward and downward flexibility deficiency probability index, deficiency expectation index and margin expectation index from short time scale, medium time scale and long time scale respectively; The upward and downward flexibility deficiency probability indicators, deficiency expectation indicators and margin expectation indicators are weighted to obtain a multi-time scale flexibility risk indicator.

[0007] In some implementable embodiments of the first aspect, the independent energy storage response flow congestion relief model is established based on the independent energy storage response flow congestion relief function, and the network transmission congestion risk index is determined, including: Construct wind power output probability model, photovoltaic power output probability model and independent energy storage output probability model respectively; According to the wind power output probability model, the photovoltaic output probability model and the independent energy storage output probability model, based on the independent energy storage response to the flow congestion relief effect, an independent energy storage response to the flow congestion relief model is established; Based on the independent energy storage response power flow congestion mitigation model, a network transmission congestion risk index is determined.

[0008] In some implementations of the first aspect, determining the comprehensive weights of the multi-time scale flexibility risk index and the network transmission congestion risk index according to the entropy weight method and the analytic hierarchy process includes: Determine first weight values ​​of the multi-time scale flexibility risk index and the network transmission congestion risk index respectively by using an entropy weight method; Determine second weight values ​​of the multi-time scale flexibility risk index and the network transmission congestion risk index respectively by using a hierarchical analysis method; According to the first weight value and the second weight value, the comprehensive weights of the multi-time scale flexibility risk index and the network transmission congestion risk index are determined respectively. In some implementations of the first aspect, the method according to claim 1 is characterized in that the urgency of energy storage demand of local power grids is classified into levels according to the comprehensive weight of the multi-time scale flexibility risk index and the network transmission congestion risk index, including: Determine the urgency score of energy storage demand for power grids in various cities based on the comprehensive weight of the multi-timescale flexibility risk index and the network transmission congestion risk index; Based on the scores of the urgency of energy storage needs in the power grids of various cities, the urgency of energy storage needs in the power grids of various cities is divided into levels, wherein the urgency of energy storage needs in the power grids of various cities includes very urgent, highly urgent, urgent, general and not urgent.

[0009] In some implementations of the first aspect, scoring the urgency of energy storage demand for local power grids according to the comprehensive weight of the multi-time scale flexibility risk index and the network transmission congestion risk index includes:

[0010] in, For the The urgency of energy storage demand in each city is scored. is the comprehensive weight of the multi-time scale flexibility risk index and the network transmission congestion risk index, It is the normalized value of the multi-time scale flexibility risk index and the network transmission congestion risk index.

[0011] According to a second aspect of the present disclosure, there is provided an independent energy storage hierarchical quantification device considering power regulation and network congestion relief, the device comprising: A modeling unit, which is used to establish an independent energy storage dispatch response model for power grid power balancing needs based on the multi-time scale power balancing effect of independent energy storage response, and determine the multi-time scale flexibility risk index; The modeling unit is further used to establish an independent energy storage response flow congestion relief model based on the independent energy storage response flow congestion relief effect, and determine a network transmission congestion risk index; A calculation unit, used to determine the comprehensive weights of the multi-time scale flexibility risk index and the network transmission congestion risk index according to an entropy weight method and a hierarchical analysis method; The scoring unit is used to classify the urgency of energy storage demand in the power grid of each city according to the comprehensive weight of the multi-time scale flexibility risk index and the network transmission congestion risk index.

[0012] According to a third aspect of the present disclosure, an electronic device is provided, comprising a processor, and a memory and a network interface connected to the processor; the network interface is connected to a non-volatile memory in a server; when the processor is running, it retrieves a computer program from the non-volatile memory through the network interface, and runs the computer program through the memory to execute the independent energy storage graded quantification method considering power regulation and network congestion relief as described above.

[0013] According to a fourth aspect of the present disclosure, a storage medium is provided, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the independent energy storage hierarchical quantification method considering power regulation and network congestion relief as described above is implemented.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. The present invention calculates flexibility indicators from different time scales, and then weights them to form a comprehensive indicator for system flexibility evaluation, comprehensively evaluates the flexibility of the system, and improves the accuracy and reliability of the indicator.

[0015] 2. The present invention uses a combination of entropy weight method and hierarchical analysis method to determine the weights of indicators to describe the urgency of the energy storage demand of local power grids. It not only overcomes the defect of strong subjectivity of the weights determined by the hierarchical analysis method, but also solves the problem that the weights determined by the entropy weight method cannot reflect the subjective needs in actual planning.

[0016] 3. The present invention classifies the urgency of energy storage needs of power grids in various cities into different levels, which helps to rationally plan energy storage layout and improve the utilization efficiency and economy of energy storage systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A flow chart of an independent energy storage hierarchical quantification method considering power regulation and network congestion relief according to an embodiment of the present invention is shown; Figure 2 A block diagram of an independent energy storage hierarchical quantization device considering power regulation and network congestion relief according to an embodiment of the present invention is shown; Figure 3 A schematic diagram of a computing device according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0020] In order to alleviate the network congestion problem and improve the flexibility and stability of the power system, the present invention provides an independent energy storage grading and quantification method that takes into account power regulation and network congestion relief. It includes: based on the multi-time scale power balance effect of independent energy storage response, an independent energy storage dispatch response model for the power balance demand of the power grid is established, and a multi-time scale flexibility risk index is determined; based on the independent energy storage response flow congestion relief effect, an independent energy storage response flow congestion relief model is established, and a network transmission congestion risk index is determined; the comprehensive weights of the multi-time scale flexibility risk index and the network transmission congestion risk index are determined according to the entropy weight method and the hierarchical analysis method; the urgency of the energy storage demand of the power grid in various cities is divided into levels according to the comprehensive weights of the multi-time scale flexibility risk index and the network transmission congestion risk index. In this way, it is helpful to rationally plan the energy storage layout and improve the utilization efficiency and economy of the energy storage system.

[0021] The independent energy storage grading and quantification method considering power regulation and network congestion relief provided by the present disclosure is described in more detail below in conjunction with the accompanying drawings and specific embodiments.

[0022] Embodiment 1 Figure 1 A flowchart of an independent energy storage hierarchical quantification method considering power regulation and network congestion relief provided by an embodiment of the present disclosure is shown; Figure 1 As shown, the method 100 may include the following steps: S110, based on the multi-time scale power balancing effect of independent energy storage response, an independent energy storage dispatch response model for power grid power balancing needs is established, and a multi-time scale flexibility risk indicator is determined.

[0023] In some embodiments, the independent energy storage response to the multi-time scale power balancing effect is used to establish an independent energy storage dispatch response model for power grid power balancing needs, and determine the multi-time scale flexibility risk index, which can be performed according to the following steps: Step 1.1: Establish the objective function of the independent energy storage dispatch response model: (1) Where: F is the comprehensive cost of the system; , , , , They are respectively the operating cost of thermal power units, the penalty cost for wind power abandonment, the penalty cost for solar power abandonment, the operating cost of independent energy storage, and the penalty cost for load shedding, as follows: (2) (3) (4) (5) (6) In formulas (2) to (6): is the start-up and shutdown cost of thermal power units, represents the sum of the rated capacities of the generator sets that are planned to be started by cluster unit g at time t, is the unit power generation cost of thermal power; is the unit wind curtailment cost, is the unit abandoned light cost; is the unit load shedding cost; is the investment cost per unit power of energy storage, is the unit capacity investment cost; is the estimated coefficient of operation and maintenance cost of energy storage, which is 0.5%. represents the sum of the rated capacities of all units in cluster unit g at time t; and They are wind power and photovoltaic power abandonment respectively; Indicates the load shedding size; Indicates the rated capacity of energy storage; Indicates the rated power of energy storage; The coefficient of equal annual value is as follows: (7) In formula (7), r is the base discount rate, and n is the operation period of energy storage (years).

[0024] Step 1.2: According to the operation status of cluster units, establish output power constraints and unit climbing constraints: (8) (9) (10) In formulas (8) to (10): represents the output upper limit of cluster unit g, Indicates the lower limit of the output of cluster unit g; represents the sum of the rated capacities of all units in cluster unit g at time t; is the ratio of the startup power variation capability of cluster unit g to the total operating capacity of the units; is the ratio of the shutdown power variation capability of cluster unit g to the total operating capacity of the units; is the ratio of the maximum upward climbing capacity of cluster unit g in unit time to the total operating capacity of the unit; is the ratio of the maximum downward ramping capacity of cluster unit g in unit time to the total operating capacity of the unit; represents the sum of the rated capacities of the online generating units of cluster unit g at time t; represents the sum of the rated capacities of the generating units planned to be started by cluster unit g at time t-1; Represents the sum of the rated capacities of the generator sets of cluster unit g that are scheduled to be shut down at time t-1;. Step 1.3: Establish minimum start and stop time constraints: (11) (12) (13) (14) (15) (16) In formulas (11) to (16): For cluster units The online operating capacity of the unit during the initial period; For cluster units The minimum startup time for cluster units The average value of the minimum start-up time of all units in the system; For cluster units The minimum startup time for cluster units The average value of the minimum start-up time of all units in the system.

[0025] Step 1.4: Establish operation constraints (17) (18) (19) Formula (17) is the system power balance constraint; Formula (18) is the backup constraint, is the rated charging power of the energy storage, is the rated discharge power of the energy storage; R is the system spare capacity; in formula (19) , and They are the theoretical load, actual load and load shedding size in the system respectively.

[0026] Step 1.5: Based on the possible abandonment of wind and solar power in clean energy, the clean energy output is divided into actual grid-connected output and theoretical output: (20) (twenty one) (twenty two) In formulas (20) to (22): and are the actual grid-connected outputs of wind power and photovoltaic power, respectively; and They are wind power and photovoltaic power abandonment respectively; and They are the theoretical outputs of wind power and photovoltaic power respectively.

[0027] Step 1.6: According to the characteristics of independent energy storage at different time scales and the different needs of the power system, a multi-time scale independent energy storage operation model is established: (twenty three) (twenty four) (25) (26) (27) (28) (29) (30) (31) (32) Formula (23) is the independent energy storage capacity allocation formula under different time scales, represents the maximum capacity of independent energy storage at different time scales, represents the rated capacity of energy storage, and formula (24) is the allocation weight constraint. , and represents the allocation weight; Formula (25) is the energy balance equation of the energy storage in adjacent time periods, represents the capacity of energy storage at time t, Indicates that storage The capacity of the moment, and are the charging and discharging efficiency of energy storage, and are the charging and discharging power of energy storage, respectively; Formula (26) limits the real-time capacity of energy storage to not exceed the rated capacity of energy storage; Formulas (27) and (28) limit the real-time charging and discharging power of energy storage to not exceed the rated power of energy storage. represents the rated power of the energy storage; Equations (29) and (30) are the energy storage charging and discharging state restriction constraints, which stipulate that the energy storage can only be in the charging state or the discharging state at the same time. is a variable of 0 or 1, 1 indicates that the energy storage is in a discharging state, 0 indicates that the energy storage is in a charging state, and M is a positive real number; Formula (31) is the maximum discharge capacity constraint of the energy storage, represents the minimum state of charge of energy storage; Formula (32) is the maximum charging capacity constraint of energy storage, Indicates the maximum state of charge of the energy storage.

[0028] Step 1.7: Calculate the upward and downward flexibility deficiency probabilities, deficiency expectations and margin expectation indicators from the short-time scale, medium-time scale and long-time scale, and then weight them to form a comprehensive indicator for system flexibility assessment.

[0029] (33) (34) (35) (36) (37) In formulas (33) to (37), , , Comprehensive reflection The scale-weighted flexibility insufficiency probability, flexibility margin expectation and flexibility insufficiency expectation of flexibility at each scale; , , They are the probability of inflexibility deficiency, the expectation of flexibility margin and the expectation of flexibility deficiency; For scale The allocation weight of For scale The size of the flexibility demanded per unit time.

[0030] S120, based on the independent energy storage response power flow congestion mitigation function, establish an independent energy storage response power flow congestion mitigation model, and determine a network transmission congestion risk index.

[0031] In some embodiments, based on the independent energy storage response flow congestion mitigation effect, establishing an independent energy storage response flow congestion mitigation model and determining a network transmission congestion risk index may include the following steps: Step 2.1: Construct a probability model for wind power output, photovoltaic power output, and independent energy storage output: (1) Wind power output model: (38) In formula (38), , , They are the cut-in wind speed, rated wind speed and cut-out wind speed of the wind turbine respectively; is the rated power of the fan; is the actual wind speed.

[0032] (2) Active output model of photovoltaic cells: (39) In formula (39), is the actual solar radiation intensity; and is the critical value of light intensity; is the rated active power of the photovoltaic cell.

[0033] (3) Independent energy storage output model exist The excess power of wind power, photovoltaic power and other renewable energy generation in the system at the moment is recorded as (40) In formula (40), for Excess power of renewable energy at any time; and Respectively The actual output and real-time load value of renewable energy at all times; It is the proportion of new energy installed capacity in the system.

[0034] when When , the energy storage is in charging state: (41) when When , the energy storage is in the discharge state: (42) (43) In formulas (41) to (43), For energy storage The amount of power stored at any time; For the efficiency of energy storage; is the maximum charge and discharge power of the energy storage device; is the sampling period; , They are respectively the minimum and maximum remaining power allowed by the energy storage device.

[0035] Step 2.2: Establish an independent energy storage response flow congestion mitigation model (1) Objective function (44) In formula (44), is the total number of nodes; For Node The amount of electricity wasted from renewable energy sources; For Node The load shedding capacity; (2) Power balance constraints (45) In formula (45), injecting active energy into the generator; For Node The active energy injected by the energy storage is set to 0 when the system is not connected to the energy storage. When the energy storage is connected, it is positive during discharge and negative during charging. For Node Active load.

[0036] (3) Unit output constraints (46) In formula (46), and Inject minimum and maximum active power into the unit respectively.

[0037] (4) Load shedding constraints (47) In formula (47), For Node Load shedding judgment vector.

[0038] (5) Constraints on abandoned electricity from new energy sources (48) In formula (48), For Node New energy power abandonment judgment vector.

[0039] (6) Branch flow constraints (49) (50) In formulas (49) to (50), is the branch active power vector; is the branch-node association matrix; is the node admittance matrix; is the admittance matrix of the branch, Inject active vector into the generator; is the node active load vector; is the node shear load vector; For branch Active power; For branch Maximum allowed active power.

[0040] Step 2.3: Establish network transmission congestion risk assessment indicators (1) Line blocking probability TLCP (51) In formula (51), is the sampling number; For the lines in the sampling The number of times blocking occurs, that is, the system status in the sampling Number of occurrences.

[0041] (2) Line blocking capacity TLCC The line blocking capacity can be divided into the maximum line blocking capacity and the average blocking capacity .

[0042] (52) (53) In formulas (52) to (53), , , and Line The blocking capacity, actual power and rated capacity; For Line The number of system states in which blocking occurs. For Line In the The blocking capacity under a system state.

[0043] (3) Line blocked electricity TLCE (54) S130, determining the comprehensive weights of the multi-time scale flexibility risk index and the network transmission congestion risk index according to the entropy weight method and the hierarchical analysis method.

[0044] In some embodiments, determining the comprehensive weights of the multi-time scale flexibility risk index and the network transmission congestion risk index according to the entropy weight method and the hierarchical analysis method may include the following steps: Step 3.1: Determine the weights of the multi-time scale flexibility risk index and the network transmission congestion risk index using the entropy weight method First, for Contains The indicator sequence matrix of indicators The dimensionless processing is performed, where the negative index uses formula (55) and the positive index uses formula (56).

[0045] (55) (56) In the formula, and They are Sequence The maximum and minimum values ​​of ; is the number of cities that participate in the classification of energy storage demand urgency. The samples after dimensionless transformation of equations (55) and (56) form a matrix and are recorded as , and use formula (57) to calculate the The proportion of an indicator in the indicator sequence.

[0046] (57) Then, calculate the weight of the indicator in the indicator sequence. The entropy value of each indicator is calculated by formula (58), and the entropy weight is calculated by formula (59).

[0047] (58) (59) Step 3.2: Determine indicator weights using the analytic hierarchy process The source-load characteristic index of the municipal power grid, the system operation safety index, the multi-time scale operation flexibility index and the network transmission congestion risk index (A, B, C, D) are selected as the criterion layer; the new energy coverage rate, the traditional energy coverage rate, the new energy supply reliability index, the annual maximum load increase rate (X1~X4), the frequency offset, the frequency change rate, the frequency safety margin (X5~X7), the probability of insufficient flexibility, the expectation of insufficient flexibility, the expectation of flexibility margin, the scale-weighted flexibility index (X8~X11), the line congestion probability, the line congestion capacity and the line blocked power (X12~X14) are selected as sub-criteria. The judgment matrix is ​​constructed according to the importance degree of Table 1.

[0048] Table 1 Meaning of importance scale Scale meaning 1 Indicates that two elements have the same importance. 3 Indicates that compared with two elements, the former is slightly more important than the latter 5 Indicates that compared with two elements, the former is obviously more important than the latter 7 Indicates that compared with two elements, the former is extremely important than the latter 9 Indicates that compared with two elements, the former is more important than the latter 2，4，6，8 Indicates the middle value of the above adjacent judgments The comparison judgment matrix is ​​set as , The characteristic value of , the maximum eigenvalue The corresponding eigenvector is , after normalization, we get the vector . Multiply each row of elements and open To the power of , we get the vector ,in, (60) After normalization, we get the weight vector ,in: (61) Step 3.3: Consistency test of judgment matrix (62) (63) In formulas (62) to (63), is a consistency indicator, is the average random consistency index, is the consistency ratio, See the table below: Table 2 Matrix consistency index table Matrix order 1 2 3 4 5 6 7 8 RI 0 0 0.58 0.9 1.12 1.24 1.32 1.41 when When , the judgment matrix satisfies the consistency test, and the obtained weights can be used to evaluate the urgency of energy storage demand.

[0049] Step 3.4: Determine the comprehensive weight of indicators: Comprehensively use entropy weight method and hierarchical analysis method to determine the The weights of each indicator in the indicator sequence are as follows: (64) S140, classifying the urgency of energy storage demand for power grids in various cities according to the comprehensive weight of the multi-time scale flexibility risk index and the network transmission congestion risk index.

[0050] In some embodiments, the formula (55) and the formula (56) can be used to calculate a OK Column Matrix ,matrix Medium Element is the normalized value of the indicator, which can be expressed by formula (65): (65) In formula (65), Represents the number of indicators, The number of cities represented, , .

[0051] Then, the comprehensive evaluation value of the data is calculated by formula (66): : (66) In formula (66), For the The urgency of energy storage demand in each city is scored.

[0052] Finally, the urgency of energy storage demand in various cities is classified according to Table 3.

[0053] Table 3 Classification of the urgency of energy storage demand in cities Assessment score (0，60] (60，70] (70，80] (80，90] (90，100] SA Very urgent Highly urgent urgent generally Not urgent According to the embodiments of the present invention, the following technical effects are achieved: 1. The present invention calculates flexibility indicators from different time scales, and then weights them to form a comprehensive indicator for system flexibility evaluation, comprehensively evaluates the flexibility of the system, and improves the accuracy and reliability of the indicator.

[0054] 2. The present invention uses a combination of entropy weight method and hierarchical analysis method to determine the weights of indicators to describe the urgency of the energy storage demand of local power grids. It not only overcomes the defect of strong subjectivity of the weights determined by the hierarchical analysis method, but also solves the problem that the weights determined by the entropy weight method cannot reflect the subjective needs in actual planning.

[0055] 3. The present invention classifies the urgency of energy storage needs of power grids in various cities into different levels, which helps to rationally plan energy storage layout and improve the utilization efficiency and economy of energy storage systems.

[0056] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described order of actions, because according to the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0057] The above is an introduction to a method embodiment. The following is a further explanation of the solution of the present invention through an apparatus embodiment.

[0058] Embodiment 2 Figure 2A block diagram of an independent energy storage hierarchical quantization device considering power regulation and network congestion relief according to an embodiment of the present disclosure is shown. Figure 2 As shown, the device 200 includes: A modeling unit 210 is used to establish an independent energy storage dispatch response model for power grid power balancing needs based on the independent energy storage response multi-time scale power balancing effect, and determine a multi-time scale flexibility risk index; The modeling unit 210 is further used to establish an independent energy storage response power flow congestion relief model based on the independent energy storage response power flow congestion relief effect, and determine the network transmission congestion risk index; A calculation unit 220, used to determine the comprehensive weights of the multi-time scale flexibility risk index and the network transmission congestion risk index according to an entropy weight method and a hierarchical analysis method; The scoring unit 230 is used to classify the urgency of energy storage demand in various municipal power grids according to the comprehensive weight of the multi-time scale flexibility risk index and the network transmission congestion risk index.

[0059] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described module can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0060] like Figure 3 As shown, an embodiment of the present invention further provides a computer-readable storage medium having computer program instructions stored thereon; when the computer program instructions are executed by a processor, the independent energy storage graded quantization method considering power regulation and network congestion relief provided in an embodiment of the present invention is implemented, or the independent energy storage graded quantization method considering power regulation and network congestion relief provided in an embodiment of the present invention is implemented.

[0061] It should be clear that the present invention is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present invention.

[0062] The functional blocks shown in the structural block diagram described above can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application-specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present invention are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link through a data signal carried in a carrier. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, read-only memories (ROM), flash memory, erasable read-only memories (EROM), floppy disks, compact disc read-only memories (CD-ROM), optical discs, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0063] It should also be noted that the exemplary embodiments mentioned in the present invention describe some methods or systems based on a series of steps or devices. However, the present invention is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiments, or in a different order from the embodiments, or several steps can be performed simultaneously.

[0064] Aspects of the present disclosure are described above with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the functions / actions specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It can also be understood that each box in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0065] The above is only a specific implementation of the present invention. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present invention is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be covered within the protection scope of the present invention.

Claims

1. A method for quantifying independent energy storage grading considering power regulation and network congestion relief, characterized in that: include: Based on the multi-time scale power balancing effect of independent energy storage response, an independent energy storage dispatch response model for power grid power balancing needs is established, and multi-time scale flexibility risk indicators are determined; Based on the independent energy storage response flow congestion mitigation function, an independent energy storage response flow congestion mitigation model is established, and the network transmission congestion risk index is determined; Determine the comprehensive weights of the multi-time scale flexibility risk index and the network transmission congestion risk index according to the entropy weight method and the hierarchical analysis method; The urgency of energy storage demand for power grids in various cities is graded according to the comprehensive weight of the multi-time scale flexibility risk index and the network transmission congestion risk index.

2. The method according to claim 1, characterized in that Based on the multi-time scale power balancing effect of independent energy storage response, an independent energy storage dispatch response model for power grid power balancing needs is established, and multi-time scale flexibility risk indicators are determined, including: According to the characteristics of independent energy storage at different time scales and the different needs of the power system, a multi-time scale independent energy storage operation model is established to generate independent energy storage operation constraints; Based on the multi-time-scale independent energy storage operation model and the independent energy storage operation constraints, the multi-time-scale flexibility risk index is determined from the short time scale, the medium time scale and the long time scale respectively.

3. The method according to claim 2, characterized in that The multi-time scale flexibility risk indicators are determined from the short time scale, the medium time scale and the long time scale, respectively, including: Calculate upward and downward flexibility deficiency probability index, deficiency expectation index and margin expectation index from short time scale, medium time scale and long time scale respectively; The upward and downward flexibility deficiency probability indicators, deficiency expectation indicators and margin expectation indicators are weighted to obtain a multi-time scale flexibility risk indicator.

4. The method according to claim 1, characterized in that: Based on the independent energy storage response flow congestion mitigation function, an independent energy storage response flow congestion mitigation model is established, and a network transmission congestion risk index is determined, including: Construct wind power output probability model, photovoltaic power output probability model and independent energy storage output probability model respectively; According to the wind power output probability model, the photovoltaic output probability model and the independent energy storage output probability model, based on the independent energy storage response to the flow congestion relief effect, an independent energy storage response to the flow congestion relief model is established; Based on the independent energy storage response power flow congestion mitigation model, a network transmission congestion risk index is determined.

5. The method according to claim 1, characterized in that The method of determining the comprehensive weights of the multi-time scale flexibility risk index and the network transmission congestion risk index according to the entropy weight method and the hierarchical analysis method includes: Determine first weight values ​​of the multi-time scale flexibility risk index and the network transmission congestion risk index respectively by using an entropy weight method; Determine second weight values ​​of the multi-time scale flexibility risk index and the network transmission congestion risk index respectively by using a hierarchical analysis method; According to the first weight value and the second weight value, the comprehensive weights of the multi-time scale flexibility risk index and the network transmission congestion risk index are determined respectively.

6. The method according to claim 1, characterized in that The urgency of the energy storage demand of the power grids in various cities is classified into different levels according to the comprehensive weight of the multi-time scale flexibility risk index and the network transmission congestion risk index, including: Determine the urgency score of energy storage demand for power grids in various cities based on the comprehensive weight of the multi-timescale flexibility risk index and the network transmission congestion risk index; Based on the scores of the urgency of energy storage needs in the power grids of various cities, the urgency of energy storage needs in the power grids of various cities is divided into levels, wherein the urgency of energy storage needs in the power grids of various cities includes very urgent, highly urgent, urgent, general and not urgent.

7. The method according to claim 5, characterized in that The urgency of the energy storage demand of the power grid in each city is scored according to the comprehensive weight of the multi-time scale flexibility risk index and the network transmission congestion risk index, including: in, For the The urgency of energy storage demand in each city is scored. is the comprehensive weight of the multi-time scale flexibility risk index and the network transmission congestion risk index, It is the normalized value of the multi-time scale flexibility risk index and the network transmission congestion risk index.

8. An independent energy storage hierarchical quantification device considering power regulation and network congestion relief, characterized in that: include: A modeling unit, which is used to establish an independent energy storage dispatch response model for power grid power balancing needs based on the multi-time scale power balancing effect of independent energy storage response, and determine the multi-time scale flexibility risk index; The modeling unit is further used to establish an independent energy storage response flow congestion relief model based on the independent energy storage response flow congestion relief effect, and determine a network transmission congestion risk index; A calculation unit, used to determine the comprehensive weights of the multi-time scale flexibility risk index and the network transmission congestion risk index according to an entropy weight method and a hierarchical analysis method; The scoring unit is used to classify the urgency of energy storage demand in the power grid of each city according to the comprehensive weight of the multi-time scale flexibility risk index and the network transmission congestion risk index.

9. An electronic device, characterized in that: It includes a processor, and a memory and a network interface connected to the processor; the network interface is connected to a non-volatile memory in a server; when the processor is running, it calls a computer program from the non-volatile memory through the network interface, and runs the computer program through the memory to execute the method described in any one of claims 1 to 7.

10. A storage medium, characterized in that: Computer program instructions are stored on the computer storage medium, and when the computer program instructions are executed by the processor, the method according to any one of claims 1 to 7 is implemented.

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