Basin node hydropower station cluster peak regulation scheduling control method and system

Abstract

The invention discloses a river basin node hydropower station cluster peak regulation scheduling control method and system. The method comprises the following steps: determining operation constraint conditions of a power grid, wind and light, river basin node hydropower stations, energy storage and demand side flexible resources; according to the operation constraint conditions, constructing a regional power grid peak regulation optimization control objective function considering the drainage basin node hydropower station group; and according to the regional power grid peak regulation optimization control objective function, solving the optimized operation state and the optimized output power of each power station in the drainage basin node hydropower station cluster at different moments. The adjustable flexible resources on the demand side are fully considered, the advantages of the flexible resources on the demand side are brought into play, and hydroelectric, wind power and photoelectric absorption and the overall peak regulation capacity of the system are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of distributed energy operation control, and in particular relates to a peak-shaving dispatching control method and system for a cluster of hydropower stations at river basin nodes. Background Art

[0002] The current power grid operation often ignores the flexible regulation characteristics of the basin node hydropower station group, which makes a large number of dispersed basin node hydropower station groups unable to fully play their flexible regulation role. At the same time, the operation of the power grid characterized by a high proportion of distributed energy still has problems such as insufficient peak-shaving capacity, power shortage during peak periods, and insufficient electricity during valley periods. Therefore, it is of great significance to improve the overall peak-shaving strategy of cross-basin cascade power station clusters based on distributed water / photovoltaic power generation resources and through the complementarity of distributed basin node hydropower stations and photovoltaic power generation clusters. However, a large number of dispersed basin node hydropower stations and demand-side flexible resources in county areas are isolated from the peak-shaving strategy of the power system. Peak-shaving still has a series of problems such as excessively high peak-shaving costs of county power grids, insufficient flexible resource management and control capabilities in county areas, and insufficient energy supply reliability. Summary of the invention

[0003] The present invention provides a peak-shaving dispatching control method and system for a cluster of hydropower stations at river basin nodes, which are used to solve the technical problem of poor peak-shaving capacity of existing county power grids.

[0004] In a first aspect, the present invention provides a peak-shaving dispatching control method for a cluster of hydropower stations at river basin nodes, comprising:

[0005] Establish operating constraints for the power grid, wind and solar power, hydropower stations at river basin nodes, energy storage and demand-side flexible resources;

[0006] According to the operation constraints, a regional power grid peak load optimization control objective function taking into account the hydropower station group at the river basin node is constructed. The expression of the regional power grid peak load optimization control objective function is:

[0007] f=min(C g +C lo +C es ωC cbr -C grid -C co2 ),

[0008] Where f is the overall operating cost, C g is the total cost of hydropower station power generation, photovoltaic power generation and wind power generation at the basin node, C lo is the load loss cost of distributed flexible resources, C es is the energy storage cost of distributed flexible resources, C dr is the demand response load shifting cost, C grid is the cost of purchasing electricity from the power grid, C co2for the cost of carbon capture;

[0009] According to the regional power grid peak load optimization control objective function, the optimized operating state and optimized output power of each power station in the basin node hydropower station cluster at different times are solved.

[0010] In a second aspect, the present invention provides a peak-shaving dispatching control system for a cluster of hydropower stations at river basin nodes, comprising:

[0011] A determination module configured to establish operating constraints for the power grid, wind and solar power, hydropower stations at river basin nodes, energy storage, and demand-side flexible resources;

[0012] A construction module is configured to construct a regional power grid peak-shaving optimization control objective function taking into account the hydropower station group at the river basin node according to the operation constraint condition, and the expression of the regional power grid peak-shaving optimization control objective function is:

[0013] f=min(C g +C lo +C es -C dr -C grid -C co2 ),

[0014] Where f is the overall operating cost, C g is the total cost of hydropower station power generation, photovoltaic power generation and wind power generation at the basin node, C lo is the load loss cost of distributed flexible resources, C es is the energy storage cost of distributed flexible resources, C dr is the demand response load shifting cost, C grid is the cost of purchasing electricity from the power grid, C co2 for the cost of carbon capture;

[0015] The solution module is configured to solve the optimized operating state and optimized output power of each power station in the basin node hydropower station cluster at different times according to the regional power grid peak load optimization control objective function.

[0016] According to a third aspect, an electronic device is provided, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the steps of the peak-shaving dispatching control method of a cluster of hydropower stations at a basin node according to any embodiment of the present invention.

[0017] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor executes the steps of the peak-shaving dispatching control method for a cluster of hydropower stations at river basin nodes according to any embodiment of the present invention.

[0018] The peak-shaving dispatching control method and system of the basin node hydropower station cluster of the present application establishes the operating constraints of the power grid, wind and solar power, small hydropower, energy storage and flexible resources on the demand side, and constructs a regional power grid peak-shaving optimization control objective function taking into account the small hydropower group. Finally, the optimized operating status and output power of each power station in the small hydropower cluster at different times are calculated and solved, which fully considers the adjustable flexible resources on the demand side, helps to give full play to the advantages of flexible resources on the demand side, and improves the absorption and overall peak-shaving capacity of hydropower, wind power and photovoltaic power. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 A flow chart of a peak-shaving dispatching control method for a cluster of hydropower stations at river basin nodes provided in one embodiment of the present invention;

[0021] Figure 2 A structural block diagram of a peak-shaving dispatching control system for a cluster of hydropower stations at river basin nodes provided in one embodiment of the present invention;

[0022] Figure 3 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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.

[0024] See also Figure 1 , which shows a flow chart of a peak-shaving dispatching control method for a cluster of hydropower stations at river basin nodes of the present application.

[0025] like Figure 1 As shown, the peak-shaving dispatching control method of the cluster of hydropower stations at the river basin nodes specifically includes the following steps:

[0026] Step S101, establish the operating constraints of the power grid, wind and solar power, basin node hydropower stations, energy storage and demand-side flexible resources.

[0027] In this step, the expression of the power grid operation constraint L1 is:

[0028]

[0029] Where P t L is the load power at dispatch time t, N h for;

[0030] The expression of water balance constraint L2 of the hydropower station at the basin node is:

[0031]

[0032] In the formula, v i，t+1 is the flow rate of hydropower station i at time t+1, v i，t is the flow rate of hydropower station i at time t, d i，t is the interval flow of the basin node hydropower station i at time t, Φ is the number of direct upstream power stations, For the direct upstream power station In the period The outbound flow, u i，t is the number of direct upstream power stations of the basin node hydropower station i, Δt is the scheduling time interval, For the direct upstream power station The water flow delay to the hydropower station i at the basin node, For the direct upstream power station In the period Outbound flow, Not greater than The largest integer of ;

[0033] The expression of the upper and lower limit constraints L3 of the output and storage capacity of the hydropower station at the basin node is:

[0034]

[0035]

[0036] Where P i h，min , P i h，max are the minimum and maximum power generation of the hydropower station i at the basin node, is the storage capacity of the hydropower station i at the dispatching time t, V i h,min 、V i h，max are the lower and upper limits of the storage capacity of the hydropower station i at the basin node, respectively;

[0037] The expression of photovoltaic and wind power output constraint L4 is:

[0038] P min ≤P t ≤P max ,

[0039] P min.w ≤P t w ≤P max，w ,

[0040] Where P min , P max are the lower and upper limits of photovoltaic output, P t is the photovoltaic output at time t, P min.w , P max，w They are the lower and upper limits of wind power output respectively.

[0041] Step S102, constructing a regional power grid peak load optimization control objective function taking into account the hydropower station groups at the river basin nodes according to the operation constraints.

[0042] In this step, the expression of the regional power grid peak load optimization control objective function is:

[0043] f=min(C g +C lo +C es -C dr -C grid -C co2 ),

[0044] Where f is the overall operating cost, C g is the total cost of hydropower station power generation, photovoltaic power generation and wind power generation at the basin node, C lo is the load loss cost of distributed flexible resources, C es is the energy storage cost of distributed flexible resources, C ar is the demand response load shifting cost, C grid is the cost of purchasing electricity from the power grid, C co2 The cost of carbon capture.

[0045] It should be noted that the expression for calculating the total cost of hydropower station power generation, photovoltaic power generation and wind power generation at the basin node is:

[0046]

[0047] In the formula, C gh is the power generation income of the hydropower station at the basin node, C gw is the revenue of wind power generation, C gp For the income from photovoltaic power generation, is the unit power generation revenue of the basin node hydropower station at the dispatching time, is the output of the basin node hydropower station i at the dispatching time t, t p is the scheduling time interval, is the unit power generation revenue of wind power at dispatching time t, is the output of wind power at dispatching time t, is the unit power generation income of photovoltaic at the scheduling time t, is the output of photovoltaic power at the dispatching time, T is the dispatching period, and T′ is the number of hydropower stations in the basin node;

[0048] The expression for calculating the load loss cost of distributed flexible resources is:

[0049]

[0050] In the formula, is the cost of unit load loss power at dispatch time t, is the load loss power of the distributed flexibility resource at the scheduling time t;

[0051] The expression for calculating the energy storage cost of distributed flexible resources is:

[0052]

[0053] In the formula, C ec is the charging cost of energy storage, C ed is the discharge cost of energy storage, is the unit charging cost of energy storage at the dispatch time, P t ec For energy storage at the dispatch time, the internal charging power is is the unit discharge cost of energy storage at dispatch time t, P t ed is the discharge power of energy storage at the dispatching time t;

[0054] The expression for calculating the demand response load shifting cost is:

[0055]

[0056] In the formula, is the unit load control cost within the dispatching time t, P t drso is the output power of the demand response load at the dispatch time t, P t drsi is the transfer power of the demand response load at the dispatch time t;

[0057] The expressions for calculating the grid electricity purchase cost and carbon capture cost are:

[0058]

[0059] In the formula, is the electricity purchase price at time t, P t grid is the purchased electricity power at time t, d is the carbon capture cost coefficient, C i is the coal consumption coefficient per unit of electricity.

[0060] Step S103, solving the optimized operating state and optimized output power of each power station in the basin node hydropower station cluster at different times according to the regional power grid peak load optimization control objective function.

[0061] In this step, the expression for the optimal output power of each basin node hydropower station at different dispatching times is solved as follows:

[0062] min C g

[0063] s j , j = 1, ..., 6,

[0064] Where, L j It represents the operating constraints of the power grid operation, basin node hydropower stations, wind power, photovoltaics, energy storage, and demand response loads.

[0065] In summary, the method of this application establishes the operating constraints of the power grid, wind and solar power, small hydropower, energy storage and flexible resources on the demand side, and constructs a regional power grid peak-shaving optimization control objective function taking into account the small hydropower group. Finally, the optimized operating status and output power of each power station in the small hydropower cluster at different times are calculated and solved, which fully considers the adjustable flexible resources on the demand side, helps to give full play to the advantages of flexible resources on the demand side, and improves the absorption and overall peak-shaving capacity of hydropower, wind power and photovoltaic power.

[0066] See also Figure 2 , which shows a structural block diagram of a peak-shaving dispatching control system for a cluster of hydropower stations at river basin nodes of the present application.

[0067] like Figure 2 As shown, the peak-shaving dispatching control system 200 of a cluster of hydropower stations at river basin nodes includes a determination module 210 , a construction module 220 and a solution module 230 .

[0068] Among them, the determination module 210 is configured to establish the operating constraints of the power grid, wind and solar power, basin node hydropower stations, energy storage and demand-side flexible resources;

[0069] The construction module 220 is configured to construct a regional power grid peak-shaving optimization control objective function taking into account the hydropower station group at the river basin node according to the operation constraint condition, and the expression of the regional power grid peak-shaving optimization control objective function is:

[0070] f=min(C g+C lo +C es -C dr -C grid -C co2 ),

[0071] Where f is the overall operating cost, C g is the total cost of hydropower station power generation, photovoltaic power generation and wind power generation at the basin node, C lo is the load loss cost of distributed flexible resources, C es is the energy storage cost of distributed flexible resources, C dr is the demand response load shifting cost, C grid is the cost of purchasing electricity from the power grid, C co2 for the cost of carbon capture;

[0072] The solution module 230 is configured to solve the optimized operating state and optimized output power of each power station in the basin node hydropower station cluster at different times according to the regional power grid peak load optimization control objective function.

[0073] It should be understood that Figure 2 Modules and references documented in Figure 1 Therefore, the operations and features described above for the method and the corresponding technical effects are also applicable to Figure 2 The modules in it will not be described in detail here.

[0074] In other embodiments, the embodiments of the present invention further provide a computer-readable storage medium having a computer program stored thereon, and when the program instructions are executed by a processor, the processor is caused to execute the peak-shaving dispatching control method for a cluster of hydropower stations at a river basin node in any of the above method embodiments;

[0075] As an implementation mode, the computer-readable storage medium of the present invention stores computer-executable instructions, and the computer-executable instructions are configured as follows:

[0076] Establish operating constraints for the power grid, wind and solar power, hydropower stations at river basin nodes, energy storage and demand-side flexible resources;

[0077] According to the operation constraints, a regional power grid peak load optimization control objective function taking into account the hydropower station group at the river basin node is constructed. The expression of the regional power grid peak load optimization control objective function is:

[0078] f=min(C g +C lo +C es -C dr -C grid -C co2 ),

[0079] Where f is the overall operating cost, C g is the total cost of hydropower station power generation, photovoltaic power generation and wind power generation at the basin node, C lo is the load loss cost of distributed flexible resources, C es is the energy storage cost of distributed flexible resources, C dr is the demand response load shifting cost, C grid is the cost of purchasing electricity from the power grid, C co2 for the cost of carbon capture;

[0080] According to the regional power grid peak load optimization control objective function, the optimized operating state and optimized output power of each power station in the basin node hydropower station cluster at different times are solved.

[0081] The computer-readable storage medium may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created according to the use of the peak-shaving dispatching control system of the cluster of hydropower stations at the river basin node, etc. In addition, the computer-readable storage medium may include a high-speed random access memory, and may also include a memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the peak-shaving dispatching control system of the cluster of hydropower stations at the river basin node via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0082] Figure 3 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, such as Figure 3 As shown, the device includes: a processor 310 and a memory 320. The electronic device may also include: an input device 330 and an output device 340. The processor 310, the memory 320, the input device 330 and the output device 340 may be connected via a bus or other means. Figure 3 In the example, the connection via bus is taken as an example. The memory 320 is the above-mentioned computer-readable storage medium. The processor 310 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions and modules stored in the memory 320, that is, the peak-shaving dispatching control method of the watershed node hydropower station cluster in the above-mentioned method embodiment is realized. The input device 330 can receive input digital or character information, and generate key signal input related to user settings and function control of the peak-shaving dispatching control system of the watershed node hydropower station cluster. The output device 340 may include display devices such as display screens.

[0083] The electronic device can execute the method provided by the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the method provided by the embodiment of the present invention.

[0084] As an implementation mode, the electronic device is applied to a peak-shaving dispatching control system of a cluster of hydropower stations at a river basin node, and is used for a client, and includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor, so that the at least one processor can:

[0085] Establish operating constraints for the power grid, wind and solar power, hydropower stations at river basin nodes, energy storage and demand-side flexible resources;

[0086] According to the operation constraints, a regional power grid peak load optimization control objective function taking into account the hydropower station group at the river basin node is constructed. The expression of the regional power grid peak load optimization control objective function is:

[0087] f=min(C g +C lo +C es -C dr -C grid -C co2 ),

[0088] Where f is the overall operating cost, C g is the total cost of hydropower station power generation, photovoltaic power generation and wind power generation at the basin node, C lo is the load loss cost of distributed flexible resources, C es is the energy storage cost of distributed flexible resources, C dr is the demand response load shifting cost, C grid is the cost of purchasing electricity from the power grid, C co2 for the cost of carbon capture;

[0089] According to the regional power grid peak load optimization control objective function, the optimized operating state and optimized output power of each power station in the basin node hydropower station cluster at different times are solved.

[0090] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of each embodiment or some parts of the embodiment.

[0091] 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 peak-shaving dispatching control method for a cluster of hydropower stations at river basin nodes, characterized in that: include: Establish operating constraints for the power grid, wind and solar power, hydropower stations at river basin nodes, energy storage and demand-side flexible resources; According to the operation constraints, a regional power grid peak load optimization control objective function taking into account the hydropower station group at the river basin node is constructed. The expression of the regional power grid peak load optimization control objective function is: f=min(C g +C lo +C es -C dr -C grid -C co2 ), Where f is the overall operating cost, C g is the total cost of hydropower station power generation, photovoltaic power generation and wind power generation at the basin node, C lo is the load loss cost of distributed flexible resources, C es is the energy storage cost of distributed flexible resources, C dr is the demand response load shifting cost, C grid is the cost of purchasing electricity from the power grid, C co2 for the cost of carbon capture; According to the regional power grid peak load optimization control objective function, the optimized operating state and optimized output power of each power station in the basin node hydropower station cluster at different times are solved.

2. A method for peak load regulation and dispatching control of a cluster of hydropower stations at river basin nodes according to claim 1, characterized in that: in, The expression for calculating the total cost of hydropower station power generation, photovoltaic power generation and wind power generation at the basin node is: In the formula, C gh is the power generation income of the hydropower station at the basin node, C gw is the revenue of wind power generation, C gp For the income from photovoltaic power generation, is the unit power generation revenue of the basin node hydropower station at the dispatching time t, is the output of the basin node hydropower station i at the dispatching time t, t p is the scheduling time interval, is the unit power generation revenue of wind power at dispatching time t, is the output of wind power at dispatching time t, is the unit power generation income of photovoltaic at the scheduling time t, is the output of PV at the dispatching time t, T is the dispatching period, and T′ is the number of hydropower stations in the basin node; The expression for calculating the load loss cost of distributed flexible resources is: In the formula, is the cost of unit load loss power at dispatch time t, P t lo is the load loss power of the distributed flexibility resource at the scheduling time t; The expression for calculating the energy storage cost of distributed flexible resources is: In the formula, C ec is the charging cost of energy storage, C ed is the discharge cost of energy storage, is the unit charging cost of energy storage at dispatch time t, is the charging power of the energy storage at the scheduling time t, is the unit discharge cost of energy storage at dispatch time t, is the discharge power of energy storage at the dispatching time t; The expression for calculating the demand response load shifting cost is: In the formula, is the unit load control cost within the dispatching time t, is the output power of the demand response load at the dispatch time t, is the transfer power of the demand response load at the dispatch time t; The expressions for calculating the grid electricity purchase cost and carbon capture cost are: In the formula, is the electricity purchase price at time t, is the purchased electricity power at time t, d is the carbon capture cost coefficient, C i is the coal consumption coefficient per unit of electricity.

3. A method for peak load regulation and dispatching control of a cluster of hydropower stations at river basin nodes according to claim 2, characterized in that: in, The expression of the power grid operation constraint L1 is: In the formula, is the load power at the dispatching time t; The expression of water balance constraint L2 of the hydropower station at the basin node is: In the formula, v i，t+1 is the flow rate of hydropower station i at time t+1, v i，t is the flow rate of hydropower station i at time t, d i，t is the interval flow of the basin node hydropower station i at time t, Φ is the number of direct upstream power stations, For the direct upstream power station In the period The outbound flow, u i，t is the number of direct upstream power stations of the basin node hydropower station i, Δt is the scheduling time interval, For the direct upstream power station The water flow delay to the hydropower station i at the basin node, For the direct upstream power station In the period Outbound flow, Not greater than The largest integer of ; The expression of the upper and lower limit constraints L3 of the output and storage capacity of the hydropower station at the basin node is: In the formula, are the minimum and maximum power generation of the hydropower station i at the basin node, is the storage capacity of the basin node hydropower station i at the dispatching time t, are the lower and upper limits of the storage capacity of the hydropower station i at the basin node, respectively; The expression of photovoltaic and wind power output constraint L4 is: P min ≤P t ≤P max , Where P min , P max are the lower and upper limits of photovoltaic output, P t is the photovoltaic output at time t, P min.w , P max，w They are the lower and upper limits of wind power output respectively.

4. A method for peak load regulation and dispatching control of a cluster of hydropower stations at river basin nodes according to claim 1, characterized in that: in, The expression for solving the optimal output power of each basin node hydropower station at different dispatching times is: my C g s.t.g L j ,j=1,,...,6, Where, L j It represents the operating constraints of the power grid operation, basin node hydropower stations, wind power, photovoltaics, energy storage, and demand response loads.

5. A peak-shaving dispatching control system for a cluster of hydropower stations at river basin nodes, characterized in that: include: A determination module configured to establish operating constraints for the power grid, wind and solar power, hydropower stations at river basin nodes, energy storage, and demand-side flexible resources; A construction module is configured to construct a regional power grid peak-shaving optimization control objective function taking into account the hydropower station group at the river basin node according to the operation constraint condition, and the expression of the regional power grid peak-shaving optimization control objective function is: f=min(C g +C lo +C es -C dr -C grid -C co2 ), Where f is the overall operating cost, C g is the total cost of hydropower station power generation, photovoltaic power generation and wind power generation at the basin node, C lo is the load loss cost of distributed flexible resources, C es is the energy storage cost of distributed flexible resources, C dr is the demand response load shifting cost, C grid is the cost of purchasing electricity from the power grid, C co2 for the cost of carbon capture; The solution module is configured to solve the optimized operating state and optimized output power of each power station in the basin node hydropower station cluster at different times according to the regional power grid peak load optimization control objective function.

6. An electronic device, characterized in that: include: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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