A multi-type power source collaborative construction method and device, equipment and medium

Abstract

The application relates to the technical field of power planning. A multi-type power source collaborative construction method, device, equipment and medium are disclosed. The method comprises the following steps: acquiring load data and power source data of multi-type power sources; constructing multi-dimensional constraint conditions, wherein the multi-dimensional constraint conditions comprise flexibility constraint conditions, adequacy constraint conditions, frequency safety constraint conditions and environmental protection requirement constraint conditions; constructing a multi-type power source collaborative planning objective function, wherein the objective of the objective function is to minimize the total system cost, and the total system cost comprises construction cost and operation cost of the multi-type power sources; based on a pre-set solving model, the multi-type power source collaborative planning objective function is solved under the multi-dimensional constraint conditions according to the load data and the power source data of the multi-type power sources, so that a multi-type power source collaborative construction scheme is obtained. The application fully mobilizes the flexibility and frequency modulation capacity of the multi-type power sources, can ensure that the system can timely respond when supply or demand changes, and can meet the safe and reliable operation requirements of the system.

Description

Technical Field

[0001] This application relates to the field of power planning technology, and more specifically, to a method, apparatus, equipment, and medium for the coordinated construction of multiple types of power sources. Background Technology

[0002] Traditional power systems primarily rely on thermal and hydropower units with controllable output, which possess strong load-tracking and regulation capabilities. However, with the continuous development of large-scale wind and solar renewable energy generation and distributed generation, the proportion of power sources with insufficient regulation capabilities and highly uncertain output in the power system's power structure has significantly increased. This results in two main problems: firstly, a significant increase in power system uncertainty, posing challenges to flexible supply-demand balance; and secondly, a gradual decrease in system inertia and a deterioration in frequency response characteristics, directly weakening the system's ability to withstand power imbalances, further increasing system frequency fluctuations under unbalanced power conditions. Existing power systems cannot fully utilize the flexibility and frequency regulation capabilities of various power sources, cannot guarantee timely responses to changes in supply or demand, and cannot meet the requirements for safe and reliable system operation.

[0003] Therefore, a multi-type power supply system is needed that can fully utilize the flexibility and frequency regulation capabilities of multiple power sources, ensure that the system can respond promptly when supply or demand changes, and meet the requirements for safe and reliable operation of the system. Summary of the Invention

[0004] In view of the above situation, this application provides a method, apparatus, equipment and medium for the coordinated construction of multiple types of power sources, which aims to solve the above problems or at least partially solve the above problems.

[0005] Firstly, this application provides a method for coordinated deployment of multiple types of power sources, including: Acquire load data and power data for multiple types of power supplies; Construct multidimensional constraints, which include flexibility constraints, adequacy constraints, frequency security constraints, and environmental protection requirement constraints; A multi-type power supply collaborative planning objective function is constructed. The objective function aims to minimize the total system cost, which includes the construction cost and operating cost of the multi-type power supply. Based on the load data and power data of the various types of power sources, and using a pre-set solution model, the objective function for the collaborative planning of the various types of power sources is solved under the multi-dimensional constraints to obtain a collaborative construction scheme for the various types of power sources.

[0006] For example, based on the load data and power data of the multiple types of power sources, and based on a pre-set solution model, the objective function for the collaborative planning of the multiple types of power sources under the multidimensional constraints is solved, including: Based on the solution model, the objective function of the multi-type power supply collaborative planning is decomposed into a master investment problem and an operational subproblem, which are connected by a cut set. Based on the load data and power data of the various types of power sources, the operation sub-problem is solved under the multidimensional constraints. The solution results of the running subproblem are input into the main investment problem, and the main investment problem is solved under the multidimensional constraints. Based on the solution results of the operation sub-problem and the solution results of the investment master problem, the multi-type power source collaborative construction scheme is obtained.

[0007] For example, based on the load data and power data of the various types of power sources, the operational sub-problem is solved under the multidimensional constraints, including: According to the solution model, the running subproblem is decomposed into a secondary principal problem and a secondary subproblem, which are connected by a frequency-safe cut. Based on the load data and power data of the various types of power sources, the secondary principal problem is solved under the multidimensional constraints that do not include the frequency security constraints. The solution result of the secondary principal problem is input into the secondary subproblem, and the secondary subproblem is solved under the frequency security constraint. The solution result of the subproblem is judged according to the frequency security constraint, and the solution result of the running subproblem is output according to the judgment result.

[0008] For example, judging the solution result of the subproblem based on the frequency security constraints, and outputting the solution result of the running subproblem based on the judgment result, includes: When the solution to the subproblem satisfies the frequency security constraint, the solution to the running subproblem is output. When the solution result of the secondary subproblem does not meet the frequency safety constraint, the frequency safety cut is added, and the updated frequency safety cut is input into the solution process of the secondary principal problem. The secondary principal problem and the secondary subproblem are solved again until the solution result of the secondary subproblem meets the frequency safety constraint, and the solution result of the running subproblem is output.

[0009] For example, the frequency security constraints include frequency drop constraints under large disturbances and maximum frequency change rate constraints; The frequency drop constraint under large disturbances is that the minimum frequency drop under large disturbances is greater than or equal to the preset frequency drop limit; The maximum frequency change rate constraint is that the maximum frequency change rate under large disturbance is less than or equal to the preset frequency change rate limit. The minimum frequency drop and the maximum frequency change rate under the large disturbance are obtained through simulation based on a pre-set frequency response model of multi-element frequency modulation resources.

[0010] For example, the flexibility constraints include the average flexibility margin constraint and the renewable energy curtailment constraint. The average flexibility margin constraint condition is that the average value of the up / down adjustment flexibility margin of multiple types of power systems over multiple time scales is greater than the preset margin value. The new energy curtailment constraint is that the ratio of wind and solar curtailment energy to expected wind and solar power generation under multiple time scales is greater than or equal to the preset curtailment rate.

[0011] For example, the adequacy constraints include capacity reserve ratio constraints, reliability index constraints, and spinning reserve constraints; The environmental protection requirements include that the total carbon dioxide emissions of multiple types of power systems are less than or equal to the preset emission level.

[0012] Secondly, this application provides a multi-type power supply collaborative construction device, comprising: The acquisition module is used to acquire load data and power data of various types of power supplies; The constraint module is used to construct multi-dimensional constraints, which include flexibility constraints, adequacy constraints, frequency security constraints, and environmental protection requirement constraints. The objective module is used to construct an objective function for collaborative planning of multiple power sources. The objective function aims to minimize the total system cost, which includes the construction and operating costs of the multiple power sources. The solution module is used to solve the objective function of the multi-type power supply collaborative planning under the multi-dimensional constraints based on the load data and power supply data of the multi-type power supply and a pre-set solution model, so as to obtain the multi-type power supply collaborative construction scheme.

[0013] Thirdly, this application provides a computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the multi-type power supply collaborative deployment method as described in the first aspect.

[0014] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the multi-type power supply collaborative deployment method as described in the first aspect.

[0015] The above-described technical solutions adopted in the embodiments of this application can achieve the following beneficial effects: This application solves a multi-type power supply collaborative deployment scheme. The corresponding multi-type power supply system can meet the constraints of flexibility, adequacy, frequency security, and environmental protection. It can fully mobilize the flexibility and frequency regulation capabilities of the multi-type power supply, ensure that the system can respond in a timely manner when supply or demand changes, and meet the requirements for safe and reliable operation of the system. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a schematic diagram of an application environment for a multi-type power supply collaborative construction method according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating a method for coordinated deployment of multiple power sources according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the probability density function for adjusting the flexibility margin up / down in one embodiment of the present invention; Figure 4 This is a schematic diagram of the solution process for step S40 in one embodiment of the present invention; Figure 5 This is a diagram showing the adjustment flexibility margin and demand analysis under a typical scenario corresponding to a specific construction plan in one embodiment of the present invention. Figure 6 This is a power balance curve diagram of a typical scenario corresponding to a specific construction scheme in one embodiment of the present invention; Figure 7 This is a curve of the maximum frequency change rate under a large disturbance in a typical scenario corresponding to a specific construction scheme in one embodiment of the present invention. Figure 8 This is a curve showing the minimum frequency drop under a large disturbance in a typical scenario corresponding to a specific construction scheme in one embodiment of the present invention. Figure 9 This is a schematic diagram of a multi-type power supply collaborative construction device according to an embodiment of the present invention; Figure 10 This is a schematic diagram of the structure of a computer device according to an embodiment of the present invention; Figure 11This is another structural schematic diagram of a computer device according to one embodiment of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such use can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the term "comprising" and its variations should be interpreted as open-ended terms meaning "including but not limited to."

[0019] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0020] As mentioned earlier, existing power systems cannot fully utilize the flexibility and frequency regulation capabilities of multiple power sources, cannot guarantee timely responses to changes in supply or demand, and cannot meet the requirements for safe and reliable system operation. To address this technical problem, embodiments of this application provide a method for the coordinated deployment of multiple power sources.

[0021] The multi-type power supply coordinated deployment method provided in this invention can be applied to, for example... Figure 1In this application environment, the device communicates with the server via a network. The server can obtain load and power data of multiple types of power sources from the device; construct multi-dimensional constraints, including flexibility constraints, adequacy constraints, frequency safety constraints, and environmental protection requirements; construct a multi-type power source collaborative planning objective function, the objective of which is to minimize the total system cost, which includes the construction and operating costs of multiple types of power sources; based on the load and power data of the multiple types of power sources, and using a pre-set solution model, solve the multi-type power source collaborative planning objective function under the multi-dimensional constraints to obtain a multi-type power source collaborative deployment scheme. The multi-type power source collaborative deployment scheme obtained in this application satisfies the flexibility, adequacy, frequency safety, and environmental protection requirements in the corresponding multi-type power source system, fully utilizes the flexibility and frequency regulation capabilities of multiple types of power sources, ensures timely response to changes in supply or demand, and meets the requirements for safe and reliable system operation.

[0022] The device side can be, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices. The server side can be implemented using a standalone server or a server cluster consisting of multiple servers. The invention will now be described in detail through specific embodiments.

[0023] Please see Figure 2 As shown, Figure 2 A flowchart illustrating a method for coordinated deployment of multiple power sources provided in an embodiment of the present invention includes the following steps: S10: Obtain load data and power data for multiple power sources.

[0024] In one embodiment, load data refers to the time-series load size of a typical scenario in a future planning year. For example, the maximum load is projected to be 580 million kilowatts in the planning year, with wind power at 100 million kilowatts and photovoltaic at 250 million kilowatts, totaling 350 million kilowatts.

[0025] In one embodiment, the power data includes the actual installed capacity of thermal power, nuclear power, pumped storage, energy storage, and wind and solar power in the current year, as well as the projected wind and solar power boundaries and the installed capacity of potential thermal power, nuclear power, pumped storage, and energy storage projects in the future planning year. For example, the potential installed capacity of coal-fired power is 39.83 million kilowatts, gas-fired power is 20.66 million kilowatts, nuclear power is 24.71 million kilowatts, pumped storage is 33.2 million kilowatts, and new energy storage is 70 million kilowatts.

[0026] In one embodiment, load data and power data for multiple power types are input through input boxes on the device's interactive interface.

[0027] S20: Construct multidimensional constraints, which include flexibility constraints, adequacy constraints, frequency security constraints, and environmental protection requirement constraints.

[0028] In one embodiment, flexibility, adequacy, frequency security, and environmental protection requirements manifest differently across different time scales. This application's method for coordinated deployment of multiple power sources does not focus on one or a few of these requirements, but rather on all of them. It considers flexibility, adequacy, frequency security, and environmental protection requirements collaboratively during power planning, ensuring the necessary regulation resources and capabilities for the new power system from the outset. In particular, the flexibility index fully considers the probabilistic and non-probabilistic characteristics of the time, space, and direction of various types of flexible resources (source, grid, load, and storage).

[0029] In one embodiment, the flexibility constraints include an average flexibility margin constraint and a renewable energy curtailment constraint. Both the average flexibility margin constraint and the renewable energy curtailment constraint are non-probabilistic constraints.

[0030] A common method for evaluating the flexibility of power systems is to compare the flexibility requirements at different time periods with the system's flexibility resource capacity.

[0031] In one embodiment, the average flexibility margin constraint is that the average of the up / down adjustment flexibility margins of multiple power system types over multiple time scales is greater than a preset margin value, as shown in the following formula:

[0032] In the formula, This represents the average up / down adjustment flexibility margin of various power systems across multiple time scales. The time scale includes weekly and longer time scales, daily time scales, hourly time scales, and minute-level time scales. In order to be in The flexibility margin at a certain time scale is the difference between the flexibility adjustment capability and the demand in the same direction during the same period.

[0033] Flexibility margin It is obtained by calculation using the following formula:

[0034] In the formula, Time scale; Characterizing a diverse and flexible set of resources; The load and wind and solar energy clusters represent the net load adjustment demand, which comes from the fluctuations in wind and solar energy power and load between adjacent times and the fluctuations at each time. For a certain time scale, multiple flexible resources in The ability to adjust up / down in real time; For a certain time scale, multiple flexible resources in Net load adjustment requirements at any given time; For a certain time scale, multiple flexible resources in The ability to adjust and regulate at any time; For a certain flexible resource at a certain time scale The ability to adjust the timing of events is preset. For a certain time scale, multiple flexible resources in The ability to adjust downwards at any given moment; For a certain flexible resource at a certain time scale The ability to adjust downtime is preset; For a certain time scale, multiple flexible resources in The net load adjustment demand at any given time; For a certain flexible resource at a certain time scale The net load adjustment demand at any given time can be preset. For a certain time scale, multiple flexible resources in The need to adjust net load at any given time; For a certain flexible resource at a certain time scale The net load reduction adjustment demand at any given time is preset. The calculation formulas for the flexibility adjustment capabilities of different resources can also be preset. The aforementioned average flexibility margin constraint can evaluate the flexibility of multiple types of energy systems across the entire time scale.

[0035] In one embodiment, for different time scales The definition of flexibility resources is as follows: Long-term flexibility resources (weekly and longer timescales) include all power sources. Daily timescale flexibility resources mainly include coal-fired power, with the start-up and shutdown schedules of large-capacity coal-fired power units determined based on the system's daily maximum load within the week. Hourly timescale flexibility resources include small-capacity coal-fired power units, gas-fired power units, and pumped-storage units. Determining the daily start-up and shutdown schedules of units based on hourly flexibility requirements is practical and reasonable. Furthermore, for coal-fired power unit output, it is assumed that they only track net load levels at the hourly timescale and do not participate in scheduling at smaller timescales. Minute-level (15-minute) timescale flexibility resources include gas-fired power units and new energy storage technologies.

[0036] The new energy curtailment constraint is that the ratio of wind and solar curtailment energy to expected wind and solar power generation over multiple time scales is greater than or equal to a preset curtailment rate, calculated using the following formula:

[0037] In the formula, This is the preset value for the energy curtailment rate, which is the lower limit required by the standard. For multiple types of energy systems on a time scale The scenery below is a waste of energy; For multiple types of energy systems on a time scale The projected electricity generation from wind and solar power is as follows. The aforementioned new energy curtailment constraint mainly measures the inadequacy of the downward adjustment flexibility of various types of energy systems, and it is usually evaluated on a calendar year timescale.

[0038] In one embodiment, the flexibility constraint further includes a probabilistic constraint, which is based on the system flexibility margin. The probability density function is as follows Figure 3 As shown, this is used to measure the frequency of events indicating insufficient flexibility. It is obtained using the following formula:

[0039] In the formula, for The probability density function for insufficient flexibility on the time scale (up / down) is about the flexibility margin. The function, in obtaining The subsequent construction can be used to verify the investment and construction plan; This is the allowed flexibility margin probability confidence level, which is preset. Because the probabilistic flexibility index needs to be expressed through the probability density function of the flexibility margin... The calculations require a sufficient sample of data, and the evaluation in this application is conducted on a yearly timescale.

[0040] In one embodiment, the frequency safety constraints include a frequency drop constraint under large disturbances, wherein the minimum frequency drop under large disturbances is greater than or equal to a pre-set frequency drop limit; and a maximum frequency change rate constraint, wherein the maximum frequency change rate under large disturbances is less than or equal to a pre-set frequency change rate limit; the minimum frequency drop under large disturbances and the maximum frequency change rate under large disturbances are obtained through simulation based on a pre-set frequency response model of multi-element frequency modulation resources.

[0041] In one embodiment, the minimum frequency drop and the maximum frequency change rate under large disturbances are obtained based on simulations using MATLAB Simulink software. Frequency response models of multi-element frequency modulation resources can be built in MATLAB Simulink. The method for building such models in MATLAB Simulink is not limited here; any model that covers the required energy source can be built according to the specific needs.

[0042] When a large disturbance occurs in a multi-type power system, resulting in a power deficit, the minimum frequency drop under the large disturbance should meet the following requirement:

[0043] In the formula, for t Minimum frequency drop under large disturbances in various types of power systems at any given time; This indicates the frequency drop limit, which is preset.

[0044] The rate of frequency drop occurs at the instant of a large disturbance ( At this moment, the frequency change rate reaches its maximum. Because the speed controller fails to respond to the frequency change in time, the primary frequency regulation power of the multi-type power system is zero at this instant. The maximum frequency change rate under a large disturbance can be established as shown below. It is related to the system's unbalanced power and total inertia level.

[0045]

[0046] In the formula, Indicates the limit of the rate of change of frequency; Indicates system inertia; This refers to the unbalanced power caused by large disturbances; This is the rated frequency value.

[0047] In one embodiment, adequacy constraints are typically used to ensure that the system can meet requirements under various operating conditions and has sufficient backup capacity to cope with uncertainties and unforeseen events. There are various methods for representing adequacy constraints; the adequacy constraints described in this application include three categories: capacity reserve rate constraints, reliability index constraints, and spinning reserve constraints.

[0048] In one embodiment, adequacy can be represented by the capacity reserve ratio. The capacity reserve ratio refers to the percentage by which the total generating capacity of the system exceeds the expected maximum load. This constraint is typically expressed as:

[0049] In the formula, Traditional generators, represented by thermal power, nuclear power, and pumped storage, The installed capacity; For wind farm The installed capacity; For photovoltaic power plants The installed capacity; The reliability of the wind farm varies depending on the time scale of the scenario. This refers to the credibility of photovoltaic power plants, and its value varies depending on the time scale and scenario. resThis is the predetermined capacity reserve rate; This is the system's peak load; This is the total number of all conventional generators; It refers to the number of wind farms; It refers to the number of photovoltaic power plants.

[0050] In one embodiment, the reliability metric constraint uses the probability of load failure. and expected value of load loss As an indicator, it is used to statistically measure the probability or frequency that the system cannot meet the load, requiring the model to satisfy:

[0051] In the formula, It is a pre-set limit for the probability of load failure; It is the limit value of the expected load loss, which is preset.

[0052] In one embodiment, the system also needs to reserve a certain capacity of spinning standby units to cope with sudden failures or demand fluctuations, i.e., spinning standby constraints at various times:

[0053] In the formula, Traditional generators, represented by thermal power, nuclear power, and pumped storage, The installed capacity; It is a traditional generator exist The power emitted at any given moment; The set rotational reserve rate; for Actual load at any given time; This represents the total number of all conventional generators.

[0054] In one embodiment, within a power system, this application uses the emission factor method to constrain emissions. The emission factor refers to the amount of pollutant emissions generated per unit of electricity generated, typically expressed in weight (e.g., tons), for example, for a generator. Its carbon dioxide emission factor can be expressed as (Unit: tons / MWh).

[0055] Total emission limits are set according to policies, regulations, or environmental targets. (For example, total carbon dioxide emissions must not exceed a certain specific value), the environmental protection requirements include that the total carbon dioxide emissions of multiple types of power systems are less than or equal to the preset emission amount.

[0056] Environmental protection requirements and constraints can be expressed as follows:

[0057] In the formula, It is the total emissions of the system; It is a coal-fired generator The amount of electricity generated; This refers to the total number of all coal-fired power plants; It is the total emission limit of the system.

[0058] In one embodiment, preset thresholds for each constraint are also acquired, such as various parameters of each constraint, including upper and lower limits of unit output, unit status, ramp rate, coal consumption rate, and wind and solar confidence capacity. This also includes conventional constraints such as capacity reserve requirements, spinning reserve requirements, and electricity demand, as well as limits related to flexibility, adequacy, frequency security, and environmental protection. The preset thresholds for each of these constraints can also be input through input boxes on the device's interactive interface.

[0059] In one embodiment, other constraints are also included, such as qualitative constraints like resource endowment constraints and energy policy constraints. Resource endowment constraints refer to the fact that newly built units and flexible resources in the system are subject to the resource endowment constraints of each region, with a development ceiling that is pre-set. Energy policy constraints refer to the different energy development paths formulated by different regions based on their energy development plans, such as the requirement that the proportion of non-fossil energy power generation must exceed a certain percentage of total power generation. The power generation proportion is pre-set. Other constraints include conventional constraints such as total power generation constraints, power balance constraints, unit output constraints, unit ramp-up constraints, new energy storage operation SOC (State of Charge) constraints, demand-side response operation constraints, and pumped storage reservoir capacity constraints, which will not be elaborated upon here.

[0060] S30: Construct a multi-type power supply collaborative planning objective function. The objective function aims to minimize the total system cost, which includes the construction and operation costs of the multi-type power supply.

[0061] In one embodiment, the objective function for multi-type power source collaborative planning considering the above-mentioned multi-dimensional constraints (flexibility constraints, adequacy constraints, frequency security constraints, and environmental protection requirements, etc.) is as follows, with the objective function being the minimum total system cost, comprising two parts: the investment and construction costs of the multi-type power sources. and the operating costs of multiple power supply types , Investment and construction costs There are investment and construction costs for thermal power, nuclear power, pumped storage, and energy storage, excluding wind and solar renewable energy; and operating costs. This mainly includes the operation and maintenance costs of each power source and the demand-side response costs; It is the cost of load shedding and energy curtailment.

[0062]

[0063] In the formula, the decision variables include whether or not to invest in conventional thermal power, nuclear power, and pumped storage power. And whether or not new energy storage (wind and solar) projects are built No construction is undertaken when the decision variable is equal to 0, and construction is undertaken when it is equal to 1. The number of candidate thermal power, nuclear power, and pumped storage power sources; The number of energy storage units to be selected; This refers to the total number of power sources (including thermal, nuclear, pumped-storage, wind, and solar power) after the planning. The total number of nodes in the set; index Indicates different scenarios; subscript Indicates different times; Indicate the number of scenes, and note the duration of different scenes. They are also different. It is a scene The probability of; This represents the unit cost of conventional thermal power, nuclear power, and pumped storage power. The superscript I indicates the construction cost. The unit cost of investing in new energy storage (wind and solar) power plants; The unit operation and maintenance cost for various types of generator units (thermal power, nuclear power, pumped storage, wind, and solar), with the superscript M representing operation and maintenance cost; This represents the unit cost of demand-side response; the superscript E indicates the demand-side response cost. Cost per unit load shedding; Cost of energy curtailment; It contributes to the power of various generating units; This refers to the demand-side response power. This refers to the load shedding power; This refers to curtailed energy; since operating costs are calculated based on energy (e.g., kWh) rather than power (e.g., kW), therefore... and The calculations were all multiplied by the number of time periods accordingly. Calculate the time length of each scene The energy value within the time frame is used to determine the operating cost. Taking a daily time scale as an example... , It should be noted that the investment and construction costs It is a concept of the present value of total investment. In order to add it to the annual operating costs, it is discounted using the formula for calculating the equivalent annual value from the present value. It refers to lifespan. It is the discount rate.

[0064] Constraints are imposed on the objective functions corresponding to formulas (16) and (17). The constraints include the multidimensional constraints, resource endowment constraints, energy policy constraints, and conventional constraints corresponding to formulas (1) to (15) in step S20. Flexibility constraints are as shown in formulas (1)-(8), frequency security constraints are as shown in formulas (9)-(10), adequacy constraints are as shown in formulas (11)-(14), and environmental protection requirements constraints are as shown in formula (15).

[0065] S40: Based on the load data and power data of the multiple types of power sources, and based on the pre-set solution model, the objective function of the collaborative planning of the multiple types of power sources is solved under the multi-dimensional constraints to obtain a collaborative construction scheme for multiple types of power sources.

[0066] In one embodiment, such as Figure 4 As shown, step S40, based on the load data and power data of the multiple power sources, and using a pre-set solution model, solves the objective function of the multi-type power source collaborative planning under the multi-dimensional constraints, including: S41: Based on the solution model, the objective function of the multi-type power supply collaborative planning is decomposed into an investment master problem and an operation subproblem, which are connected by a cut set.

[0067] S42: Based on the load data and power data of the various types of power sources, solve the operation sub-problem under the multidimensional constraints.

[0068] S43: Input the solution result of the running subproblem into the main investment problem, and solve the main investment problem under the multidimensional constraints.

[0069] S44: Based on the solution results of the operation sub-problem and the solution results of the investment master problem, the multi-type power source collaborative construction scheme is obtained.

[0070] In one embodiment, the objective function for multi-type power source collaborative planning in step S41 is a typical planning + operation model. Since there are many 0-1 variables in the objective function, the Benders algorithm is first used for decomposition, dividing the objective function into a master investment problem and an operation sub-problem for solution. The master investment problem coordinates investments in multiple types of power sources, including energy storage, while the operation sub-problem is simulated at the day-ahead unit combination, intraday hourly level, and intraday minute level. Let... , , for , The decision variables constituted As the operating variables related to unit output, demand-side response output, load shedding, etc., Equation (17) and its constraints can be expressed as Equation (18).

[0071]

[0072] In the formula, F ( x ) represents all variables in the aforementioned model that are only related to the decision variables. The relevant equality and inequality constraints; H ( x, y ) represents the decision variables in the aforementioned model. and runtime variables All related equality and inequality constraints; Represents a set of integer variables; Represents the set of real number variables.

[0073] When fixed When the value is , Is with The relevant operational subproblems, and when we are given different conditions satisfying the investment constraints When the value is, The optimal values ​​are different, therefore, the running subproblem can be regarded as a problem about... The function, denoted as Introducing slack variables This leads to the following equivalent form of the main investment problem:

[0074] in, This is called a Benders cut, used to connect the investment master problem and the running subproblem. Equation (19) is a low-dimensional mixed integer programming problem that can be solved using the CPLEX toolkit. Therefore, it is only necessary to obtain... The specific expression for the investment decision variables can be obtained by solving equation (19). A feasible solution. Using the dual problem of equation (19), according to Taylor expansion, we can obtain... exist Nearby function expressions:

[0075] In the formula, The sensitivity coefficient is the number of investment decision variables. Substituting it into equation (19) yields a feasible solution. The value is determined, but it is not necessarily the optimal solution. To obtain the overall optimal solution, an iterative process is introduced. The core of the Benders decomposition method lies in decomposing investment and operational optimization, and exchanging information through Benders cuts in the middle.

[0076] like Figure 4 As shown, in step S42, the operation sub-problem is solved under the multi-dimensional constraints based on the load data and power data of the various power sources. The upper bound, sensitivity coefficient, and Benders cut of the operation sub-problem are obtained through iterative solving. In step S43, the solution result of the operation sub-problem is input into the main investment problem, and the main investment problem is solved under the multi-dimensional constraints. That is, the Benders cut is added to the main investment problem for solving, forming the lower bound and a new investment scheme result. In step S44, based on the solution results of the running subproblem and the solution results of the investment master problem, the multi-type power source collaborative construction scheme is obtained. This includes comparing whether the relative error between the upper bound obtained in step S42 and the lower bound obtained in step S43 converges. The convergence condition is preset. When the convergence condition is met, the planning scheme is output; otherwise, it is updated. In step S42, the subproblem is solved again to obtain a new upper bound, a new sensitivity coefficient, and a new Benders cut. In step S43, a new lower bound is obtained. In step S44, the next round of comparison is performed until the relative error between the upper and lower bounds reaches the convergence condition, and then the planning scheme is output.

[0077] In one embodiment, step S42, based on the load data and power data of the multiple types of power sources, solves the operational sub-problem under the multidimensional constraints, including: S421: According to the solution model, the running subproblem is decomposed into a secondary principal problem and a secondary subproblem, and the secondary principal problem and the secondary subproblem are connected by a frequency-safe cut; S422: Based on the load data and power data of the various types of power sources, solve the secondary principal problem under the multidimensional constraint conditions that do not include the frequency safety constraint condition; S423: Input the solution result of the secondary principal problem into the secondary subproblem, and solve the secondary subproblem under the frequency security constraint; S424: Judge the solution result of the subproblem according to the frequency security constraint, and output the solution result of the running subproblem according to the judgment result.

[0078] In one embodiment, such as Figure 4As shown, in step S421, based on the two-stage iterative optimization idea of ​​L-shape, the running subproblem is decomposed into a secondary principal problem and a secondary subproblem, which are connected by a frequency safety cut constraint. Because the frequency safety constraint is a special type of constraint that needs to be calculated using MATLAB Simulink software, decomposition makes solving the running subproblem more convenient. In step S422, based on the load data and power data of the various power sources, the secondary principal problem is solved under the multidimensional constraint conditions excluding the frequency safety constraint, to obtain a feasible solution. The feasible solution The secondary subproblem is solved under the frequency security constraints described in step S423, that is, the problem is solved by... The frequency safety constraints are input and solved. In step S424, it is determined whether the frequency safety constraints are met. If the frequency safety constraints are met, the unit output and objective function value are output, forming the upper bound, sensitivity coefficient, and Benders cut (i.e., the solution result of the operating subproblem). If the frequency safety constraints are not met, the frequency safety cut is added, and the updated frequency safety cut is input into the solution process of the secondary master problem. The secondary master problem and the secondary subproblem are resolved until the solution result of the secondary subproblem satisfies the frequency safety constraints. The solution result of the operating subproblem is then output. The frequency safety cut includes the number of synchronous units in operation or the system inertia at that moment. The rules for adding the frequency safety cut are preset, and adding the frequency safety cut improves the frequency regulation capability of the system.

[0079] The multi-type power source co-construction scheme includes construction schemes for conventional thermal power, nuclear power, pumped storage, and new energy storage, as well as various technical and economic indicators of the scheme and its balanced performance in different scenarios.

[0080] In a specific embodiment, the data in step S10 includes load data: the planned maximum annual load is 580 million kilowatts, wind power is 100 million kilowatts, photovoltaic power is 250 million kilowatts, totaling 350 million kilowatts. Power source data: coal-fired power capacity is 39.83 million kilowatts, gas-fired power capacity is 20.66 million kilowatts, nuclear power capacity is 24.71 million kilowatts, pumped storage capacity is 33.2 million kilowatts, and new energy storage capacity is 70 million kilowatts. The parameters in the multidimensional constraints include: coal-fired power construction cost of 3,500 yuan / kW, gas-fired power cost of 2,500 yuan / kW, pumped storage cost of 6,000 yuan / kW, nuclear power cost of 18,320 yuan / kW, and new energy storage cost of 1,500 yuan / kW, which are consistent with the objective function (17) of the model. , Correspondingly, for coal-fired units, the start-up and shutdown cost of a 1 million kW unit is 1 million yuan per cycle, and the operating cost is 0.28 yuan / kWh; for a 600,000 kW unit, the start-up and shutdown cost is 700,000 yuan per cycle, and the operating cost is 0.3 yuan / kWh; for units of 300,000 kW and below, the start-up and shutdown cost is 500,000 yuan per cycle, and the operating cost is 0.32 yuan / kWh; for gas-fired units, the start-up and shutdown cost is 100,000 yuan per cycle, and the operating cost is 0.6 yuan / kWh; and for pumped-storage units, the operating cost is 0.08 yuan / kWh, which corresponds to the objective function in the model (17). Correspondingly, the energy curtailment penalty cost is 5 yuan / kWh, the load shedding penalty cost is 50 yuan / kWh, and the demand-side response cost is 4 yuan / kWh, which correspond to the objective function (17) of the model. , , Corresponding. Confidence level of probability for insufficient flexibility. Take 1%, energy curtailment limit Take 5%, capacity reserve rate res Take 15%, rotating reserve rate Take 5%, frequency drop limit Take 0.2Hz as the limit for the rate of frequency change. Take 0.24 Hz / s, total emission limit The settings are configured according to requirements. In addition, this embodiment obtained 21 scenarios with different time scales in advance, considering that coal-fired and gas-fired units may experience certain output obstruction rates due to unit non-stop rates and limitations in coal and gas sources under different scenarios.

[0081] Under the constraints of step S20, the above parameters are solved in step S40 by solving the objective function of the multi-type power source collaborative planning in step S30, and the multi-type power source collaborative construction scheme is obtained as follows: coal-fired power units with a planned capacity of 22.66 million kilowatts, pumped storage units with a planned capacity of 15.13 million kilowatts, gas-fired power units with a planned capacity of 9.52 million kilowatts, nuclear power units with a planned capacity of 19.5 million kilowatts, and new energy storage with a planned capacity of 11.45 million kilowatts. The corresponding technical and economic indicators are shown in Table 1.

[0082] Table 1. Technical and economic indicators of the investment and construction plan

[0083] Taking a typical scenario from the 21 scenarios corresponding to the above investment plan as an example, this scenario is characterized by light load during holidays, and its flexibility margin for downward adjustment and demand analysis are as follows: Figure 5 As shown, the power balance curve is as follows: Figure 6As shown. Overall, starting at 7:00 AM, when the net load is at its lowest, the system begins to schedule pumped storage and energy storage charging. Subsequently, as photovoltaic (PV) power output increases, the PV growth rate outpaces the load, and pumped storage and energy storage continue to pump water and charge to support PV power consumption. Around 8:00 PM, due to a sharp decrease in PV power, the load remains at a high level during the evening peak, and pumped storage and energy storage power generation meet the load demand. The system's renewable energy installations are mainly (distributed) PV, therefore, curtailment is most likely to occur during the mid-load period of holidays when the load is low and PV power generation is high. After incorporating a reduced flexibility constraint, deep peak shaving of coal-fired power units allows for the scheduling of energy storage / pumped storage and other flexible resources to operate / pump water during the mid-load period, consuming excess PV power and achieving full intraday consumption in this typical scenario. From a flexibility margin perspective, around 4:00 PM is a point where the flexibility margin is relatively low. The maximum frequency change rate curve and the minimum frequency drop curve under large disturbances are shown below. Figure 7 , Figure 8 As shown, the planned investment scheme's system frequency change rate is less than the frequency change rate limit, and the actual minimum system drop value is greater than the allowable minimum drop value, thus meeting frequency safety constraints under large disturbances. Furthermore, the system has sufficient margin, with a load shedding probability of only 0.04%, and monthly carbon emissions of 1.01 billion tons, meeting policy expectations.

[0084] As can be seen, in the above scheme, this application establishes four types of special constraints: flexibility, adequacy, frequency security, and environmental protection requirements. Among them, the flexibility constraint covers probabilistic and non-probabilistic indicators at different time scales, the adequacy constraint includes three types: capacity reserve rate constraint, reliability indicator constraint, and spinning reserve constraint. The frequency security constraint ensures that the minimum frequency drop and frequency drop rate under large disturbances meet the requirements. The environmental protection requirement constraint measures the total carbon emission limit, realizing the quantification of key constraints in the construction process of multiple types of power sources.

[0085] This application proposes and effectively solves objective functions for collaborative planning of multiple types of power sources, covering flexibility, adequacy, frequency security, and environmental protection requirements. It addresses the problem of insufficient consideration of factors such as flexibility and frequency security during power investment and construction, which leads to difficulties in renewable energy consumption and insufficient supply capacity in typical scenarios. This improves the foresight and adaptability of power planning schemes.

[0086] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0087] In one embodiment, a multi-type power supply collaborative construction device is provided, which corresponds one-to-one with the multi-type power supply collaborative deployment method described in the above embodiments. For example... Figure 9As shown, the multi-type power source collaborative construction device includes an acquisition module 101, a constraint module 102, an objective module 103, and a solution module 104. Detailed descriptions of each functional module are as follows: The acquisition module 101 is used to acquire load data and power data of multiple types of power supplies; Constraint module 102 is used to construct multi-dimensional constraints, which include flexibility constraints, adequacy constraints, frequency security constraints, and environmental protection requirement constraints. The objective module 103 is used to construct a multi-type power supply collaborative planning objective function. The objective function aims to minimize the total system cost, which includes the construction cost and operating cost of the multi-type power supply. The solution module 104 is used to solve the objective function of the multi-type power supply collaborative planning under the multi-dimensional constraints based on the load data and power supply data of the multi-type power supply and a pre-set solution model, so as to obtain the multi-type power supply collaborative construction scheme.

[0088] Specifically, module 101 is used to obtain the time-series load size of typical scenarios in future planning years; it is also used to obtain the actual construction capacity size of thermal power, nuclear power, pumped storage, energy storage, and wind and solar power in the current year, as well as the expected wind and solar power boundaries and the construction capacity size of thermal power, nuclear power, pumped storage, and energy storage projects to be selected in future planning years.

[0089] Specifically, the constraint module 102 is used to construct the flexibility constraint conditions, including the average flexibility margin constraint conditions and the new energy curtailment constraint conditions; or it is used to construct the flexibility constraint conditions, including probabilistic constraint conditions. Specifically, constraint module 102 is used to construct frequency safety constraints, including a frequency drop constraint under large disturbances, where the minimum frequency drop under large disturbances is greater than or equal to a pre-set frequency drop limit; and a maximum frequency change rate constraint, where the maximum frequency change rate under large disturbances is less than or equal to a pre-set frequency change rate limit. The minimum frequency drop under large disturbances and the maximum frequency change rate under large disturbances are obtained through simulation based on a pre-set frequency response model of multi-element frequency modulation resources.

[0090] Specifically, constraint module 102 is used to construct adequacy constraints, including capacity reserve ratio constraints, reliability index constraints, and spinning reserve constraints.

[0091] Specifically, the constraint module 102 is used to ensure that the total carbon dioxide emissions of multiple types of power systems are less than or equal to a preset emission level.

[0092] Specifically, target module 103 is used to calculate the investment and construction costs for building multiple types of power sources. and the operating costs of multiple power supply types , Among them, investment and construction costs There are investment and construction costs for thermal power, nuclear power, pumped storage, and energy storage, excluding wind and solar renewable energy; and operating costs. This mainly includes the operation and maintenance costs of each power source and the demand-side response costs; It is the cost of load shedding and energy curtailment.

[0093] Specifically, the solution module 104 is used to decompose the objective function of the multi-type power source collaborative planning into an investment master problem and an operation sub-problem based on the solution model, wherein the investment master problem and the operation sub-problem are connected by cut sets; to solve the operation sub-problem under the multi-dimensional constraints based on the load data and power data of the multi-type power sources; to input the solution result of the operation sub-problem into the investment master problem, and to solve the investment master problem under the multi-dimensional constraints; and to obtain the multi-type power source collaborative construction scheme based on the solution results of the operation sub-problem and the solution results of the investment master problem.

[0094] Specifically, the solution module 104 is used to decompose the operational subproblem into a secondary principal problem and a secondary subproblem according to the solution model, wherein the secondary principal problem and the secondary subproblem are connected by a frequency safety cut; to solve the secondary principal problem under the multidimensional constraints excluding the frequency safety constraints, based on the load data and power data of the multiple types of power sources; to input the solution result of the secondary principal problem into the secondary subproblem and solve the secondary subproblem under the frequency safety constraints; to judge the solution result of the secondary subproblem according to the frequency safety constraints, and to output the solution result of the operational subproblem based on the judgment result.

[0095] Specifically, the solution module 104 is used to output the solution result of the running subproblem when the solution result of the secondary subproblem satisfies the frequency security constraint; when the solution result of the secondary subproblem does not satisfy the frequency security constraint, the frequency security cut is added, the updated frequency security cut is input into the solution process of the secondary principal problem, the secondary principal problem and the secondary subproblem are resolved until the solution result of the secondary subproblem satisfies the frequency security constraint, and the solution result of the running subproblem is output.

[0096] This invention provides a multi-type power source collaborative construction device, which establishes four special constraints: flexibility, adequacy, frequency security, and environmental protection requirements. Among them, the flexibility constraint covers probabilistic and non-probabilistic indicators at different time scales, the adequacy constraint includes three types: capacity reserve rate constraint, reliability index constraint, and spinning reserve constraint. The frequency security constraint ensures that the minimum frequency drop and frequency drop rate under large disturbances meet the requirements. The environmental protection requirement constraint measures the total carbon emission limit, realizing the quantification of key constraints in the construction process of multi-type power sources.

[0097] This application proposes and effectively solves objective functions for collaborative planning of multiple types of power sources, covering flexibility, adequacy, frequency security, and environmental protection requirements. It addresses the problem of insufficient consideration of factors such as flexibility and frequency security during power investment and construction, which leads to difficulties in renewable energy consumption and insufficient supply capacity in typical scenarios. This improves the foresight and adaptability of power planning schemes.

[0098] Specific limitations regarding the multi-type power supply collaborative construction device can be found in the limitations of the multi-type power supply collaborative construction method described above, and will not be repeated here. Each module in the aforementioned multi-type power supply collaborative construction device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0099] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 10 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used for communication with external devices via a network connection. When the computer program is executed by the processor, it implements the functions or steps of a multi-type power supply collaborative deployment method on the server side.

[0100] In one embodiment, a computer device is provided, which may be a device terminal, and its internal structure diagram may be as follows: Figure 11As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used to communicate with an external server via a network connection. When the computer program is executed by the processor, it implements the functions or steps of a multi-type power supply collaborative deployment method on the device side.

[0101] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps: Acquire load data and power data for multiple types of power supplies; Construct multidimensional constraints, which include flexibility constraints, adequacy constraints, frequency security constraints, and environmental protection requirement constraints; A multi-type power supply collaborative planning objective function is constructed. The objective function aims to minimize the total system cost, which includes the construction cost and operating cost of the multi-type power supply. Based on the load data and power data of the various types of power sources, and using a pre-set solution model, the objective function for the collaborative planning of the various types of power sources is solved under the multi-dimensional constraints to obtain a collaborative construction scheme for the various types of power sources.

[0102] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor: Acquire load data and power data for multiple types of power supplies; Construct multidimensional constraints, which include flexibility constraints, adequacy constraints, frequency security constraints, and environmental protection requirement constraints; A multi-type power supply collaborative planning objective function is constructed. The objective function aims to minimize the total system cost, which includes the construction cost and operating cost of the multi-type power supply. Based on the load data and power data of the various types of power sources, and using a pre-set solution model, the objective function for the collaborative planning of the various types of power sources is solved under the multi-dimensional constraints to obtain a collaborative construction scheme for the various types of power sources.

[0103] It should be noted that the functions or steps that can be implemented by the computer-readable storage medium or computer device described above can be referred to the relevant descriptions on the server side and device side in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0104] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0105] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0106] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for coordinated deployment of multiple types of power sources, characterized in that, include: Acquire load data and power data for multiple types of power supplies; Construct multidimensional constraints, which include flexibility constraints, adequacy constraints, frequency security constraints, and environmental protection requirement constraints; A multi-type power supply collaborative planning objective function is constructed. The objective function aims to minimize the total system cost, which includes the construction cost and operating cost of the multi-type power supply. Based on the load data and power data of the various types of power sources, and based on the pre-set solution model, the objective function of the collaborative planning of the various types of power sources is solved under the multi-dimensional constraints to obtain a collaborative construction scheme for the various types of power sources. Based on the load data and power data of the various power sources, and using a pre-set solution model, the objective function for the collaborative planning of the various power sources under the multi-dimensional constraints is solved, including: Based on the solution model, the objective function of the multi-type power supply collaborative planning is decomposed into a master investment problem and an operational subproblem, which are connected by a cut set. Based on the load data and power data of the various types of power sources, the operation sub-problem is solved under the multidimensional constraints. The solution results of the running subproblem are input into the main investment problem, and the main investment problem is solved under the multidimensional constraints. Based on the solution results of the operation sub-problem and the solution results of the investment master problem, the multi-type power source collaborative construction scheme is obtained; Based on the load data and power data of the various power sources, the operational sub-problem is solved under the multidimensional constraints, including: According to the solution model, the running subproblem is decomposed into a secondary principal problem and a secondary subproblem, which are connected by a frequency-safe cut. Based on the load data and power data of the various types of power sources, the secondary principal problem is solved under the multidimensional constraints that do not include the frequency security constraints. The solution result of the secondary principal problem is input into the secondary subproblem, and the secondary subproblem is solved under the frequency security constraint. The solution result of the subproblem is judged according to the frequency security constraint, and the solution result of the running subproblem is output according to the judgment result.

2. The method according to claim 1, characterized in that, The solution result of the subproblem is judged based on the frequency security constraints, and the solution result of the running subproblem is output based on the judgment result, including: When the solution to the subproblem satisfies the frequency security constraint, the solution to the running subproblem is output. When the solution result of the secondary subproblem does not meet the frequency safety constraint, the frequency safety cut is added, and the updated frequency safety cut is input into the solution process of the secondary principal problem. The secondary principal problem and the secondary subproblem are solved again until the solution result of the secondary subproblem meets the frequency safety constraint, and the solution result of the running subproblem is output.

3. The method according to any one of claims 1 to 2, characterized in that, The frequency security constraints include frequency drop constraints under large disturbances and maximum frequency change rate constraints. The frequency drop constraint under large disturbances is that the minimum frequency drop under large disturbances is greater than or equal to the preset frequency drop limit; The maximum frequency change rate constraint is that the maximum frequency change rate under large disturbance is less than or equal to the preset frequency change rate limit. The minimum frequency drop and the maximum frequency change rate under the large disturbance are obtained through simulation based on a pre-set frequency response model of multi-element frequency modulation resources.

4. The method according to claim 1, characterized in that, The flexibility constraints include the average flexibility margin constraint and the new energy curtailment constraint. The average flexibility margin constraint condition is that the average value of the up / down adjustment flexibility margin of multiple types of power systems over multiple time scales is greater than the preset margin value. The new energy curtailment constraint is that the ratio of wind and solar curtailment energy to expected wind and solar power generation under multiple time scales is greater than or equal to the preset curtailment rate.

5. The method according to claim 1, characterized in that, The adequacy constraints include capacity reserve ratio constraints, reliability index constraints, and spinning reserve constraints. The environmental protection requirements include that the total carbon dioxide emissions of multiple types of power systems are less than or equal to the preset emission level.

6. A multi-type power supply collaborative construction device, characterized in that, The apparatus comprising, using the method as described in any one of claims 1 to 5, includes: The acquisition module is used to acquire load data and power data of various types of power supplies; The constraint module is used to construct multi-dimensional constraints, which include flexibility constraints, adequacy constraints, frequency security constraints, and environmental protection requirement constraints. The objective module is used to construct an objective function for collaborative planning of multiple power sources. The objective function aims to minimize the total system cost, which includes the construction and operating costs of the multiple power sources. The solution module is used to solve the objective function of the multi-type power supply collaborative planning under the multi-dimensional constraints based on the load data and power supply data of the multi-type power supply and a pre-set solution model, so as to obtain the multi-type power supply collaborative construction scheme.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the multi-type power supply collaborative construction method as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the multi-type power supply collaborative construction method as described in any one of claims 1 to 5.

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