Regional power grid optimization scheduling method and device, electronic equipment and medium

Abstract

The invention provides a regional power grid optimal scheduling method and device, electronic equipment and a medium, and the method comprises an obtaining module which is used for obtaining a single operation model of each flexible resource; the first establishing module is used for establishing single power regulation feasible regions of the plurality of flexible resources according to the plurality of single operation models; the clustering module is used for summing the plurality of monomer power regulation feasible regions and determining a power aggregation feasible region of each flexible resource; and the second establishment module is used for establishing a regional power grid optimization scheduling model containing aggregation distributed flexible resources according to the plurality of power aggregation feasible regions. According to the method, the technical problem that the adjustable power is relatively small due to participation of flexible resources of single equipment in regional power grid optimization scheduling in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method for optimizing and dispatching a regional power grid containing aggregated distributed flexible resources, a device for optimizing and dispatching a regional power grid containing aggregated distributed flexible resources, an electronic device and a readable storage medium. Background Art

[0002] With the advancement of the electric energy substitution strategy, the power load has been rising year by year, bringing major challenges to the safe and economical operation of the power system. Exploiting flexible and adjustable resources such as air conditioners, electric vehicles and energy storage to participate in the optimal dispatch of regional power grids can delay power investment and is an effective way to improve the safe and economical operation of power systems. The flexible characteristic curve of a single device has the characteristics of periodic jumps and small adjustable power. In contrast, the flexible resource group has the characteristics of considerable quantity, flexible dispatching methods and huge potential. Therefore, how to aggregate the adjustable capacity of flexible resources to participate in the optimal dispatch of regional power grids has become a technical problem that needs to be solved urgently.

[0003] However, in actual use, there is a problem: in the existing technology, the flexible resources of single devices are used to participate in the optimization and scheduling of regional power grids, and the adjustable power is relatively small. Summary of the invention

[0004] The present invention solves the technical problem in the prior art that the flexible resources of a single device participate in the optimization dispatch of a regional power grid and the adjustable power is relatively small.

[0005] To solve the above problems, the present invention provides a method for optimizing and dispatching a regional power grid containing aggregated distributed flexible resources, comprising: an acquisition module, the acquisition module is used to acquire a single-unit operation model of each flexible resource; a first establishment module, the first establishment module is used to establish a single-unit power regulation feasible domain of multiple flexible resources according to multiple single-unit operation models; a clustering module, the clustering module is used to sum up multiple single-unit power regulation feasible domains to determine the power aggregation feasible domain of each flexible resource; a second establishment module, the second establishment module is used to establish a regional power grid optimizing and dispatching model containing aggregated distributed flexible resources according to multiple power aggregation feasible domains.

[0006] Compared with the prior art, the technical effects achieved by adopting this technical solution are: determining the power aggregation feasible domain of each flexible resource, and participating in the regional power grid optimization scheduling through the flexible resource group, with a more substantial number, a more flexible scheduling method, and greater potential; the present invention can aggregate the adjustable capacity of flexible resources to participate in the regional power grid optimization scheduling, delay power investment, and improve the safety and economy of power system operation.

[0007] In one example of the present invention, the flexible resources include: air conditioning resources, electric vehicle resources and energy storage resources.

[0008] Compared with existing technologies, the technical effect achieved by adopting this technical solution is that tapping into flexible and adjustable resources such as air conditioners, electric vehicles and energy storage to participate in regional power grid optimization and scheduling can delay electricity investment.

[0009] In an example of the present invention, multiple individual power regulation feasible regions are summed to determine the power aggregation feasible region of each flexible resource, including: summing the individual power regulation feasible regions of each flexible resource by using an inner approximate Minkowski sum method.

[0010] Compared with the existing technology, the technical effect achieved by adopting this technical solution is as follows: Minkowski sum is an addition applicable to Euclidean space, and its physical essence is the expansion set of multiple spaces. By adopting the Minkowski sum method, the feasible domain of single power regulation of each type of flexible resource is summed, and the aggregation feasible domain of each type of flexible resource group to be aggregated can be determined.

[0011] In one example of the present invention, multiple single-unit power regulation feasible domains are summed to determine the power aggregation feasible domain of each flexible resource, and the method also includes: setting a benchmark for each flexible resource to obtain a first benchmark formula and a second benchmark formula; establishing an approximate constraint space of the flexible resource according to the first benchmark formula and the second benchmark formula; and integrating multiple approximate constraint spaces to obtain the power aggregation feasible domain of each flexible resource.

[0012] In one example of the present invention, a regional power grid optimization scheduling model containing aggregated distributed flexible resources is established based on multiple power aggregation feasible domains, including: based on multiple power aggregation feasible domains, taking minimizing operating costs as the goal, considering the operating constraints and network topology constraints of various types of devices; establishing a regional power grid optimization scheduling model based on the operating constraints and network topology constraints; wherein the devices include: photovoltaic units, wind turbines and flexible resources.

[0013] On the other hand, an embodiment of the present invention also provides a regional power grid optimization and scheduling device containing aggregated distributed flexible resources, which applies a regional power grid optimization and scheduling method as described in any of the above embodiments, including: an acquisition module, the acquisition module is used to acquire the individual operation model of each flexible resource; a first establishment module, the first establishment module is used to establish a plurality of individual power regulation feasible domains of flexible resources according to multiple individual operation models; a clustering module, the clustering module is used to sum up multiple individual power regulation feasible domains to determine the power aggregation feasible domain of each flexible resource; a second establishment module, the second establishment module is used to establish a regional power grid optimization and scheduling model containing aggregated distributed flexible resources according to multiple power aggregation feasible domains.

[0014] Compared with the prior art, the technical effect achieved by adopting this technical solution is as follows: the regional power grid optimization and dispatching device containing aggregated distributed flexible resources in this embodiment is used to implement the regional power grid optimization and dispatching method containing aggregated distributed flexible resources as in any embodiment of the present invention, so it has all the beneficial effects of the regional power grid optimization and dispatching method containing aggregated distributed flexible resources as in any embodiment of the present invention, which will not be repeated here.

[0015] On the other hand, an embodiment of the present invention further provides an electronic device, comprising: a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein when the program or instruction is executed by the processor, the steps of the method for optimizing and scheduling a regional power grid containing aggregated distributed flexible resources are implemented as in any one of the above embodiments.

[0016] Compared with the prior art, the technical effect achieved by adopting this technical solution is as follows: the electronic device in this embodiment operates the regional power grid optimization scheduling method containing aggregated distributed flexible resources as in any embodiment of the present invention, so it has all the beneficial effects of the regional power grid optimization scheduling method containing aggregated distributed flexible resources as in any embodiment of the present invention, which will not be repeated here.

[0017] On the other hand, an embodiment of the present invention further provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the regional power grid optimization scheduling method containing aggregated distributed flexible resources as in any of the above embodiments are implemented.

[0018] Compared with the prior art, the technical effect achieved by adopting this technical solution is as follows: the readable storage medium in this embodiment is used to store the regional power grid optimization scheduling method containing aggregated distributed flexible resources as in any embodiment of the present invention, so it has all the beneficial effects of the regional power grid optimization scheduling method containing aggregated distributed flexible resources as in any embodiment of the present invention, which will not be repeated here.

[0019] After adopting the technical solution of the present invention, the following technical effects can be achieved: (1) Determine the power aggregation feasible domain of each flexible resource, and participate in the regional power grid optimization dispatch through the flexible resource group, which will have a larger number, more flexible dispatch mode and greater potential; (2) The present invention can aggregate the adjustable capacity of flexible resources to participate in the optimization and dispatching of regional power grids, delay power investment, and improve the safety and economy of power system operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A flowchart of a method for optimizing and dispatching a regional power grid with aggregated distributed flexible resources provided in Embodiment 1 of the present invention; Figure 2A schematic diagram of an IEEE33 node provided for an exemplary embodiment of the present invention; Figure 3 An operation scenario diagram provided for an exemplary embodiment of the present invention; Figure 4 A graph showing the aggregation results of adjustable capacity of an air conditioning cluster provided by an exemplary embodiment of the present invention; Figure 5 A graph showing the aggregation results of adjustable capabilities of an electric vehicle cluster provided by an exemplary embodiment of the present invention; Figure 6 A graph showing the aggregated results of adjustable capacity of an energy storage cluster provided by an exemplary embodiment of the present invention; Figure 7 A diagram of system optimization scheduling results provided by an exemplary embodiment of the present invention; Figure 8 A schematic block diagram of the structure of a regional power grid optimization dispatching device containing aggregated distributed flexible resources provided in the second embodiment of the present invention; Fig. 9 A block diagram of an electronic device according to a third embodiment of the present invention; Fig.10 A schematic diagram of the structure of a readable storage medium provided in Embodiment 4 of the present invention.

[0021] Description of reference numerals: 100-regional power grid optimization and dispatching device containing aggregated distributed flexible resources; 101-acquisition module; 102-first establishment module; 103-clustering module; 104-second establishment module; 200-electronic device; 210-memory; 211-computer program; 220-processor; 230-input device; 240-output device; 300-readable storage medium; 310-computer executable instructions. DETAILED DESCRIPTION

[0022] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention are described clearly and completely. 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.

[0023] [Example 1] See also Figure 1 , which is a flow chart of a method for optimizing and dispatching a regional power grid with aggregated distributed flexible resources provided by the first embodiment of the present invention, the method for optimizing and dispatching a regional power grid comprising: Step S100: obtaining a single operating model of each flexible resource; Step S200: establishing a feasible domain of power regulation of multiple flexible resources according to multiple single-unit operation models; Step S300: summing up multiple single-unit power regulation feasible regions to determine the power aggregation feasible region of each flexible resource; Step S400: establishing a regional power grid optimization scheduling model containing aggregated distributed flexible resources according to multiple power aggregation feasible domains.

[0024] Furthermore, flexible resources include: air-conditioning resources, electric vehicle resources and energy storage resources.

[0025] In a specific embodiment, the flexible resources may include: air conditioning resources, electric vehicle resources and energy storage resources. According to the unit operation models of different types of flexible resources, the unit power regulation feasible domain of flexible resources such as air conditioning, electric vehicle, energy storage, etc. is established, and the unit power regulation feasible domain of each type of flexible resource is rewritten into a compact constraint space form, which is the unit power regulation feasible domain.

[0026] 1) For the establishment of air conditioning models, most studies use a discrete first-order equivalent thermal parameters (ETP) model to describe the temperature evolution principle of air conditioning loads. However, in scenarios with a large number of air conditioning loads, the results obtained by using discrete models and continuous models for air conditioning loads are similar. In order to facilitate the evaluation of the aggregation flexibility of air conditioning load clusters, this paper uses an approximate model with continuous power changes as follows: Formula 1: ; Formula 2: ; Formula 3: ; Formula 4: ; Where: , , For the Air conditioning load Indoor temperature, ambient temperature and set temperature at the moment; AC is air conditioning, , For the Air conditioning load Power consumption and rated power at all times; A user-specified tolerance range for temperature fluctuations; , No. Thermal resistance and thermal capacity of air conditioner load; For the The energy efficiency curve of the air conditioner load is approximately expressed as a linear function of the outdoor temperature, c represents the rate at which the energy efficiency changes with the outdoor temperature, and d represents the energy efficiency value when the outdoor temperature is zero; is the scheduling period interval. Wherein, Formula 1 is the single operation model of air conditioning resources.

[0027] Make the following transformation on the above formula: Formula 5: ; Formula 6: ; Where: Air conditioning load The baseline power of , then the energy storage model of the air conditioning load can be obtained as follows: Formula 7: ; Formula 8: ; Formula 9: ; Formula 10: ; In the formula, is the constraint space of the air conditioner n; the diagonal of the E matrix is ​​1, and the rest of the elements are 0; is the constraint matrix, is the parameter matrix of air conditioner n; is the power matrix of air conditioner n at each moment; is the boundary vector, is the power upper and lower limit matrix of air conditioner n; is the energy upper and lower limit matrix of air conditioner n. Wherein, formula 10 is the feasible domain of single power regulation of air conditioner resources.

[0028] 2) For the establishment of the electric vehicle model, this paper describes its constraint space based on the operation records of the charging pile, including the start time, end time, charging amount, initial SOC and maximum battery capacity of the EV. In the formula E: and Respectively Air conditioning storage Power and power at all times; and Respectively Air conditioning storage The upper and lower power limits at each moment; and Respectively Air conditioning storage The upper and lower limits of power at the moment. Its compact form is: V is an electric vehicle. In the adjustable charging mode, the charging power of the EV grid connection process can be flexibly adjusted. However, in order not to affect the life of the electric vehicle battery during the grid connection process, the EV is prohibited from being in a discharge state. satisfy: Formula 11: ; Formula 12: ; Where: , , , , and Electric vehicles Grid-connected period, off-grid period, initial SOC of grid connection, rated charging power, charging efficiency and battery capacity; , Electric vehicles exist The actual charging power during the period, SOC at the end of the period; The unit duration.

[0029] Vehicle power when off-grid satisfy . , They are The initial grid-connected power and the expected off-grid power, For electric vehicles Off-grid power consumption. , the upper and lower boundaries of the limit are expressed as: Formula 13: ; Its compact form is: Formula 14: ; In the formula, , Electric vehicles l exist t The upper and lower limits of energy at each moment; is the constraint space of electric vehicle n; is the constraint matrix, is the parameter matrix of electric vehicle n; is the power vector; is the power matrix of electric vehicle n at each moment; is the boundary vector, is the power upper and lower limit matrix of electric vehicle n; is the energy upper and lower limit matrix of electric vehicle n. Wherein, formula 13 is the single operation model of electric vehicle resources, and formula 14 is the feasible domain of single power regulation of electric vehicle resources.

[0030] 3) For the establishment of energy storage model, Formula 15: ; Formula 16: ; Formula 17: ; Formula 18: ; Formula 19: ; Formula 20: ; In the formula, ESS is energy storage, and Energy storage devices exist and Energy storage level at the moment; and Energy storage efficiency coefficient and energy release efficiency coefficient respectively; and They are Time storage device The charging and discharging power; and Energy storage devices The minimum and maximum charging power; and Energy storage devices The minimum and maximum values ​​of the discharge power; and Energy storage devices The minimum and maximum capacity of and Energy storage devices The energy storage level at the beginning and end of the dispatch period. Its compact form is: Formula 21: In the formula, is the constraint space of energy storage n; is the parameter matrix of energy storage n; is the power matrix of energy storage n at each moment; is the power upper and lower limit matrix of energy storage n; is the energy upper and lower limit matrix of energy storage n. Among them, formulas 15-20 are the single-unit operation models of energy storage resources, and formula 21 is the feasible domain of single-unit power regulation of energy storage resources.

[0031] Furthermore, step S300 includes: summing up the feasible regions of power regulation of each flexible resource by using an inner approximate Minkowski sum method.

[0032] Specifically, the Minkowski sum is an addition applicable to Euclidean space, and its physical essence is the expansion set of multiple spaces. By using the Minkowski sum method to sum the feasible domain of individual power regulation of each type of flexible resource, the aggregated feasible domain of each type of flexible resource group to be aggregated can be determined.

[0033] Furthermore, step S300 also includes: Step S310: performing benchmark setting on each flexible resource to obtain a first benchmark formula and a second benchmark formula; Step S320: establishing an approximate constraint space of the flexible resource according to the first reference formula and the second reference formula; Step S330: Integrate multiple approximate constraint spaces to obtain a power aggregation feasible region for each flexible resource.

[0034] Specifically, step S300 mainly includes three steps: benchmark setting, inner approximation and cluster integration.

[0035] Among them, the average method is used to set the benchmark as follows: Formula 22: ; Formula 23: ; Where N is the number of flexible resources in this type of cluster, is the benchmark parameter matrix, Flexible resources n The parameter matrix of is the reference boundary matrix, Flexible resources n The boundary matrix of . Formula 23 is the first benchmark formula, and Formula 24 is the second benchmark formula.

[0036] The inner approximation stage mainly achieves the benchmark representation of the differentiated constraint space by translation and scaling. That is, based on the benchmarks of formulas 22 and 23, the constraint spaces of different individuals are approximated according to formula 24: Formula 24: ; In the formula, is the approximate constraint space of individual n; is the scaling factor of individual n; is the translation factor of individual n, and formula 24 is the approximate constraint space. The specific value can be obtained by solving the following LP problem: Formula 25: ; Formula 26: ; Formula 27: ; In the formula, ; , is the auxiliary variable matrix.

[0037] After completing the inner approximation link (that is, the inner approximation link), the shapes of all individual constraint spaces are the same, so their Minkowski sums can be calculated by direct addition and integrated into the form of formula 28: Formula 28: ; In the formula, is the power aggregation feasible domain of this type of flexible resource cluster, and p is the power matrix after aggregation. Formula 28 is the power aggregation feasible domain of various types of flexible resources.

[0038] Furthermore, step S400 includes: According to multiple power aggregation feasible regions, the operation constraints of various devices and network topology constraints are considered with the goal of minimizing the operation cost; Establish a regional power grid optimization dispatch model based on operation constraints and network topology constraints; Among them, the devices include: photovoltaic units, wind turbines and flexible resources.

[0039] Specifically, step S400 is as follows: based on the feasible domain aggregation result (that is, the power aggregation feasible domain), with the goal of minimizing the operating cost, considering the operating constraints of various devices and the network topology constraints, a regional power grid optimization scheduling model containing aggregated distributed flexible resources is established and solved.

[0040] (1) Objective function Formula 29: ; Formula 30: ; Formula 31: ; Formula 32: ; In the formula, is the total operating cost of the system; is the total cost of electricity purchase; The operating costs of distributed new energy devices; The compensation cost for the flexible resource cluster to participate in the scheduling; for The electricity price for the time period; for Power of electricity and gas purchased during time periods; , The unit power generation cost of wind power and photovoltaic units; for Photovoltaic power during the period; for Wind power during the period; , and They are the response costs of air conditioning load, electric vehicle load and user-side energy storage respectively.

[0041] (2) Distribution network flow constraints This paper adopts the DC power flow constraints of the distribution network and converts the power flow constraints into MISOCP constraints using the second-order cone relaxation method: Formula 33: ; Formula 34: ; Formula 35: ; Formula 36: ; Formula 37: ; Formula 38: ; Formula 39: ; Formula 40: ; In the formula, Indicates p is the power of the line flowing into the node; q represents the power flowing out of the node; Indicates q is the power of the line out of the node; Indicates q is the resistance of the line leaving the node; and They are slave nodes in DN. The set of branches that start and end; and are the sets of nodes and branches in DN respectively; For Node exist Output power at the moment; , , and Node Voltage, branch exist Current, power, and resistance at each moment; Indicates line L The square of the current; Representation Node n The square of the voltage; and They are and The upper limit of Representation Node i The square of the current; Representation Node j The square of the current.

[0042] (3) Constraints on photovoltaic units Formula 41: ; Formula 42: ; Formula 43: ; In the formula, For photovoltaic power stations Total output power at all times; for Light intensity at the time; is the photoelectric conversion efficiency of the photovoltaic power station; and Indicates the power supplied by the photovoltaic unit to the load and the abandoned power; Indicates the upper limit of the unit's climbing rate.

[0043] (4) Wind turbine constraints Formula 44: ; Formula 45: ; Formula 46: ; In the formula, For the moment wind speed; and are the cut-in and cut-out wind speeds, respectively; and are the rated wind speed and rated power of the wind turbine respectively; is the total power of the wind turbine; and Indicates the power supplied to the load by the wind turbine and the power abandoned; Indicates the upper limit of the climbing rate of the wind turbine.

[0044] (5) Flexible resource cluster constraints Formula 47: ; In the formula, , and They are the parameter matrices of the air conditioning cluster, electric vehicle cluster, and energy storage cluster respectively; , and are the vectors of air conditioning cluster, electric vehicle cluster and energy storage cluster respectively; , and They are the operating power of the air-conditioning cluster, electric vehicle cluster and energy storage cluster respectively.

[0045] So far, the regional power grid optimization scheduling model with aggregated distributed flexible resources has been established and can be applied to actual examples.

[0046] In order to verify the effectiveness of the proposed method, the following example is set: Example: Figure 2 As shown in the figure, taking the IEEE33-node distribution network as an example, the proposed optimized collaborative operation model is studied. Figure 3 The predicted output curve, electric load curve and outdoor temperature curve of wind turbine and photovoltaic unit are shown in Table 1.

[0047] Table 1 Parameters of various equipment in the system Figure 4 The aggregation result of the adjustable capacity of the air conditioning cluster shows that the air conditioning cluster has the ability to adjust upward from 0:00 to 6:00, but not the ability to adjust downward. This is because the outdoor temperature is low at night, and most air conditioners are not running, with an operating power of 0. During the day, the cluster has both the ability to adjust upward and downward. From 12:00 to 16:00, the ability to adjust upward decreases, while the ability to adjust downward increases. This is because the outdoor temperature rises during this period, and the air conditioner needs more power to maintain the set temperature, resulting in a smaller ability to adjust upward. Figure 5 Aggregation results of the adjustable capacity of electric vehicle clusters show that the upward adjustment capacity of electric vehicle clusters is greater than the downward adjustment capacity, because the charging needs of users need to be met; Figure 6 Aggregation results of the adjustable capacity of energy storage clusters show that the upward adjustment capacity of energy storage clusters is equivalent to the downward adjustment capacity because it is less affected by weather and human factors; Figure 7The results of system optimization scheduling show that during 23:00-24:00 and 0:00-6:00, the electricity price is low, and the electric load is mainly supplied by wind power and electricity purchased from the superior power grid. During this period, low electricity prices are used to charge and reserve energy for the energy storage system; during 6:00-16:00, the power generation of photovoltaic and wind power can not only meet the load demand, but the excess power is also used to charge electric vehicle groups, air conditioning groups and energy storage systems, thereby increasing the new energy consumption rate; during 17:00-22:00, the electricity price is high and the output of WTG and PV is reduced. The AC group, EV group and ESS group are all discharged during this time period to meet the electric load demand, and the shortfall is supplemented by purchasing electricity from the power grid. Through such a scheduling strategy, not only the electricity cost of 89,443 yuan is reduced, but also the flexibility and safety of the power grid are significantly improved, ensuring efficient energy utilization and economy, and enhancing the ability to cope with load fluctuations.

[0048] Therefore, the present invention establishes the feasible domain of single power regulation of flexible resources such as air conditioners, electric vehicles, and energy storage based on the operation models of different types of flexible resources, and then obtains the aggregation feasible domain of each type of flexible resource group to be aggregated through the inner approximate Minkowski sum. According to the aggregation result of the feasible domain, with the goal of minimizing the operating cost, considering the operation constraints of various devices and the network topology constraints, a regional power grid optimization scheduling model containing aggregated distributed flexible resources is established and solved.

[0049] [Example 2] See also Figure 8 This embodiment also provides a regional power grid optimization scheduling device 100 containing aggregated distributed flexible resources, which applies the regional power grid optimization scheduling method as in the first embodiment, including: an acquisition module 101, a first establishment module 102, a clustering module 103 and a second establishment module 104, wherein the acquisition module 101 is used to acquire a single-unit operation model of each flexible resource; the first establishment module 102 is used to establish a single-unit power regulation feasible domain of multiple flexible resources according to multiple single-unit operation models; the clustering module 103 is used to sum multiple single-unit power regulation feasible domains to determine the power aggregation feasible domain of each flexible resource; the second establishment module 104 is used to establish a regional power grid optimization scheduling model containing aggregated distributed flexible resources according to multiple power aggregation feasible domains.

[0050] In a specific embodiment, the acquisition module 101, the first establishment module 102, the clustering module 103 and the second establishment module 104 of the regional power grid optimization and scheduling device 100 containing aggregated distributed flexible resources cooperate to implement the regional power grid optimization and scheduling method containing aggregated distributed flexible resources as described in the first embodiment above, which will not be repeated here.

[0051] [Example 3] See also Fig. 9This embodiment provides a structural diagram of an electronic device 200, where the electronic device 200 includes, for example, a processor 220 and a memory 210 electrically connected to the processor 220, where a computer program 211 is stored in the memory 210, and the processor 220 loads the computer program 211 to implement a regional power grid optimization scheduling method containing aggregated distributed flexible resources as in the first embodiment.

[0052] The memory 210 may include one or more computer program products, and the computer program product may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 220 may run the program instructions to implement the methods of the software programs of the various embodiments of the present invention above and / or other desired functions. In one example, the electronic device 200 may also include: an input device 230 and an output device 240, which are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0053] In addition, the input device 230 may also include, for example, a keyboard, a mouse, and the like.

[0054] The output device 240 can output various information to the outside, and can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto.

[0055] Of course, to simplify, Fig. 9 Only some of the components related to the present invention in the electronic device are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, according to specific application situations, the electronic device 200 may also include any other appropriate components.

[0056] [Example 4] See also Fig.10 This embodiment also provides a readable storage medium 300, which stores computer executable instructions 310. When the computer executable instructions 310 are read and executed by the processor, the electronic device 200 where the readable storage medium 300 is located is controlled to implement the regional power grid optimization scheduling method containing aggregated distributed flexible resources as in the first embodiment.

[0057] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are only illustrative. For example, the functional modules in the various embodiments of the present invention can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0058] If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned readable storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program codes.

[0059] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the dispatching of a regional power grid containing aggregated distributed flexible resources, characterized in that: include: An acquisition module, the acquisition module is used to acquire a single operation model of each flexible resource; A first establishing module, the first establishing module is used to establish a plurality of monomer power adjustment feasible domains of the flexible resources according to the plurality of monomer operation models; A clustering module, the clustering module is used to sum the multiple monomer power regulation feasible domains to determine the power aggregation feasible domain of each flexible resource; The second establishing module is used to establish a regional power grid optimization scheduling model containing aggregated distributed flexible resources according to multiple power aggregation feasible domains.

2. The regional power grid optimization dispatching method according to claim 1, characterized in that: The flexible resources include: air conditioning resources, electric vehicle resources and energy storage resources.

3. The regional power grid optimization dispatching method according to claim 1, characterized in that: The step of summing up the plurality of single-unit power regulation feasible domains to determine the power aggregation feasible domain of each flexible resource includes: The individual power regulation feasible regions of the flexible resources are summed up by using an inner approximate Minkowski sum method.

4. The regional power grid optimization dispatching method according to claim 3, characterized in that: The summing up of the plurality of the single power regulation feasible domains to determine the power aggregation feasible domain of each of the flexible resources further includes: Performing benchmark setting on each of the flexible resources to obtain a first benchmark formula and a second benchmark formula; Establishing an approximate constraint space of the flexible resource according to the first reference formula and the second reference formula; A plurality of the approximate constraint spaces are integrated to obtain the power aggregation feasible domain of each of the flexible resources.

5. The regional power grid optimization dispatching method according to claim 1, characterized in that: The method of establishing a regional power grid optimization dispatching model containing aggregated distributed flexible resources according to the plurality of power aggregation feasible domains includes: According to the plurality of power aggregation feasible domains, with the goal of minimizing the operation cost, the operation constraints of various devices and the network topology constraints are considered; Establishing the regional power grid optimization dispatching model according to the operation constraints and the network topology constraints; Wherein, the device comprises: a photovoltaic unit, a wind turbine unit and the flexible resource.

6. A regional power grid optimization dispatching device containing aggregated distributed flexible resources, characterized in that: The regional power grid optimization dispatching device applies the regional power grid optimization dispatching method according to any one of claims 1 to 5, including: An acquisition module, the acquisition module is used to acquire a single operation model of each flexible resource; A first establishing module, the first establishing module is used to establish a plurality of monomer power adjustment feasible domains of the flexible resources according to the plurality of monomer operation models; A clustering module, the clustering module is used to sum the multiple monomer power regulation feasible domains to determine the power aggregation feasible domain of each flexible resource; The second establishing module is used to establish a regional power grid optimization scheduling model containing aggregated distributed flexible resources according to multiple power aggregation feasible domains.

7. An electronic device, characterized in that: The electronic device includes: a processor, a memory, and a program or instruction stored in the memory and executable on the processor. When the program or instruction is executed by the processor, the steps of the method for optimizing and dispatching a regional power grid containing aggregated distributed flexible resources are implemented as described in any one of claims 1 to 5.

8. A readable storage medium, characterized in that: The readable storage medium stores programs or instructions, and when the programs or instructions are executed by the processor, the steps of the method for optimizing and dispatching a regional power grid containing aggregated distributed flexible resources as described in any one of claims 1 to 5 are implemented.

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