New energy station transformation and multi-type power supply collaborative optimization method, system, equipment and medium

Abstract

The invention relates to the technical field of new energy station transformation and multi-type power supply collaborative optimization, and discloses a new energy station transformation and multi-type power supply collaborative optimization method, system and device and a medium, and the method comprises the steps: obtaining a power system target parameter, and building and solving a first power supply planning model; preset disturbance is applied to the model, and frequency safety is checked. If not, adding system parameters and obtaining sensitivity cut constraints, and rebuilding and solving the model according to the system parameters and the sensitivity cut constraints. And after the conditions are met, outputting an optimization scheme. According to the method, the efficiency of a new energy station is improved, multi-power-supply collaborative optimization is realized, and the stability and reliability of a power system are enhanced. A power system is optimized in a targeted manner by accurately acquiring target parameters and establishing a first power supply planning model. And the model is solved and preset disturbance is configured, so that the frequency safety problem can be found and solved in time. In addition, by adding system parameters and obtaining sensitivity cut constraints, the scheme can be further refined and optimized, and accuracy and efficiency are improved.

Description

Technical Field

[0001] The present application relates to the technical field of new energy power station transformation and multi-type power source collaborative optimization, and particularly relates to a method, system, device and medium for new energy power station transformation and multi-type power source collaborative optimization. Background Art

[0002] The traditional high-proportion new energy power system power source planning mainly focuses on the construction decisions of traditional units and new energy power stations. In this process, the frequency security constraint is usually a non-linear constraint, and researchers approximate it as a linear constraint through various methods so that the planning problem can be solved. For example, a primary frequency regulation response model of the system is established, the expression of the frequency changing with time after the system receives a power disturbance is deduced, and a linearized constraint of the frequency change rate and the lowest value of the frequency drop is formed to ensure the system frequency security, and it is embedded in the source-network-load collaborative planning model. This method of linearizing the frequency security constraint is an approximate method of representing the frequency, and there are certain errors in itself. For example, for the unit commitment problem considering multi-region frequency security, aiming at this complex mixed-integer non-linear programming problem, a multi-region frequency security constraint unit commitment solving method based on the direction decomposition algorithm is proposed. The original mixed-integer non-linear programming problem is decomposed into a master problem and a sub-problem. The sub-problem establishes a system frequency response model, accurately calculates the frequency index, and generates a linear optimization cut to supplement the master problem to improve the frequency security of the master problem. However, the traditional cutting is prone to falling into a local optimum and it is difficult to find the global optimum solution.

[0003] In summary, the existing power source planning methods considering frequency security have problems that the error of the frequency index solution gradually increases with the increase of the system scale, or it is easy to fall into a local optimum solution, resulting in the planning model being difficult to directly and effectively solve. Summary of the Invention

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the present application provides a method, system, device and medium for new energy power station transformation and multi-type power source collaborative optimization, which can solve the problems of low operation efficiency, energy waste and insufficient power grid stability existing in the existing new energy power stations when accessing multi-type power sources.

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] In a first aspect, the present application provides a method for new energy power station transformation and multi-type power source collaborative optimization, including:

[0008] Obtain the target parameters of the power system to be optimized, and establish a first power source planning model according to the target parameters;

[0009] The first power planning model includes an objective function and a set of constraint conditions;

[0010] Solve the first power planning model;

[0011] Configure a preset perturbation for the solved first power planning model, and determine whether the perturbation result meets the preset frequency safety value;

[0012] If not, add several system parameters to the first power planning model separately, and obtain several sensitivity cut constraints after the separate addition;

[0013] Re - establish the first power planning model according to the several sensitivity cut constraints, and re - solve the first power planning model;

[0014] If it meets the requirement, output the optimization plan.

[0015] As a preferred solution of the new energy power station transformation and multi - type power source collaborative optimization method described in this application, where: establishing the first power planning model according to the target parameters includes:

[0016] Establish a frequency response model according to the target parameters, and determine the frequency safety constraint regarding the first power planning model according to the frequency response model;

[0017] The first power planning model includes an objective function with the minimum cost as the goal, and a set of constraint conditions including frequency safety constraints.

[0018] As a preferred solution of the new energy power station transformation and multi - type power source collaborative optimization method described in this application, where: configuring a preset perturbation for the solved first power planning model includes:

[0019] The preset perturbation includes applying a load surge of a fixed magnitude to the target power system;

[0020] According to the load surge, obtain two frequency indicators, namely the lowest frequency drop value and the maximum frequency change rate, in combination with the frequency response model;

[0021] Determine whether the lowest frequency drop value and the maximum frequency change rate meet the preset frequency safety value.

[0022] This preferred solution can simulate the operating state of the actual power system in case of emergencies by configuring a preset perturbation for the solved first power planning model, especially applying a load surge of a fixed magnitude. This method can accurately evaluate the frequency response ability of the system, including the lowest frequency drop value and the maximum frequency change rate, so as to ensure the frequency safety of the system when facing load mutations.

[0023] As a preferred embodiment of the new energy power station transformation and multi-type power source collaborative optimization method described in this application, where: the obtaining of several sensitivity cut constraints after separate addition includes:

[0024] Separate addition of several system parameters to the first power source planning model, where the several system parameters include frequency regulation reserve capacity, system inertia, and system unit regulation power;

[0025] The several sensitivity cut constraints include a first sensitivity cut constraint obtained according to the frequency regulation reserve capacity, a second sensitivity cut constraint obtained according to the system inertia, and a third sensitivity cut constraint obtained according to the system unit regulation power.

[0026] As a preferred embodiment of the new energy power station transformation and multi-type power source collaborative optimization method described in this application, where: the re-establishment of the first power source planning model according to the several sensitivity cut constraints includes:

[0027] Incorporate the several sensitivity cut constraints as new constraint conditions into the constraint condition set;

[0028] The several sensitivity cut constraints are parallel constraints;

[0029] Solve the re-established first power source planning model.

[0030] As a preferred embodiment of the new energy power station transformation and multi-type power source collaborative optimization method described in this application, where: the frequency response model includes: a thermal power unit frequency response model, a new energy frequency response model, and a energy storage frequency response model.

[0031] As a preferred embodiment of the new energy power station transformation and multi-type power source collaborative optimization method described in this application, where: the constraint condition set includes system total reserve constraint, unit output upper and lower limit constraints, unit climbing down constraints, node power balance constraints, new energy abandonment constraints, system spinning reserve constraints, unit minimum on-time constraints, frequency safety constraints, transformation path mutual exclusion constraints, and energy storage charge and discharge constraints.

[0032] In a second aspect, this application provides a new energy power station transformation and multi-type power source collaborative optimization system, including:

[0033] A model establishment module, configured to obtain target parameters of a power system to be optimized, and establish a first power source planning model according to the target parameters;

[0034] The first power source planning model includes an objective function and a constraint condition set;

[0035] A solution module, configured to solve the first power source planning model;

[0036] A judgment module, configured to configure a preset perturbation for the first power planning model after solution, and judge whether the perturbation result meets a preset frequency safety value;

[0037] A first judgment execution module, configured to judge that if it does not meet the requirement, several system parameters are separately added to the first power planning model, and several sensitivity cut constraints after the separate addition are obtained;

[0038] A re-planning module, configured to re-establish the first power planning model according to the several sensitivity cut constraints, and re-solve the first power planning model;

[0039] A second judgment execution module, configured to judge that if it meets the requirement, an optimization plan is output.

[0040] In a third aspect, the present application provides an electronic device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the method described above are implemented.

[0041] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method described above are implemented.

[0042] Compared with the prior art, the beneficial effects of the present application are as follows: The present application proposes a method for the transformation of new energy power stations and the collaborative optimization of multiple types of power sources, obtains the target parameters of the power system to be optimized, and establishes a first power planning model according to the target parameters; solves the first power planning model; configures a preset perturbation for the first power planning model after solution, and judges whether the perturbation result meets a preset frequency safety value; if it does not meet the requirement, several system parameters are separately added to the first power planning model, and several sensitivity cut constraints after the separate addition are obtained; re-establish the first power planning model according to the several sensitivity cut constraints, and re-solve the first power planning model; if it meets the requirement, an optimization plan is output. This method not only improves the operation efficiency of new energy power stations, but also realizes the collaborative optimization of multiple types of power sources, enhancing the stability and reliability of the power system. Through accurate acquisition of target parameters and establishment of the first power planning model, the present application can perform targeted optimization for specific power system situations. At the same time, by solving the first power planning model and configuring a preset perturbation, the present application can timely discover and solve potential frequency safety problems, ensuring the safe and stable operation of the power system. In addition, by adding system parameters and obtaining sensitivity cut constraints, the present application can further refine the optimization plan, improving the accuracy and efficiency of optimization.

[0043] More specifically, this application establishes a power planning model considering the primary frequency regulation transformation of new energy power stations and including frequency security constraints. Aiming at the problem that early-built new energy power stations lack frequency response capabilities, two primary frequency regulation transformation paths are proposed to enable new energy power stations to have frequency response capabilities, increase the frequency regulation resources of the system, and reduce the investment in future frequency regulation power sources such as thermal power and energy storage.

[0044] This application establishes a multi-directional sensitivity optimization cut that can identify key influencing factors from multiple dimensions. Through the design of the multi-directional sensitivity optimization cut, the risk of the algorithm falling into local optimality is avoided, the search scope of the solution space is broadened, and it is ensured that a better solution can be found in a shorter time. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of this application, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of this application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0046] Figure 1 It is a flowchart of a method for the transformation of new energy power stations and the collaborative optimization of multiple types of power sources provided by an embodiment of this application.

[0047] Figure 2 It is a schematic diagram of the thermal power frequency response model of a method for the transformation of new energy power stations and the collaborative optimization of multiple types of power sources provided by an embodiment of this application.

[0048] Figure 3 It is a schematic diagram of the pumped storage power station frequency response model of a method for the transformation of new energy power stations and the collaborative optimization of multiple types of power sources provided by an embodiment of this application.

[0049] Figure 4 It is a schematic diagram of the wind power frequency response model of a method for the transformation of new energy power stations and the collaborative optimization of multiple types of power sources provided by an embodiment of this application.

[0050] Figure 5 It is a schematic diagram of the photovoltaic frequency response model of a method for the transformation of new energy power stations and the collaborative optimization of multiple types of power sources provided by an embodiment of this application.

[0051] Figure 6 It is a schematic diagram of the primary frequency regulation response curve after considering the multi-directional sensitivity optimization cut of a method for the transformation of new energy power stations and the collaborative optimization of multiple types of power sources provided by an embodiment of this application.

[0052] Figure 7An internal structure diagram of an electronic device for a new energy station transformation and multi-type power supply collaborative optimization method provided in one embodiment of the present application. DETAILED DESCRIPTION

[0053] To make the above-mentioned purposes, features, and advantages of this application more clearly understood, the following detailed description of the specific embodiments of this application is given in conjunction with the accompanying drawings. It is obvious that the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of this application.

[0054] Example 1, with reference to Figure 1 - Figure 7 , which is the first embodiment of this application, provides a method for collaborative optimization of new energy station transformation and multi-type power sources, including:

[0055] There are some problems in existing related technologies, such as the lack of unified planning during the transformation of new energy stations, which leads to waste of resources; insufficient coordination between multiple types of power sources, which affects overall energy efficiency; the system's slow response to emergencies, making it difficult to ensure power supply stability; serious equipment aging and low operating efficiency; and backward data storage and processing methods that cannot meet the needs of intelligent management.

[0056] This application provides a method that can effectively solve the above-mentioned problems. Next, we will combine multiple embodiments to elaborate on how to implement the new energy station transformation and multi-type power supply collaborative optimization method;

[0057] Figure 1 A method flow chart of a new energy station transformation and multi-type power supply collaborative optimization method is shown, including:

[0058] S101, obtaining target parameters of the power system to be optimized, and establishing a first power supply planning model according to the target parameters;

[0059] It should be noted that this application is to achieve coordinated optimization of new energy station transformation and multiple types of power sources, so it is necessary to obtain relevant parameters of the power system including new energy station transformation and multiple types of power sources.

[0060] In an optional embodiment, the target parameters of the power system to be optimized may include the construction capacity of thermal power and pumped storage power stations, the primary frequency regulation transformation path of new energy stations; frequency response parameters such as the inertia constant, droop coefficient, frequency regulation dead zone of thermal power, hydropower, wind power, photovoltaics, and energy storage; unit construction cost, operating cost, new energy abandonment cost, and load shedding cost of various types of power sources; climbing rate, upper and lower limits of output, minimum start-up time, frequency regulation standby coefficient, maximum limit of system frequency change rate and minimum limit of system frequency drop, and wind and solar load timing data of various types of power sources.

[0061] In an alternative embodiment, the means for obtaining the above-mentioned relevant parameters may be to monitor the operating state of the power system and the operating data of the new energy power station in real time, and use data acquisition and processing technologies to extract and organize various parameters.

[0062] It should be noted that by obtaining the above-mentioned relevant parameters, the current situation and requirements of the power system can be comprehensively and accurately understood, thereby providing a scientific basis for the transformation of new energy power stations and the collaborative optimization of multiple types of power sources. Specifically, these parameters help to formulate a reasonable power source planning model, optimize the power source layout and construction capacity, and avoid resource waste; at the same time, they can also improve the coordination between multiple types of power sources, achieve the maximum utilization of resources, and improve the overall energy efficiency.

[0063] In an alternative embodiment, the economic and technical characteristics of the primary frequency regulation transformation path of the new energy power station are analyzed. The primary frequency regulation transformation path of the new energy power station mainly includes installing a frequency regulator and installing an electrochemical energy storage system at the power station. When the power station adds a primary frequency regulation device, the primary frequency regulation device mirrors the active power regulation command of the automatic generation control (AGC) system in real time. When it is detected that the grid frequency exceeds the primary frequency regulation threshold, the primary frequency regulation device sends a signal to the AGC system to lock the reverse regulation command of the AGC system. The primary frequency regulation device algebraically superimposes the primary frequency regulation adjustment amount and the active power regulation value sent by the AGC system of the grid and directly issues it to the communication power regulation device through a fast channel for active power regulation. The time required from when the frequency crosses the dead zone of the new energy power station's frequency regulation to when the power generation output reliably starts to change in the frequency regulation direction does not exceed 2s. Therefore, installing a frequency regulator can only achieve the primary frequency regulation function and does not participate in inertia response. The investment in a single 20MW power station is about 800,000 yuan. The technical route for installing an electrochemical energy storage system is that the energy storage system is connected to the AC bus through a step-up transformer. When it is detected that the grid frequency exceeds the primary frequency regulation threshold, the energy storage system is controlled to absorb or emit active power. At the same time, the energy storage can also participate in system balancing and peak shaving and valley filling. The electrochemical energy storage system also has fast response characteristics and can quickly release or absorb power instantaneously when the frequency deviation occurs, making up for the deficiency of the lag in response of traditional frequency regulation means, thereby effectively enhancing the inertia support ability of the system and improving the frequency safety level. At present, the most widely used energy storage system in the market is the lithium battery energy storage system. The total project contracting price for a 4h lithium battery energy storage project is 1,023,000 yuan / MWh, and the operating cost of the lithium battery is 0.06 yuan / kWh.

[0064] In an alternative embodiment, the first power source planning model is used to comprehensively consider the power generation characteristics, frequency regulation requirements of the new energy power station, and the performance of the energy storage system to achieve the optimal configuration and scheduling of the power source.

[0065] In an optional embodiment, the first power planning model can optimize the power generation plan, frequency regulation strategy, and charge-discharge plan of the energy storage system of the new energy power station by introducing multi-objective optimization algorithms such as genetic algorithms and particle swarm algorithms to maximize economic benefits and frequency stability.

[0066] Specifically, after introducing the multi-objective optimization algorithm, the model can comprehensively consider multiple economic indicators such as power generation cost, frequency regulation revenue, and operating efficiency of the energy storage system, as well as frequency stability indicators such as frequency deviation degree and recovery speed, so as to formulate a more reasonable power generation plan, frequency regulation strategy, and charge-discharge plan of the energy storage system. This optimization method helps to improve the economy of the new energy power station and the frequency stability of the power system.

[0067] In an optional embodiment, the first power planning model can also establish a dynamic power planning model by considering the power demand and frequency fluctuation conditions at different time scales to achieve real-time scheduling and control of the new energy power station and the energy storage system.

[0068] Specifically, after considering the power demand and frequency fluctuation conditions at different time scales, the dynamic power planning model can more accurately predict the future power demand and frequency change trends, so as to adjust the power generation output of the new energy power station and the charge-discharge state of the energy storage system in advance to meet the real-time needs of the power system. This real-time scheduling and control method helps to further improve the stability and reliability of the power system.

[0069] However, the above method of establishing the first power planning model cannot fully adapt to all scenarios and requirements. Therefore, the first power planning model based on linear programming is designed as follows.

[0070] In the embodiment of the present application, the first power planning model includes an objective function and a set of constraint conditions.

[0071] In the embodiment of the present application, establishing the first power planning model according to the target parameters includes:

[0072] Establishing a frequency response model according to the target parameters, and determining the frequency safety constraint regarding the first power planning model according to the frequency response model;

[0073] The first power planning model includes an objective function with the minimum cost as the goal, and a set of constraint conditions including frequency safety constraints.

[0074] In the embodiment of the present application, the frequency response model includes: a thermal power unit frequency response model, a new energy frequency response model, and an energy storage frequency response model.

[0075] In the embodiments of the present application, the constraint set includes the total system reserve constraint, the upper and lower limits of unit output constraint, the unit down-ramp constraint, the nodal power balance constraint, the new energy curtailment constraint, the system spinning reserve constraint, the minimum unit on-time constraint, the frequency security constraint, the transformation path mutual exclusion constraint, and the energy storage charge and discharge constraint.

[0076] Specifically, a system frequency response (SFR) model is established. As Figure 2 - 5 shown, a coordinated frequency regulation strategy for energy storage and various types of power sources is adopted. The influence of the boiler is considered in the thermal energy storage module. K m,T,1 represents the capacity ratio coefficient, and T c,1 , T g,1 , and T r,1 represent the prime mover, governor time constant, and reheater time constant, respectively; F h,1 represents the work ratio of the high-pressure cylinder; in the pumped storage power station module, T d is the pumped storage response time, H d is the pumped storage inertia constant, R p is the permanent droop rate, and R C is the comprehensive droop rate; in the wind energy storage module, H H,1 is the virtual inertia constant; K H,1 is the droop coefficient; K m,H,1 is the capacity ratio coefficient; T H,1 is the response delay. In the photovoltaic energy storage module, H U,1 is the virtual inertia constant; K U,1 is the droop coefficient; K m,U,1 is the capacity ratio coefficient; T U,1 is the response delay. In the energy storage configuration of each module, K EU,1 is the droop coefficient; H EU,1 is the virtual inertia constant; T EU,1 represents the response delay of the energy storage device; K m,EU,1 is the capacity ratio coefficient. In the inertia and load damping response module, H eq is the system equivalent inertia constant; D is the load damping coefficient.

[0077] It should be noted that the system equivalent inertia constant is related to the unit inertia constant, rated capacity, frequency regulation status, start-stop status, and fault status of the unit.

[0078] In an alternative embodiment, according to the existing power grid frequency regulation mode, synchronous units participate in frequency regulation as soon as they are started, while new energy units participate in frequency regulation in an autonomous load shedding operation mode. Therefore, their frequency regulation status can be determined. In addition, both thermal power and new energy units can provide synchronous inertia or virtual inertia support. The calculation formula for the system equivalent inertia constant is as follows:

[0079]

[0080] Where: ν i represents the start-up and shutdown status of unit i; is a thermal power unit, is a pumped storage power station, H i is the inertia constant of the frequency modulation device i; p w is the maximum possible output of the new energy unit; u w is the decision variable of whether the new energy participates in frequency modulation; S B,sys is the total installed capacity of the system.

[0081] In an alternative embodiment, the minimum value of the frequency drop and the maximum value of the frequency change rate are calculated as two frequency indicators to measure the frequency security of the system. Assume that the system is subjected to a load surge of magnitude ΔP L , and the system frequency change can be calculated from the first-order swing equation, as shown in Equation (2):

[0082]

[0083] In an alternative embodiment, the objective function is to minimize the sum of the thermal power construction cost, the pumped storage power station construction cost, the new energy field transformation cost, the energy storage operation cost, and the thermal power operation cost, as shown in Equation (3):

[0084] min(C 1set +C 2set +C tf +C 1run +C e,run )(3)

[0085]

[0086] Where, C 1set is the annual equivalent value of the thermal power investment and construction cost, x g is the unit investment and construction decision variable, x g =1 indicates investment and construction, and conversely x g =0 indicates no investment and construction, C 2set is the annual equivalent value of the pumped storage unit investment and construction cost, C g is the unit investment and construction cost of the power source, i is the discount rate, T s is the unit life, C run is the annual operation cost of each type of unit, including thermal power, pumped storage, and energy storage, C r,i is the unit power operation cost of unit i, E i is the total power of each type of unit, C tf is the new energy field transformation cost, tf is the set of transformation paths, tf = 1 indicates installing a frequency modulation device, tf = 2 indicates installing energy storage, x tf represents the transformation path decision variable, Ctf is the unit cost corresponding to the transformation path, P w,max is the installed capacity of the new energy power station.

[0087] In an alternative embodiment, the constraint conditions mainly include the total system reserve constraint, the upper and lower limits of unit output constraint, the unit down-ramp constraint, the node power balance constraint, the new energy curtailment constraint, the system spinning reserve constraint, the minimum unit on-time constraint and the frequency security constraint, the transformation path mutual exclusion constraint, the energy storage charge and discharge constraint. The frequency security constraints are shown in equations (8) and (9). Equation (8) is the frequency change rate constraint, and equation (9) is the frequency drop value constraint.

[0088]

[0089] f nadir,t ≤f min (9)

[0090] In the formula, V RoCoF,t is the instantaneous frequency change rate of the system after being disturbed at time t, is the limit value of the frequency change rate, f nadir,t is the lowest value of the frequency drop after the disturbance, f min is the limit value of the frequency drop.

[0091] It should be noted that due to the highly non-linear frequency security constraints of equations (8) and (9), the power supply planning model cannot be directly solved using a conventional optimization toolbox. Therefore, it is considered to transform it into a two-stage problem consisting of a master problem and a sub-problem for iterative solution. The master and sub-problems are connected through sensitivity optimization cuts. The master problem is a multi-type power supply planning problem considering the primary frequency regulation transformation of new energy power stations without considering the frequency security constraints. The solution of the master problem determines the primary frequency regulation response ability of the system. The construction of the sub-problem is to check whether the solution of the master problem satisfies the frequency security constraints. If the solution of the master problem does not satisfy the frequency security constraints, the sub-problem will generate sensitivity cut constraints and add them to the master problem to improve the frequency security of the solution of the master problem. When the solution of the master problem satisfies the frequency security constraints, the problem is terminated and the planning results are output.

[0092] It should be noted that obtaining the target parameters of the power system to be optimized and establishing the first power supply planning model based on the target parameters can ensure that the planning process fully considers the actual situation and requirements of the system. First, the acquisition of target parameters can reflect the current state and future development trend of the power system, providing accurate data support for planning. Second, the first power supply planning model established based on these target parameters can more accurately simulate the operation of the power system and various possible scenarios, thus helping decision-makers more comprehensively evaluate the effects of different planning schemes. This not only improves the accuracy and reliability of planning, but also helps to optimize resource allocation, reduce operating costs, and enhance the overall performance and stability of the power system.

[0093] S102. Solve the first power supply planning model;

[0094] In an alternative embodiment, solving the first power supply planning model is to verify whether the established first power supply planning model is solvable. The existing technology's solution method can be used, and through continuous iteration and optimization, gradually approach the optimal solution. For example, a mixed-integer linear programming (MILP) solver can be used, which performs well in dealing with complex optimization problems containing integer variables. During the solution process, reasonable iteration times and convergence criteria can be set to ensure the accuracy and efficiency of the solution. Through solving, a set of optimal power supply planning schemes can be obtained. These schemes can maximize the utilization of new energy on the premise of ensuring the safe and stable operation of the power system, improving the economy and environmental protection of the system. In addition, sensitivity analysis can also be performed on the solution results to evaluate the impact of different parameter changes on the planning results, providing more comprehensive information support for decision-making.

[0095] It should be noted that solving the first power supply planning model can provide a solid foundation for subsequent collaborative optimization of multiple types of power supplies. By solving the first power supply planning model, this application can obtain a set of preliminary optimal power supply planning schemes. These schemes not only consider the maximized utilization of new energy, but also take into account the safe and stable operation of the power system. These preliminary schemes provide clear goals and directions for subsequent collaborative optimization of multiple types of power supplies, making the optimization process more targeted and efficient.

[0096] S103. Configure a preset perturbation for the solved first power supply planning model, and determine whether the perturbation result meets the preset frequency safety value;

[0097] In an alternative embodiment, configuring a preset perturbation for the solved first power planning model is to simulate various emergencies that the power system may encounter during actual operation. By introducing the preset perturbation, the response of the power planning scheme under these perturbations can be observed, thereby evaluating the stability and reliability of the scheme. Such perturbation testing is an essential part of power system planning, which helps to discover potential safety hazards and take measures to address them in advance.

[0098] In an alternative embodiment, the frequency safety preset value can be set according to the actual situation and operation requirements of the power system. It represents the minimum frequency requirement for the power system to maintain stable operation under perturbations. By comparing the perturbation results with the frequency safety preset value, it can be determined whether the power planning scheme meets the frequency safety requirements of the power system. If not, the scheme needs to be adjusted to improve the frequency stability of the system.

[0099] In the embodiment of the present application, configuring a preset perturbation for the solved first power planning model includes:

[0100] The preset perturbation includes imposing a load surge of a fixed magnitude on the target power system;

[0101] According to the load surge, two frequency indicators, namely, the minimum value of the frequency drop and the maximum value of the frequency change rate, are obtained by combining with the frequency response model;

[0102] Judge whether the minimum value of the frequency drop and the maximum value of the frequency change rate meet the frequency safety preset value.

[0103] Exemplarily, based on the initial solution of the main problem, the initial state of the system is determined, including the unit on / off state, the curtailment power of the new energy power station, the output of each type of power source, the load level, etc. A load surge equivalent to 5% of the load size is applied to the system every hour, and the frequency drop curve is simulated through the SFR model to obtain the two frequency indicators of the initial minimum value of the frequency drop and the maximum value of the frequency change rate, and judge whether they exceed the limit.

[0104] It should be noted that by configuring a preset perturbation for the solved first power planning model and judging whether the perturbation result meets the frequency safety preset value, potential frequency safety problems that may be caused after the new energy power station is connected to the grid can be discovered in advance, so as to be considered and optimized in the subsequent power planning adjustment, avoiding frequency safety over-limit problems when the new energy power station is actually connected, and improving the reliability and safety of the new energy power station connected to the grid. At the same time, by configuring a preset perturbation for the first power planning model and judging the perturbation result, it can also provide guidance for the transformation of the new energy power station, enabling it to better adapt to the frequency safety requirements of the power grid and realizing the efficient utilization of new energy and the safe and stable operation of the power grid.

[0105] S104. If not satisfied, several system parameters are separately added to the first power planning model, and several sensitivity cut constraints after the separate addition are obtained.

[0106] The first power planning model is re-established according to several sensitivity cut constraints, and the first power planning model is re-solved.

[0107] In the embodiment of the present application, obtaining several sensitivity cut constraints after the separate addition includes:

[0108] Several system parameters are separately added to the first power planning model. The several system parameters include frequency modulation reserve capacity, system inertia, and system unit regulation power.

[0109] The several sensitivity cut constraints include a first sensitivity cut constraint obtained according to the frequency modulation reserve capacity, a second sensitivity cut constraint obtained according to the system inertia, and a third sensitivity cut constraint obtained according to the system unit regulation power.

[0110] In the embodiment of the present application, re-establishing the first power planning model according to several sensitivity cut constraints includes:

[0111] The several sensitivity cut constraints are incorporated into the constraint set as new constraint conditions.

[0112] The several sensitivity cut constraints are parallel constraints.

[0113] The re-established first power planning model is solved.

[0114] It should be noted that the system inertia level and the frequency modulation reserve capacity are often used to measure the primary frequency modulation response ability of the system. According to the power-frequency characteristics of the power system, the more sufficient the frequency modulation reserve capacity in the system, the smaller the frequency drop value. And the rate of change of frequency at the moment when the system is disturbed is the maximum value and is directly affected by the system inertia level. Generally speaking, as the system inertia level rises, the rate of change of frequency will gradually decrease. Therefore, the main factor affecting the system frequency drop value is the system frequency modulation reserve capacity. Therefore, the frequency modulation reserve capacity can be changed to generate sensitivity optimization cuts and supplemented to the main problem iterative solution to achieve power planning considering frequency safety. For the minimum value of frequency drop and the maximum value of the rate of change of frequency, the frequency modulation reserve is only one of the influencing factors. This means that even if the frequency modulation reserve is changed to a certain extent, the lowest frequency point can be raised by changing other influencing factors. Obviously, continuously increasing the frequency modulation reserve capacity in the system while shrinking the feasible region may exclude the unit combination scheme with stronger frequency modulation ability and lower cost. Therefore, here, a method of multi-direction parallel supplementary optimization cuts is used for optimization and solution. The sensitivity optimization cuts include the increase of system inertia H, the increase of unit regulation power R -1 increase, and the system frequency modulation reserve P frMultiple inequality directions are added, and the multi-directional sensitivity cuts are respectively supplemented to the master problem to form three parallel optimization problems. Finally, the solution with the minimum cost among them is taken as the optimal solution.

[0115] Specifically, the first direction is the sensitivity optimization cut of frequency regulation reserve. The relationship between the frequency index and the frequency regulation reserve is expressed by a function (i.e., the first sensitivity cut constraint), as shown in Equation (10).

[0116]

[0117] The sensitivity S of the frequency index to unit i at time τ FI,iτ is defined as the first-order analytical derivative of the function g at its solution . According to the definition of sensitivity, Equation (10) can be approximately written as Equation (11).

[0118]

[0119] Since the function g is highly non-linear, it is difficult to accurately obtain its first-order derivative. Therefore, this paper adopts a simulation-based numerical analysis method to calculate the sensitivity. The specific steps are as follows:

[0120] First, when the frequency security constraint is violated at time τ, this application approximates the sensitivity of the frequency index to the unit's on-off state as Equations (12) and (13).

[0121]

[0122] Then, the frequency regulation reserve capacity is increased. After updating the system's frequency regulation reserve capacity, the primary frequency response curve is simulated again through the SFR model, and two updated values of the frequency index are obtained. The sensitivity calculation of the frequency index is as shown in Equations (12) and (13).

[0123] Assume that P fr,τ (n) is the solution of the master problem in the nth iteration. The solution in the next iteration can be expressed as Equation (14).

[0124] P fr,τ (n + 1) = P fr,τ (n) + ΔP fr,τ (14)

[0125] The frequency index in the (n + 1)th iteration should be improved. Therefore, constraints are added as shown in Equations (15) and (16).

[0126] RoCoF τ (n + 1) < RoCoF τ (n) (15)

[0127] f nadir,τ (n + 1) > fnadir,τ (n) (16)

[0128] Combining equations (11)-(13) gives equations (17) and (18).

[0129]

[0130] Equations (17) and (18) are the sensitivity optimization cut constraints of the frequency index with respect to the frequency regulation reserve capacity. At the same time, a constraint on the frequency regulation reserve capacity is added, as shown in equation (19).

[0131]

[0132] The second direction is the system inertia sensitivity cut. The relationship between the frequency index and the system inertia is expressed as a function (i.e., the second sensitivity cut constraint), as shown in equation (20).

[0133]

[0134] The sensitivity S of the frequency index to the system inertia at time τ FI,iτ is defined as the first-order analytical derivative of the function g at its solution H τ0 According to the definition of sensitivity, equation (20) can be approximated as equation (21).

[0135]

[0136] Since the function g is highly nonlinear, it is difficult to accurately obtain its first-order derivative. Therefore, this paper uses a simulation-based numerical analysis method to calculate the sensitivity. The specific steps are as follows:

[0137] First, when the frequency security constraint is violated at time τ, this application approximates the sensitivity of the frequency index to the unit's on / off state as equations (22) and (23).

[0138]

[0139] Then, the system inertia is increased. After updating the system inertia, the primary frequency response curve is simulated again through the SFR model, and two updated values of the frequency index are obtained. The sensitivity calculation of the frequency index is shown in equations (22) and (23).

[0140] Assume P fr,τ (n) is the solution of the master problem in the nth iteration. The solution in the next iteration can be expressed as equation (24).

[0141] H τ (n + 1) = H τ (n) + ΔH τ (24)

[0142] The frequency index in the (n + 1)-th iteration should be improved, so constraints as shown in Eqs. (25) and (26) are added.

[0143] RoCoF τ (n + 1) < RoCoF τ (n) (25)

[0144] f nadir,τ (n + 1) > f nadir,τ (n) (26)

[0145] By combining Eqs. (22)-(24), Eqs. (27) and (28) are obtained.

[0146]

[0147] Eqs. (27) and (28) are the sensitivity optimization cutting constraints of the frequency index with respect to the system inertia.

[0148] The third direction is the system inertia sensitivity cutting. The relationship between the frequency index and the system inertia is expressed by a function (i.e., the third sensitivity cutting constraint), as shown in Eq. (29).

[0149]

[0150] The sensitivity of the frequency index to the system inertia at time τ is defined as the first-order analytical derivative of the function g at its solution According to the definition of sensitivity, Eq. (29) can be approximated as Eq. (30).

[0151]

[0152] Since the function g is highly nonlinear, it is difficult to accurately obtain its first-order derivative. Therefore, a simulation-based numerical analysis method is adopted in this paper to calculate the sensitivity, and the specific steps are as follows:

[0153] First, when the frequency security constraint is violated at time τ, the sensitivity of the frequency index to the on-off state of the unit is approximated as Eqs. (31) and (32) in this application.

[0154]

[0155] Then, the system inertia is increased. After updating the system inertia, the primary frequency response curve is simulated again through the SFR model, and two updated values of the frequency index are obtained. The sensitivity calculation of the frequency index is as shown in Eqs. (31) and (32).

[0156] Assume P fr,τ (n) is the solution of the master problem in the n-th iteration. The solution in the next iteration can be expressed as Eq. (33).

[0157]

[0158] The frequency index in the (n + 1)-th iteration should be improved, so constraints such as those shown in Eqs. (34) and (35) are added.

[0159] RoCoF τ (n + 1) < RoCoF τ (n) (34)

[0160] f nadir,τ (n + 1) > f nadir,τ (n) (35)

[0161] By combining Eqs. (30)-(32), Eqs. (36) and (37) are obtained.

[0162]

[0163] Eqs. (36) and (37) are the sensitivity optimization cut constraints of the frequency index with respect to the system inertia.

[0164] It should be noted that after the main problem is solved, the three sub-problems respectively supplement the sensitivity optimization cut constraints to the main problem, generating three parallel optimization problems, which are iteratively solved in turn until the frequency security constraints are met, then the solution is stopped, and the planning scheme with the lowest cost is output.

[0165] S105, if satisfied, output the optimization scheme.

[0166] In summary, the present application proposes a method for the transformation of new energy power stations and the collaborative optimization of multiple types of power sources, obtains the target parameters of the power system to be optimized, and establishes a first power source planning model according to the target parameters; solves the first power source planning model; configures a preset disturbance for the solved first power source planning model, and determines whether the disturbance result meets the preset frequency security value; if not, add several system parameters to the first power source planning model separately, and obtain several sensitivity cut constraints after the separate addition; re-establish the first power source planning model according to the several sensitivity cut constraints, and re-solve the first power source planning model; if satisfied, output the optimization scheme. This method not only improves the operation efficiency of new energy power stations, but also realizes the collaborative optimization of multiple types of power sources, enhancing the stability and reliability of the power system. Through accurate acquisition of target parameters and establishment of the first power source planning model, the present application can perform targeted optimization according to the specific situation of the power system. At the same time, by solving the first power source planning model and configuring a preset disturbance, the present application can timely discover and solve potential frequency security problems, ensuring the safe and stable operation of the power system. In addition, by adding system parameters and obtaining sensitivity cut constraints, the present application can further refine the optimization scheme, improving the accuracy and efficiency of optimization.

[0167] Example 2. In a preferred embodiment, taking the improved IEEE 24-node system as an example, there are 3405 MW of thermal power units in the example. The installed capacities of wind power and photovoltaic power are both 2000 MW, and neither wind power nor photovoltaic power has the primary frequency regulation response ability and needs to be transformed. There are two transformation paths. One is to install a frequency regulator, and the other is to configure energy storage. The cost of the frequency regulator is 40,000 yuan / MW, and the cost of energy storage is 1,023,000 yuan / MWh. And a 2-hour energy storage system is configured according to 20% of the installed capacity of new energy. The thermal power to be selected is 2153 MW, and the construction cost is 3.5 million yuan / MWh. The pumped-storage to be selected is 500 MW, and the construction cost is 6 million yuan / MW. The operation and maintenance cost of new energy is 80 yuan / MWh. The spinning reserve ratio is not less than 5%, the total system reserve ratio is not less than 15%, the allowable maximum frequency change rate is 0.5 Hz / s, and the allowable maximum frequency drop value is 0.2 Hz.

[0168] Two planning schemes are set. Scheme 1 only considers the unidirectional sensitivity cut constraint of the frequency index with respect to the system frequency regulation reserve capacity, while Scheme 2 is a power source planning including the sensitivity cut constraints in three directions of unit on-off state, system inertia, and frequency regulation reserve capacity. The results are shown in Table 1.

[0169] Table 1 Various indicators of the planning scheme with or without considering frequency safety

[0170] Category Planning results without considering frequency security Planning results considering frequency security Annual equivalent value of investment and construction costs of thermal power units (10,000 yuan) 21312 18654 Annual equivalent value of transformation costs of new energy power stations (10,000 yuan) 19123 19123 Annual equivalent value of investment and construction costs of pumped storage (10,000 yuan) 10659 10659 Annual load shedding cost (10,000 yuan) 0 0 Annual energy curtailment cost (10,000 yuan) 0 100 Annual operation cost of units (10,000 yuan) 639814 628794 Annual operation cost of new energy (10,000 yuan) 543 543 Annual total cost (10,000 yuan) 691451 677873

[0171] It can be seen from Table 1 that when using the unidirectional sensitivity optimization cut, the annual investment and construction cost is 213.12 million yuan, the annual load shedding cost and annual energy abandonment cost are both 0, the annual unit operation cost is 639.814 million yuan, the annual new energy operation cost is 5.43 million yuan, and the annual total cost is 691.451 million yuan. After using the multi-directional sensitivity cut constraint, the annual investment and construction cost is reduced by 26.58 million yuan to 186.54 million yuan, and the energy abandonment cost increases to 1 million yuan.

[0172] Specific analysis shows that when only considering the unidirectional sensitivity optimization cut to supplement the master problem, 500 MW of thermal power units are invested and built. When considering the multi-directional sensitivity optimization cut to supplement the master problem, a total of 400 MW of thermal power units are invested and built. When only considering the sensitivity of frequency regulation reserve with respect to the frequency index, more thermal power units are invested and built, ensuring the frequency safety of the system, but falling into a local optimum. When considering the multi-directional sensitivity optimization cut constraint, the installed capacity of the invested units is reduced, and the frequency safety constraint can still be satisfied, achieving the global optimum. As shown in the attached Figure 6 The following is a frequency regulation response curve considering the multi-directional optimization cut. After the first solution of the master problem, a perturbation is applied and it is found that the frequency drop value exceeds the limit. Then the sub-problem generates the sensitivity optimization cut constraint and iteratively solves it. Finally, the frequency index meets the constraint and the final scheme is output.

[0173] Embodiment 3 also provides a new energy power station transformation and multi-type power source collaborative optimization system, including:

[0174] A model establishment module, configured to obtain target parameters of the power system to be optimized and establish a first power source planning model according to the target parameters;

[0175] The first power source planning model includes an objective function and a set of constraint conditions;

[0176] A solution module, configured to solve the first power source planning model;

[0177] A judgment module, configured to configure a preset perturbation for the solved first power source planning model and judge whether the perturbation result meets a preset value of frequency security;

[0178] A first judgment execution module, configured to judge that if not satisfied, several system parameters are separately added to the first power source planning model, and several sensitivity cut constraints after the separate addition are obtained;

[0179] A re-planning module, configured to re-establish the first power source planning model according to the several sensitivity cut constraints and re-solve the first power source planning model;

[0180] A second judgment execution module, configured to judge that if satisfied, an optimization plan is output.

[0181] The above-mentioned each unit module can be embedded in the processor in the electronic device in a hardware form or be independent of the processor, or can be stored in the memory in the electronic device in a software form, so as to facilitate the processor to call and execute the operations corresponding to the above-mentioned each module.

[0182] This embodiment also provides an electronic device, which can be a terminal, and its internal structure diagram can be as shown in Figure 7As shown. The electronic device includes a processor, a memory, a communication interface, a display screen, and an input device connected via a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the electronic device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it realizes a method for the transformation of a new energy power station and the collaborative optimization of multiple types of power sources. The display screen of the electronic device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the electronic device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the electronic device, or an external keyboard, a touchpad, or a mouse, etc.

[0183] This embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0184] Obtain the target parameters of the power system to be optimized, and establish a first power supply planning model according to the target parameters;

[0185] The first power supply planning model includes an objective function and a set of constraint conditions;

[0186] Solve the first power supply planning model;

[0187] Configure a preset perturbation for the solved first power supply planning model, and determine whether the perturbation result meets the preset frequency safety value;

[0188] If not, add several system parameters to the first power supply planning model separately, and obtain several sensitivity cut constraints after adding them separately;

[0189] Re-establish the first power supply planning model according to the several sensitivity cut constraints, and re-solve the first power supply planning model;

[0190] If it meets the requirements, output the optimization plan.

[0191] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit them. Although the present application has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present application can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present application, and they should all be covered within the scope of the claims of the present application.

[0192] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer-usable program code. The solutions in the embodiments of the present application can be implemented in various computer languages.

[0193] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0194] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0195] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0196] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they know the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present application.

[0197] Obviously, those skilled in the art can make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalent technologies, this application is also intended to include these modifications and variations.

Claims

1. A method for the transformation of a new energy power station and the collaborative optimization of multiple types of power sources, characterized in that, Including: Obtain the target parameters of the power system to be optimized, and establish a first power supply planning model according to the target parameters; The first power supply planning model includes an objective function and a set of constraint conditions; Solve the first power supply planning model; Configure a preset perturbation for the solved first power supply planning model, and determine whether the perturbation result meets the preset value of frequency security; If not, separately add several system parameters to the first power supply planning model, and obtain several sensitivity cut constraints after the separate addition; Re-establish the first power supply planning model according to the several sensitivity cut constraints, and re-solve the first power supply planning model; If satisfied, output the optimization plan.

2. The new energy power station transformation and multi-type power source collaborative optimization method according to claim 1, wherein, The establishing the first power supply planning model according to the target parameters includes: Establish a frequency response model according to the target parameters, and determine the frequency security constraint for the first power supply planning model according to the frequency response model; The first power supply planning model includes an objective function with the minimum cost as the goal, and a set of constraint conditions including frequency security constraints.

3. A new energy power station transformation and multi-type power source collaborative optimization method according to claim 2, characterized in that The configuring a preset perturbation for the solved first power supply planning model includes: The preset perturbation includes applying a load surge of a fixed size to the target power system; According to the load surge, obtain two frequency indicators, namely the minimum value of frequency drop and the maximum value of frequency change rate, in combination with the frequency response model; Determine whether the minimum value of frequency drop and the maximum value of frequency change rate meet the preset value of frequency security.

4. The new energy power station transformation and multi-type power source collaborative optimization method according to claim 3, characterized in that The obtaining several sensitivity cut constraints after the separate addition includes: Separate add several system parameters to the first power supply planning model, and the several system parameters include frequency modulation reserve capacity, system inertia, and system unit regulation power; The several sensitivity cut constraints include a first sensitivity cut constraint obtained according to the frequency modulation reserve capacity, a second sensitivity cut constraint obtained according to the system inertia, and a third sensitivity cut constraint obtained according to the system unit regulation power.

5. The new energy power station transformation and multi-type power source collaborative optimization method according to claim 4, characterized in that, The re-establishing the first power supply planning model according to the several sensitivity cut constraints includes: Incorporate the several sensitivity cut constraints as new constraint conditions into the set of constraint conditions; The several sensitivity cut constraints are parallel constraints; Solve the re-established first power supply planning model.

6. The new energy power station transformation and multi-type power source collaborative optimization method according to claim 5, wherein, The frequency response model includes: a thermal power unit frequency response model, a new energy frequency response model, and a energy storage frequency response model.

7. A new energy power station transformation and multi-type power source collaborative optimization method according to claim 6, characterized in that The set of constraint conditions includes system total reserve constraint, unit output upper and lower limit constraints, unit down-ramp constraint, node power balance constraint, new energy curtailment constraint, system spinning reserve constraint, unit minimum on-time constraint, frequency security constraint, transformation path mutual exclusion constraint, and energy storage charge and discharge constraint.

8. A new energy power station transformation and multi-type power source collaborative optimization system, which applies the method according to any one of claims 1 to 7, is characterized in that Including: A model establishment module, configured to obtain the target parameters of the power system to be optimized, and establish a first power supply planning model according to the target parameters; The first power supply planning model includes an objective function and a set of constraint conditions; A solving module, configured to solve the first power supply planning model; A judgment module, configured to configure a preset perturbation for the solved first power supply planning model, and determine whether the perturbation result meets the preset value of frequency security; The first judgment execution module is used to judge that if the condition is not met, several system parameters are separately added to the first power planning model, and several sensitivity cut constraints after the separate addition are obtained; The re-planning module is used to re-establish the first power planning model according to the several sensitivity cut constraints and re-solve the first power planning model; The second judgment execution module is used to judge that if the condition is met, the optimization plan is output.

9. An electronic device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of a new energy station transformation and multi-type power source collaborative optimization method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of a new energy station transformation and multi-type power source collaborative optimization method described in any one of claims 1 to 7 are implemented.

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