A low-carbon operation method for virtual pumped storage power plants considering spatiotemporal coupling characteristics

Abstract

The present invention discloses a low-carbon operation method of a virtual pumped-storage power plant considering the spatiotemporal coupling characteristics. Based on the matrix of various hydropower station types aggregated by the virtual power plant, the water area association matrix and the water flow time-delay matrix, an operation model of the spatiotemporal coupling characteristics of the virtual pumped-storage power plant is constructed; based on the coordination of the changes in the carbon emissions per unit of electricity between the virtual power plant and the external power grid, a constraint method of the low-carbon operation model of the virtual power plant that aggregates different types of hydropower resources is constructed, so as to achieve a balance between pumped storage supply and demand internally, participate in system regulation externally, perform operation steps, and realize the self-balancing low-carbon coordination of the virtual power plant. The present invention solves the problem that the current power market lacks flexible aggregation of resources that can adapt to virtual power plants. By adjusting the weight of the operation mode adjustment, the virtual pumped-storage power plant can be flexibly adapted to different operation scenarios, effectively solving the problem of optimal configuration of power data, achieving a balance between supply and demand in the power market, and improving the effective utilization rate, while realizing the social value of energy conservation and carbon reduction.

Description

Technical Field

[0001] The present invention relates to the field of electric power systems, and more specifically, to the technical field of hydropower system operation optimization. Background Art

[0002] With the proposal of the "carbon peak and carbon neutrality" goals and the need to build a new power system, the pace of clean and low-carbon transformation of energy and electricity has accelerated, and decentralized unit-based entity operations have become a new form of business for the development of new power systems. In this context, virtual power plants, as a system form with flexible resource allocation in the construction of new power systems, can achieve flexible operation in a "quasi-centralized" and "quasi-real-time" manner, promoting power generation and consumption balance internally and participating in system regulation externally, through the aggregation and coordinated control of resources of small non-centralized power plants with low voltage levels and small installed capacities in traditional power systems.

[0003] At present, there are a large number of small hydropower stations, which are widely distributed and have the physical conditions to aggregate into virtual power plants. However, at this stage, there is a lack of methods to coordinate the output of various power stations considering low-carbon operation, which affects the effective utilization of power resources. The optimization of the coupling relationship between power stations to achieve a balance between supply and demand has become an urgent problem to be solved in the virtual pumped storage power plant system. Summary of the invention

[0004] In view of the shortcomings of the existing technology, the present invention ensures the coordinated output of each power station by considering the optimization of the time and space coupling characteristics between hydropower stations, thereby ensuring the balance of supply and demand of the virtual pumped storage power plant, thereby achieving effective utilization of power resources and realizing the social benefits of energy conservation and carbon reduction.

[0005] The present invention proposes a technical solution to the optimization problem of the spatiotemporal coupling characteristics of electric power resources. The low-carbon operation method of a virtual pumped-storage power plant taking into account the spatiotemporal coupling characteristics between hydropower stations is helpful to improve resource utilization and has practical application value in energy conservation and carbon reduction.

[0006] The technical solution adopted by the present invention to solve its technical problems is: a low-carbon operation method of a virtual pumped-storage power plant considering the time-space coupling characteristics, comprising the following steps: Step 1, based on the type matrix of each hydropower station aggregated by the virtual power plant, the water area association matrix and the water flow time-delay matrix, an operation model of the time-space coupling characteristics of the virtual pumped-storage power plant is constructed; Step 2, based on the coordination of the changes in the carbon emissions per unit electricity of the virtual power plant and the external power grid, a constraint method for constructing a low-carbon operation model of a virtual power plant that aggregates different types of hydropower resources is constructed, internally achieving a balance between pumped storage supply and demand, externally participating in system regulation, and performing operation steps to achieve self-balancing and low-carbon coordination of the virtual power plant.

[0007] In the above technical solution, further, the operation model constructed in step 1 is composed of the hydropower station type, the coupling relationship between the hydropower stations, and the time lag relationship of the flow, respectively represented by N-dimensional vectors M1 , N×N dimensional matrix M 2 and N×N dimensional matrix M 3 Characterization, hydropower station type vector M 1 ,

[0008] M 1 =[h i ] N×1

[0009] Where M 1 It represents the types of N hydropower stations, and its element h i =0,h i =1 (i=1,2,…,N) respectively indicates that the hydropower station i is a run-of-river hydropower station and a reservoir-adjustable hydropower station.

[0010] Water area correlation matrix M 2 ,

[0011] M 2 =[c ij ] N×N

[0012] Where M 2 It represents the flow direction of N hydropower stations, and its element c ij =0, c ij =1, c ij =-1 respectively indicates that there is no hydraulic connection between hydropower stations i and j, the water flow direction is from hydropower station j to hydropower station i, and the water flow direction is from hydropower station i to hydropower station j.

[0013] Water flow delay matrix M 3 ,

[0014] M 3 =[d ij ] N×N

[0015] Where M 3 It represents the water flow time lag of N hydropower stations, and its element d ij It indicates the water flow time lag when hydropower station j transmits flow to hydropower station i, and the time lag unit is the scheduling period.

[0016] Furthermore, the construction and operation model is:

[0017]

[0018] In the formula, f represents the equivalent carbon emission intensity of the power input from the external power grid to the virtual power plant. C_in and the equivalent carbon reduction of the virtual power plant outputting power to the external grid f C_out The difference reflects the low-carbon nature of virtual power plant operation; and They represent the power input and output of the virtual power plant at the dispatching time t respectively; It represents the equivalent carbon emissions of a unit of electricity input or output by the virtual power plant at the dispatch time t; α represents the adjustment weight of the virtual power plant operation mode, α∈[1,+∞), α=1, α=+∞ respectively represent the virtual power plant adopts the operation mode that fully considers the coordination with the external power grid carbon emission intensity and the virtual power plant considers the coordination with the external carbon emission intensity on the basis of self-balancing. The larger α is, the more the virtual power plant attaches importance to self-balancing and low-carbon operation; Δt represents the dispatch time interval; T represents the dispatch cycle.

[0019] Furthermore, the operation model includes a power balance constraint method,

[0020]

[0021] In the formula, represents the power generation of the virtual power plant at the dispatching time t of hydropower station i; Represents the on-site load power of the virtual power plant.

[0022] Furthermore, the operation model includes a hydropower station water-to-electricity conversion relationship constraint method,

[0023]

[0024] Where g represents the acceleration due to gravity; represents the power generation efficiency of hydropower station i; and They represent the net water head and power flow of hydropower station i at time t. Considering that the water head does not change much within a day, we can set is a constant.

[0025] Furthermore, the operation model includes a method for constraining the spatiotemporal coupling characteristics of a hydropower station.

[0026]

[0027] In the formula, It represents the associated flow of hydropower station i at dispatching time t considering the hydraulic correlation and water flow time lag between hydropower stations; represents the discharge flow of hydropower station j at dispatching time t before reaching the initial water flow delay; H and S t They represent the set of all hydropower stations and the set of hydropower stations that reach the initial water flow delay at the dispatching time t; Δt ij It represents the magnitude of the water flow time lag between hydropower stations i and j considering the hydraulic connection topology of the hydropower stations; and They respectively represent the discharge flow, power generation flow and abandoned water flow of the hydropower station at the scheduling time t.

[0028] Furthermore, the operation model includes a hydropower station flow balance relationship constraint method,

[0029]

[0030] In the formula, h i represents the i-th hydropower station, and They respectively represent the reservoir capacity, natural water inflow and abandoned water flow of hydropower station i at the scheduling time t.

[0031] Furthermore, the operation model includes a hydropower station power generation constraint method,

[0032]

[0033] In the formula, and They represent the minimum and maximum power generation of hydropower station i respectively.

[0034] Furthermore, the operation model includes a method for constraining the upper and lower limits of the reservoir capacity of a hydropower station.

[0035]

[0036] In the formula, and They represent the lower and upper limits of the storage capacity of hydropower station i respectively.

[0037] Furthermore, the operation steps include:

[0038] Step 1: Input the hydropower station information, load and external grid unit electricity carbon emission curve and construct the virtual power plant spatiotemporal coupling characteristic matrix.

[0039] Step 2: Construction of virtual power plant operation constraints considering the spatiotemporal coupling characteristics of hydropower stations.

[0040] Step 3: Determine the adjustment weight α of the virtual power plant operation mode according to the operation requirements, construct the objective function of minimizing the equivalent carbon emissions of the virtual power plant output and input power, and form a low-carbon operation model of the virtual pumped storage power plant considering the spatiotemporal coupling characteristics.

[0041] Step 4: Solve the low-carbon operation model of the virtual pumped storage power plant considering the time-space coupling characteristics.

[0042] The present invention aims at the optimization problem of the spatiotemporal coupling characteristics of electric power resources, uses the processing method of topological information, and utilizes the matrix form to store the information of the type of hydropower station, the coupling relationship between hydropower stations, and the time-lag relationship of the flow rate, and expresses the spatiotemporal characteristics between hydropower stations in a concise form for storing correction information, which is convenient for calculation optimization and ensures the coordinated output of each power station, thereby ensuring the balance of supply and demand of the virtual pumped-storage power plant, thereby achieving the effective utilization rate of electric power resources and realizing the social benefits of energy conservation and carbon reduction.

[0043] The present invention proposes a low-carbon operation method for a virtual pumped-storage power plant that takes into account the spatiotemporal coupling characteristics between hydropower stations, constructs a low-carbon operation model of a virtual power plant that aggregates different types of hydropower resources, achieves a pumped-storage supply and demand balance internally, participates in system regulation externally, and realizes self-balancing and low-carbon coordination of the virtual power plant, which helps to improve resource utilization and has practical application value in energy conservation and carbon reduction.

[0044] The present invention has the following advantages: Since the current electricity market lacks a unified modeling method that can adapt to the flexible aggregation of virtual power plant resources, the present invention uses the processing method of topological information in power network theory to construct a unified model of various operating modes of virtual pumped-storage power plants, and through the adjustment of operating mode adjustment weights, the virtual pumped-storage power plant can flexibly adapt to different operating scenarios, thereby effectively solving the problem of optimal configuration of power data and achieving supply and demand balance in the electricity market. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The drawings described herein are used to provide a further understanding of the present invention and should not constitute an improper limitation of the present invention. The terms in the figures are only used to more conveniently describe and explain the present invention and should not be understood as any additional limitation.

[0046] Figure 1 This is a topological map of the watershed of a hydropower station.

[0047] Figure 2 Schematic diagram of the process of modeling the spatiotemporal coupling characteristics of a virtual power plant.

[0048] Figure 3 Parameters of hydropower plants with different aggregation types.

[0049] Figure 4 , Figure 5 , Figure 6 , Figure 7 and Figure 8 , which is a schematic diagram of various typical loads and carbon emissions per unit of electricity in different seasons.

[0050] Fig. 9 This is a comparison chart of the results of Class A aggregated virtual power plants using different operating modes in different seasons, winter operating mode 1.

[0051] Fig.10This is a comparison chart of the results of Class A aggregated virtual power plants using different operating modes in different seasons, operating mode 1 in spring and autumn.

[0052] Fig.11 This is a comparison chart of the results of Class A aggregated virtual power plants using different operating modes in different seasons, summer operating mode 1.

[0053] Fig.12 This is a comparison chart of the results of Class A aggregated virtual power plants using different operating modes in different seasons, winter operating mode 2.

[0054] Fig.13 This is a comparison chart of the results of Class A aggregated virtual power plants using different operating modes in different seasons, operating mode 2 in spring and autumn.

[0055] Fig.14 This is a comparison chart of the results of different operation modes of Class A aggregated virtual power plants in different seasons, summer operation mode 2.

[0056] Fig.15 This is a comparison chart of the results of different operation modes of Class B aggregated virtual power plants in different seasons, winter operation mode 1.

[0057] Fig.16 This is a comparison chart of the results of different operating modes of Class B aggregated virtual power plants in different seasons, operating mode 1 in spring and autumn.

[0058] Fig.17 This is a comparison chart of the results of different operation modes of Class B aggregated virtual power plants in different seasons, summer operation mode 1.

[0059] Fig.18 This is a comparison chart of the results of different operating modes of Class B aggregated virtual power plants in different seasons, winter operating mode 2.

[0060] Fig.19 This is a comparison chart of the results of different operation modes of Class B aggregated virtual power plants in different seasons, operation mode 2 in spring and autumn.

[0061] Fig. 20 This is a comparison chart of the results of different operating modes of Class B aggregated virtual power plants in different seasons, summer operating mode 2.

[0062] Fig.21 This is a graph showing the equivalent carbon emissions results for the operation of virtual power plants with different aggregation types. DETAILED DESCRIPTION

[0063] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments so that the invention can be understood more clearly and intuitively.

[0064] The drawings are only for illustrative purposes and cannot be construed as limiting the present invention. It is understandable to those skilled in the art that some well-known structures and their descriptions in the drawings may be omitted. The positional relationships described in the drawings are only for illustrative purposes and cannot be construed as limiting the present invention.

[0065] Embodiment: An improved master-slave control method for multiple parallel energy storage converters. This embodiment constructs a spatiotemporal coupling characteristic model of a virtual pumped storage power plant based on a node association matrix, flexibly adjusts the matrix assignment of the storage virtual power plant operation characteristics according to different virtual power plant aggregation conditions, and realizes efficient storage and convenient call of information. The specific method is as follows:

[0066] Virtual power plants flexibly aggregate different types of hydropower stations for joint scheduling. Different types of hydropower stations have different operating models, and the spatiotemporal coupling characteristics of water flow between hydropower stations with hydraulic connections affect the actual operating conditions of each hydropower station. Therefore, it is necessary to establish models for the types of hydropower stations, whether there is a coupling relationship between hydropower stations, and the time-lag relationship of flow. However, overly complex physical models will increase the difficulty of aggregating and operating diverse resources in virtual power plants. Drawing on the processing method of topological information in power network theory, this embodiment uses N-dimensional vectors M to represent the types of hydropower stations, whether there is a coupling relationship between hydropower stations, and the time-lag relationship of flow. 1 , N×N dimensional matrix M 2 and N×N dimensional matrix M 3 express.

[0067] like Figure 1 As shown, it is the topology of a hydropower station basin, which consists of 1 main stream and 3 tributaries, where power stations 1-5 are built on the main stream, and power stations 6-8, 9-10 and 11 are built on the 3 tributaries in the figure respectively. The matrix assignments that characterize the spatiotemporal coupling characteristics of the virtual power plant defined in this embodiment are as follows:

[0068] M 1 =[10010010010] T

[0069]

[0070]

[0071] This embodiment constructs a low-carbon operation model of a virtual pumped storage power plant that takes into account the spatiotemporal coupling characteristics, and then proposes a revised low-carbon operation model of a virtual pumped storage power plant that needs to be coordinated with the carbon emission intensity of each dispatching period of the external power grid. The specific method is as follows:

[0072] Considering the spatiotemporal coupling characteristics, the upper and lower hydropower stations establish a collaborative operation model according to the aggregated hydropower station types, and utilize the hydraulic spatiotemporal coupling relationship between multiple stations to expand their operating output range, improve their controllability, and achieve the effect of a virtual pumped-storage power station.

[0073] The goal of the virtual pumped-storage power plant that takes into account the time-space coupling characteristics is to achieve a balance between power generation and electricity consumption as much as possible under the new power system, provide clean and low-carbon hydropower for the loads within the power supply range, and reduce dependence on external electricity with high carbon emission intensity; externally, it participates in system regulation, improves the ability to export hydropower during periods when the carbon emission intensity per unit electricity of the external power grid is high, and minimizes the equivalent carbon emissions in the power supply area.

[0074] Based on this, the objective function of the low-carbon operation model of the virtual pumped storage power plant considering the time-space coupling characteristics is constructed as follows.

[0075]

[0076] In the formula, f represents the equivalent carbon emission intensity of the power input from the external power grid to the virtual power plant. C_in and the equivalent carbon reduction of the virtual power plant outputting power to the external grid f C_out The difference reflects the low-carbon nature of virtual power plant operation; and They represent the power input and output of the virtual power plant at the dispatching time t respectively; It represents the equivalent carbon emissions of a unit of electricity input or output by the virtual power plant at the dispatch time t; α represents the adjustment weight of the virtual power plant operation mode, α∈[1,+∞), α=1, α=+∞ respectively represent the virtual power plant adopts the operation mode that fully considers the coordination with the external power grid carbon emission intensity and the virtual power plant considers the coordination with the external carbon emission intensity on the basis of self-balancing. The larger α is, the more the virtual power plant attaches importance to self-balancing and low-carbon operation; Δt represents the dispatch time interval; T represents the dispatch cycle.

[0077] A virtual power plant that considers the spatiotemporal coupling characteristics jointly dispatches multiple hydropower stations. By utilizing the coupling relationship between the flows between the upper and lower hydropower stations, the run-of-river hydropower stations and the hydropower stations with adjustable reservoir capacity share the latter's reservoir capacity, thereby achieving coordination between the hydropower station output and the carbon emission intensity of the external power grid. The constraints considered in the model include: power balance constraints, hydropower station hydropower conversion relationship constraints, hydropower station spatiotemporal coupling characteristics constraints, hydropower station flow balance relationship constraints, hydropower station power generation constraints, and upper and lower limit constraints on the reservoir capacity of the hydropower station.

[0078] like Figure 2 The figure shows a schematic diagram of the modeling process of the time-space coupling characteristics of the virtual power plant. The low-carbon operation process of the virtual pumped storage power plant considering the time-space coupling characteristics is as follows:

[0079] Step 1: Input the hydropower station information, load and external grid unit electricity carbon emission curve and construct the virtual power plant spatiotemporal coupling characteristic matrix.

[0080] Step 2: Construction of virtual power plant operation constraints considering the spatiotemporal coupling characteristics of hydropower stations.

[0081] Step 3: Determine the adjustment weight α of the virtual power plant operation mode according to the operation requirements, construct the objective function of minimizing the equivalent carbon emissions of the virtual power plant output and input power, and form a low-carbon operation model of the virtual pumped storage power plant considering the spatiotemporal coupling characteristics.

[0082] Step 4: Solve the low-carbon operation model of the virtual pumped storage power plant considering the time-space coupling characteristics.

[0083] like Figure 3 As shown, the types of dispersed hydropower resources that can be aggregated into a virtual pumped-storage power plant in this embodiment. The virtual power plant aggregation types defined and constructed are divided into Class A: the upper hydropower station is a hydropower station with adjustable reservoir capacity, and the lower hydropower station is a run-of-river hydropower station, and Class B: both the upper and lower hydropower stations are hydropower stations with adjustable reservoir capacity. The load of the virtual power plant is composed of four different types of loads, namely residential, office, commercial and industrial, which are weighted in equal proportions. The definition of operating mode 1 indicates that the virtual pumped-storage power plant adopts an operating mode that is completely coordinated with the carbon emissions of the external power grid, that is, corresponding to α=1; operating mode 2 indicates that the virtual pumped-storage power plant adopts an operating mode that is coordinated with the carbon emissions of the external power grid on the basis of self-balancing, that is, corresponding to α=+∞.

[0084] like Figure 4 , Figure 5 , Figure 6 , Figure 7 and Figure 8 As shown, the equivalent carbon emission curve per unit electricity of the external power grid coordinated by various loads and virtual power plants on typical days in different seasons.

[0085] like Fig. 9 , Fig.10 , Fig.11 , Fig.12 , Fig.13 and Fig.14 As shown in the figure, for type A aggregation, the dispatch results of the virtual pumped storage power plant considering the time-space coupling characteristics under the above two operation modes.

[0086] like Fig.15 , Fig.16 , Fig.17 , Fig.18 , Fig.19 and Fig. 20 As shown in the figure, for type B aggregation, the scheduling results of the virtual pumped storage power plant considering the time-space coupling characteristics under the above two operating modes.

[0087] from Figures 9 to 20 As shown in the figure, when the virtual pumped storage power plant is dispatched in operation mode 1, the output of each hydropower station is mainly coordinated with the carbon emission intensity of the external power grid, that is, the power output is concentrated in the period of high carbon emission intensity, and the power input is concentrated in the dispatch period of low carbon emission intensity, so as to achieve the operation goal of minimum carbon emission. When the virtual pumped storage power plant is dispatched in operation mode 2, the output of each hydropower station is mainly coordinated with the change of load, that is, firstly ensure the low-carbon supply of the internal load of the virtual power plant, and then consider the coordination with the carbon emission intensity of the external power grid.

[0088] Therefore, the virtual power plant operation method generated by this embodiment can realize the sharing of the reservoir capacity regulation capability of the upper-level hydropower station with reservoir capacity and the lower-level run-of-river hydropower station through the coordinated scheduling of the upper and lower hydropower stations with a hydraulic spatiotemporal coupling relationship, or the sharing of reservoir capacity of the upper and lower hydropower stations with reservoir capacity, thereby increasing the overall regulation and storage capacity and having the advantage of flexible operation to meet the needs of different operation scenarios.

[0089] like Fig.21 As shown in the figure, the equivalent carbon emission results of virtual power plants of different aggregation types using the above two operation modes are shown. A positive carbon emission data value indicates that the total operation of the virtual power plant in one day is manifested as carbon emissions, and a negative value indicates that the total operation of the virtual power plant in one day is manifested as the output of low-carbon electricity to the external power grid to achieve energy conservation and carbon reduction. The self-balancing zero-carbon operation hours are converted from the typical daily operation results of each season in the example.

[0090] Compared with operation mode 2, operation mode 1 can reduce carbon emissions of Class A and Class B aggregate scheduling by 55.39%, 10%, 16.82%, 14.77%, 23.2% and 0.4% on typical days in winter, spring and summer, respectively. Compared with operation mode 1, operation mode 2 can increase the self-balancing zero-carbon operation hours of Class A and Class B aggregate scheduling by 3.1% and 1.1%, respectively. The two operation modes have different low-carbon benefits due to their different demand scenarios. Therefore, the virtual pumped storage power plant operation method considering the spatiotemporal coupling characteristics proposed in the present invention can achieve optimal low-carbon operation under different scenario requirements according to different operation scenario requirements.

[0091] As described in the above embodiments, by processing topological information, information on the type of hydropower station, the coupling relationship between hydropower stations, and the time-lag relationship of the flow rate is stored in matrix form, and the spatiotemporal characteristics between hydropower stations are represented in a concise form for storing correction information and for calculation optimization to ensure coordinated output of each power station, thereby ensuring the balance of supply and demand of the virtual pumped-storage power plant. A low-carbon operation method for a virtual pumped-storage power plant that utilizes the spatiotemporal coupling characteristics between hydropower stations is used to construct a low-carbon operation model of a virtual power plant that aggregates different types of hydropower resources, achieve a balance in pumped-storage supply and demand internally, participate in system regulation externally, and realize self-balancing, low-carbon coordination of the virtual power plant, thereby achieving effective utilization of power resources and realizing the social value of energy conservation and carbon reduction.

[0092] The present invention solves the problem that the current power market lacks flexible aggregation of resources that can adapt to virtual power plants. By adjusting the weights of operating mode regulation, the virtual pumped storage power plant can be flexibly adapted to different operating scenarios, thereby effectively solving the problem of optimal configuration of power data and achieving a balance between supply and demand in the power market.

[0093] In addition to the above-mentioned embodiments, within the scope disclosed in the claims and the specification of the present invention, the technical features of the present invention can be reselected and the steps can be adjusted to form new embodiments. These are all achievable by those skilled in the art without creative work. Therefore, the embodiments of the present invention that are not described in detail should also be regarded as specific embodiments of the present invention and within the protection scope of the present invention.

Claims

1. A low-carbon operation method for a virtual pumped storage power plant considering the spatiotemporal coupling characteristics, characterized in that: The following steps are involved: S1: Based on the matrix of hydropower station types, water area association matrix and water flow time-delay matrix aggregated by the virtual power plant, an operation model of the spatiotemporal coupling characteristics of the virtual pumped storage power plant is constructed; The operation model uses the virtual power plant to import power equivalent carbon emission intensity f from the external power grid. C_in and the equivalent carbon reduction of power output to the external grid f C_out The difference reflects the low-carbon nature of the operation; Where: Considering the equivalent carbon emissions of a unit of electricity imported or exported by the virtual power plant at the dispatch time t The virtual power plant operation mode adjustment weight α, which indicates the importance of self-balancing low-carbon operation, is constructed to import power from the outside during the dispatch period T. and the output power The equivalent carbon emissions are minimized objective function; S2: Based on the coordination between the virtual power plant and the changes in carbon emissions per unit of electricity of the external power grid, a constraint method for the low-carbon operation model of the virtual power plant that aggregates different types of hydropower resources is constructed to achieve a balance between pumped storage supply and demand internally, participate in system regulation externally, and perform operation steps to achieve self-balancing and low-carbon coordination of the virtual power plant.

2. A low-carbon operation method of a virtual pumped storage power plant considering time-space coupling characteristics according to claim 1, characterized in that: The operation model constructed in S1 is composed of the hydropower station type, the coupling relationship between the hydropower stations, and the time lag relationship of the flow, which are represented by N-dimensional vectors M 1 , N×N dimensional matrix M 2 and N×N dimensional matrix M 3 Characterization, Hydropower station type vector M 1 , M 1 =[h i ] N×1 Where M 1 It represents the types of N hydropower stations, and its element h i =0,h i =1, i=1,2,…,N respectively indicate that the hydropower station i is a run-of-river hydropower station and a hydropower station with adjustable reservoir capacity; Water area correlation matrix M 2 , M 2 =[c ij ] N×N Where M 2 It represents the flow direction of N hydropower stations, and its element c ij =0, c ij =1, c ij = -1 respectively indicates that there is no hydraulic connection between hydropower stations i and j, the water flow direction is from hydropower station j to hydropower station i, and the water flow direction is from hydropower station i to hydropower station j; Water flow delay matrix M 3 , M 3 =[d ij ] N×N Where M 3 It represents the water flow time lag of N hydropower stations, and its element d ij It represents the water flow delay when hydropower station j transmits flow to hydropower station i.

3. A low-carbon operation method of a virtual pumped storage power plant considering the time-space coupling characteristics according to claim 2, characterized in that: The operating model constructed in S1 is: In the formula, f represents the equivalent carbon emission intensity of the power input from the external power grid to the virtual power plant. C_in and the equivalent carbon reduction of the virtual power plant outputting power to the external grid f C_out The difference reflects the low-carbon nature of virtual power plant operation; and They represent the power input and output of the virtual power plant at the dispatching time t respectively; It represents the equivalent carbon emissions of a unit of electricity input or output by the virtual power plant at the dispatch time t; α represents the adjustment weight of the virtual power plant operation mode, α∈[1,+∞), α=1, α=+∞ respectively represent the virtual power plant adopts the operation mode that fully considers the coordination with the external power grid carbon emission intensity and the virtual power plant considers the coordination with the external carbon emission intensity on the basis of self-balancing. The larger α is, the more the virtual power plant attaches importance to self-balancing and low-carbon operation; Δt represents the dispatch time interval; T represents the dispatch cycle.

4. A low-carbon operation method of a virtual pumped storage power plant considering time-space coupling characteristics according to claim 1, characterized in that: The operation model in S2 includes a power balance constraint method, In the formula, represents the power generation of the virtual power plant at the dispatching time t of hydropower station i; Represents the on-site load power of the virtual power plant.

5. A low-carbon operation method of a virtual pumped storage power plant considering time-space coupling characteristics according to claim 1, characterized in that: The operation model in S2 includes a hydropower station water-to-electricity conversion relationship constraint method, Where g represents the acceleration due to gravity; represents the power generation efficiency of hydropower station i; and They represent the net water head and power flow of hydropower station i at time t respectively. Considering that the water head does not change much in a day, we assume is a constant.

6. A low-carbon operation method of a virtual pumped storage power plant considering time-space coupling characteristics according to claim 1, characterized in that: The operation model in S2 includes a constraint method for the spatiotemporal coupling characteristics of a hydropower station. In the formula, It represents the associated flow of hydropower station i at dispatching time t considering the hydraulic correlation and water flow time lag between hydropower stations; represents the discharge flow of hydropower station j at the dispatching time t before the initial water flow delay is reached; H and S t They represent the set of all hydropower stations and the set of hydropower stations that reach the initial water flow delay at the dispatching time t; Δt ij It represents the magnitude of the water flow time lag between hydropower stations i and j considering the hydraulic connection topology of the hydropower stations; and They respectively represent the discharge flow, power generation flow and abandoned water flow of the hydropower station at the scheduling time t.

7. A low-carbon operation method for a virtual pumped storage power plant considering time-space coupling characteristics according to claim 1, characterized in that: The operation model in S2 includes a hydropower station flow balance relationship constraint method, In the formula, h i represents the i-th hydropower station, and They respectively represent the reservoir capacity, natural water inflow and abandoned water flow of hydropower station i at the scheduling time t.

8. A low-carbon operation method for a virtual pumped storage power plant considering time-space coupling characteristics according to claim 1, characterized in that: The operation model in S2 includes a power generation constraint method for a hydropower station. In the formula, and They represent the minimum and maximum power generation of hydropower station i respectively.

9. A low-carbon operation method for a virtual pumped storage power plant considering time-space coupling characteristics according to claim 1, characterized in that: The operation model in S2 includes the upper and lower limit constraints of the reservoir capacity of the hydropower station. In the formula, and They represent the lower and upper limits of the storage capacity of hydropower station i respectively.

10. A low-carbon operation method for a virtual pumped storage power plant considering time-space coupling characteristics according to any one of claims 1 to 9, characterized in that: The operation steps include: Step 1: Input the hydropower station information, load and external grid unit electricity carbon emission curve and construct the virtual power plant spatiotemporal coupling characteristic matrix; Step 2: Construction of virtual power plant operation constraints considering the spatiotemporal coupling characteristics of hydropower stations; Step 3: Determine the adjustment weight α of the virtual power plant operation mode according to the operation requirements, construct the objective function of minimizing the equivalent carbon emissions of the virtual power plant output and input power, and form a low-carbon operation model of the virtual pumped storage power plant considering the spatiotemporal coupling characteristics; Step 4: Solve the low-carbon operation model of the virtual pumped storage power plant considering the time-space coupling characteristics.

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