A multi-region power grid wind-solar-storage coordination planning method and device, a terminal device, and a storage medium

Abstract

The application discloses a kind of multi-zone power grid wind light storage coordination planning method, device, terminal equipment and storage medium, the method with wind power plant planning capacity in target area, photovoltaic power station planning capacity and energy storage power station configuration capacity as decision variable, with wind power plant equal annual value investment cost, photovoltaic power station equal annual value investment cost and the sum of energy storage power station equal annual value investment cost minimum as target, constructs upper planning model in wind light storage joint optimization planning model;With the sum of start-stop cost, penalty cost and fuel cost of thermal power unit of target area minimum as target, construct lower planning model in wind light storage joint optimization planning model;Under the constraint of construction, wind light storage joint optimization planning model is solved, and final wind light storage configuration scheme is obtained;According to final wind light storage configuration scheme, wind power plant, photovoltaic power station and energy storage power station in target area are configured;From this, the complementary characteristics between regions are fully utilized, and the overall utilization rate of renewable energy is improved.

Description

Technical Field

[0001] The present invention relates to the field of new energy planning, and in particular to a method, device, terminal equipment and storage medium for coordinated planning of wind, solar and storage systems in a multi-regional power grid. Background Art

[0002] In recent years, the installed capacity of wind and solar power has continued to increase, making it crucial to optimize the allocation of wind, solar, and energy storage resources. Wind and solar energy are highly random and intermittent, and their development and utilization are subject to significant constraints from natural conditions. Wind and solar energy are naturally highly complementary in terms of time and location. Compared to independent wind power or photovoltaic power generation, interconnecting regional power grids and leveraging load complementarity can better integrate wind and solar resources. For example, when wind and solar power generation in one region is insufficient (such as when solar power generation is low on cloudy days or when wind power is weak), electricity can be obtained from power grids in other regions to compensate. When wind and solar power generation in a region is excessive, the excess electricity can be transmitted to other regions, thereby enhancing the utilization and integration capabilities of the entire interconnected power grid for wind and solar power resources. However, previous studies have typically conducted new energy and energy storage planning based on a specific region or system, without considering the joint planning of new energy and energy storage across multiple regions. Summary of the Invention

[0003] The present invention provides a multi-regional power grid wind, solar power and storage coordinated planning method, device, terminal equipment and storage medium. The method determines the wind, solar power and storage configuration plan based on the established wind, solar power and storage joint optimization planning model, thereby effectively coordinating the power output between different target areas, making full use of the complementary characteristics between regions, significantly improving the configuration efficiency of wind energy, solar energy and energy storage resources, and thus improving the overall utilization rate of renewable energy.

[0004] An embodiment of the present invention provides a method for wind, solar, and energy storage coordinated planning in a multi-regional power grid, including:

[0005] Obtain a typical scenario set, the total number of target areas, the fixed investment cost of the target area, the maximum equal annual value investment cost, the discount rate, wind farm capacity data, photovoltaic power station capacity data, installed capacity ratio data, capacity-to-power ratio of energy storage power stations in the target area, the set of thermal power units in the target area, the unit penalty cost of the target area, the start-up and shutdown cost data of thermal power units in the target area, the maximum output per unit capacity of wind farms and photovoltaic power stations in the target area, the installed capacity of thermal power units in the target area, the charging and discharging efficiency of energy storage power stations in the target area, the ramp rate of thermal power units in the target area, the interconnection line power data of the target area, the load demand of the target area, and the target scheduling period;

[0006] Based on the acquired data, the upper-level planning model of the wind, solar, and energy storage joint planning model is constructed, with the planned capacity of wind farms, planned capacity of photovoltaic power stations, and configured capacity of energy storage stations in the target area as decision variables, and the goal of minimizing the sum of the annual investment costs of wind farms, photovoltaic power stations, and energy storage stations. Furthermore, based on the acquired data, the lower-level planning model of the wind, solar, and energy storage joint optimization planning model is constructed, with the goal of minimizing the sum of the start-up and shutdown costs, penalty costs, and fuel costs of thermal power units in the target area.

[0007] Based on the acquired data, build upper-level constraints and lower-level constraints;

[0008] Under the constructed constraints, combined with the typical scenario set, the wind, solar and storage joint optimization planning model is coordinated and solved until the upper-level planning model and the lower-level planning model are coordinated, thereby obtaining the final wind, solar and storage configuration plan for the target area;

[0009] According to the final wind, solar and storage configuration plan, the wind farm, photovoltaic power station and energy storage power station in the target area are configured; wherein, the final wind, solar and storage configuration plan includes the planned capacity of the wind farm, the planned capacity of the photovoltaic power station and the configured capacity of the energy storage power station in the target area.

[0010] Furthermore, under the constructed constraints, the wind, solar and storage joint optimization planning model is coordinated and solved in combination with the typical scenario set until the upper-level planning model and the lower-level planning model are coordinated, thereby obtaining a final wind, solar and storage configuration plan for the target area, including:

[0011] Inputting the typical scenario set into the wind-solar-storage joint optimization planning model;

[0012] Repeat the coordination and iteration operation until the upper-level planning model and the lower-level planning model reach coordination, and output the final wind-solar-storage configuration plan for the target area;

[0013] The coordinated iterative operation includes:

[0014] The current decision variables of the upper-level planning model are transferred to the lower-level planning model, so that the lower-level planning model solves the updated wind farm output, photovoltaic power station output, and thermal power unit output in each typical scenario of the target area according to the current decision variables and the lower-level constraints;

[0015] According to the output of the wind farm, photovoltaic power station and thermal power unit in each typical scenario of the updated target area, the updated annual utilization hours are calculated, and it is determined whether the difference between the updated annual utilization hours and the corresponding annual utilization hours before the update is less than the corresponding preset threshold value.

[0016] If so, it is determined that the upper-level planning model and the lower-level planning model have reached coordination, the iterative calculation is stopped, and the updated annual utilization hours are passed to the upper-level planning model, so that the upper-level planning model solves the updated decision variables based on the updated annual utilization hours, and uses the updated decision variables as the final wind-solar-storage configuration plan.

[0017] If not, the updated annual utilization hours are passed to the upper-level planning model, so that the upper-level planning model obtains updated decision variables based on the updated annual utilization hours, and performs the next coordinated iterative operation according to the updated decision variables; wherein, when the lower-level planning model is for the first iterative calculation, the current decision variables of the upper-level planning model are generated by random initialization, and the annual utilization hours before the update are preset empirical values.

[0018] Furthermore, the upper-level planning model includes:

[0019]

[0020] Where C inv is the total annual investment cost of the target area; k represents the kth target area; K is the total number of target areas; Plan the capacity of wind farms in target area k; Plan the capacity of the photovoltaic power station in the target area k; B W B is the unit capacity cost of the wind farm; PV The unit capacity cost of the photovoltaic power station; The unit capacity investment cost of the energy storage power station; Configure the capacity of the energy storage power station in the target area k; Y W Y is the age of the wind farm units; PV Y is the age of the photovoltaic power station unit; ESS is the life of the energy storage power station unit; θ is the discount rate.

[0021] Furthermore, the lower-level planning model includes:

[0022]

[0023] Where C oper is the total operating cost; is the fuel cost of the thermal power unit in the target area k under the jth typical scenario; represents the start-stop cost in the target area k under the jth typical scenario; represents the penalty cost of wind and solar curtailment in the jth typical scenario within the target area k; J represents the number of typical scenarios; ρ j is the probability of occurrence of typical scenario j; T is the target scheduling period; is the set of thermal power units in target area k; a k,g is the quadratic term of the quadratic function of fuel cost of thermal power unit g; b k,g is the linear term of the quadratic function of the fuel cost of thermal power unit g; c k,g is the constant term coefficient of the quadratic function of the fuel cost of thermal power unit g; is the output of thermal power unit g in target area k at time t under the jth typical scenario; is the penalty cost for curtailing k units of wind power in the target area; The penalty cost for abandoning k units in the target area; is the wind curtailment amount of target area k at the tth moment under the jth scenario; is the amount of abandoned light in target area k at the tth moment in the jth scene; d k,g is the startup cost of thermal power unit g; e k,g is the shutdown cost of thermal power unit g; u k,g,j,t is the starting state of thermal power unit g, v k,g,j,t is the shutdown state of thermal power unit g, when u k,g,j,t =1, indicating that the thermal power unit g is in the starting state; when v k,g,j,t When =1, it indicates that the thermal power unit g is in shutdown state.

[0024] Furthermore, the annual utilization hours include: annual utilization hours of wind farms, annual utilization hours of photovoltaic power stations, and annual utilization hours of thermal power units;

[0025] The updated annual utilization hours are calculated based on the wind farm output, photovoltaic power station output, and thermal power unit output of the updated target area under each typical scenario, including:

[0026] The updated annual utilization hours of wind farms, photovoltaic power stations, and thermal power units are calculated using the following formula:

[0027]

[0028] Where, is the annual utilization hours of thermal power units in the target area k; is the annual utilization hours of the photovoltaic power station in the target area k; is the annual utilization hours of wind farms in target area k; is the installed capacity of PV power plants in target area k; is the installed capacity of wind farms in target area k; is the installed capacity of thermal power units in target area k; Plan the capacity of wind farms in target area k; Plan the capacity of the photovoltaic power station in the target area k; ρ j is the probability of occurrence of typical scenario j; is the wind farm output of target area k at the tth moment under the jth scenario; is the output of the photovoltaic power station in the target area k under the jth scenario; is the output of thermal power unit g in target area k at the tth moment under the jth typical scenario.

[0029] Furthermore, the upper-level constraints include: installed capacity constraints, wind, solar and energy storage joint investment cost constraints, new energy installed capacity proportion constraints and reserve constraints; the lower-level constraints include: regional power balance constraints, interconnection line power constraints, new energy output constraints, thermal power output constraints and energy storage battery constraints.

[0030] An embodiment of the present invention further provides a multi-regional power grid wind, solar and storage coordination planning device, comprising: a data acquisition module, a model construction module, a constraint construction module, a configuration scheme determination module and a configuration module;

[0031] The data acquisition module is used to obtain a typical scenario set, the total number of target areas, the fixed investment cost of the target area, the maximum annual investment cost, the discount rate, wind farm capacity data, photovoltaic power station capacity data, installed capacity ratio data, capacity-to-power ratio of energy storage power stations in the target area, the set of thermal power units in the target area, the unit penalty fee of the target area, the start-up and shutdown cost data of thermal power units in the target area, the maximum output per unit capacity of wind farms and photovoltaic power stations in the target area, the installed capacity of thermal power units in the target area, the charging and discharging efficiency of energy storage power stations in the target area, the ramp rate of thermal power units in the target area, the tie line power data of the target area, the load demand of the target area, and the target scheduling period;

[0032] The model construction module is used to construct an upper-level planning model in the wind-solar-storage joint planning model based on the acquired data, with the planned capacity of wind farms, the planned capacity of photovoltaic power stations, and the configured capacity of energy storage power stations in the target area as decision variables, and with the goal of minimizing the sum of the annual investment costs of wind farms, photovoltaic power stations, and energy storage power stations; and to construct a lower-level planning model in the wind-solar-storage joint optimization planning model based on the acquired data, with the goal of minimizing the sum of the start-up and shutdown costs, penalty costs, and fuel costs of thermal power units in the target area;

[0033] The constraint construction module is used to construct upper-level constraints and lower-level constraints based on the acquired data;

[0034] The configuration scheme determination module is used to coordinate and solve the wind, solar and storage joint optimization planning model under the constructed constraints and in combination with the typical scenario set until the upper-level planning model and the lower-level planning model are coordinated to obtain the final wind, solar and storage configuration scheme for the target area;

[0035] The configuration module is used to configure the wind farm, photovoltaic power station and energy storage power station in the target area according to the final wind, solar and storage configuration plan; wherein the final wind, solar and storage configuration plan includes the planned capacity of the wind farm, the planned capacity of the photovoltaic power station and the configured capacity of the energy storage power station in the target area.

[0036] Furthermore, under the constructed constraints, the wind, solar and storage joint optimization planning model is coordinated and solved in combination with the typical scenario set until the upper-level planning model and the lower-level planning model are coordinated, thereby obtaining a final wind, solar and storage configuration plan for the target area, including:

[0037] Inputting the typical scenario set into the wind-solar-storage joint optimization planning model;

[0038] Repeat the coordination iteration operation until the upper-level planning model and the lower-level planning model reach coordination, and output the final wind-solar-storage configuration plan for the target area;

[0039] The coordinated iterative operation includes:

[0040] The current decision variables of the upper-level planning model are transferred to the lower-level planning model, so that the lower-level planning model solves the updated wind farm output, photovoltaic power station output, and thermal power unit output in each typical scenario of the target area according to the current decision variables and the lower-level constraints;

[0041] According to the output of the wind farm, photovoltaic power station and thermal power unit in each typical scenario of the updated target area, the updated annual utilization hours are calculated, and it is determined whether the difference between the updated annual utilization hours and the corresponding annual utilization hours before the update is less than the corresponding preset threshold value.

[0042] If so, it is determined that the upper-level planning model and the lower-level planning model have reached coordination, the iterative calculation is stopped, and the updated annual utilization hours are passed to the upper-level planning model, so that the upper-level planning model solves the updated decision variables based on the updated annual utilization hours, and uses the updated decision variables as the final wind-solar-storage configuration plan.

[0043] If not, the updated annual utilization hours are passed to the upper-level planning model, so that the upper-level planning model obtains updated decision variables based on the updated annual utilization hours, and performs the next coordinated iterative operation according to the updated decision variables; wherein, when the lower-level planning model is for the first iterative calculation, the current decision variables of the upper-level planning model are generated by random initialization, and the annual utilization hours before the update are preset empirical values.

[0044] The present application also provides a terminal device, including:

[0045] one or more processors;

[0046] a memory, coupled to the processor, for storing one or more programs;

[0047] When the one or more programs are executed by the one or more processors, the one or more processors implement the multi-regional power grid wind, solar and storage coordination planning method as described in the above-mentioned embodiment of the invention.

[0048] The present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the multi-regional power grid wind, solar and storage coordinated planning method as described in the above-mentioned embodiment of the invention is implemented.

[0049] The following beneficial effects are achieved by implementing the present invention:

[0050] The present invention provides a method, apparatus, terminal device, and storage medium for coordinated planning of wind, solar, and storage systems in a multi-regional power grid. The method first acquires basic data, then constructs upper-level and lower-level constraints to ensure that the results obtained when solving the model subsequently do not exceed the range.

[0051] Secondly, taking the planned capacity of wind farms, planned capacity of photovoltaic power stations and configured capacity of energy storage power stations in the target area as decision variables, and taking the minimization of the sum of the annual investment costs of wind farms, photovoltaic power stations and energy storage power stations as the goal, an upper-level planning model in the wind-solar-storage joint planning model is constructed, and taking the minimization of the sum of the start-up and shutdown costs, penalty costs and fuel costs of thermal power units in the target area as the goal, a lower-level planning model in the wind-solar-storage joint optimization planning model is constructed. Thus, the constructed wind-solar-storage joint optimization planning model comprehensively considers multiple factors such as power system construction, operation costs, and resource endowment. The established wind-solar-storage joint optimization planning model effectively coordinates the power output between different regions, fully utilizes the complementary characteristics between regions, significantly improves the configuration efficiency of wind energy, solar energy and energy storage resources, and thus improves the overall utilization rate of renewable energy.

[0052] Finally, the master-slave game model is solved based on the constructed constraints to obtain the final wind-solar-storage configuration plan. The wind farms, photovoltaic power stations and energy storage power stations in the target area are configured according to the final wind-solar-storage configuration plan. At this point, the reasonable allocation of planned capacity is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solution of the present application, the following is a brief introduction to the drawings required for use in the implementation. Obviously, the drawings described below are only some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0054] Figure 1 This is a flowchart of a method for coordinated planning of wind, solar, and storage in a multi-regional power grid provided in one embodiment of the present application;

[0055] Figure 2 This is a schematic diagram of the installed capacity planning results of renewable energy and energy storage provided in one embodiment of the present application;

[0056] Figure 3 This is a schematic diagram of the reduction in power consumption and reduction rate of renewable energy provided in one embodiment of the present application;

[0057] Figure 4 This is a graph showing the error between the virtual solution and the real solution of the coupled variables in the process of solving the augmented Lagrangian alternating direction inexact Newton method provided in one embodiment of the present application;

[0058] Figure 5 This is a structural diagram of a multi-regional power grid wind, solar, and storage coordination planning device provided by an embodiment of the present application;

[0059] Figure 6 This is a schematic diagram of the structure of a terminal device provided in a certain embodiment of the present application. DETAILED DESCRIPTION

[0060] To make the objectives, technical solutions, and advantages of this application more clear, the technical solutions in this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments 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 technicians in this field without making any creative efforts are within the scope of protection of this application.

[0061] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned figure descriptions are intended to cover non-exclusive inclusions.

[0062] In the description of the embodiments of this application, the technical terms "first" and "second" are used only to distinguish different objects and should not be understood to indicate or imply relative importance or implicitly specify the quantity, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, the meaning of "plurality" is more than two, unless otherwise clearly and specifically defined.

[0063] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0064] In the description of the embodiments of this application, the term "and / or" is simply a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent the following three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0065] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0066] In the description of the embodiments of the present application, unless otherwise expressly specified or limited, technical terms such as "installed," "connected," "connected," and "fixed" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integration; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; internal connections between two components or interactions between two components. Those skilled in the art can understand the specific meanings of the above terms in the embodiments of the present application based on specific circumstances.

[0067] See also Figure 1 , is a flow chart of a method for wind, solar, and energy storage coordination planning in a multi-regional power grid provided by an embodiment of the present invention, including:

[0068] S1. Obtain a typical scenario set, the total number of target areas, the fixed investment cost of the target area, the maximum annual investment cost, the discount rate, the capacity data of wind farms, the capacity data of photovoltaic power stations, the installed capacity ratio data, the capacity-to-power ratio of energy storage power stations in the target area, the set of thermal power units in the target area, the unit penalty cost of the target area, the start-up and shutdown cost data of thermal power units in the target area, the maximum output per unit capacity of wind farms and photovoltaic power stations in the target area, the installed capacity of thermal power units in the target area, the charging and discharging efficiency of energy storage power stations in the target area, the ramp rate of thermal power units in the target area, the tie line power data of the target area, the load demand of the target area, and the target scheduling period.

[0069] Specifically, the fixed investment cost in the target area includes the unit capacity cost of the wind farm in the target area, the age of the wind farm units in the target area, the unit capacity cost of the photovoltaic power station in the target area, the age of the photovoltaic power station units in the target area, the unit capacity investment cost of the energy storage power station in the target area, and the age of the energy storage power station units in the target area; the wind farm capacity data includes the upper limit of the construction capacity of the wind farm in the target area and the installed capacity of the wind farm in the target area; the photovoltaic power station capacity data includes the upper limit of the construction capacity of the photovoltaic power station in the target area and the installed capacity of the photovoltaic power station in the target area;

[0070] Specifically, the installed capacity proportion data includes the upper limit of the configured capacity of the energy storage power station in the target area to the installed capacity of new energy, the capacity-to-power ratio of the energy storage power station, the total installed capacity of the power plant, the maximum installed capacity proportion, the minimum installed capacity proportion, the maximum output per unit capacity of the wind farm in the target area, and the maximum output per unit capacity of the photovoltaic power station in the target area.

[0071] Specifically, the unit penalty fee of the target area includes the unit wind curtailment penalty fee and the unit solar curtailment penalty fee in the target area; the start-up and shutdown cost data of the thermal power units in the target area include the startup cost and the shutdown cost of the thermal power units; the load demand of the target area includes the load demand and annual electricity demand of the target area under typical scenario j in the typical scenario set; the interconnection line power data of the target area includes the upper and lower limits of the interconnection line power between each target area.

[0072] S2. Based on the acquired data, the planned capacity of wind farms, the planned capacity of photovoltaic power stations, and the configured capacity of energy storage power stations in the target area are used as decision variables, and the sum of the equal annual investment costs of wind farms, photovoltaic power stations, and energy storage power stations is minimized as the goal. The upper-level planning model of the wind, solar, and energy storage joint planning model is constructed. Furthermore, based on the acquired data, the lower-level planning model of the wind, solar, and energy storage joint optimization planning model is constructed, with the sum of the start-up and shutdown costs, penalty costs, and fuel costs of thermal power units in the target area as the goal.

[0073] Schematically, the multi-region wind, solar and energy storage joint planning problem can be divided into two levels. The upper level problem is to determine the configuration capacity of power sources (i.e. wind farms and photovoltaic power stations) and energy storage power stations in each target area. The lower level problem is to solve the problem of maximizing the consumption of new energy according to the planned capacity given after solving the upper level problem, so as to determine the operating status of power sources (i.e. wind farms, thermal power units and photovoltaic power stations) in each target area, and return these operating statuses to the upper level problem. Through iterative solutions between the upper and lower levels of problems, the final wind, solar and energy storage configuration plan is obtained;

[0074] In a preferred embodiment, the upper-level planning model includes:

[0075]

[0076] Where C inv is the total annual investment cost of the target area; k represents the kth target area; K is the total number of target areas; Plan the capacity of wind farms in target area k; Plan the capacity of the photovoltaic power station in the target area k; B W B is the unit capacity cost of the wind farm; PV The unit capacity cost of the photovoltaic power station; The unit capacity investment cost of the energy storage power station; Configure the capacity of the energy storage power station in the target area k; Y W Y is the age of the wind farm units; PV Y is the age of the photovoltaic power station unit; ESS is the life of the energy storage power station unit; θ is the discount rate.

[0077] In a preferred embodiment, the lower-level planning model includes:

[0078]

[0079] Where C oper is the total operating cost; is the fuel cost of the thermal power unit in the target area k under the jth typical scenario; represents the start-stop cost in the target area k under the jth typical scenario; represents the penalty cost of wind and solar curtailment in the jth typical scenario within the target area k; J represents the number of typical scenarios; ρ j is the probability of occurrence of typical scenario j; T is the target scheduling period; is the set of thermal power units in target area k; a k,g is the quadratic term of the quadratic function of fuel cost of thermal power unit g; b k,g is the linear term of the quadratic function of the fuel cost of thermal power unit g; c k,g is the constant term coefficient of the quadratic function of the fuel cost of thermal power unit g; is the output of thermal power unit g in target area k at time t under the jth typical scenario; is the penalty cost for curtailing k units of wind power in the target area; The penalty cost for abandoning k units in the target area; is the wind curtailment amount of target area k at the tth moment under the jth scenario; is the amount of abandoned light in target area k at the tth moment in the jth scene; d k,g is the startup cost of thermal power unit g; e k,g is the shutdown cost of thermal power unit g; u k,g,j,t is the starting state of thermal power unit g, v k,g,j,t is the shutdown state of thermal power unit g, when u k,g,j,t =1, indicating that the thermal power unit g is in the starting state; when v k,g,j,t When =1, it indicates that the thermal power unit g is in shutdown state.

[0080] S3. Construct upper-level constraints and lower-level constraints based on the acquired data;

[0081] In a preferred embodiment, the upper constraints include: installed capacity constraints, wind, solar and energy storage joint investment cost constraints, new energy installed capacity ratio constraints and reserve constraints; the lower constraints include: regional power balance constraints, tie line power constraints, new energy output constraints, thermal power output constraints and energy storage battery constraints;

[0082] Specifically, the installed capacity constraints include:

[0083]

[0084] Where, Build an upper bound on the capacity of wind farms in the kth target area; The upper bound of the photovoltaic power station construction capacity for the kth target area is established; The upper limit ratio of energy storage capacity to installed capacity of new energy sources for the kth target area is calculated; is the installed capacity of wind farms in the kth target area; is the installed capacity of the photovoltaic power station in the kth target area; k es is the capacity-power ratio of the energy storage device; is the total rated power of the energy storage power station in the kth target area; Allocate capacity for energy storage power stations within target area k;

[0085] Specifically, formula (6) indicates that the planned capacity of wind turbines in the wind power station must be less than the upper limit of wind turbine capacity that can be installed in the target area, and formula (7) indicates that the planned capacity of the photovoltaic power station must be less than the capacity of the photovoltaic power station that can be installed; the rated total power of the energy storage power station in the kth target area is is an unknown quantity to be determined, the capacity-power ratio k of the energy storage device es is a known quantity.

[0086] Specifically, the wind, solar and energy storage joint investment cost constraints include:

[0087]

[0088] Where C inv_max is the maximum equal annual value investment cost;

[0089] Specifically, the constraints on the proportion of new energy installed capacity include:

[0090]

[0091] Where S is the total installed capacity of the power plant, R max is the maximum value of installed capacity; R min is the maximum and minimum value of the installed capacity proportion; is the installed capacity of PV power plants in target area k; is the installed capacity of wind farms in target area k.

[0092] Specifically, the backup constraints include:

[0093]

[0094] Where, is the annual utilization hours of the wind farm; is the annual utilization hours of the photovoltaic power station; is the annual utilization hours of thermal power units; K is the regional set; E r is the annual power demand; R is the reserve coefficient; is the installed capacity of thermal power units in target area k.

[0095] Specifically, the installed capacity constraints (6)-(9), the wind, solar and energy storage joint investment cost constraint (10), the new energy installed capacity proportion constraint (11) and the backup constraint (12) constitute the upper-level constraints.

[0096] Specifically, the regional power balance constraints include:

[0097]

[0098] Where, is the load demand of target area k under typical scenario j; is the output of thermal power unit g in target area k; is the output of wind turbines in target area k under typical scenario j; is the output of the photovoltaic power station in the target area k under the typical scenario j; is the output of the energy storage power station in target area k under typical scenario j; A represents the power flowing from target area z to target area k through the tie line; k is the set of regions connected to the target region k by contact lines;

[0099] Specifically, the output of wind turbines in target area k under typical scenario j is The output of the photovoltaic power station in the target area k under the typical scenario j Output of the energy storage power station in target area k under typical scenario j The power flowing from target area z to target area k through the tie line All are quantities to be sought.

[0100] Specifically, the tie line power constraint includes:

[0101]

[0102] Where, is the upper limit of the tie line power between area z and area k, is the lower limit of the tie line power between area z and area k.

[0103] Specifically, the new energy output constraints include:

[0104]

[0105] Where, is the maximum output per unit capacity of the wind farm; The maximum output per unit capacity of the photovoltaic power station;

[0106] Specifically, the thermal power output constraints include:

[0107]

[0108] Where, is the unit's upward climbing rate; is the downward climbing rate of the unit; Z k,g,j,t is a binary variable, indicating the operating status of the unit; H on is the minimum start-up time of the unit; H off The minimum downtime of the unit.

[0109] Specifically, the energy storage battery constraints include:

[0110]

[0111] Where, E k,j,t is the amount of electricity; is the charging power; are the discharge power; is charging efficiency; is the discharge efficiency; α k is the lower limit coefficient of power; M e is the coefficient of the large M method; It is the energy storage charging and discharging state. represents the energy storage discharge, and It means energy storage charging; is the installed energy storage capacity of the kth target area; is the total rated power of the installed energy storage in the kth target area;

[0112] Specifically, the amount of electricity E k,j,t , charging power Discharge power the amount of intercession for the unknown;

[0113] Specifically, the regional power balance constraint (13), the interconnection line power constraints (14)-(15), the new energy output constraints (16)-(19), the thermal power output constraint (20) and the energy storage battery constraint (21) constitute the lower-level constraints.

[0114] S4. Under the constructed constraints, combined with the typical scenario set, coordinate and solve the wind, solar and storage joint optimization planning model until the upper-level planning model and the lower-level planning model are coordinated to obtain the final wind, solar and storage configuration plan for the target area;

[0115] In a preferred embodiment, under the constructed constraints, the wind, solar and storage joint optimization planning model is coordinated and solved in combination with the typical scenario set until the upper-level planning model and the lower-level planning model are coordinated to obtain the final wind, solar and storage configuration plan for the target area, including:

[0116] Inputting the typical scenario set into the wind-solar-storage joint optimization planning model;

[0117] Repeat the coordination iteration operation until the upper-level planning model and the lower-level planning model reach coordination, and output the final wind-solar-storage configuration plan for the target area;

[0118] The coordinated iterative operation includes:

[0119] The current decision variables of the upper-level planning model are transferred to the lower-level planning model, so that the lower-level planning model solves the updated wind farm output, photovoltaic power station output, and thermal power unit output in each typical scenario of the target area according to the current decision variables and the lower-level constraints;

[0120] According to the output of the wind farm, photovoltaic power station and thermal power unit in each typical scenario of the updated target area, the updated annual utilization hours are calculated, and it is determined whether the difference between the updated annual utilization hours and the corresponding annual utilization hours before the update is less than the corresponding preset threshold value.

[0121] If so, it is determined that the upper-level planning model and the lower-level planning model have reached coordination, the iterative calculation is stopped, and the updated annual utilization hours are passed to the upper-level planning model, so that the upper-level planning model solves the updated decision variables based on the updated annual utilization hours, and uses the updated decision variables as the final wind-solar-storage configuration plan.

[0122] If not, the updated annual utilization hours are passed to the upper-level planning model, so that the upper-level planning model obtains updated decision variables based on the updated annual utilization hours, and performs the next coordinated iteration operation based on the updated decision variables; wherein, when the lower-level planning model is in the initial iterative calculation, the current decision variables of the upper-level planning model are generated by random initialization, and the annual utilization hours before the update are preset empirical values;

[0123] Specifically, when solving the wind-solar-storage joint optimization planning model, the typical scenario set is input into the wind-solar-storage joint optimization planning model, and the acquired data is substituted into the upper-level constraints and the lower-level constraints;

[0124] Then, since this is the first iterative calculation, the upper-level planning model will randomly initialize and generate initial decision variables. The initial annual utilization hours of the wind farm, the annual utilization hours of the photovoltaic power station, and the annual utilization hours of the thermal power unit are preset empirical values.

[0125] During the first coordinated iteration operation, the upper-level planning model inputs randomly generated initial decision variables into the lower-level planning model, so that after receiving the initial decision variables transmitted by the upper-level planning model, the lower-level planning model performs a time-series operation simulation for each typical scenario using a preset augmented Lagrangian alternating direction inexact Newton method, thereby solving for the wind farm output, photovoltaic power station output, and thermal power unit output of the target area under each typical scenario;

[0126] Based on the wind farm output, photovoltaic power station output, and thermal power unit output obtained in each typical scenario, the updated annual utilization hours of the wind farm, the updated annual utilization hours of the photovoltaic power station, and the updated annual utilization hours of the thermal power unit are calculated;

[0127] Subtract the updated annual utilization hours of the wind farm from the annual utilization hours of the wind farm before the update to obtain a first difference value; wherein, in the first iterative calculation, the annual utilization hours of the wind farm before the update is the previously input empirical value; in non-first iterative calculations, the annual utilization hours of the wind farm before the update is the annual utilization hours of the wind farm obtained by solving the previous iterative calculation;

[0128] The second difference is obtained by subtracting the updated annual utilization hours of the photovoltaic power station from the annual utilization hours of the photovoltaic power station before the update. In the first iterative calculation, the annual utilization hours of the photovoltaic power station before the update is the empirical value input in advance. In the non-first iterative calculation, the annual utilization hours of the photovoltaic power station before the update is the annual utilization hours of the photovoltaic power station solved in the previous iterative calculation.

[0129] The third difference is obtained by subtracting the annual utilization hours of the thermal power unit after the update from the annual utilization hours of the thermal power unit before the update. In the first iterative calculation, the annual utilization hours of the thermal power unit before the update is the empirical value input in advance. In the non-first iterative calculation, the annual utilization hours of the thermal power unit before the update is the annual utilization hours of the thermal power unit solved in the previous iterative calculation.

[0130] It should be noted that the update of the annual utilization hours of wind farms, photovoltaic power stations and thermal power units is reflected in the reserve constraints in the upper-level constraints, i.e., formula (12).

[0131] Simultaneously determining whether the first difference is less than a preset first convergence threshold, determining whether the second difference is less than a preset second convergence threshold, and determining whether the third difference is less than a preset third convergence threshold;

[0132] If both are less than the corresponding convergence threshold, it is determined that the upper-level planning model and the lower-level planning model have reached coordination, the iterative calculation is stopped, and the updated annual utilization hours are passed to the upper-level planning model, so that the upper-level planning model solves the updated decision variables based on the updated annual utilization hours, and uses the updated decision variables as the final wind-solar-storage configuration plan;

[0133] If not, the updated annual utilization hours are passed to the upper-level planning model, so that the upper-level planning model solves the updated decision variables based on the updated annual utilization hours, and performs the next coordination iteration operation based on the updated decision variables.

[0134] In a preferred embodiment, the annual utilization hours include: annual utilization hours of wind farms, annual utilization hours of photovoltaic power stations, and annual utilization hours of thermal power units;

[0135] The updated annual utilization hours are calculated based on the wind farm output, photovoltaic power station output, and thermal power unit output of the updated target area under each typical scenario, including:

[0136] The updated annual utilization hours of wind farms, photovoltaic power stations, and thermal power units are calculated using the following formula:

[0137]

[0138] Where, is the annual utilization hours of thermal power units in the target area k; is the annual utilization hours of the photovoltaic power station in the target area k; is the annual utilization hours of wind farms in target area k; is the installed capacity of PV power plants in target area k; is the installed capacity of wind farms in target area k; is the installed capacity of thermal power units in target area k; Plan the capacity of wind farms in target area k; Plan the capacity of the photovoltaic power station in the target area k; ρ j is the probability of occurrence of typical scenario j; is the wind farm output of target area k at the tth moment under the jth scenario; is the output of the photovoltaic power station in the target area k under the jth scenario; is the output of thermal power unit g in target area k at the tth moment under the jth typical scenario.

[0139] Schematically, the upper-level programming model can be solved using an existing solver, while the lower-level programming model is solved using the augmented Lagrangian alternating direction inexact Newton method;

[0140] Specifically, the augmented Lagrangian alternating direction inexact Newton method (ALADN) ensures convergence after a sufficient number of iterations, offering significant advantages in distributed problem solving. In each iteration, distributed individuals solve their own decoupled problems, while the computational center solves a centralized optimization problem. Because the underlying planning models for different target areas in each typical scenario are coupled, the AADN method can be used for distributed solution of each scenario.

[0141] Specifically, the lower-level constraint (15) in the lower-level planning model is converted into an equality constraint of coupled variables, which is expressed as The inequality constraints of the coupled variables in equation (14) are expressed as

[0142] Formula (13), Formula (16)-(21) are the equality constraints and inequality constraints of non-coupled variables, denoted as p k (x k )=0 and q k (x k )≤0.

[0143] Therefore, the lower-level planning model can be expressed as:

[0144]

[0145] Where, The objective function for running simulations for multiple target areas under typical scenario j; x k,j is the uncoupled decision variable for a single target region,

[0146] are coupled decision variables, including tie line power constraints is the coefficient matrix of the coupled equality constraints, is the corresponding Lagrange multiplier; is the corresponding Lagrange multiplier.

[0147] According to the augmented Lagrangian alternating direction inexact Newton method, the solution steps of the lower-level planning model in a typical scenario j are as follows:

[0148] Step S401: Give the initial value of the coupling variable and the initial value of the Lagrange multiplier Choose a positive semidefinite matrix Σ k,j , Lagrange multiplier ρ and convergence threshold ε, and define the number of iterations s = 1.

[0149] Step S402: solve the following problem for each sub-region:

[0150]

[0151] Step S403: If the condition in formula (27) is met, return x k,j and The loop terminates, otherwise continues to step S404;

[0152]

[0153] Step S404: Calculate local gradient Calculate the Hessian approximation matrix and the Jacobian matrix

[0154] Step S405: Solve the following quadratic programming:

[0155]

[0156] Where λ QP is the corresponding Lagrange multiplier.

[0157] Step S406: update the variables and return to step S402;

[0158]

[0159] Where α1, α2, and α3 are corresponding coefficients, which can be set to the unit matrix I.

[0160] S5. Configure the wind farm, photovoltaic power station and energy storage power station in the target area according to the final wind, photovoltaic and energy storage configuration plan; wherein the final wind, photovoltaic and energy storage configuration plan includes the planned capacity of the wind farm, the planned capacity of the photovoltaic power station and the configured capacity of the energy storage power station in the target area.

[0161] Specifically, the actual parameters of a power grid in a certain region of China are used for verification. The power grid consists of three interconnected subgrids. The installed capacity of wind, solar and storage is as follows: Figure 2 As shown in the figure, the development of renewable energy sources such as wind and solar power is primarily concentrated in Region 3. This planning result is consistent with the distribution of natural resources in each subregion. The expected renewable energy reduction is defined as the total amount of energy reduced in each scenario multiplied by the probability of the scenario. Figure 3 The projected curtailment of renewable energy systems (RESs) in different regions and their corresponding curtailment rates are shown. The curtailment rates in each subregion are very low, remaining below 5%. This demonstrates that the proposed collaborative planning approach can help reduce curtailment of renewable energy. Figure 4The error curve of the virtual solution and the real solution of the coupling variable in the augmented Lagrange alternating direction inexact Newton method is obtained.

[0162] Referring to Figure 5 The device for multi-region power grid wind-solar-storage coordinated planning provided by an embodiment of the present application comprises a data acquisition module, a model construction module, a constraint construction module, a configuration scheme determination module, and a configuration module.

[0163] The data acquisition module is configured to acquire a typical scenario set, a total number of target regions, fixed investment costs of the target regions, maximum equal annual investment costs, a discount rate, wind farm capacity data, photovoltaic power station capacity data, installed capacity proportion data, a capacity-power ratio of energy storage power stations in the target regions, a set of thermal power units in the target regions, a unit penalty cost of the target regions, start-stop cost data of the thermal power units in the target regions, maximum output of a unit capacity of the wind farms and maximum output of a unit capacity of the photovoltaic power stations in the target regions, installed capacity of the thermal power units in the target regions, charge-discharge efficiency of the energy storage power stations in the target regions, ramping rate of the thermal power units in the target regions, tie-line power data of the target regions, load demand of the target regions, and a target scheduling period.

[0164] The model construction module is configured to, according to the acquired data, take the wind farm planning capacity, the photovoltaic power station planning capacity, and the energy storage power station configuration capacity in the target regions as decision variables, take the sum of the equal annual investment costs of the wind farms, the equal annual investment costs of the photovoltaic power stations, and the equal annual investment costs of the energy storage power stations as the target, and construct an upper planning model in a wind-solar-storage joint planning model; and according to the acquired data, take the sum of the start-stop cost, the penalty cost, and the fuel cost of the thermal power units in the target regions as the target, and construct a lower planning model in the wind-solar-storage joint optimization planning model.

[0165] The constraint construction module is configured to, according to the acquired data, construct upper constraints and lower constraints.

[0166] The configuration scheme determination module is configured to, under the constructed constraints, combine the typical scenario set, and coordinately solve the wind-solar-storage joint optimization planning model until the upper planning model and the lower planning model are coordinated, so as to obtain a final wind-solar-storage configuration scheme of the target regions.

[0167] The configuration module is configured to, according to the final wind-solar-storage configuration scheme, configure the wind farms, the photovoltaic power stations, and the energy storage power stations in the target regions; and the final wind-solar-storage configuration scheme comprises the wind farm planning capacity, the photovoltaic power station planning capacity, and the energy storage power station configuration capacity in the target regions.

[0168] In a preferred embodiment, under the constructed constraints, the wind, solar and storage joint optimization planning model is coordinated and solved in combination with the typical scenario set until the upper-level planning model and the lower-level planning model are coordinated to obtain the final wind, solar and storage configuration plan for the target area, including:

[0169] Inputting the typical scenario set into the wind-solar-storage joint optimization planning model;

[0170] Repeat the coordination iteration operation until the upper-level planning model and the lower-level planning model reach coordination, and output the final wind-solar-storage configuration plan for the target area;

[0171] The coordinated iterative operation includes:

[0172] The current decision variables of the upper-level planning model are transferred to the lower-level planning model, so that the lower-level planning model solves the updated wind farm output, photovoltaic power station output, and thermal power unit output in each typical scenario of the target area according to the current decision variables and the lower-level constraints;

[0173] According to the output of the wind farm, photovoltaic power station and thermal power unit in each typical scenario of the updated target area, the updated annual utilization hours are calculated, and it is determined whether the difference between the updated annual utilization hours and the corresponding annual utilization hours before the update is less than the corresponding preset threshold value.

[0174] If so, it is determined that the upper-level planning model and the lower-level planning model have reached coordination, the iterative calculation is stopped, and the updated annual utilization hours are passed to the upper-level planning model, so that the upper-level planning model solves the updated decision variables based on the updated annual utilization hours, and uses the updated decision variables as the final wind-solar-storage configuration plan.

[0175] If not, the updated annual utilization hours are passed to the upper-level planning model, so that the upper-level planning model obtains updated decision variables based on the updated annual utilization hours, and performs the next coordinated iterative operation according to the updated decision variables; wherein, when the lower-level planning model is for the first iterative calculation, the current decision variables of the upper-level planning model are generated by random initialization, and the annual utilization hours before the update are preset empirical values.

[0176] See also Figure 6 , an embodiment of the present application further provides a terminal device, including:

[0177] one or more processors;

[0178] a memory coupled with the processors, storing one or more programs;

[0179] The one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of multi-regional power grid wind-solar-storage coordination planning as described above.

[0180] The processors are configured to control overall operations of the terminal device to complete all or part of the steps of the method of multi-regional power grid wind-solar-storage coordination planning as described above. The memory is configured to store various types of data to support the operations of the terminal device, which may, for example, include instructions for any application or method operating on the terminal device, and application-related data. The memory can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0181] In an exemplary embodiment, the terminal device can be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic elements for executing the method of multi-regional power grid wind-solar-storage coordination planning as described in any of the above embodiments and achieving the technical effects consistent with the above method.

[0182] In another exemplary embodiment, a computer-readable storage medium including a computer program is further provided. When executed by a processor, the computer program implements the steps of the method for coordinated planning of wind, solar, and energy storage for a multi-regional power grid as described in any of the aforementioned embodiments. For example, the computer-readable storage medium may be the aforementioned memory including the computer program. The computer program may be executed by a processor of a terminal device to implement the method for coordinated planning of wind, solar, and energy storage for a multi-regional power grid as described in any of the aforementioned embodiments, thereby achieving the same technical effects as the aforementioned methods.

[0183] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A multi-regional power grid wind, solar and energy storage coordinated planning method, characterized by: include: Obtain a typical scenario set, the total number of target areas, the fixed investment cost of the target area, the maximum equal annual value investment cost, the discount rate, wind farm capacity data, photovoltaic power station capacity data, installed capacity ratio data, capacity-to-power ratio of energy storage power stations in the target area, the set of thermal power units in the target area, the unit penalty cost of the target area, the start-up and shutdown cost data of thermal power units in the target area, the maximum output per unit capacity of wind farms and photovoltaic power stations in the target area, the installed capacity of thermal power units in the target area, the charging and discharging efficiency of energy storage power stations in the target area, the ramp rate of thermal power units in the target area, the interconnection line power data of the target area, the load demand of the target area, and the target scheduling period; Based on the acquired data, the upper-level planning model of the wind, solar, and energy storage joint planning model is constructed, with the planned capacity of wind farms, planned capacity of photovoltaic power stations, and configured capacity of energy storage stations in the target area as decision variables, and the goal of minimizing the sum of the annual investment costs of wind farms, photovoltaic power stations, and energy storage stations. Furthermore, based on the acquired data, the lower-level planning model of the wind, solar, and energy storage joint optimization planning model is constructed, with the goal of minimizing the sum of the start-up and shutdown costs, penalty costs, and fuel costs of thermal power units in the target area. Based on the acquired data, build upper-level constraints and lower-level constraints; Inputting the typical scenario set into the wind-solar-storage joint optimization planning model; Repeat the coordination iteration operation until the upper-level planning model and the lower-level planning model reach coordination, and output the final wind-solar-storage configuration plan for the target area; The coordinated iterative operation includes: The current decision variables of the upper-level planning model are transferred to the lower-level planning model, so that the lower-level planning model solves the updated wind farm output, photovoltaic power station output, and thermal power unit output in each typical scenario of the target area according to the current decision variables and the lower-level constraints; According to the output of the wind farm, photovoltaic power station and thermal power unit in each typical scenario of the updated target area, the updated annual utilization hours are calculated, and it is determined whether the difference between the updated annual utilization hours and the corresponding annual utilization hours before the update is less than the corresponding preset threshold value. If so, it is determined that the upper-level planning model and the lower-level planning model have reached coordination, the iterative calculation is stopped, and the updated annual utilization hours are passed to the upper-level planning model, so that the upper-level planning model solves the updated decision variables based on the updated annual utilization hours, and uses the updated decision variables as the final wind-solar-storage configuration plan. If not, the updated annual utilization hours are passed to the upper-level planning model, so that the upper-level planning model obtains updated decision variables based on the updated annual utilization hours, and performs the next coordinated iteration operation based on the updated decision variables; wherein, when the lower-level planning model is in the initial iterative calculation, the current decision variables of the upper-level planning model are generated by random initialization, and the annual utilization hours before the update are preset empirical values; According to the final wind, solar and storage configuration plan, the wind farm, photovoltaic power station and energy storage power station in the target area are configured; wherein, the final wind, solar and storage configuration plan includes the planned capacity of the wind farm, the planned capacity of the photovoltaic power station and the configured capacity of the energy storage power station in the target area.

2. The multi-regional power grid wind, solar and energy storage coordinated planning method according to claim 1, characterized in that: The upper-level planning model includes: ； Where, is the total equivalent annual value investment cost of the target area; Indicates the target areas; is the total number of target areas; Target area Planned capacity of wind farms within the area; Target area Planned capacity of photovoltaic power stations within the area; is the unit capacity cost of the wind farm; The unit capacity cost of the photovoltaic power station; The unit capacity investment cost of the energy storage power station; Target area The configuration capacity of the energy storage power station within the facility; is the age of the wind farm units; The age of the photovoltaic power station unit; is the life of the energy storage power station unit; ϑ is the discount rate.

3. The multi-regional power grid wind, solar and energy storage coordinated planning method according to claim 1, characterized in that: The lower-level planning model includes: ； ； ； ； Where, is the total operating cost; Target area The thermal power units in Fuel costs in a typical scenario; Indicates the target area Inner Start-up and shutdown costs in a typical scenario; Indicates the target area Inner The penalty cost of curtailing wind and solar power under a typical scenario; J represents the number of typical scenarios; For a typical scenario probability of occurrence; The target scheduling period; Target area A collection of thermal power units; is the quadratic term of the quadratic function of the fuel cost of thermal power unit g; is the linear term of the quadratic function of the fuel cost of thermal power unit g; is the constant term coefficient of the quadratic function of the fuel cost of thermal power unit g; Target area In the In a typical scenario The output of thermal power unit g at a certain moment; Target area Penalty fees for units that curtail wind power; Target area Penalty fees for unit abandonment of light; Target area In the In this scenario The amount of wind curtailment at a given moment; Target area In the In this scenario The amount of abandoned light at each moment; is the startup cost of thermal power unit g; is the shutdown cost of thermal power unit g; is the startup state of thermal power unit g, is the shutdown state of thermal power unit g. When , it indicates that the thermal power unit g is in the starting state; when When , it indicates that the thermal power unit g is in shutdown state.

4. The multi-regional power grid wind, solar and energy storage coordinated planning method according to claim 1, characterized in that: The annual utilization hours include: annual utilization hours of wind farms, annual utilization hours of photovoltaic power stations and annual utilization hours of thermal power units; The updated annual utilization hours are calculated based on the wind farm output, photovoltaic power station output, and thermal power unit output of the updated target area under each typical scenario, including: The updated annual utilization hours of wind farms, photovoltaic power stations, and thermal power units are calculated using the following formula: ； ； ； Where, Target area Annual utilization hours of thermal power units within Target area Annual utilization hours of photovoltaic power stations within the area; Target area Annual utilization hours of wind farms within the area; Target area The installed capacity of the photovoltaic power station in the city; Target area the installed capacity of internal wind farms; Target area The installed capacity of domestic thermal power units; Target area Planned capacity of wind farms within the area; Target area Planned capacity of photovoltaic power stations within the area; For a typical scenario probability of occurrence; Target area In the In this scenario The output of the wind farm at a given moment; Target area In the The output of the photovoltaic power station in each scenario; Target area In the In a typical scenario The output of thermal power unit g at a certain moment.

5. The multi-regional power grid wind, solar and storage coordination planning method according to any one of claims 1 to 4, characterized in that: The upper-level constraints include: installed capacity constraints, wind, solar and energy storage joint investment cost constraints, new energy installed capacity proportion constraints and reserve constraints; the lower-level constraints include: regional power balance constraints, interconnection line power constraints, new energy output constraints, thermal power output constraints and energy storage battery constraints.

6. A multi-regional power grid wind, solar and storage coordination planning device, characterized in that: include: Data acquisition module, model building module, constraint building module, configuration scheme determination module and configuration module; The data acquisition module is used to obtain a typical scenario set, the total number of target areas, the fixed investment cost of the target area, the maximum annual investment cost, the discount rate, wind farm capacity data, photovoltaic power station capacity data, installed capacity ratio data, capacity-to-power ratio of energy storage power stations in the target area, the set of thermal power units in the target area, the unit penalty fee of the target area, the start-up and shutdown cost data of thermal power units in the target area, the maximum output per unit capacity of wind farms and photovoltaic power stations in the target area, the installed capacity of thermal power units in the target area, the charging and discharging efficiency of energy storage power stations in the target area, the ramp rate of thermal power units in the target area, the tie line power data of the target area, the load demand of the target area, and the target scheduling period; The model construction module is used to construct an upper-level planning model in the wind-solar-storage joint planning model based on the acquired data, with the planned capacity of wind farms, the planned capacity of photovoltaic power stations, and the configured capacity of energy storage power stations in the target area as decision variables, and with the goal of minimizing the sum of the annual investment costs of wind farms, photovoltaic power stations, and energy storage power stations; and to construct a lower-level planning model in the wind-solar-storage joint optimization planning model based on the acquired data, with the goal of minimizing the sum of the start-up and shutdown costs, penalty costs, and fuel costs of thermal power units in the target area; The constraint construction module is used to construct upper-level constraints and lower-level constraints based on the acquired data; The configuration scheme determination module is used to input the typical scenario set into the wind-solar-storage joint optimization planning model; Repeat the coordination iteration operation until the upper-level planning model and the lower-level planning model reach coordination, and output the final wind-solar-storage configuration plan for the target area; The coordinated iterative operation includes: The current decision variables of the upper-level planning model are transferred to the lower-level planning model, so that the lower-level planning model solves the updated wind farm output, photovoltaic power station output, and thermal power unit output in each typical scenario of the target area according to the current decision variables and the lower-level constraints; According to the output of the wind farm, photovoltaic power station and thermal power unit in each typical scenario of the updated target area, the updated annual utilization hours are calculated, and it is determined whether the difference between the updated annual utilization hours and the corresponding annual utilization hours before the update is less than the corresponding preset threshold value. If so, it is determined that the upper-level planning model and the lower-level planning model have reached coordination, the iterative calculation is stopped, and the updated annual utilization hours are passed to the upper-level planning model, so that the upper-level planning model solves the updated decision variables based on the updated annual utilization hours, and uses the updated decision variables as the final wind-solar-storage configuration plan. If not, the updated annual utilization hours are passed to the upper-level planning model, so that the upper-level planning model obtains updated decision variables based on the updated annual utilization hours, and performs the next coordinated iteration operation based on the updated decision variables; wherein, when the lower-level planning model is in the initial iterative calculation, the current decision variables of the upper-level planning model are generated by random initialization, and the annual utilization hours before the update are preset empirical values; The configuration module is used to configure the wind farm, photovoltaic power station and energy storage power station in the target area according to the final wind, solar and storage configuration plan; wherein the final wind, solar and storage configuration plan includes the planned capacity of the wind farm, the planned capacity of the photovoltaic power station and the configured capacity of the energy storage power station in the target area.

7. A terminal device, characterized in that: include: one or more processors; a memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the multi-regional power grid wind, solar and storage coordination planning method as described in any one of claims 1-5.

8. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the multi-regional power grid wind, solar and storage coordination planning method as described in any one of claims 1 to 5 is implemented.

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