Configuration determination method and device of power system and nonvolatile storage medium

Abstract

The invention discloses a configuration determination method and device of a power system and a nonvolatile storage medium. The method comprises the steps that a first optimization model corresponding to an electric power system is constructed, the first optimization model takes the lowest operation cost of the electric power system as the target, and the electric power system comprises a combination system of a new energy group and a thermal power generating unit; acquiring electric power parameters; inputting the electric power parameters into a first optimization model, and determining a first cost; on the basis of the first optimization model, replacing the combined system with a thermal power generating unit system to obtain a second optimization model; inputting the electric power parameters into a second optimization model, and determining a second cost; and based on the first cost and the second cost, adjusting the configuration proportion of the new energy group and the thermal power generating unit in the power system. The method solves the technical problem that the operation cost and the power generation effect of a power system are affected by the unreasonable configuration proportion of the new energy group and the thermal power generating unit due to the fact that the overall operation cost after the new energy assembly is purely considered at present is not fully considered.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and more specifically, to a method, apparatus, and non-volatile storage medium for determining the configuration of a power system. Background Technology

[0002] Against the backdrop of the current power system transformation, the large-scale integration of new energy sources such as wind and solar power presents unprecedented challenges to the operation of the power system. The volatility and intermittency of new energy power generation require the power system to have greater flexibility and regulation capabilities to ensure real-time balance between power supply and demand and stable system operation.

[0003] Existing cost calculation methods for renewable energy systems primarily focus on estimating the overall increase in operating costs to the power system after renewable energy integration. This insufficient consideration leads to an unreasonable configuration ratio of renewable energy units to thermal power units. Over-reliance on renewable energy may result in frequent start-ups and shutdowns of thermal power plants, increasing peak-shaving costs. On the other hand, an excessively high proportion of thermal power may lead to low returns on renewable energy investments, wasting renewable energy resources and increasing unnecessary social costs. Furthermore, it may affect the frequency and voltage stability of the power system. An excessively high proportion of renewable energy (such as wind and solar power) without sufficient flexible adjustment resources may make it difficult to maintain a balance between power supply and demand due to the uncertainties of renewable energy generation (such as weather changes), leading to grid instability or even collapse.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This invention provides a method, apparatus, and non-volatile storage medium for determining the configuration of a power system, in order to at least solve the technical problem that the current approach of simply considering the overall operating cost after the installation of new energy units is not comprehensive enough, resulting in an unreasonable configuration ratio of new energy units and thermal power units, which affects the operating cost and power generation efficiency of the power system.

[0006] According to one aspect of the present invention, a method for determining the configuration of a power system is provided, comprising: constructing a first optimization model corresponding to the power system, wherein the first optimization model aims to minimize the operating cost of the power system, and the power system includes a combination system of renewable energy units and thermal power units; obtaining power parameters corresponding to the power system; inputting the power parameters into the first optimization model to determine a first cost corresponding to the power system; based on the first optimization model, replacing the combination system of renewable energy units and thermal power units in the power system with a thermal power unit system to obtain a second optimization model; inputting the power parameters into the second optimization model to determine a second cost corresponding to the power system; and adjusting the configuration ratio of renewable energy units and thermal power units in the power system based on the first cost and the second cost.

[0007] Optionally, the first optimization model includes constraints, which include conventional constraints and thermal power peak shaving constraints. The conventional constraints include at least one of the following: power balance constraints, new energy unit output constraints, state of charge inequality constraints, state of charge equality constraints, line transmission power constraints, and DC power constraints. The thermal power peak shaving constraints include thermal power unit output constraints, start-up and shutdown cost constraints, and thermal power unit ramping constraints.

[0008] Optionally, based on the first optimization model, the combined system corresponding to the new energy group and the thermal power unit in the power system is replaced with the thermal power unit system to obtain the second optimization model, including: calculating the power generation of the new energy group in the power system according to the first optimization model; determining the equivalent thermal power unit power generation based on the power generation of the new energy group; and replacing the combined system corresponding to the new energy group and the thermal power unit in the power system with the thermal power unit system based on the equivalent thermal power unit power generation to determine the second optimization model.

[0009] Optionally, based on the equivalent power generation of thermal power units, the combined system corresponding to the new energy units and thermal power units in the power system is replaced with the thermal power unit system to determine the second optimization model, including: adjusting the objective function in the first optimization model based on the equivalent power generation of thermal power units to obtain the adjusted objective function; and replacing the combined system corresponding to the new energy units and thermal power units in the power system with the thermal power unit system based on the adjusted objective function to obtain the second optimization model.

[0010] Optionally, based on the first cost and the second cost, the configuration ratio of new energy units and thermal power units in the power system is adjusted, including: calculating the difference between the first cost and the second cost to determine the difference cost; determining the ratio of multiple costs in the difference cost, wherein the multiple costs include the cost of retrofitting thermal power units; and adjusting the configuration ratio of new energy units and thermal power units in the power system based on the ratio of the multiple costs.

[0011] Optionally, the objective function in the first optimization model is as follows:

[0012]

[0013] in, Y For the operating costs of the power system, t =1,2,3,…, T For simulation time, N This refers to the number of busbars in a power system. m For the first m busbar n For the first n busbar m Not equal to n , thThis refers to the thermal power plant number corresponding to the thermal power unit. pw This is the number corresponding to the new energy group. s The energy storage system is designated as the energy storage number in the power system. l This refers to the numbering of lines in the power system. c This is the initial investment amount. o For operating costs, P For the corresponding power, SUC This indicates the start-up cost of a thermal power unit. SDC For the downtime cost of thermal power units, This indicates the capacity of the optimized energy storage system. The initial capacity of the energy storage system, This indicates the capacity of the optimized line. This represents the initial capacity of the line. ot This is the designation for other power sources in the power system. i It is the virtual machine group number used to calculate the peak-shaving cost of thermal power units.

[0014] According to another aspect of the present invention, a power system configuration determination apparatus is also provided, comprising: a construction module for constructing a first optimization model corresponding to the power system, wherein the first optimization model aims to minimize the operating cost of the power system, and the power system includes a combination system of new energy units and thermal power units; an acquisition module for acquiring power parameters corresponding to the power system; an input module for inputting the power parameters into the first optimization model to determine a first cost corresponding to the power system; a replacement module for replacing the combination system of new energy units and thermal power units in the power system with a thermal power unit system based on the first optimization model to obtain a second optimization model; a determination module for inputting the power parameters into the second optimization model to determine a second cost corresponding to the power system; and an adjustment module for adjusting the configuration ratio of new energy units and thermal power units in the power system based on the first cost and the second cost.

[0015] According to another aspect of the present invention, a non-volatile storage medium is also provided, the non-volatile storage medium including a stored program, wherein, when the program is running, the device where the non-volatile storage medium is located is controlled to execute any of the above-described power system configuration determination methods.

[0016] According to another aspect of the present invention, a computer device is also provided, the computer device including a processor, the processor being configured to run a program, wherein the program executes any of the above-described power system configuration determination methods during runtime.

[0017] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements any of the above-described power system configuration determination methods.

[0018] In this embodiment of the invention, a power system configuration determination method is adopted. A first optimization model corresponding to the power system is constructed, wherein the first optimization model aims to minimize the operating cost of the power system. The power system includes a combination system of renewable energy units and thermal power units. Power parameters corresponding to the power system are obtained; these parameters are input into the first optimization model to determine the first cost of the power system; based on the first optimization model, the combination system of renewable energy units and thermal power units in the power system is replaced with a thermal power unit system to obtain a second optimization model; power parameters are input into the second optimization model to determine the second cost of the power system; based on the first and second costs, the configuration ratio of renewable energy units and thermal power units in the power system is adjusted, achieving a more reasonable adjustment of the configuration ratio of renewable energy units and thermal power units. This achieves the technical effect of reducing operating costs and improving the reliability of power system operation, thereby solving the technical problem that the current approach of simply considering the overall operating cost after renewable energy unit installation is not comprehensive enough, leading to an unreasonable configuration ratio of renewable energy units and thermal power units that affects the operating cost and power generation efficiency of the power system. Attached Figure Description

[0019] 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:

[0020] Figure 1 A hardware block diagram of a computer terminal for implementing a configuration determination method for a power system is shown.

[0021] Figure 2 This is a flowchart illustrating a power system configuration determination method provided according to an embodiment of the present invention;

[0022] Figure 3 This is a structural block diagram of a power system configuration determination device provided according to an embodiment of the present invention. Detailed Implementation

[0023] 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.

[0024] 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.

[0025] According to an embodiment of the present invention, a method embodiment for determining the configuration of a power system is provided. 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.

[0026] The method embodiment provided in Embodiment 1 of this application can be executed on a mobile terminal, computer terminal, or similar computing device. Figure 1 A hardware block diagram of a computer terminal for implementing a configuration determination method for a power system is shown. Figure 1 As shown, the computer terminal 10 may include one or more processors (shown as 102a, 102b, ..., 102n in the figure) (the processor may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0027] It should be noted that the aforementioned one or more processors and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10. As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0028] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the power system configuration determination method in this embodiment of the invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby implementing the power system configuration determination method of the aforementioned application. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0029] The display can be, for example, a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer terminal 10.

[0030] Figure 2 This is a flowchart illustrating a power system configuration determination method provided according to an embodiment of the present invention, as shown below. Figure 2 As shown, the method includes the following steps:

[0031] Step S202: Construct the first optimization model corresponding to the power system. The first optimization model aims to minimize the operating cost of the power system. The power system includes a combination system of new energy units and thermal power units.

[0032] In this step, a first optimization model is constructed with the objective of minimizing the operating cost of the power system. For a combined system including renewable energy units and thermal power units, all factors affecting the operating cost of the power system can be identified first. Based on these factors, an objective function is established, which sums up all costs to represent the total operating cost of the system. The function form is ensured to be suitable for subsequent optimization algorithms; it is typically a linear or nonlinear function, depending on the interactions between cost terms. Constraints can also be set to ensure that the optimized scheme meets the actual conditions.

[0033] The objective function and all constraints can be integrated to form a complete optimization model. This model may take the form of linear programming, quadratic programming, nonlinear programming, or mixed integer programming, depending on the nature of the cost function and the characteristics of the constraints.

[0034] As an optional implementation, the objective function in the first optimization model is as follows:

[0035]

[0036] in, Y For the operating costs of the power system, t =1,2,3,…, T For simulation time, N This refers to the number of busbars in a power system. m For the first m busbar n For the first n busbar m Not equal to n , th This refers to the thermal power plant number corresponding to the thermal power unit. pw This is the number corresponding to the new energy group. s The energy storage system is designated as the energy storage number in the power system. l This refers to the numbering of lines in the power system. c This is the initial investment amount. o For operating costs, P For the corresponding power, SUC This indicates the start-up cost of a thermal power unit. SDC For the downtime cost of thermal power units, This indicates the capacity of the optimized energy storage system. The initial capacity of the energy storage system, This indicates the capacity of the optimized line. This represents the initial capacity of the line. ot This is the designation for other power sources in the power system. i It is the virtual machine group number used to calculate the peak-shaving cost of thermal power units.

[0037] Step S204: Obtain the power parameters corresponding to the power system.

[0038] In this step, obtaining the corresponding power parameters of the power system is crucial for understanding the system's operating status and designing effective control strategies. These power parameters can include generation parameters, such as those for thermal power generation, renewable energy generation, energy storage, and transmission and distribution.

[0039] Step S206: Input the power parameters into the first optimization model to determine the first cost corresponding to the power system.

[0040] In this step, power parameters are input into the first optimization model to determine the initial cost of the power system. Specifically, all variables and parameters involved in the model can be initialized to ensure they match the collected power parameters. This includes, but is not limited to, the generation cost, maximum and minimum output, and ramp rate of each power source; the charging and discharging efficiency, energy storage capacity, and cost of energy storage devices; the resistance, reactance, and maximum allowable current of transmission lines; and power demand forecasts. The collected power parameters are input into the optimization model according to the format and structure required by the model. For example, generation cost and output limits should correspond to power source type and time period; load forecast data should be correlated with time series; and energy storage and grid parameters should match the system configuration. Constraints are set for the model based on the power parameters to ensure that the model considers all relevant technical and economic constraints during the solution process. This may include power balance constraints, power source output constraints, energy storage state-of-charge constraints, and line capacity constraints. An appropriate optimization algorithm is used to solve the model. The choice of algorithm depends on the complexity and type of the model (linear, nonlinear, mixed integer programming, etc.). The solution process may require multiple iterations to find the minimum cost solution that satisfies all constraints. The analysis of the model's solution results extracts the optimal power output configuration, energy storage device operation strategies, and necessary grid transformation plans for the system at different time points. These results directly reflect the operating costs of the power system.

[0041] Specifically, parameters such as power source, grid, load, and wind and solar power output are input into the optimization model to obtain various costs of the renewable energy generation scenario. The optimization model combining renewable energy and thermal power units is solved to obtain optimization results for various power source outputs, energy storage capacity, and line capacity, thus obtaining various costs of the renewable energy generation scenario, including:

[0042] When the output of a thermal power unit is below 50%, it is considered to be in peak-shaving mode. The cost of thermal power flexibility retrofit (thermal power peak-shaving retrofit) is calculated according to the following formula:

[0043] ;

[0044] in, Costs associated with the flexibility retrofitting of thermal power plants (peak shaving retrofitting of thermal power plants), for n On the motherboard line th The rated capacity of the No. 1 thermal power unit The output of thermal power units calculated using the production simulation model. c m The cost per kilowatt for retrofitting this type of unit. th TH represents the number of thermal power units.

[0045] Calculate the peak-shaving cost of thermal power plants based on the following:

[0046] ,

[0047] in, for th The cost of the No. 1 thermal power unit i This represents the peak-shaving operation status of thermal power plants. o n,th for n On the motherboard line th Peak shaving costs of thermal power units.

[0048] Calculate the startup cost of a thermal power unit using the following formula:

[0049] ,

[0050] in, suc n,th for n On the motherboard line th The cost of a single start-up of a No. 1 thermal power unit S n,th This refers to the number of times a thermal power plant is started.

[0051] Calculate the shutdown cost of thermal power units using the following formula:

[0052] ,

[0053] in, sdc n,th for n On the motherboard line th Cost of a single shutdown of a thermal power unit No. S n,th This refers to the number of times thermal power plants have been shut down.

[0054] Calculate the energy storage construction cost using the following formula:

[0055] ,

[0056] in, For energy storage unit investment cost, Indicates energy storage capacity.

[0057] Calculate the energy storage operating cost using the following formula:

[0058] ,

[0059] in, For the unit operating cost of energy storage, This refers to the operating power of the energy storage system.

[0060] Calculate the power transmission network upgrade cost using the following formula:

[0061] ,

[0062] in, This represents the average unit investment cost of the line. n ( n =1,2,3,…,N) is the first n busbar m ( m = 1,2,3,…,N) is the first... m One busbar, and m Not equal to n , This indicates the optimized line capacity; This represents the initial capacity of the line.

[0063] Based on the model's output, the total operating cost of the power system, i.e., the first cost, is calculated. This includes the generation cost of all power sources, the operating cost of energy storage, the investment and operating costs of transmission lines, and the additional peak-shaving and retrofitting costs incurred to accommodate the volatility of new energy sources.

[0064] By following the steps above, power parameters can be effectively input into the optimization model to calculate the first cost of the power system under a specific configuration, providing key economic analysis basis for power system planning and operation decisions.

[0065] Step S208: Based on the first optimization model, the combined system corresponding to the new energy group and the thermal power unit in the power system is replaced with the thermal power unit system to obtain the second optimization model.

[0066] In this step, the constraints related to new energy power sources (such as wind power and photovoltaics) in the first optimization model can be removed. Based on the power generation of the new energy group and the utilization hours of the corresponding thermal power units, the total capacity and output of the thermal power units theoretically required for "replacement" are calculated. Especially during peak periods of new energy output, thermal power units need sufficient reserve capacity to ensure system supply and demand balance. The objective function still aims to minimize the operating cost of the power system, but in the new model, only the cost of thermal power units is considered, and the cost of new energy groups is no longer included. The first business optimization model includes the system cost under the combined effect of new energy groups and thermal power units; in the second optimization model, these cost items should all be converted to costs involving only thermal power units. Keeping the power demand unchanged during the conversion process ensures the comparability of the two models. Furthermore, the flexibility modification cost of thermal power units needs to be considered, because increasing thermal power capacity or output to compensate for the withdrawal of new energy may require modifications to existing thermal power units to adapt to higher frequency start-stop and peak-shaving demands. In the "replacing thermal power" scenario, it may be necessary to increase the number of reserve thermal power units to ensure the reliability and stability of the power system; this should also be reflected in the cost calculation.

[0067] By constructing and solving the second optimization model, we can gain a deeper understanding of the differences in operating costs between new energy units and traditional thermal power units, providing strong data support for assessing the economics of new energy, planning power structure, and formulating energy policies.

[0068] Step S210: Input the power parameters into the second optimization model to determine the second cost corresponding to the power system.

[0069] In this step, power parameters are input into the second optimization model to determine the second cost of the power system. This process mainly involves cost optimization analysis of the power system composed of thermal power units. An appropriate optimization algorithm is used to solve the second optimization model. This may involve linear programming, mixed integer programming, or more complex nonlinear optimization methods, depending on the complexity of the model and the nature of the objective function. The solution process will calculate the optimal output plan and total operating cost of the thermal power units under all system requirements and constraints. The solution results of the second optimization model can be analyzed to check whether the output plan of the thermal power units is reasonable and whether all constraints are met. It is necessary to ensure that the configuration of thermal power units in the second model can fully meet the system load demand, which may require increasing the total capacity of the thermal power units. Furthermore, when calculating the second cost, the additional costs and efficiency losses that may result from frequent start-ups and shutdowns and deep peak shaving of thermal power units should be fully considered.

[0070] By following the steps above, the second cost of the power system under the "replacing thermal power" scenario can be accurately calculated, providing important data for subsequent cost analysis and new energy economic benefit assessment.

[0071] Step S212: Based on the first cost and the second cost, adjust the configuration ratio of new energy units and thermal power units in the power system.

[0072] In this step, the difference or percentage change between the first and second costs can be calculated to quantify the impact of introducing new energy sources on the total system cost. The changes in each cost item within the cost difference, such as the cost of thermal power plant flexibility retrofitting, peak-shaving costs, energy storage costs, and transmission costs, are studied to identify the cost advantages and challenges of new energy. If the goal is to minimize the total system cost, the cost-effectiveness of new energy at different proportions is analyzed. Considering the reliability of system operation, the unpredictability of new energy and the stability of thermal power are balanced to find the optimal balance between cost and system stability. Based on the cost analysis results, different scenarios with varying configuration ratios of new energy and thermal power units are set, and the optimization model is rerun. For example, reducing the configuration of thermal power units and increasing the proportion of new energy is analyzed to assess cost changes. While increasing new energy, the addition of energy storage equipment or the retrofitting of thermal power units to enhance system flexibility and peak-shaving capabilities can be considered. The adaptability of the system to the fluctuations in new energy output and the total operating cost under different configuration ratios can be evaluated. The cost-benefit ratios under different configuration ratios are compared, and the contribution of increasing the proportion of new energy to reducing carbon emissions and environmental pollution is assessed, translating this into economic value and incorporating it into the total cost or benefit analysis. Based on the cost and benefit analysis, a configuration strategy for new energy and thermal power units is formulated.

[0073] Through the above steps, the goal of more rationally adjusting the configuration ratio of new energy units and thermal power units can be achieved, thereby reducing operating costs and improving the reliability of power system operation. This solves the technical problem that the current approach of simply considering the overall operating cost after the installation of new energy units is not comprehensive enough, which leads to an unreasonable configuration ratio of new energy units and thermal power units, affecting the operating cost and power generation efficiency of the power system.

[0074] As an optional embodiment, the first optimization model includes constraints, which include conventional constraints and thermal power peak shaving constraints. The conventional constraints include at least one of the following: power balance constraints, new energy unit output constraints, state of charge inequality constraints, state of charge equality constraints, line transmission power constraints, and DC power constraints. The thermal power peak shaving constraints include thermal power unit output constraints, start-up and shutdown cost constraints, and thermal power unit ramping constraints.

[0075] Optionally, the constraints include conventional constraints and thermal power peak shaving constraints. This invention proposes a method for calculating the cost of thermal power peak shaving based on virtual machine groups, which adds thermal power peak shaving constraints on the basis of conventional constraints.

[0076] Specifically, the conventional constraints include: node power balance constraints, renewable energy output constraints, energy storage discharge constraints, energy storage charging constraints, state of charge inequality constraints, state of charge equality constraints, line transmission power constraints, and DC power constraints.

[0077] (1) Node power balance constraints, as shown in the following formula:

[0078]

[0079] In the formula, L n,t for n The busbar is t The load at any given time, on the right side of the equals sign, are, in order: thermal power units, energy storage discharge, energy storage charging, new energy units, hydropower units, nuclear power units, and power lines. t Power at any given moment. s The energy storage system is designated as the energy storage number in the power system. pw Numbering of new energy generating units ho Number the hydroelectric generating units. nc Numbering of nuclear power units l This is the line number.

[0080] (2) The power output constraint of new energy sources is shown in the following formula:

[0081] ,

[0082] In the formula, for n On the motherboard line pw No. 0 New Energy Group t Discharge power at any given time To provide theoretical support for new energy units at all times.

[0083] (3) Energy storage discharge constraint, as shown in the following formula:

[0084] ,

[0085] In the formula, yes n On the motherboard line s The No. 1 energy storage system t Discharge power at any given time This indicates the optimized energy storage capacity.

[0086] (4) Energy storage charging constraints, as shown in the following formula:

[0087] ,

[0088] In the formula, yes n On the motherboard lines The No. 1 energy storage system t Charging power at any time This indicates the optimized energy storage capacity.

[0089] (5) Charge state inequality constraints, as shown in the following formula:

[0090] ,

[0091] In the formula, yes n On the motherboard line s The No. 1 energy storage system t State of charge at time t, yes n On the motherboard line s The continuous discharge time of the No. 1 energy storage system.

[0092] (6) The state of charge equation constraint is shown in the following formula:

[0093] ,

[0094] in, yes n On the motherboard line s Energy loss of the No. 1 energy storage system yes n On the motherboard line s Charging losses of the energy storage system yes n On the motherboard line s The discharge loss of the energy storage system. If the energy storage is pumped storage, two additional parameters are required: yes n On the motherboard line s The No. 1 energy storage system t The reservoir receives water naturally at all times. yes n On the motherboard line s The No. 1 energy storage system t The reservoir naturally consumes water through constant pumping.

[0095] (7) Line transmission power constraints, as shown in the following formula:

[0096] ,

[0097] In the formula, It is a line nm exist t Transmission power at any given moment It is a line nm Maximum transmission power.

[0098] (8) Line DC power constraint, as shown in the following formula:

[0099] ,

[0100] In the formula, It is the power distribution factor matrix. It is a line nm exist t The line transmission power matrix corresponding to each moment. It is the first n busbars in t The net injected power matrix at time t.

[0101] The specific constraints for peak shaving in thermal power plants include: thermal power unit output constraints, start-up and shutdown cost constraints, and thermal power unit ramping constraints.

[0102] (1) Output constraints of thermal power units are shown in the following formula:

[0103] The thermal power unit is divided into I virtual machine groups. For the effort put into each segment, The constraints are:

[0104] ,

[0105] Where: the minimum output of each virtual machine group is Maximum output is ; and These represent the maximum and minimum output of the thermal power unit, and also the maximum output of the I-th virtual machine group and the minimum output of the first unit. These are 0-1 variables, representing the operating status of the virtual thermal power unit. Must meet .

[0106] (2) Start-up and shutdown cost constraints, as shown in the following formula:

[0107] ,

[0108] in, , For the first n The busbar is numbered as th The cost of a single start-up and shutdown of a thermal power unit.

[0109] (3) The ramping constraint of thermal power units is shown in the following formula:

[0110] ,

[0111] in, , These are the limits for the decrease and increase in power output of thermal power units per unit time, respectively. , These are the limits for the reduction in power output during a single shutdown and the increase in power output during a single startup of a thermal power unit.

[0112] As an optional embodiment, based on the first optimization model, the combined system corresponding to the new energy group and the thermal power unit in the power system is replaced with the thermal power unit system to obtain the second optimization model, including: calculating the power generation of the new energy group in the power system according to the first optimization model; determining the equivalent thermal power unit power generation based on the power generation of the new energy group; and replacing the combined system corresponding to the new energy group and the thermal power unit in the power system with the thermal power unit system based on the equivalent thermal power unit power generation to determine the second optimization model.

[0113] Optionally, a replacement thermal power scenario can be designed, replacing the new energy units in the power system with thermal power units. With the lowest cost of the new energy system as the objective function, a thermal power unit combination optimization model containing the objective function and constraints can be established to obtain the various costs of the replacement thermal power scenario.

[0114] The results of the first optimization model can be used to extract the power generation data of new energy power generation units (such as wind power and photovoltaic power) within a specific time period. Based on the calculated power generation of new energy units, it is necessary to determine the power generation of thermal power units that can equivalently replace these new energy power generation units. This conversion process needs to consider factors such as the efficiency, operating cost, and carbon emission standards of thermal power units, as well as the impact of the intermittency and uncertainty of new energy on thermal power demand. Specifically, the average operating efficiency of thermal power units can be determined based on their technical parameters. Using the equivalent efficiency, the new energy power generation is converted into equivalent thermal power generation. Considering that thermal power units may operate at lower efficiency during peak shaving, the impact of this factor should be included in the calculation. Based on the determined equivalent thermal power generation and the utilization hours of thermal power units, the required total capacity of thermal power units is calculated to ensure that the system can meet the equivalent power output demand. The original power system's new energy power sources are completely replaced with thermal power units, and the system structure and power configuration are adjusted. This may include adjusting or increasing the number of thermal power units and making corresponding modifications to the transmission network to ensure system stability and power supply-demand balance. Under the new system configuration consisting entirely of thermal power units, a second optimization model is constructed. The goal of this model remains to minimize the system operating cost, but its structure and constraints will differ, incorporating only the cost and operating characteristics of the thermal power units.

[0115] Through the above steps, the optimization model can be transformed from a hybrid system of new energy and thermal power to an optimization model of a fully thermal power system, providing a more comprehensive cost-benefit analysis for the planning and operation of the power system.

[0116] As an optional embodiment, based on the equivalent power generation of thermal power units, the combined system corresponding to the new energy groups and thermal power units in the power system is replaced with the thermal power unit system to determine the second optimization model, including: adjusting the objective function in the first optimization model based on the equivalent power generation of thermal power units to obtain the adjusted objective function; and replacing the combined system corresponding to the new energy groups and thermal power units in the power system with the thermal power unit system based on the adjusted objective function to obtain the second optimization model.

[0117] Optionally, the equivalent power generation of thermal power units can be calculated based on the theoretical or actual power generation of the renewable energy units in the first optimization model. In this step, the uncertainties and smoothed output curves of renewable energy generation are transformed into acceptable output curves for thermal power units, ensuring that the system can provide the same amount of electricity service as in the scenario including renewable energy under the "replacing thermal power" scenario. In the first optimization model, the objective function typically includes the initial investment costs, operating costs, peak-shaving costs, and other renewable energy-related cost items for renewable energy units, thermal power units, energy storage facilities, and transmission and distribution networks. To construct the second optimization model, the objective function needs to be adjusted to reflect only the costs of thermal power units, energy storage facilities, and transmission and distribution networks under the "replacing thermal power" scenario. After determining the adjusted objective function, the second optimization model is constructed based on the equivalent power generation of thermal power units and the new objective function. This model still includes all constraints, but the power structure is modified to include only thermal power units. This means that the output plans of thermal power units, the capacity and operation plans of energy storage facilities, and the capacity of the transmission network need to be recalculated to ensure that the system still meets all constraints in pure thermal power mode, including but not limited to node power balance constraints, line transmission power constraints, and DC power constraints.

[0118] Specifically, based on the scenario including renewable energy power generation, the renewable energy power generation on each bus is calculated according to the following formula. :

[0119] ,

[0120] in, for n On the motherboard line pw No. 0 New Energy Group t Discharge power at any given moment.

[0121] Based on a scenario including renewable energy generation, the utilization hours of thermal power on each busbar are calculated using the following formula. :

[0122] ,

[0123] in, for n On the motherboard line th No. 1 thermal power unit t Efforts are made at all times.

[0124] According to the formula Calculate the capacity of "replacing thermal power" on each bus. .

[0125] The objective function for replacing renewable energy units with thermal power units in the power system, with the goal of minimizing system investment and operating costs, is as follows:

[0126] .

[0127] Furthermore, the following constraints are added to the constraints regarding the power generation of the replacement thermal power units:

[0128] ,

[0129] in, This is to replace the output of the thermal power plant on each bus at time t.

[0130] As an optional embodiment, adjusting the configuration ratio of new energy units and thermal power units in the power system based on a first cost and a second cost includes: calculating the difference between the first cost and the second cost to determine the difference cost; determining the ratio of multiple costs in the difference cost, wherein the multiple costs include the cost of retrofitting thermal power units; and adjusting the configuration ratio of new energy units and thermal power units in the power system based on the ratio of the multiple costs.

[0131] Alternatively, the cost of the new energy system can be obtained by calculating the difference between the system cost of the new energy power generation scenario and the system cost of replacing the thermal power scenario, according to the following formula. SC :

[0132] ,

[0133] in, SC For the cost of new energy systems, Q VRE It is renewable energy power generation. CT th , CY th , CSU, CSD, CT S CY S CT scan L The costs listed in order are: coal-fired power plant flexibility retrofitting cost, coal-fired power plant peak-shaving cost, coal-fired power plant start-up cost, coal-fired power plant shutdown cost, energy storage construction cost, energy storage operation cost, and transmission line investment cost, obtained from the optimization model of the combination of new energy and thermal power units under the new energy scenario. RP This represents the production simulation solution obtained from the thermal power unit combination optimization model under the replacement thermal power scenario.

[0134] Next, the differential cost is broken down to determine the contribution of each cost item (such as thermal power unit retrofitting cost, peak-shaving cost, grid retrofitting cost, and energy storage construction cost). The ratios of these cost items to the differential cost are calculated to identify which cost items play a key role in system cost changes. Particular attention should be paid to the thermal power unit retrofitting cost ratio, as it reflects the economic burden of adapting the thermal power system to the volatility of renewable energy sources. Based on the cost ratios, the impact of the configuration ratio of renewable energy and thermal power units on the total system cost is analyzed. For example, a high thermal power unit retrofitting cost ratio may indicate that the current renewable energy penetration rate is too high, increasing the economic burden on the system. Considering the environmental benefits of renewable energy (such as reduced carbon emissions), both economic and environmental impacts must be taken into account when adjusting the configuration ratio. A possible goal is to find the lowest-cost configuration ratio while ensuring system stability and environmental quality. It is essential to ensure that the adjusted configuration ratio does not reduce the overall flexibility and stability of the system, especially its ability to handle intermittent renewable energy output.

[0135] A series of configuration ratio scenarios for new energy and thermal power can be set up, and an optimization model can be used to solve each scenario, collecting corresponding cost data. The impact of changes in the configuration ratio on the total system cost is analyzed to identify cost-sensitive areas, i.e., the intervals where changes in the configuration ratio have a significant impact on costs. Based on the cost sensitivity analysis, the configuration ratio of new energy and thermal power that minimizes the total system cost or achieves the optimal cost-benefit ratio is sought.

[0136] Through the above steps, the configuration ratio of new energy units and thermal power units in the power system can be scientifically and rationally adjusted based on the comparative analysis of the first cost and the second cost, thereby optimizing the system operating cost and improving the reliability of the power system.

[0137] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0138] Through the above description of the embodiments, those skilled in the art can clearly understand that the power system configuration determination method according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0139] According to an embodiment of the present invention, a power system configuration determination apparatus for implementing the above-described power system configuration determination method is also provided. Figure 3 This is a structural block diagram of a power system configuration determination device provided according to an embodiment of the present invention, such as... Figure 3 As shown, the configuration determination device for the power system includes: a construction module 302, an acquisition module 304, an input module 306, a replacement module 308, a determination module 310, and an adjustment module 312. The configuration determination device for the power system will be described below.

[0140] Module 302 is used to construct the first optimization model corresponding to the power system. The first optimization model aims to minimize the operating cost of the power system, which includes a combination of new energy units and thermal power units.

[0141] The acquisition module 304, connected to the construction module 302, is used to acquire the power parameters corresponding to the power system.

[0142] The input module 306, connected to the acquisition module 304, is used to input power parameters into the first optimization model to determine the first cost corresponding to the power system.

[0143] Replacement module 308, connected to input module 306, is used to replace the combined system corresponding to new energy groups and thermal power units in the power system with thermal power unit system based on the first optimization model, so as to obtain the second optimization model.

[0144] The determination module 310, connected to the replacement module 308, is used to input power parameters into the second optimization model to determine the second cost corresponding to the power system.

[0145] The adjustment module 312, connected to the determination module 310, is used to adjust the configuration ratio of new energy groups and thermal power units in the power system based on the first cost and the second cost.

[0146] It should be noted that the aforementioned construction module 302, acquisition module 304, input module 306, replacement module 308, determination module 310, and adjustment module 312 correspond to steps S202 to S212 in the embodiments. Multiple modules and their corresponding steps implement the same instances and application scenarios, but are not limited to the content disclosed in the above embodiments. It should also be noted that the aforementioned modules, as part of the device, can run on the computer terminal 10 provided in the embodiments.

[0147] Embodiments of the present invention may provide a computer device. Optionally, in this embodiment, the computer device may be located in at least one of a plurality of network devices in a computer network. The computer device includes a memory and a processor.

[0148] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the power system configuration determination method and apparatus in this embodiment of the invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby realizing the aforementioned power system configuration determination method. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to a computer terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0149] The processor can access information and application programs stored in memory via a transmission device to perform the following steps: constructing a first optimization model for the power system, wherein the first optimization model aims to minimize the operating cost of the power system, and the power system includes a combination system of renewable energy units and thermal power units; obtaining the power parameters corresponding to the power system; inputting the power parameters into the first optimization model to determine the first cost corresponding to the power system; based on the first optimization model, replacing the combination system of renewable energy units and thermal power units in the power system with a thermal power unit system to obtain a second optimization model; inputting the power parameters into the second optimization model to determine the second cost corresponding to the power system; and adjusting the configuration ratio of renewable energy units and thermal power units in the power system based on the first cost and the second cost.

[0150] Optionally, the processor may also execute program code for the following steps: The first optimization model includes constraints, wherein the constraints include conventional constraints and thermal power peak shaving constraints, wherein the conventional constraints include at least one of the following: power balance constraints, new energy unit output constraints, state of charge inequality constraints, state of charge equality constraints, line transmission power constraints, and DC power constraints, and the thermal power peak shaving constraints include thermal power unit output constraints, start-up and shutdown cost constraints, and thermal power unit ramping constraints.

[0151] Optionally, the processor may also execute program code for the following steps: based on the first optimization model, replacing the combined system corresponding to the new energy group and the thermal power unit in the power system with the thermal power unit system to obtain the second optimization model, including: calculating the power generation of the new energy group in the power system according to the first optimization model; determining the equivalent thermal power unit power generation based on the power generation of the new energy group; and replacing the combined system corresponding to the new energy group and the thermal power unit in the power system with the thermal power unit system based on the equivalent thermal power unit power generation to determine the second optimization model.

[0152] Optionally, the processor may also execute program code for the following steps: based on the equivalent power generation of thermal power units, replace the combined system of new energy units and thermal power units in the power system with the thermal power unit system to determine the second optimization model, including: based on the equivalent power generation of thermal power units, adjusting the objective function in the first optimization model to obtain the adjusted objective function; based on the adjusted objective function, replacing the combined system of new energy units and thermal power units in the power system with the thermal power unit system to obtain the second optimization model.

[0153] Optionally, the processor may also execute program code for the following steps: adjusting the configuration ratio of new energy units and thermal power units in the power system based on a first cost and a second cost, including: calculating the difference between the first cost and the second cost to determine the difference cost; determining the ratio of multiple costs in the difference cost, wherein the multiple costs include the cost of retrofitting thermal power units; and adjusting the configuration ratio of new energy units and thermal power units in the power system based on the ratio of the multiple costs.

[0154] Optionally, the processor may also execute program code with the following steps: The objective function in the first optimization model is as follows:

[0155]

[0156] in, Y For the operating costs of the power system, t =1,2,3,…, T For simulation time, N This refers to the number of busbars in a power system. m For the first mbusbar n For the first n busbar m Not equal to n , th This refers to the thermal power plant number corresponding to the thermal power unit. pw This is the number corresponding to the new energy group. s The energy storage system is designated as the energy storage number in the power system. l This refers to the numbering of lines in the power system. c This is the initial investment amount. o For operating costs, P For the corresponding power, SUC This indicates the start-up cost of a thermal power unit. SDC For the downtime cost of thermal power units, This indicates the capacity of the optimized energy storage system. The initial capacity of the energy storage system, This indicates the capacity of the optimized line. This represents the initial capacity of the line. ot This is the designation for other power sources in the power system. i It is the virtual machine group number used to calculate the peak-shaving cost of thermal power units.

[0157] This invention provides a method for determining the configuration of a power system. It involves constructing a first optimization model for the power system, where the objective is to minimize the operating cost of the power system. The power system comprises a combination of renewable energy units and thermal power units. The method involves obtaining the power parameters corresponding to the power system, inputting these parameters into the first optimization model to determine the first cost of the power system, and then replacing the combination of renewable energy units and thermal power units in the power system with a thermal power unit system to obtain a second optimization model. The power parameters are then input into the second optimization model to determine the second cost of the power system. Based on the first and second costs, the configuration ratio of renewable energy units and thermal power units in the power system is adjusted, achieving a more reasonable adjustment of their configuration ratio. This reduces operating costs and improves the reliability of the power system, thus solving the technical problem that current methods, which only consider the overall operating cost after renewable energy unit installation, are not comprehensive enough, leading to an unreasonable configuration ratio of renewable energy units and thermal power units that affects the operating cost and power generation efficiency of the power system.

[0158] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a non-volatile storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0159] Embodiments of the present invention also provide a non-volatile storage medium. Optionally, in this embodiment, the aforementioned non-volatile storage medium can be used to store the program code executed by the power system configuration determination method provided in the above embodiments.

[0160] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0161] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: constructing a first optimization model corresponding to the power system, wherein the first optimization model aims to minimize the operating cost of the power system, and the power system includes a combined system of new energy units and thermal power units; obtaining the power parameters corresponding to the power system; inputting the power parameters into the first optimization model to determine the first cost corresponding to the power system; based on the first optimization model, replacing the combined system of new energy units and thermal power units in the power system with a thermal power unit system to obtain a second optimization model; inputting the power parameters into the second optimization model to determine the second cost corresponding to the power system; and adjusting the configuration ratio of new energy units and thermal power units in the power system based on the first cost and the second cost.

[0162] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: The first optimization model includes constraints, wherein the constraints include conventional constraints and thermal power peak shaving constraints, wherein the conventional constraints include at least one of the following: power balance constraints, new energy unit output constraints, state of charge inequality constraints, state of charge equality constraints, line transmission power constraints, and DC power constraints, and the thermal power peak shaving constraints include thermal power unit output constraints, start-up and shutdown cost constraints, and thermal power unit ramping constraints.

[0163] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: based on a first optimization model, replacing the combined system corresponding to the new energy group and the thermal power unit in the power system with the thermal power unit system to obtain a second optimization model, including: calculating the power generation of the new energy group in the power system according to the first optimization model; determining the equivalent thermal power unit power generation based on the power generation of the new energy group; and replacing the combined system corresponding to the new energy group and the thermal power unit in the power system with the thermal power unit system based on the equivalent thermal power unit power generation to determine the second optimization model.

[0164] Optionally, based on the equivalent power generation of thermal power units, the combined system corresponding to the new energy units and thermal power units in the power system is replaced with the thermal power unit system to determine the second optimization model, including: adjusting the objective function in the first optimization model based on the equivalent power generation of thermal power units to obtain the adjusted objective function; and replacing the combined system corresponding to the new energy units and thermal power units in the power system with the thermal power unit system based on the adjusted objective function to obtain the second optimization model.

[0165] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: adjusting the configuration ratio of new energy groups and thermal power units in the power system based on a first cost and a second cost, including: calculating the difference between the first cost and the second cost to determine the difference cost; determining the ratio of multiple costs in the difference cost, wherein the multiple costs include the cost of retrofitting thermal power units; and adjusting the configuration ratio of new energy groups and thermal power units in the power system based on the ratio of the multiple costs.

[0166] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: The objective function in the first optimization model is as follows:

[0167]

[0168] in, Y For the operating costs of the power system, t =1,2,3,…, T For simulation time, N This refers to the number of busbars in a power system. m For the first m busbar n For the first n busbar m Not equal to n , th This refers to the thermal power plant number corresponding to the thermal power unit. pw This is the number corresponding to the new energy group. s The energy storage system is designated as the energy storage number in the power system. lThis refers to the numbering of lines in the power system. c This is the initial investment amount. o For operating costs, P For the corresponding power, SUC This indicates the start-up cost of a thermal power unit. SDC For the downtime cost of thermal power units, This indicates the capacity of the optimized energy storage system. The initial capacity of the energy storage system, This indicates the capacity of the optimized line. This represents the initial capacity of the line. ot This is the designation for other power sources in the power system. i It is the virtual machine group number used to calculate the peak-shaving cost of thermal power units.

[0169] Embodiments of the present invention also provide a computer program product, including a computer program. Optionally, in this embodiment, when the computer program is executed by a processor, it can: construct a first optimization model corresponding to a power system, wherein the first optimization model aims to minimize the operating cost of the power system, and the power system includes a combined system of renewable energy units and thermal power units; obtain power parameters corresponding to the power system; input the power parameters into the first optimization model to determine a first cost corresponding to the power system; based on the first optimization model, replace the combined system of renewable energy units and thermal power units in the power system with a thermal power unit system to obtain a second optimization model; input the power parameters into the second optimization model to determine a second cost corresponding to the power system; and adjust the configuration ratio of renewable energy units and thermal power units in the power system based on the first cost and the second cost.

[0170] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0171] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0172] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0173] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0174] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0175] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a non-volatile storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0176] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for determining the configuration of a power system, characterized in that, include: A first optimization model is constructed for the power system, wherein the first optimization model aims to minimize the operating cost of the power system, and the power system includes a combination system of new energy units and thermal power units. Obtain the power parameters corresponding to the power system; The power parameters are input into the first optimization model to determine the first cost corresponding to the power system; Based on the first optimization model, the combined system corresponding to the new energy group and the thermal power unit in the power system is replaced with the thermal power unit system to obtain the second optimization model; The power parameters are input into the second optimization model to determine the second cost corresponding to the power system; Based on the first cost and the second cost, the configuration ratio of the new energy group and the thermal power unit in the power system is adjusted.

2. The method according to claim 1, characterized in that, The first optimization model includes constraints, which include conventional constraints and thermal power peak shaving constraints. The conventional constraints include at least one of the following: power balance constraints, new energy unit output constraints, state of charge inequality constraints, state of charge equality constraints, line transmission power constraints, and DC power constraints. The thermal power peak shaving constraints include thermal power unit output constraints, start-up and shutdown cost constraints, and thermal power unit ramping constraints.

3. The method according to claim 1, characterized in that, Based on the first optimization model, the combined system corresponding to the new energy group and the thermal power unit in the power system is replaced with the thermal power unit system to obtain the second optimization model, including: Calculate the power generation of the new energy group in the power system based on the first optimization model; Based on the power generation of the new energy group, determine the power generation of the equivalent thermal power unit. Based on the equivalent power generation of the thermal power unit, the combined system corresponding to the new energy group and the thermal power unit in the power system is replaced with the thermal power unit system to determine the second optimization model.

4. The method according to claim 3, characterized in that, The step of replacing the combined system of the new energy group and the thermal power group in the power system with the thermal power group system based on the equivalent thermal power unit power generation, and determining the second optimization model, includes: Based on the equivalent thermal power unit's power generation, the objective function in the first optimization model is adjusted to obtain the adjusted objective function. Based on the adjusted objective function, the combined system corresponding to the new energy group and the thermal power unit in the power system is replaced with the thermal power unit system to obtain the second optimization model.

5. The method according to claim 1, characterized in that, The adjustment of the configuration ratio of new energy units and thermal power units in the power system based on the first cost and the second cost includes: Calculate the difference between the first cost and the second cost to determine the difference cost; Determine the ratio of multiple costs in the differential cost, wherein the multiple costs include the cost of retrofitting thermal power units; Based on the ratio of the aforementioned multiple costs, the configuration ratio of new energy units and thermal power units in the power system is adjusted.

6. The method according to any one of claims 1 to 5, characterized in that, The objective function in the first optimization model is as follows: ； in, Y The operating cost of the power system, t =1,2,3,…, T For simulation time, N The number of buses in the power system. m For the first m busbar n For the first n busbar m Not equal to n , th This refers to the thermal power plant number corresponding to the thermal power unit. pw This refers to the number corresponding to the new energy group. s The energy storage system in the power system is designated as its energy storage number. l This refers to the number of the lines in the power system. c This is the initial investment amount. o For operating costs, P For the corresponding power, SUC This indicates the start-up cost of the thermal power unit. SDC The shutdown cost of the aforementioned thermal power unit. This indicates the capacity of the optimized energy storage system. This is the initial capacity of the energy storage system. This indicates the optimized capacity of the line. This represents the initial capacity of the line. ot This refers to the numbering of other power sources in the power system. i It is the virtual machine group number used to calculate the peak-shaving cost of the thermal power unit.

7. A power system configuration determination device, characterized in that, include: A construction module is used to construct a first optimization model corresponding to the power system, wherein the first optimization model aims to minimize the operating cost of the power system, and the power system includes a combined system of new energy units and thermal power units. The acquisition module is used to acquire the power parameters corresponding to the power system; The input module is used to input the power parameters into the first optimization model to determine the first cost corresponding to the power system; The replacement module is used to replace the combined system corresponding to the new energy group and the thermal power unit in the power system with the thermal power unit system based on the first optimization model, so as to obtain the second optimization model. The determination module is used to input the power parameters into the second optimization model to determine the second cost corresponding to the power system; An adjustment module is used to adjust the configuration ratio of the new energy group and the thermal power unit in the power system based on the first cost and the second cost.

8. A non-volatile storage medium, characterized in that, The non-volatile storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the non-volatile storage medium to perform the power system configuration determination method according to any one of claims 1 to 6.

9. A computer device, characterized in that, include: Memory and processor The memory stores computer programs; The processor is configured to execute a computer program stored in the memory, wherein when the computer program is executed, the processor performs the configuration determination method for the power system according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the configuration determination method for the power system according to any one of claims 1 to 6.

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