Multi-regional power grid wind-solar-storage coordinated planning method and device, terminal equipment and storage medium

Abstract

The invention discloses a multi-regional power grid wind and light storage coordinated planning method and device, terminal equipment and a storage medium. According to the method, a wind power plant planning capacity, a photovoltaic power station planning capacity and an energy storage power station configuration capacity in a target region are taken as decision variables; constructing an upper-layer planning model in the wind-solar-storage combined optimization planning model by taking the minimum sum of the equivalent annual investment cost of the wind power plant, the equivalent annual investment cost of the photovoltaic power station and the equivalent annual investment cost of the energy storage power station as a target; taking the minimum sum of the start-stop cost, the punishment cost and the fuel cost of the thermal power generating unit of the target area as a target, and constructing a lower-layer planning model in the wind-solar-storage combined optimization planning model; solving the wind and light storage joint optimization planning model under the constructed constraint to obtain a final wind and light storage configuration scheme; configuring a wind power plant, a photovoltaic power station and an energy storage power station in the target area according to the final wind and light storage configuration scheme; therefore, the complementary characteristics among the areas are fully utilized, and the overall utilization rate of renewable energy sources 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 energy storage in a multi-regional power grid. Background Art

[0002] In recent years, the installed capacity of wind and solar power has continued to increase, and it has become crucial to optimize the configuration of wind, solar and energy storage resources. Wind and solar energy are highly random and intermittent, and their development and utilization are subject to greater constraints from natural conditions. Wind and solar energy are naturally highly complementary in time and space. Compared with independent wind power generation or photovoltaic power generation, wind and solar resources can be better integrated by interconnecting regional power grids and utilizing the load complementarity characteristics. For example, when wind and solar resources in a region are insufficient to generate electricity (such as low solar power generation on cloudy days or low wind power), electricity can be obtained from power grids in other regions to make up for the shortfall; and when wind and solar resources in the region generate excess electricity, 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 resources. However, previous studies usually carried out new energy and energy storage planning based on a certain region or system, and did not consider the joint planning of new energy and energy storage in multiple regions. Summary of the invention

[0003] The present invention provides a method, device, terminal equipment and storage medium for coordinated planning of wind, solar and storage in a multi-regional power grid. The method determines a wind, solar and storage configuration plan based on an established wind, solar 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 thereby improving the overall utilization rate of renewable energy.

[0004] An embodiment of the present invention provides a multi-regional power grid wind, solar and energy storage coordination planning method, 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 investment cost, the discount rate, the wind farm capacity data, the photovoltaic power station capacity data, the installed capacity ratio data, the capacity-power ratio of the energy storage power station 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 the thermal power units in the target area, the maximum output per unit capacity of the wind farm and the maximum output per unit capacity of the photovoltaic power station in the target area, the installed capacity of the thermal power units in the target area, the charging and discharging efficiency of the energy storage power station in the target area, the ramp rate of the 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 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 taken 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, and the upper planning model in the wind-solar-storage joint planning model is constructed; and based on the acquired data, the lower planning model in the wind-solar-storage joint optimization planning model is constructed with the goal of minimizing the sum of the start-stop cost, penalty cost and fuel cost of thermal power units in the target area;

[0007] Construct upper-level constraints and lower-level constraints based on the acquired data;

[0008] Under the constructed constraints, combined with the typical scenario set, the wind-solar-storage joint optimization planning model is coordinated and solved until the upper-level planning model and the lower-level planning model are coordinated to obtain the final wind-solar-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-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-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 are coordinated, 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 wind farm output, photovoltaic power station output and thermal power unit output of the updated target area under each typical scenario according to the current decision variables and the lower-level constraints;

[0015] According to the wind farm output, photovoltaic power station output and thermal power unit output of the updated target area in each typical scenario, 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 are coordinated, 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 solves the updated decision variables based on the updated annual utilization hours, and performs the next coordinated iteration 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] In the formula, 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; θ is the discount rate.

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

[0022]

[0023] In the formula, C oper is the total operating cost; is the fuel cost of the thermal power units 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 power abandonment in the jth typical scenario in the target area k; J represents the number of typical scenarios; ρ j is the occurrence probability 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 the fuel cost of thermal power unit g; b k,g is the first 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 the thermal power unit g; is the output of thermal power unit g at the target area k at the tth moment in the jth typical scenario; is the penalty cost for curtailing k units of wind in the target area; The penalty cost for abandoning k units in the target area; is the wind abandonment volume of target area k at the tth moment in 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 startup state of thermal power unit g, v k,g,j,t is the shutdown state of thermal power unit g. 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 in 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] In the formula, 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 the target area k; is the installed capacity of the PV power station in the 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 PV power station in the target area k; ρ j is the occurrence probability of typical scenario j; is the wind farm output of target area k at the tth moment in 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 in 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 ratio 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 building module, a constraint building 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 equal annual value investment cost, the discount rate, the wind farm capacity data, the photovoltaic power station capacity data, the installed capacity ratio data, the capacity-power ratio of the energy storage power station in the target area, the set of thermal power units in the target area, the unit penalty cost of the target area, the start-stop cost data of the thermal power units in the target area, the maximum output per unit capacity of the wind farm and the maximum output per unit capacity of the photovoltaic power station in the target area, the installed capacity of the thermal power units in the target area, the charging and discharging efficiency of the energy storage power station in the target area, the climbing rate of the 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;

[0032] The model building module is used to build an upper-level planning model in the wind-solar-storage joint planning model based on the acquired data, taking 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 taking the sum of the equal annual investment costs of wind farms, photovoltaic power stations and energy storage power stations as the minimum as the goal; and to build a lower-level planning model in the wind-solar-storage joint optimization planning model based on the acquired data, taking the sum of the start-stop cost, penalty cost and fuel cost of thermal power units in the target area as the minimum as the goal;

[0033] The constraint building module is used to build upper-level constraints and lower-level constraints according to the acquired data;

[0034] The configuration scheme determination module is used to coordinate and solve the wind-solar-storage joint optimization planning model under the constructed constraints 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-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, 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.

[0036] Furthermore, under the constructed constraints, the wind-solar-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-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 and iteration operation until the upper-level planning model and the lower-level planning model are coordinated, 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 wind farm output, photovoltaic power station output and thermal power unit output of the updated target area under each typical scenario according to the current decision variables and the lower-level constraints;

[0041] According to the wind farm output, photovoltaic power station output and thermal power unit output of the updated target area in each typical scenario, 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 are coordinated, 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 solves the updated decision variables based on the updated annual utilization hours, and performs the next coordinated iteration 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 coordination 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 multi-regional power grid wind, photovoltaic and storage coordination planning method, device, terminal equipment and storage medium. The method first obtains basic data, and then constructs upper-level constraints and lower-level constraints, so as to ensure that the results obtained in the subsequent model solving will not exceed the range;

[0051] Secondly, taking 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 taking the minimization of the sum of the equal annual investment costs of wind farms, photovoltaic power stations and energy storage power stations as the goal, the upper planning model in the wind-solar-storage joint planning model is constructed, and taking the minimization of the sum of the start-stop cost, penalty cost and fuel cost of thermal power units in the target area as the goal, the lower planning model in the wind-solar-storage joint optimization planning model is constructed. Therefore, the constructed wind-solar-storage joint optimization planning model comprehensively considers multiple factors such as power system construction, operation cost, resource endowment, etc. The established wind-solar-storage joint optimization planning model effectively coordinates the power output between different regions, makes full use of 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, and 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 drawings required for use in the implementation manner will be briefly introduced below. Obviously, the drawings described below are only some implementation manners of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0054] Figure 1 It is a flowchart of a multi-regional power grid wind, solar and storage coordination planning method provided by a certain embodiment of the present application;

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

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

[0057] Figure 4 is an error curve diagram of a virtual solution and a real solution of coupling variables in a solution process of an augmented Lagrangian alternating direction inexact Newton method provided in a certain embodiment of the present application;

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

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

[0060] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are 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 creative work 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 technicians in the technical field 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" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0062] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0063] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood 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 the present application, the term "and / or" is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after 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 clearly specified and limited, technical terms such as "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the internal connection of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0067] See also Figure 1 , is a flow chart of a method for wind, solar and storage coordination planning of 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 equal 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-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-stop 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;

[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 proportion of the configuration capacity of the energy storage power station in the target area to the installed capacity of new energy, the capacity-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 abandonment penalty fee and the unit solar abandonment penalty fee of 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 the target areas.

[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 taken 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, to build the upper planning model in the wind-solar-storage joint planning model; and based on the acquired data, the sum of the start-stop cost, penalty cost and fuel cost of thermal power units in the target area is minimized as the goal, to build the lower planning model in the wind-solar-storage joint optimization planning model;

[0073] Schematically, the multi-regional 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 the 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 given planning capacity after solving the upper level problem, so as to determine the operating status of the power sources (i.e. wind farms, thermal power units and photovoltaic power stations) in each target area, and return these operating states 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] In the formula, 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; θ is the discount rate.

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

[0078]

[0079] In the formula, C oper is the total operating cost; is the fuel cost of the thermal power units 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 power abandonment in the jth typical scenario in the target area k; J represents the number of typical scenarios; ρ j is the occurrence probability 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 the fuel cost of thermal power unit g; b k,g is the first 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 the thermal power unit g; is the output of thermal power unit g at the target area k at the tth moment in the jth typical scenario; is the penalty cost for curtailing k units of wind in the target area; The penalty cost for abandoning k units in the target area; is the wind abandonment volume of target area k at the tth moment in 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 startup state of thermal power unit g, v k,g,j,t is the shutdown state of thermal power unit g. 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 proportion 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] In the formula, The upper bound of wind farm capacity construction is established for the kth target area; The upper bound of the PV power plant construction capacity for the kth target area; The upper limit ratio of energy storage configuration capacity to new energy installed capacity for the kth target area; is the installed capacity of wind farms in the kth target area; is the installed capacity of the PV power plant 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 in 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 the unknown quantity to be determined, and 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] In the formula, 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] In the formula, 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 installed capacity proportion; is the installed capacity of the PV power station in the target area k; is the installed capacity of wind farms in target area k.

[0092] Specifically, the backup constraints include:

[0093]

[0094] In the formula, 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 factor; 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 constraint includes:

[0097]

[0098] In the formula, 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 Output of photovoltaic power station in target area k under typical scenario j Output of 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 demanded.

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

[0101]

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

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

[0104]

[0105] In the formula, 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] In the formula, is the upward climbing rate of the unit; 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] In the formula, E k,j,t is the amount of electricity; is the charging power; are the discharge power respectively; For charging efficiency; is the discharge efficiency; α k M is the lower limit coefficient of power; e is the big M method coefficient; 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 layer constraints.

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

[0115] In a preferred embodiment, under the constructed constraints, the wind-solar-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-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 and iteration operation until the upper-level planning model and the lower-level planning model are coordinated, 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 wind farm output, photovoltaic power station output and thermal power unit output of the updated target area under each typical scenario according to the current decision variables and the lower-level constraints;

[0120] According to the wind farm output, photovoltaic power station output and thermal power unit output of the updated target area in each typical scenario, 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 are coordinated, 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 transmitted 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 according to the updated decision variables; wherein, when the lower-level planning model is 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;

[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 it is the first time to perform iterative calculation, the upper-level planning model will randomly initialize and generate initial decision variables, and 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] When the coordinated iteration operation is performed for the first time, the upper-level planning model inputs the 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 through the preset augmented Lagrangian alternating direction inexact Newton method, thereby solving the wind farm output, photovoltaic power station output and thermal power unit output in the target area under each typical scenario;

[0126] According to the output of wind farms, photovoltaic power stations and thermal power units in each typical scenario, the updated annual utilization hours of wind farms, the updated annual utilization hours of photovoltaic power stations and the updated annual utilization hours of thermal power units are calculated;

[0127] Subtract the annual utilization hours of the wind farm after the update from the annual utilization hours of the wind farm before the update to obtain a first difference; wherein, in the first iterative calculation, the annual utilization hours of the wind farm before the update is the empirical value input in advance, and in the non-first iterative calculation, the annual utilization hours of the wind farm before the update is the annual utilization hours of the wind farm solved in the previous iterative calculation;

[0128] The annual utilization hours of the photovoltaic power station after the update are subtracted from the annual utilization hours of the photovoltaic power station before the update to obtain a second difference value; wherein, in the first iterative calculation, the annual utilization hours of the photovoltaic power station before the update is the empirical value input in advance, and 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 annual utilization hours of the thermal power unit after the update are subtracted from the annual utilization hours of the thermal power unit before the update to obtain a third difference value; wherein, in the first iterative calculation, the annual utilization hours of the thermal power unit before the update is the empirical value input in advance, and 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 according to 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 in 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] In the formula, 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 the target area k; is the installed capacity of the PV power station in the 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 PV power station in the target area k; ρ j is the occurrence probability of typical scenario j; is the wind farm output of target area k at the tth moment in 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 in the jth typical scenario.

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

[0140] Specifically, the augmented Lagrangian alternating direction inexact Newton method can ensure that the problem converges after a sufficient number of iterations, and has great advantages in distributed solving. In each iteration, the distributed individuals solve their own decoupling problems, and then the computing center solves a centralized optimization problem. Since the lower-level planning models of different target areas in each typical scenario are coupled, the augmented Lagrangian alternating direction inexact Newton method can be used for distributed solving of each typical scenario.

[0141] Specifically, the lower-level constraint (15) in the lower-level planning model is converted into an equality constraint of coupled variables, denoted 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 uncoupled variables, denoted by p k (x k )=0 and q k (x k )≤0.

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

[0144]

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

[0146] is the coupled decision variable, including the tie line power constraint 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, continue to step S404;

[0152]

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

[0154] Step S405, solve the following quadratic programming:

[0155]

[0156] In the formula, 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, actual parameters from a power grid in a certain region of China are used for verification. The power grid consists of three interconnected subgrids. Figure 2 As shown. It can be seen that the development of new energy sources such as wind and light is mainly concentrated in area 3. This planning result is consistent with the distribution of natural resources in each sub-region. The expected reduction in renewable energy is defined as the sum of the energy reduced in each scenario multiplied by the probability of the scenario. Figure 3 The expected curtailment of renewable energy systems (RESs) in different regions and the corresponding curtailment ratios are shown. The curtailment ratio in each sub-region is very low, remaining below 5%. This indicates that the collaborative planning method proposed in this application is helpful in reducing the curtailment of renewable energy. Figure 4This is the error curve between the virtual solution and the real solution of the coupling variables in the augmented Lagrangian alternating direction inexact Newton method. It can be seen that with the increase in the number of iterations, the error between the virtual solution and the real solution gradually approaches zero, and when the error between the two is less than a certain value, the iteration is terminated.

[0162] See also Figure 5 , is a multi-regional power grid wind, photovoltaic and storage coordination planning device provided by an embodiment of the present invention, comprising: 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 used to 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, the wind farm capacity data, the photovoltaic power station capacity data, the installed capacity ratio data, the capacity-power ratio of the energy storage power station in the target area, the set of thermal power units in the target area, the unit penalty cost of the target area, the start-stop cost data of the thermal power units in the target area, the maximum output per unit capacity of the wind farm and the maximum output per unit capacity of the photovoltaic power station in the target area, the installed capacity of the thermal power units in the target area, the charging and discharging efficiency of the energy storage power station in the target area, the climbing rate of the 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;

[0164] The model building module is used to build an upper-level planning model in the wind-solar-storage joint planning model based on the acquired data, taking 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 taking the sum of the equal annual investment costs of wind farms, photovoltaic power stations and energy storage power stations as the minimum as the goal; and to build a lower-level planning model in the wind-solar-storage joint optimization planning model based on the acquired data, taking the sum of the start-stop cost, penalty cost and fuel cost of thermal power units in the target area as the minimum as the goal;

[0165] The constraint building module is used to build upper-level constraints and lower-level constraints according to the acquired data;

[0166] The configuration scheme determination module is used to coordinate and solve the wind-solar-storage joint optimization planning model under the constructed constraints 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-storage configuration scheme for the target area;

[0167] 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, 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.

[0168] In a preferred embodiment, under the constructed constraints, the wind-solar-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-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 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;

[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 wind farm output, photovoltaic power station output and thermal power unit output of the updated target area under each typical scenario according to the current decision variables and the lower-level constraints;

[0173] According to the wind farm output, photovoltaic power station output and thermal power unit output of the updated target area in each typical scenario, 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 are coordinated, 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 solves the updated decision variables based on the updated annual utilization hours, and performs the next coordinated iteration 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 , a certain embodiment of the present application further provides a terminal device, including:

[0177] one or more processors;

[0178] A memory, coupled to the processor, for storing one or more programs;

[0179] 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 above.

[0180] The processor is used to control the overall operation of the terminal device to complete all or part of the steps of the above-mentioned multi-regional power grid wind, solar and storage coordination planning method. The memory is used to store various types of data to support the operation of the terminal device, and these data may include, for example, instructions for any application or method used to operate on the terminal device, as well as 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 (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Read-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, disk or optical disk.

[0181] In an exemplary embodiment, the terminal device can be implemented by one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the multi-regional power grid wind, solar and storage coordination planning method as described in any of the above embodiments, and achieve the same technical effect as the above method.

[0182] In another exemplary embodiment, a computer-readable storage medium including a computer program is also provided, and when the computer program is executed by a processor, the steps of the multi-regional power grid wind, photovoltaic and storage coordinated planning method as described in any of the above embodiments are implemented. For example, the computer-readable storage medium can be the above-mentioned memory including the computer program, and the above-mentioned computer program can be executed by the processor of the terminal device to complete the multi-regional power grid wind, photovoltaic and storage coordinated planning method as described in any of the above embodiments, and achieve the same technical effect as the above method.

[0183] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle 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 storage coordination planning method, characterized in that: include: Obtain a typical scenario set, the total number of target areas, the fixed investment cost of the target area, the maximum equal annual investment cost, the discount rate, the wind farm capacity data, the photovoltaic power station capacity data, the installed capacity ratio data, the capacity-power ratio of the energy storage power station 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 the thermal power units in the target area, the maximum output per unit capacity of the wind farm and the maximum output per unit capacity of the photovoltaic power station in the target area, the installed capacity of the thermal power units in the target area, the charging and discharging efficiency of the energy storage power station in the target area, the ramp rate of the 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 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 taken 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, and the upper planning model in the wind-solar-storage joint planning model is constructed; and based on the acquired data, the lower planning model in the wind-solar-storage joint optimization planning model is constructed with the goal of minimizing the sum of the start-stop cost, penalty cost and fuel cost of thermal power units in the target area; Construct upper-level constraints and lower-level constraints based on the acquired data; Under the constructed constraints, combined with the typical scenario set, the wind-solar-storage joint optimization planning model is coordinated and solved until the upper-level planning model and the lower-level planning model are coordinated to obtain the final wind-solar-storage configuration plan for the target area; 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 coordination planning method according to claim 1 is characterized in that: Under the constructed constraints, in combination with the typical scenario set, the wind-solar-storage joint optimization planning model is coordinated and solved until the upper-level planning model and the lower-level planning model are coordinated to obtain the final wind-solar-storage configuration plan for the target area, including: Inputting the typical scenario set into the wind-solar-storage joint optimization planning model; 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; 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 wind farm output, photovoltaic power station output and thermal power unit output of the updated target area under each typical scenario according to the current decision variables and the lower-level constraints; According to the wind farm output, photovoltaic power station output and thermal power unit output of the updated target area in each typical scenario, 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 are coordinated, 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 solves the updated decision variables based on the updated annual utilization hours, and performs the next coordinated iteration 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.

3. The multi-regional power grid wind, solar and energy storage coordination planning method according to claim 2 is characterized in that: The upper-level planning model includes: In the formula, 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; θ is the discount rate.

4. The multi-regional power grid wind, solar and energy storage coordination planning method according to claim 2 is characterized in that: The lower-level planning model comprises: In the formula, C oper is the total operating cost; is the fuel cost of the thermal power units 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 power abandonment in the jth typical scenario in the target area k; J represents the number of typical scenarios; ρ j is the occurrence probability 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 the fuel cost of thermal power unit g; b k,g is the first 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 the thermal power unit g; is the output of thermal power unit g at the target area k at the tth moment in the jth typical scenario; is the penalty cost for curtailing k units of wind in the target area; The penalty cost for abandoning k units in the target area; is the wind abandonment volume of target area k at the tth moment in 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 startup state of thermal power unit g, v k,g,j,t is the shutdown state of thermal power unit g. 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.

5. The multi-regional power grid wind, solar and storage coordination planning method according to claim 4 is 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 in 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: In the formula, 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 the target area k; is the installed capacity of the PV power station in the 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 PV power station in the target area k; ρ j is the occurrence probability of typical scenario j; is the wind farm output of target area k at the tth moment in 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 in the jth typical scenario.

6. The multi-regional power grid wind, solar and storage coordination planning method according to any one of claims 1 to 5, characterized in that: The upper-level 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-level constraints include: regional power balance constraints, interconnection line power constraints, new energy output constraints, thermal power output constraints and energy storage battery constraints.

7. 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 equal annual value investment cost, the discount rate, the wind farm capacity data, the photovoltaic power station capacity data, the installed capacity ratio data, the capacity-power ratio of the energy storage power station in the target area, the set of thermal power units in the target area, the unit penalty cost of the target area, the start-stop cost data of the thermal power units in the target area, the maximum output per unit capacity of the wind farm and the maximum output per unit capacity of the photovoltaic power station in the target area, the installed capacity of the thermal power units in the target area, the charging and discharging efficiency of the energy storage power station in the target area, the climbing rate of the 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; The model building module is used to build an upper-level planning model in the wind-solar-storage joint planning model based on the acquired data, taking 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 taking the sum of the equal annual investment costs of wind farms, photovoltaic power stations and energy storage power stations as the minimum as the goal; and to build a lower-level planning model in the wind-solar-storage joint optimization planning model based on the acquired data, taking the sum of the start-stop cost, penalty cost and fuel cost of thermal power units in the target area as the minimum as the goal; The constraint building module is used to build upper-level constraints and lower-level constraints according to the acquired data; The configuration scheme determination module is used to coordinate and solve the wind-solar-storage joint optimization planning model under the constructed constraints 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-storage configuration scheme for the target area; 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, 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.

8. The multi-regional power grid wind, solar and storage coordination planning device according to claim 7, characterized in that: Under the constructed constraints, in combination with the typical scenario set, the wind-solar-storage joint optimization planning model is coordinated and solved until the upper-level planning model and the lower-level planning model are coordinated to obtain the final wind-solar-storage configuration plan for the target area, including: Inputting the typical scenario set into the wind-solar-storage joint optimization planning model; 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; 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 wind farm output, photovoltaic power station output and thermal power unit output of the updated target area under each typical scenario according to the current decision variables and the lower-level constraints; According to the wind farm output, photovoltaic power station output and thermal power unit output of the updated target area in each typical scenario, 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 are coordinated, 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 solves the updated decision variables based on the updated annual utilization hours, and performs the next coordinated iteration 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.

9. 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 1-6.

10. 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 1-6 is implemented.

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