Power system generation scheduling strategy determination method and device and storage medium

Abstract

The application discloses a power system generation scheduling strategy determination method and device and a storage medium. The method comprises the following steps: determining a plurality of operation scenes in a target power system, and determining generation scheduling strategies corresponding to the plurality of operation scenes respectively; determining a similar scene with the maximum similarity to a current scene in the plurality of operation scenes, and a similar scheduling strategy corresponding to the similar scene; and obtaining a target scheduling strategy corresponding to the current scene based on the similar scheduling strategy. The application solves the technical problem that the decision efficiency of the power system is not ideal in the related art.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and more specifically, to a method, apparatus, and storage medium for determining power system generation dispatch strategy. Background Technology

[0002] Generally, the total generating capacity of a power grid is larger than its load. The goal of power system optimal dispatch is to minimize total operating costs or total coal consumption by rationally allocating loads while meeting constraints. Currently, related technologies mainly employ mathematical programming for power system optimal dispatch, obtaining the optimal dispatch result by solving a mathematical optimization model. This method is applicable to large-scale power grid optimal dispatch problems with multiple operating constraints. However, its drawback is low solution efficiency, especially for large-scale power systems, where the solution time is typically on the order of minutes. Furthermore, with the integration of a high proportion of renewable energy sources, wind and solar power output exhibits strong randomness. During real-time optimal dispatch, the large fluctuations in renewable energy output cause frequent changes in operating modes, requiring dispatch decisions to be made within a very short time. This results in the computational efficiency of related technologies being unable to meet the demands of real-time dispatch.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This invention provides a method, apparatus, and storage medium for determining power system generation dispatching strategies, in order to at least solve the technical problem of unsatisfactory decision-making efficiency in power systems in related technologies.

[0005] According to one aspect of the present invention, a method for determining a power system generation dispatch strategy is provided, comprising: determining multiple operating scenarios in a target power system, and determining generation dispatch strategies corresponding to the multiple operating scenarios respectively; determining a similar scenario with the highest similarity to the current scenario among the multiple operating scenarios, and a similar dispatch strategy corresponding to the similar scenario; and obtaining a target dispatch strategy corresponding to the current scenario based on the similar dispatch strategy.

[0006] According to another aspect of the present invention, a power system generation dispatch strategy determination apparatus is provided, comprising: a first determination module, configured to determine multiple operating scenarios in a target power system, and to determine generation dispatch strategies corresponding to the multiple operating scenarios respectively; a second determination module, configured to determine the similar scenario with the highest similarity to the current scenario among the multiple operating scenarios, and the similar dispatch strategy corresponding to the similar scenario; and an acquisition module, configured to obtain a target dispatch strategy corresponding to the current scenario based on the similar dispatch strategy.

[0007] According to another aspect of the present invention, a non-volatile storage medium is provided, the non-volatile storage medium storing a plurality of instructions, the instructions being adapted to be loaded by a processor and executed any one of the power system generation scheduling strategy determination methods described herein.

[0008] According to another aspect of the present invention, an electronic device is provided, comprising: one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement any one of the power system generation dispatch strategy determination methods.

[0009] In this embodiment of the invention, a data-driven approach is adopted. This involves determining multiple operating scenarios within a target power system and their corresponding generation dispatch strategies; identifying the most similar scenario to the current scenario among these scenarios, and its corresponding similar dispatch strategy; and then, based on the similar dispatch strategy, obtaining the target dispatch strategy corresponding to the current scenario. This achieves the goal of shortening the real-time optimization dispatch of the power system, improving the efficiency of real-time decision-making for the power system, and thus solving the technical problem of unsatisfactory decision-making efficiency in related technologies. Attached Figure Description

[0010] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0011] Figure 1 This is a flowchart of an optional power system generation dispatch strategy determination method provided by an embodiment of the present invention;

[0012] Figure 2 This is a schematic diagram of an optional power system generation dispatch strategy determination device provided according to an embodiment of the present invention. Detailed Implementation

[0013] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0014] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0015] Generally, the total generating capacity of a power grid is larger than its load. The goal of optimal power system scheduling is to minimize total operating costs or total coal consumption by rationally allocating loads while meeting constraints. Currently, optimal power system scheduling mainly employs mathematical programming, which transforms the optimal scheduling problem into a mathematical optimization problem through mathematical modeling. The optimal scheduling result is obtained by solving the mathematical optimization model. This method is applicable to large-scale power grid optimal scheduling problems with multiple operating constraints. It often uses an iterative loop to optimize the scheduling model. The disadvantage is its low solution efficiency, especially for large-scale power systems, where the model solution time is typically on the order of minutes, making it difficult to respond promptly to real-time decision-making needs.

[0016] With the integration of a high proportion of new energy sources, the output of wind and solar power exhibits strong randomness. During real-time optimal dispatching, the large fluctuations in new energy output cause frequent changes in operating modes, requiring dispatching decisions to be made in an extremely short time. This places higher demands on the computational efficiency of power system optimal dispatching, which is not achievable in related technologies. To achieve real-time optimal dispatching of the power system within milliseconds, dispatching methods that solve mathematical optimization models using high-complexity methods in related technologies are not feasible.

[0017] To address the aforementioned problems, embodiments of the present invention provide a method for determining power system generation dispatching strategies. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0018] Figure 1 This is a flowchart of a power system generation dispatch strategy determination method according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0019] Step S102: Determine multiple operating scenarios in the target power system, and determine the generation dispatch strategies corresponding to each of the multiple operating scenarios.

[0020] It is understandable that there may be multiple operating scenarios in the target power system. Different generation dispatch strategies are adopted as the optimal solution for different operating scenarios. Therefore, it is necessary to determine the generation dispatch strategies corresponding to multiple operating scenarios to lay the foundation for subsequent selection of target dispatch decisions in a data-driven manner.

[0021] In one optional embodiment, determining the power generation scheduling strategies corresponding to the plurality of operating scenarios includes: obtaining scenario setting parameters corresponding to the plurality of operating scenarios; and processing the scenario setting parameters corresponding to the plurality of operating scenarios using a preset scheduling model to obtain the power generation scheduling strategies corresponding to the plurality of operating scenarios.

[0022] It is understandable that there is a one-to-one mapping between scenario setting parameters and the power generation dispatching strategy of the operating scenario. The scenario setting parameters affect the operating status of multiple operating scenarios. By processing the scenario setting parameters using a preset dispatching model, the power generation dispatching strategies corresponding to each of the multiple operating scenarios can be obtained. By obtaining different scenario setting parameters for multiple operating scenarios, a comprehensive consideration of the operating scenarios of the target power system can be achieved.

[0023] Optionally, the types of parameters set in the above scenarios can be various, such as: power grid line and node connection relationships, line parameters, generator set parameters, system load size, and power transmission and transformation maintenance plans.

[0024] In one optional embodiment, the plurality of operating scenarios includes at least one of the following: historical operating scenarios, random operating scenarios, and extreme operating scenarios. The acquisition of scenario setting parameters corresponding to each of the plurality of operating scenarios includes: when the plurality of operating scenarios include the historical operating scenarios, acquiring historical setting parameters of the target power system within a preset time period; when the plurality of operating scenarios include the random operating scenarios, randomly adjusting preset parameters within a preset adjustment range to obtain random setting parameters of the target power system; and when the plurality of operating scenarios include extreme operating scenarios, determining a predetermined type of fault corresponding to the target power system, and extreme setting parameters for forming the predetermined type of fault.

[0025] It is understandable that, in order to comprehensively consider multiple operating scenarios, these scenarios include at least one of the following: historical operating scenarios, random operating scenarios, and extreme operating scenarios, or one or more combinations thereof. To obtain the scenario setting parameters corresponding to each of these multiple operating scenarios, when historical operating scenarios are included, the historical setting parameters of the target power system within a preset time period are obtained using massive amounts of historical data from the target power system. When random operating scenarios are included, to obtain as many possible scenarios as possible, the preset parameters are randomly adjusted within a preset adjustment range to obtain the random setting parameters of the target power system. When extreme operating scenarios are included, to avoid a lack of response capability for power generation dispatch decisions due to the target power system's lack of experience with fault operation in historical operations, a predetermined type of fault corresponding to the target power system and the extreme setting parameters used to form this predetermined type of fault are determined. Through the above methods, one or more combinations of historical operating scenarios, random operating scenarios, and extreme operating scenarios are made to give the target dispatch strategy comprehensive capabilities, providing real-time response capabilities to multiple operating scenarios under short-term dispatch processing conditions.

[0026] Optionally, the aforementioned predetermined type of fault can be of various types, such as: faults in multi-circuit power transmission equipment, short circuits, open circuits, etc.

[0027] In one optional embodiment, the preset parameters include at least one of the following: the system load size of the target power system, and the number of operational generator sets in the target power system.

[0028] It is understood that the preset parameters can be settings that may change in the target power system, including at least one of the following: the system load size of the target power system, and the number of operational generator units in the target power system.

[0029] In one optional embodiment, a preset scheduling model is used to represent the total generation cost of the generator units included in the target power system, and the following mathematical expression is established:

[0030]

[0031] Among them, a g b g c g Let p be the quadratic coefficient, linear coefficient, and constant term of the cost function for the g-th generator unit. g Let G be the output power of the g-th generator set, and G be the total number of generator sets.

[0032] Optionally, constraints can be set for the aforementioned preset scheduling model. These constraints can be of various types, such as load balancing constraints. Where D is the system load, p g Let G be the output power of the g-th generator set, and G be the total number of generator sets.

[0033] Generator output upper and lower limit constraints: in, For the minimum technical output of the g-th generator unit, This is for the maximum technical output of the g-th generator unit.

[0034] Generator set ramp rate constraint: in, Let g be the initial output of the g-th generator unit. Let g be the upward climbing rate of the g-th generator unit. Let g be the downhill ramp rate of the g-th generator unit.

[0035] Power flow security constraints: Among them, L i γ represents the maximum transmission power of the i-th line in the target power system. i-g Let be the power generation transfer distribution factor of line i to generator g in the target power system.

[0036] Optionally, the above-mentioned preset scheduling model can be solved in various ways. For example, the preset scheduling model is a linear programming model, which can be solved using mathematical optimization software such as GAMS, AIMMS, CPLEX, etc.

[0037] Step S104: Determine the most similar scenario to the current scenario among the above multiple running scenarios, and the similar scheduling strategy corresponding to the above similar scenario;

[0038] It is understandable that there are similar scenarios among multiple operating scenarios to the current scenario that needs to be scheduled. By selecting the similar scenario with the highest similarity, a similar discharge scheduling strategy corresponding to the similar scenario can be obtained. Through the above processing, the power generation scheduling strategies corresponding to multiple operating scenarios can be used as a basis to support the target scheduling strategy in a data-driven manner, effectively improving computational efficiency.

[0039] In an optional embodiment, determining the most similar scenario among the plurality of operating scenarios to the current scenario includes: determining a first indicator value corresponding to a predetermined indicator in each of the plurality of operating scenarios, and a second indicator value corresponding to the predetermined indicator in the current scenario of the target power system; obtaining the similarity between each of the plurality of operating scenarios and the current scenario based on the first indicator value corresponding to the predetermined indicator in each of the plurality of operating scenarios and the second indicator value corresponding to the predetermined indicator in the current scenario of the target power system; and determining the most similar scenario among the plurality of operating scenarios to the current scenario.

[0040] It is understandable that using predetermined indicators as a way to identify features of multiple operating scenarios improves the efficiency of selecting operating scenarios. This involves determining the first indicator value corresponding to each predetermined indicator in multiple operating scenarios, and the second indicator value corresponding to each predetermined indicator in the current scenario of the target power system. Similar operating scenarios are then identified based on these indicators. Based on the first indicator values ​​corresponding to each predetermined indicator in multiple operating scenarios, and the second indicator values ​​corresponding to each predetermined indicator in the current scenario of the target power system, the similarity between each of the multiple operating scenarios and the current scenario is obtained. Finally, the operating scenario with the highest similarity to the current scenario is selected as the similar scenario.

[0041] Optionally, if there are multiple similar scenarios, these scenarios can be merged.

[0042] Optionally, the aforementioned predetermined indicators can be various, such as: the system load of the target power system, the system's maximum generating capacity, the system's supply-demand ratio, and the average cost level on the generation side. There are multiple ways to obtain the similarity between the aforementioned multiple operating scenarios and the current scenario, for example: determining the similarity W between the nth operating scenario and the current scenario. n It can be expressed mathematically as follows:

[0043]

[0044] The system load size in the current scenario is D. c D n Let n be the system load size for the nth running scenario. This represents the sum of the installed capacity of all operational generators in the nth operating scenario. Q represents the sum of the installed capacity of all operational generators in the current scenario. n Let Q be the system supply-demand ratio for the nth operating scenario. c For the current system supply and demand ratio, pav n Let pav be the average cost level on the generation side for the nth operating scenario. cThis represents the average cost level on the power generation side in the current scenario.

[0045] In an optional embodiment, determining the first indicator values ​​corresponding to the predetermined indicators in the plurality of operating scenarios includes: when there are multiple predetermined indicators, including the system load of the target power system, the maximum generation capacity of the system, and the system supply-demand ratio, and the first indicator values ​​include the first load value, the first generation capacity value, and the first supply-demand ratio value, determining the first load value corresponding to the system load in the plurality of operating scenarios and the first generation capacity value corresponding to the maximum generation capacity of the system in the plurality of operating scenarios, wherein the maximum generation capacity is the sum of the installed capacity of the operable generator units in the target power system in the plurality of operating scenarios; and obtaining the first supply-demand ratio value corresponding to the system supply-demand ratio in the plurality of operating scenarios based on the ratio of the first generation capacity value corresponding to the first load ...

[0046] It is understandable that multiple predetermined indicators are set to quickly identify the required operating scenarios. These predetermined indicators include the system load of the target power system, the system's maximum generating capacity, and the system supply-demand ratio. The first load value corresponding to the system load in each of the multiple operating scenarios, and the first generating capacity value corresponding to the system's maximum generating capacity in each of the multiple operating scenarios, are determined. Using the ratio of the first load value to the first generating capacity value corresponding to each of the multiple operating scenarios, the first supply-demand ratio value corresponding to the predetermined indicator can be obtained for each of the multiple operating scenarios. Through the above processing, predetermined indicators reflecting the operating status of the power system in multiple operating scenarios are established, which is beneficial for scenario identification based on these indicators and effectively improves the efficiency of decision-making calculations.

[0047] Optionally, the first power generation capacity value corresponding to the system's maximum power generation capacity in multiple operating scenarios can be implemented in various ways, for example, by using variables. The formula for calculating the sum of the installed capacity of all operational (i.e., non-maintenance) generators within a preset time period (e.g., within a day) is as follows:

[0048]

[0049] Where G represents the total number of generator sets, and g represents the identifier of the generator set. These represent the maximum technical output of the g-th generator unit.

[0050] Optionally, the first supply-demand ratio corresponding to the system supply-demand ratio in the above-mentioned multiple operating scenarios can be implemented in various ways. For example, using the variable Q to represent the ratio of the system's maximum power generation capacity to the system load within a preset time period (such as within a day), the calculation formula is as follows:

[0051]

[0052] Where D represents the system load.

[0053] Optionally, the aforementioned predetermined indicators may also include, for example, the average cost level on the generation side, denoted by pav, which is the average cost of all generators in the target power system, calculated as follows:

[0054]

[0055] Among them, a g b g c g Let be the coefficients of the quadratic term, the coefficients of the linear term, and the constant term of the cost function for the g-th generator unit, respectively. These represent the maximum technical output of the g-th generator unit, where G is the total number of generator units and g is the identifier of the generator unit.

[0056] Step S106: Based on the above similar scheduling strategy, obtain the target scheduling strategy corresponding to the current scenario.

[0057] It is understandable that by using similar scheduling strategies, the target scheduling strategy corresponding to the current scenario can be obtained.

[0058] In an optional embodiment, obtaining the target scheduling strategy corresponding to the current scenario based on the aforementioned similar scheduling strategy includes: modifying the aforementioned similar scheduling strategy based on the index deviation value of a predetermined index between the aforementioned similar scenario and the aforementioned current scenario to obtain the aforementioned target scheduling strategy.

[0059] It is understandable that the selected similar scenarios are not the same as the current scenario, and there will generally be a deviation. In order to make the target scheduling strategy more closely fit the current scenario, the similar scheduling strategy is modified based on the deviation value of the predetermined indicators between the similar scenarios and the current scenario, thus obtaining the target scheduling strategy. Through the above processing, the accuracy of the target scheduling strategy is improved.

[0060] Optionally, the target scheduling strategy can be obtained in various ways. For example, if there are multiple predetermined indicators, and the predetermined indicator is the system load size of the target power system, the system load size of similar scenarios can be set as D. n’ Set the system load size for the current scenario to D. c D n’and D c They are not equal, and similar scheduling decisions are corrected in the following ways:

[0061]

[0062] Where, p g For similar scheduling decisions, p g "The target scheduling decision is G, where G is the total number of generator sets."

[0063] Through the above steps S102 to S106, the goal of shortening the real-time optimization scheduling of the power system can be achieved, and the technical effect of improving the real-time decision-making efficiency of the power system can be realized, thereby solving the technical problem of unsatisfactory decision-making efficiency of the power system in related technologies.

[0064] Based on the above embodiments and optional embodiments, the present invention proposes an optional implementation method, which specifically includes the following steps:

[0065] Step S1: Establish a preset scheduling model. The preset scheduling model is used to represent the total generation cost of the generator units included in the target power system, and is expressed mathematically as follows:

[0066]

[0067] Among them, a g b g c g Let p be the quadratic coefficient, linear coefficient, and constant term of the cost function for the g-th generator unit. g Let G be the output power of the g-th generator set, and G be the total number of generator sets.

[0068] The above-mentioned preset scheduling model sets constraints, which can be of various types, such as load balancing constraints. Where D is the system load, p g Let G be the output power of the g-th generator set, and G be the total number of generator sets.

[0069] Generator output upper and lower limit constraints: in, For the minimum technical output of the g-th generator unit, This is for the maximum technical output of the g-th generator unit.

[0070] Generator set ramp rate constraint: in, Let g be the initial output of the g-th generator unit. Let g be the upward climbing rate of the g-th generator unit. Let g be the downhill ramp rate of the g-th generator unit.

[0071] Power flow security constraints: Among them, L i γ represents the maximum transmission power of the i-th line in the target power system. i-g Let be the power generation transfer distribution factor of line i to generator g in the target power system.

[0072] The aforementioned preset scheduling model can be solved in various ways. For example, if the preset scheduling model is a linear programming model, it can be solved using mathematical optimization software such as GAMS, AIMMS, and CPLEX.

[0073] Step S2 involves constructing multiple operational scenarios and processing them using a preset scheduling model to obtain the power generation scheduling strategies corresponding to each scenario. When these operational scenarios include historical scenarios, random scenarios, and extreme scenarios, the following sub-steps are included:

[0074] Step S21: Obtain the generation dispatch strategy corresponding to the historical operating scenarios. Obtain the scenario setting parameters of the target power system over a preset period of time, process them using a preset dispatch model, and obtain the optimal dispatch scheme corresponding to these historical operating states, which constitutes the generation dispatch strategy corresponding to the historical operating scenarios.

[0075] Step S22: Obtain the generation dispatch strategy corresponding to the random operating scenario. In the target power system, the system load will be adjusted according to demand, and transmission and transformation maintenance plans will also be set according to demand. During maintenance, generator units cannot operate, affecting the target power system. Therefore, to enrich the possible operating conditions, the system load is randomly adjusted within a certain range, and different transmission and transformation maintenance plans are randomly set to obtain scenario setting data under random operating scenarios. This data is then processed using a preset dispatch model to obtain the generation dispatch strategy corresponding to the random operating scenario, thus obtaining the optimal dispatch scheme for these random scenarios.

[0076] Step S23: Obtain the power generation dispatch strategy corresponding to extreme operating scenarios. Considering extreme changes in scenario setting parameters, such as significantly larger or smaller system loads, and multiple transmission and transformation equipment failures, input data corresponding to extreme operating scenarios is generated. This data is then processed using a preset dispatch model to obtain the power generation dispatch strategy corresponding to these extreme operating scenarios.

[0077] Step S3: Construct predetermined indicators reflecting the state of the target power system in multiple operating scenarios. Multiple predetermined indicators are set to quickly identify the required operating scenarios. These indicators include the target power system's system load, maximum system generation capacity, system supply-demand ratio, and average generation-side cost level. The system load is set to D.

[0078] The system load corresponds to the first generating capacity value in multiple operating scenarios, using variables. The formula for calculating the sum of the installed capacity of all operational (i.e., non-maintenance) generators within a preset time period (e.g., within a day) is as follows:

[0079]

[0080] Where G represents the total number of generator sets, and g represents the identifier of the generator set. These represent the maximum technical output of the g-th generator unit.

[0081] The first supply-demand ratio value corresponding to the system supply-demand ratio in the above-mentioned multiple operating scenarios is represented by the variable Q. It is used to calculate the ratio of the system's maximum power generation capacity to the system load within a preset time period (such as within a day). The calculation formula is as follows:

[0082]

[0083] Where D represents the system load.

[0084] The average cost level on the generation side, denoted by pav, is the average cost of all generators in the target power system, and is calculated using the following formula:

[0085]

[0086] Among them, a g b g c g Let be the coefficients of the quadratic term, the coefficients of the linear term, and the constant term of the cost function for the g-th generator unit, respectively. These represent the maximum technical output of the g-th generator unit, where G is the total number of generator units and g is the identifier of the generator unit.

[0087] Step S4 involves identifying the optimal scheduling strategy among multiple running scenarios for the current scenario, which will serve as the target scheduling strategy. This includes the following sub-steps:

[0088] Step S41, based on the system load, maximum system power generation capacity, system supply-demand ratio, and average cost level of the power generation side obtained in step S3 for multiple operating scenarios. n Let n be the system load size for the nth running scenario. Q is the sum of the installed capacity of all operable generators in the nth operating scenario. n Let pav be the system supply-demand ratio for the nth operating scenario. n This represents the average cost level on the power generation side for the nth operating scenario.

[0089] Step S42: Under real-time optimized scheduling, based on the scenario setting parameters of the target power system, the system load size D for the current scenario is obtained. c , Q represents the sum of the installed capacity of all operational generators in the current scenario. c For the current system supply and demand ratio, pav c This represents the average cost level on the power generation side in the current scenario.

[0090] Step S43: Obtain the similarity between the above-mentioned multiple running scenarios and the above-mentioned current scenario, and determine the similarity W between the nth running scenario and the current scenario. n It can be expressed mathematically as follows:

[0091]

[0092] The system load size in the current scenario is D. c D n Let n be the system load size for the nth running scenario. This represents the sum of the installed capacity of all operational generators in the nth operating scenario. Q represents the sum of the installed capacity of all operational generators in the current scenario. n Let Q be the system supply-demand ratio for the nth operating scenario. c For the current system supply and demand ratio, pav n Let pav be the average cost level on the generation side for the nth operating scenario. c This represents the average cost level on the power generation side in the current scenario.

[0093] Step S44: By comparing the similarity between each of the multiple running scenarios and the current scenario, the scenario with the highest similarity is selected, which can be denoted as n'. The similarity scheduling strategy corresponding to the similar scenario can be denoted as p. g '.

[0094] Step S5: Correct the similarity scheduling strategy based on the load deviation to obtain the target scheduling strategy. Set the system load size of the similar scenario to D. n’ Set the system load size for the current scenario to D. c D n’ and D c They are not equal, and similar scheduling decisions are corrected in the following ways:

[0095]

[0096] Where, p g For similar scheduling decisions, p g "The target scheduling decision is G, where G is the total number of generator sets."

[0097] The target scheduling strategy for the current scenario can be obtained through the above step S5.

[0098] The above-mentioned optional implementation methods achieve at least the following effects: By utilizing generation dispatch strategies corresponding to multiple operating scenarios, the target dispatch strategy for the current scenario can be quickly selected. This method is data-driven, and compared to related technologies that use mathematical optimization models to obtain optimized dispatch results, the solution time can reach the minute level when the power system is large-scale. Furthermore, the embodiments of this invention improve the computational efficiency of real-time optimized dispatch of the power system through data-driven technology, shortening the real-time dispatch decision time to the millisecond level, effectively addressing the problem of optimized operation of the power system under a high proportion of renewable energy access.

[0099] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0100] This embodiment also provides a power system generation dispatch strategy determination device, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the terms "module" and "device" can refer to a combination of software and / or hardware that performs a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0101] According to embodiments of the present invention, an apparatus embodiment for implementing a power system generation dispatch strategy determination method is also provided. Figure 2 This is a schematic diagram of a power system generation dispatch strategy determination device according to an embodiment of the present invention, such as... Figure 2 As shown, the above-mentioned power system generation dispatch strategy determination device includes: a first determination module 202, a second determination module 204, and an acquisition module 206. The device will be described below.

[0102] The first determining module 202 is used to determine multiple operating scenarios in the target power system and to determine the generation dispatch strategies corresponding to the multiple operating scenarios respectively.

[0103] The second determining module 204 is connected to the first determining module 202 and is used to determine the similar scenario with the highest similarity to the current scenario among the above multiple running scenarios, as well as the similar scheduling strategy corresponding to the above similar scenario.

[0104] The acquisition module 206 is connected to the second determination module 204 and is used to obtain the target scheduling strategy corresponding to the current scenario based on the above-mentioned similar scheduling strategy.

[0105] This invention provides a power system generation dispatch strategy determination device. A first determination module 202 is configured to determine multiple operating scenarios in a target power system and the corresponding generation dispatch strategies for each of these scenarios. A second determination module 204, connected to the first determination module 202, is configured to determine the most similar scenario among the multiple operating scenarios and the corresponding similar dispatch strategy. An acquisition module 206, connected to the second determination module 204, is configured to obtain the target dispatch strategy corresponding to the current scenario based on the similar dispatch strategy. This achieves the goal of shortening the real-time optimization dispatch of the power system, improves the efficiency of real-time decision-making for the power system, and solves the technical problem of unsatisfactory decision-making efficiency in related technologies.

[0106] It should be noted that the above modules can be implemented by software or hardware. For example, for the latter, it can be implemented in the following ways: the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0107] It should be noted that the first determining module 202, the second determining module 204, and the obtaining module 206 mentioned above correspond to steps S102 to S106 in the embodiments. The instances and application scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the content disclosed in the above embodiments. It should be noted that the above modules, as part of the device, can run in a computer terminal.

[0108] It should be noted that the optional or preferred implementation methods of this embodiment can be found in the relevant descriptions in the embodiments, and will not be repeated here.

[0109] The aforementioned power system generation dispatch strategy determination device may further include a processor and a memory. The first determination module 202, the second determination module 204, the acquisition module 206, etc., are all stored in the memory as program units, and the processor executes the aforementioned program units stored in the memory to realize the corresponding functions.

[0110] The processor contains a core that retrieves the corresponding program unit from memory. One or more cores may be configured. Memory may include non-persistent memory in computer-readable media, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory includes at least one memory chip.

[0111] This invention provides a non-volatile storage medium storing a program that, when executed by a processor, implements a method for determining power system generation dispatch strategies.

[0112] This invention provides an electronic device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs the following steps: determining multiple operating scenarios in a target power system, and determining generation dispatch strategies corresponding to each of the multiple operating scenarios; determining the most similar scenario among the multiple operating scenarios to the current scenario, and the similar dispatch strategy corresponding to the similar scenario; and obtaining a target dispatch strategy corresponding to the current scenario based on the similar dispatch strategy. The device in this document can be a server, PC, etc.

[0113] The present invention also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program having the following method steps: determining multiple operating scenarios in a target power system, and determining the generation dispatch strategies corresponding to the multiple operating scenarios respectively; determining the similar scenario with the highest similarity to the current scenario among the multiple operating scenarios, and the similar dispatch strategy corresponding to the similar scenario; and obtaining the target dispatch strategy corresponding to the current scenario based on the similar dispatch strategy.

[0114] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0115] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0116] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0117] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0118] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0119] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0120] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0121] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0122] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0123] The above are merely embodiments of the present invention and are not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within the scope of the claims of the present invention.

Claims

1. A method for determining a power system generation dispatch strategy, characterized in that, include: Multiple operating scenarios in the target power system are identified, and generation dispatch strategies corresponding to the multiple operating scenarios are determined respectively. The generation dispatch strategies are obtained based on a preset dispatch model, which is used to represent the total generation cost of the generator units included in the target power system. Identify the most similar scenario to the current scenario among the multiple running scenarios, and the similar scheduling strategy corresponding to the similar scenario; Based on the similarity scheduling strategy, a target scheduling strategy corresponding to the current scenario is obtained; The step of obtaining a target scheduling strategy corresponding to the current scenario based on the similar scheduling strategy includes: modifying the similar scheduling strategy based on the index deviation value between the similar scenario and the current scenario using a predetermined index to obtain the target scheduling strategy. The predetermined index includes the system load of the target power system. The modification is performed in the following manner: , in, For similar scheduling decisions, For the target scheduling decision, G is the total number of generator sets, and the system load size of the similar scenario is D. n’ The system load size of the current scenario is D. c D n’ and D c They are not equal.

2. The method according to claim 1, characterized in that, The step of determining the most similar scene among the multiple running scenarios to the current scene includes: Determine the first indicator value corresponding to the predetermined indicator in the multiple operating scenarios, and the second indicator value corresponding to the predetermined indicator in the current scenario of the target power system; Based on the first indicator value corresponding to the predetermined indicator in the multiple operating scenarios, and the second indicator value corresponding to the predetermined indicator in the current scenario of the target power system, the similarity between the multiple operating scenarios and the current scenario is obtained. Identify the similar scenario with the highest similarity to the current scenario among the multiple operating scenarios.

3. The method according to claim 2, characterized in that, The determination of the first indicator value corresponding to the predetermined indicator in the multiple operating scenarios includes: When there are multiple predetermined indicators, including the system load of the target power system, the maximum power generation capacity of the system, and the system supply-demand ratio, and the first indicator value includes a first load value, a first power generation capacity value, and a first supply-demand ratio value, the first load value corresponding to the system load in the multiple operating scenarios and the first power generation capacity value corresponding to the maximum power generation capacity in the multiple operating scenarios are determined respectively, wherein the maximum power generation capacity is the sum of the installed capacity of the operable generator units in the target power system corresponding to the multiple operating scenarios respectively; Based on the ratio of the first power generation capacity value corresponding to each of the multiple operating scenarios to the first load value corresponding to each of the multiple operating scenarios, the first supply-demand ratio value corresponding to the system supply-demand ratio in each of the multiple operating scenarios is obtained.

4. The method according to any one of claims 1 to 3, characterized in that, The step of determining the power generation dispatch strategy corresponding to each of the multiple operating scenarios includes: Obtain the scene setting parameters corresponding to the multiple running scenarios; Based on the scenario setting parameters corresponding to the multiple operating scenarios, a preset scheduling model is used for processing to obtain the power generation scheduling strategies corresponding to the multiple operating scenarios.

5. The method according to claim 4, characterized in that, The plurality of operating scenarios includes at least one of the following: historical operating scenarios, random operating scenarios, and extreme operating scenarios. Obtaining the scenario setting parameters corresponding to each of the plurality of operating scenarios includes: When the historical operating scenario is included among the multiple operating scenarios, the historical setting parameters of the target power system within a preset time period are obtained; When the random operation scenario is included in the multiple operation scenarios, the preset parameters are randomly adjusted within a preset adjustment range to obtain the random setting parameters of the target power system. In cases where extreme operating scenarios are included among the multiple operating scenarios, a predetermined type of fault corresponding to the target power system is determined, along with extreme setting parameters for generating the predetermined type of fault.

6. The method according to claim 5, characterized in that, The preset parameters include at least one of the following: the system load size of the target power system, and the number of operational generator sets in the target power system.

7. A device for determining power system generation dispatch strategy, characterized in that, include: The first determining module is used to determine multiple operating scenarios in the target power system and to determine the power generation dispatching strategies corresponding to the multiple operating scenarios respectively. The power generation dispatching strategies are obtained based on a preset dispatching model, which is used to represent the total power generation cost of the generator units included in the target power system. The second determining module is used to determine the similar scenario with the highest similarity to the current scenario among the multiple running scenarios, and the similar scheduling strategy corresponding to the similar scenario; The acquisition module is used to obtain the target scheduling strategy corresponding to the current scenario based on the similar scheduling strategy; The acquisition module is further configured to modify the similar scheduling strategy based on the index deviation value between the similar scenario and the current scenario using a predetermined index, thereby obtaining the target scheduling strategy. The predetermined index includes the system load of the target power system, and the modification is performed in the following manner: , in, For similar scheduling decisions, For the target scheduling decision, G is the total number of generator sets, and the system load size of the similar scenario is D. n’ The system load size of the current scenario is D. c D n’ and D c They are not equal.

8. A non-volatile storage medium, characterized in that, The non-volatile storage medium stores multiple instructions, which are adapted to be loaded by a processor and executed by the power system generation dispatch strategy determination method according to any one of claims 1 to 6.

9. An electronic device, characterized in that, include: One or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the power system generation dispatch strategy determination method according to any one of claims 1 to 6.

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