Energy storage planning method and device of power system, electronic equipment and storage medium

Abstract

The invention provides an energy storage planning method and device of a power system, electronic equipment and a storage medium. The energy storage planning method comprises the steps that an upper-layer planning model is constructed based on structural information of a target power system and the number of energy storage stations planned for the target power system, and decision variables of the upper-layer planning model comprise site selection information and capacity information of all the energy storage stations; on the basis of the supply and demand information of the new energy generator set and the load of the target power system, a lower-layer operation model is constructed, and the optimization target of the lower-layer operation model is that an objective function based on the intraday operation cost and the safety index of the target power system is minimized; the safety index is used for representing the line overload severity of the target power system; and solving a double-layer optimization model comprising the upper-layer planning model and the lower-layer operation model to obtain site selection information and capacity information of each energy storage site.

Description

Technical Field

[0001] The present disclosure generally relates to the field of electric power technology, and more specifically, to an energy storage planning method, device, electronic device, and storage medium for an electric power system. Background Art

[0002] Renewable energy sources like wind and solar power have abundant reserves, but the large-scale integration of these renewables into the power system increases uncertainty due to the uncertainty of their output. Furthermore, the harsh climate in some regions, with large fluctuations in load demand and a high rate of power system accidents, further exacerbates this uncertainty. Reasonable planning of power system energy storage facilities can improve system operational safety and risk mitigation. Summary of the Invention

[0003] An exemplary embodiment of the present disclosure provides a method, device, electronic device, and storage medium for energy storage planning in a power system, which can reasonably plan the site selection information and capacity information of each energy storage site to improve the safety and risk resistance of the power system.

[0004] According to a first aspect of an embodiment of the present disclosure, a method for energy storage planning of an electric power system is provided, comprising: constructing an upper-level planning model based on structural information of a target electric power system and the number of energy storage sites planned for the target electric power system, wherein the decision variables of the upper-level planning model include the siting information and capacity information of each energy storage site, and the optimization objective of the upper-level planning model is to minimize an objective function for characterizing the annualized total cost of the target electric power system; constructing a lower-level operation model based on supply and demand information of new energy power generation units and loads of the target electric power system, wherein the decision variables of the lower-level operation model include the operation strategy of the target electric power system, and the optimization objective of the lower-level operation model is to minimize an objective function based on the intraday operation cost and safety index of the target electric power system, and the safety index is used to characterize the severity of line overload of the target electric power system; and obtaining the siting information and capacity information of each energy storage site by solving a two-layer optimization model including the upper-level planning model and the lower-level operation model.

[0005] Optionally, the safety index is determined based on the line overload severity index at multiple times during the day; wherein the line overload severity index at each moment is determined based on the line overload severity of each branch of the target power system at that moment.

[0006] Optionally, if the power transmitted by any branch at time t is less than the overload risk threshold of the branch, the line overload severity of the branch at time t is 0; if the power transmitted by any branch at time t is greater than or equal to the overload risk threshold of the branch, the line overload severity of the branch at time t is determined based on the full load transmission power limit of the branch, the overload risk threshold and the power transmitted at time t; wherein, the overload risk threshold of a branch is determined based on the full load transmission power limit of the branch.

[0007] Optionally, the constraints of the upper-level planning model include: positional relationship constraints between each energy storage site and each node of the target power system, and energy storage site capacity constraints.

[0008] Optionally, the positional relationship constraints between the various energy storage sites and the various nodes of the target power system include: the total number of elements with a value of 1 in the matrix used to describe the positional relationship is the number of the energy storage sites, the sum of the values ​​of the elements in each row of the matrix is ​​less than or equal to 1, and the sum of the values ​​of the elements in each column of the matrix is ​​equal to 1; wherein, when the value of the element located in the i-th row and k-th column is 1, it indicates that the i-th node is connected to the k-th energy storage site, and when the value of the element located in the i-th row and k-th column is 0, it indicates that the i-th node is not connected to the k-th energy storage site.

[0009] Optionally, the constraints of the lower-level operation model include: supply and demand balance constraints based on source and load uncertainty of the target power system, branch N-1 safety constraints of the target power system, equipment operation constraints of the target power system, and flow constraints of the target power system.

[0010] Optionally, the equipment operation constraints of the target power system include: operation constraints of each new energy power generation unit, output ramping constraints of each energy storage site, and energy storage constraints of each energy storage site; and / or, the supply and demand balance constraints based on the source and load uncertainty of the target power system are constructed using a probability distribution method to describe the relationship constraints between the input power and output power of each node of the target power system considering the source and load uncertainty; and / or, the branch N-1 safety constraints of the target power system are constructed using the large M method based on the N-1 expected accident set.

[0011] Optionally, the intraday operating cost includes: the transaction cost between the target power system and the external power grid, the operating cost of each energy storage site, the load shedding cost, and the energy abandonment cost of each new energy generator set; and / or, the annualized total cost of the target power system includes: the annualized investment cost of the energy storage site and the annualized operating cost obtained based on the intraday operating cost.

[0012] Optionally, the site selection information and capacity information of each energy storage site are obtained by solving a two-layer optimization model including the upper-layer planning model and the lower-layer operation model, including: converting the two-layer optimization model into a mixed integer linear programming model by linearizing the safety index and the supply and demand balance constraint; and obtaining the site selection information and capacity information of each energy storage site and the operation strategy of the target power system by solving the mixed integer linear programming model.

[0013] According to a second aspect of an embodiment of the present disclosure, a device for energy storage planning of an electric power system is provided, comprising: an upper-level construction unit configured to construct an upper-level planning model based on structural information of a target electric power system and the number of energy storage sites planned for the target electric power system, wherein the decision variables of the upper-level planning model include the siting information and capacity information of each energy storage site, and the optimization objective of the upper-level planning model is to minimize an objective function used to characterize the annualized total cost of the target electric power system; a lower-level construction unit configured to construct a lower-level operation model based on supply and demand information of new energy power generation units and loads of the target electric power system, wherein the decision variables of the lower-level operation model include the operation strategy of the target electric power system, and the optimization objective of the lower-level operation model is to minimize an objective function based on the intraday operation cost and safety index of the target electric power system, and the safety index is used to characterize the severity of line overload of the target electric power system; and a solution unit configured to obtain the siting information and capacity information of each energy storage site by solving a two-level optimization model including the upper-level planning model and the lower-level operation model.

[0014] According to a third aspect of an embodiment of the present disclosure, a computer-readable storage medium storing a computer program is provided. When the computer program is executed by a processor, the processor is prompted to execute the energy storage planning method for the power system as described above.

[0015] According to a fourth aspect of an embodiment of the present disclosure, an electronic device is provided, comprising: a processor; and a memory storing a computer program, wherein when the computer program is executed by the processor, the processor is prompted to execute the energy storage planning method for the power system as described above.

[0016] According to a fifth aspect of an embodiment of the present disclosure, there is provided a computer program product, comprising a computer program, which, when executed by a processor, implements the energy storage planning method for a power system as described above.

[0017] According to the exemplary embodiments of the present disclosure, the energy storage planning method, device, electronic device, and storage medium for the power system plan the site selection information and capacity information of each energy storage site based on the severity of the line overload of the power system to improve the safety and risk resistance of the power system.

[0018] In the following description, some aspects and / or advantages of the general inventive concept of the present disclosure will be set forth, and some aspects and / or advantages will be known through the following description or implementation of the general inventive concept of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] These and / or other aspects and advantages of the present application will become more clear and easier to understand from the following detailed description of the embodiments of the present application in conjunction with the accompanying drawings, in which: Figure 1 A flowchart illustrating an energy storage planning method for a power system according to an exemplary embodiment of the present disclosure is shown; Figure 2 An example of a two-tier optimization model according to an exemplary embodiment of the present disclosure is shown; Figure 3 shows a structural example of a power system according to an exemplary embodiment of the present disclosure; Figure 4 An example of an accident load shedding situation according to an exemplary embodiment of the present disclosure is shown; Figure 5 An example illustrating the impact of security indicators on system optimization operation effects according to an exemplary embodiment of the present disclosure; Figure 6 A structural block diagram of an energy storage planning device for a power system according to an exemplary embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0020] Reference will now be made in detail to the embodiments of the present disclosure, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to like parts throughout. The embodiments are described below with reference to the drawings in order to explain the present disclosure.

[0021] It should be noted that the terms "first," "second," and the like in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the numbers used in this manner are interchangeable where appropriate so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure as detailed in the appended claims.

[0022] It should be noted that the phrase "at least one of the several items" in this disclosure includes three types of parallel situations: "any one of the several items", "a combination of any multiple of the several items", and "all of the several items". For example, "including at least one of A and B" includes the following three parallel situations: (1) including A; (2) including B; (3) including A and B. For another example, "performing at least one of step 1 and step 2" means the following three parallel situations: (1) performing step 1; (2) performing step 2; and (3) performing both step 1 and step 2.

[0023] In the prior art, there is a lack of consideration of transmission redundancy (for example, line transmission overload) in response to emergencies during the optimization of the power system. Emergency regulation for N-1 accidents may cause overload of power system lines. Therefore, it is necessary to improve the system's ability to respond to emergencies based on existing lines. Both N-1 accidents and source-load uncertainty will have an impact on the safety of the system. In order to improve the safety of the system, this disclosure proposes to construct a power system energy storage site selection and sizing model that considers N-1 constraints and source-load uncertainty, and proposes a set of reasonable safety evaluation indicators to further improve the safety of the system. The following will be combined with Figures 1 to 6 Exemplary embodiments of the present disclosure are described in detail.

[0024] Figure 1 A flow chart illustrating an energy storage planning method for a power system according to an exemplary embodiment of the present disclosure is shown.

[0025] As an example, the energy storage planning method for the power system according to the exemplary embodiment of the present disclosure can be executed by an electronic device with data processing capabilities, for example, it can be a terminal (such as a personal notebook, desktop computer, etc.) or a server (such as an independent server, server cluster, cloud platform, etc.), and the embodiment of the present disclosure does not limit this.

[0026] Reference Figure 1 In step S101, an upper-level planning model is constructed based on the structural information of the target power system and the number of energy storage sites planned for the target power system.

[0027] As an example, the target power system may be a microgrid, for example, the target power system may be a 12.66 kV, 10 MW-level power system.

[0028] As an example, the structural information of the target power system may include but is not limited to: location information of each node, connection relationship between nodes (i.e., branch information), access information of new energy power stations (e.g., identification information of access nodes of new energy power stations (e.g., Figure 3 E7, E14, E28 in the )), identification information of nodes connected to the external power grid (such as Figure 3It should be understood that a new energy power station may include at least one new energy generator set. For example, the types of new energy generator sets may include but are not limited to: wind generator sets and photovoltaic generator sets.

[0029] The number of energy storage sites planned for the target power system is the number of energy storage sites planned to be configured for the target power system.

[0030] The decision variables of the upper-level planning model include the site selection information and capacity information of each energy storage site.

[0031] The optimization goal of the upper-level planning model is to minimize an objective function representing the annualized total cost of the target power system. For example, this annualized total cost includes the annualized investment cost of the energy storage site and the annualized operating cost derived from daily operating costs.

[0032] As an example, as shown in Equation (1), the objective function of the upper-level planning model is The annual investment cost of the energy storage site and the annual operating cost of the system It consists of two parts. Among them, represents the number of energy storage sites planned for the target power system; and They represent the unit capacity installation cost and installed capacity of the k-th energy storage site respectively; r represents the base discount rate; m represents the planning period; Represents the typical day's operating cost (i.e., the intra-day operating cost).

[0033] (1) As an example, the constraints of the upper-level planning model may include: positional relationship constraints between each energy storage site and each node of the target power system, and energy storage site capacity constraints.

[0034] As an example, the positional relationship constraints between each energy storage site and each node of the target power system may include: the total number of elements with a value of 1 in the matrix used to describe the positional relationship is the number of energy storage sites planned for the target power system, the sum of the values ​​of each element in each row of the matrix is ​​less than or equal to 1, and the sum of the values ​​of each element in each column of the matrix is ​​equal to 1; wherein, when the value of the element located in the i-th row and k-th column is 1, it means that the k-th energy storage site is connected to the i-th node, and when the value of the element located in the i-th row and k-th column is 0, it means that the k-th energy storage site is not connected to the i-th node.

[0035] As an example, the constraints of the upper-level planning model can be shown as Equation (2), where the matrix used to describe the positional relationship between each energy storage site and each node of the target power system is For one OK, A matrix of columns, represents the number of nodes in the target power system, Each element in is a 0-1 variable. When represents the access to the kth energy storage site at the i-th node; It is represented as the maximum configurable capacity of the kth energy storage site.

[0036] (2) In step S102, a lower-level operation model is constructed based on the supply and demand information of the new energy power generation units and loads of the target power system.

[0037] The decision variables of the lower-level operation model include the operation strategy of the target power system.

[0038] The optimization objective of the lower-level operation model is to minimize an objective function based on the target power system's daily operating costs and a safety indicator, which characterizes the severity of line overload in the target power system. For example, daily operating costs may include transaction costs between the target power system and the external grid, operating costs of individual energy storage sites, load shedding costs, and the energy curtailment costs of individual renewable energy generators (e.g., wind and solar curtailment).

[0039] As an example, as shown in Equation (3), the objective function of the lower-level running model ( ) by daily operating costs and a safety penalty term (i.e., safety index) It consists of two parts: daily operating cost of the target power system May include: transaction costs between the target power system and the external power grid , energy storage operating costs , load shedding and wind and solar curtailment costs .in, represents the electricity purchase price at time t ( ); represents the power purchased by the i-th node at time t (MW); Represents the unit power operating cost of the energy storage site; and They represent the charging / discharging power of the energy storage station connected to the i-th node at time t; 、 Respectively represent the load shedding cost per unit power, the wind and solar curtailment cost of new energy generators ( ); 、 They represent the load shedding amount of the i-th node at time t and the wind and solar power curtailment of the renewable energy station connected to the i-th node at time t (MW); Indicates the number of nodes in the target power system. For example, the coefficient Ks>>1.

[0040] (3) Aiming at the key parameter of power system branch transmission power, in order to reduce the energy burden of system branch transmission, this paper develops a safety index based on the line overload severity index at multiple times of the day (for example, at various scheduling times of the day). (For example, as shown in Equation (4)), the line overload severity index at each moment is The determination may be based on the severity of line overload of each branch of the target power system at that moment.

[0041] (4) As an example, if the power transmitted by any branch at time t is less than the overload risk threshold of the branch, the line overload severity of the branch at time t is 0; if the power transmitted by any branch at time t is greater than or equal to the overload risk threshold of the branch, the line overload severity of the branch at time t is determined based on the full-load transmission power limit of the branch, the overload risk threshold, and the power transmitted at time t; wherein the overload risk threshold of a branch is determined based on the full-load transmission power limit of the branch. For example, as shown in equations (5) and (6), represents the line overload severity function of branch ij (i.e., the branch between the i-th node and the j-th node) at time t; represents the power transmitted by branch ij at time t; represents the full-load transmission power limit of branch ij (i.e., the maximum power transmission value); represents the overload risk threshold of branch ij, for example, ; Represents the set of all branches of the target power system; Indicates the number of branches in the target power system.

[0042] (5) (6) As an example, the constraints of the lower-level operation model may include: supply and demand balance constraints based on source and load uncertainty of the target power system, branch N-1 safety constraints of the target power system, equipment operation constraints of the target power system, and flow constraints of the target power system.

[0043] As an example, the equipment operation constraints of the target power system may include: the operation constraints of each new energy generator set, the output ramp constraints of each energy storage site, and the storage capacity constraints of each energy storage site. For example, the storage capacity constraints of each energy storage site are shown in formula (7), where: represents the storage capacity of the k-th energy storage site at time t; and represent the upper and lower limits of the energy storage capacity of the kth energy storage site, respectively. For example, The value of 0.1 times, The value of 1 times.

[0044] (7) As an example, a probability distribution method can be used to construct a supply and demand balance constraint based on the source and load uncertainty of the target power system. The supply and demand balance constraint is used to describe the relationship between the input power and output power of each node of the target power system under the condition of considering the source and load uncertainty. For example, as shown in equations (8) to (10), represents the charging power of the energy storage station connected to the i-th node at time t; represents the discharge power of the energy storage station connected to the i-th node at time t; represents the charging power of the kth energy storage station at time t; represents the discharge power of the kth energy storage station at time t; represents the number of energy storage sites planned for the target power system; represents the standard normal distribution.

[0045] (8) (9) (10) In the above formula, the uncertainty of the output and load power of the new energy generator set is taken into account. In order to minimize the impact of insufficient energy supply, the prediction error is considered when establishing the source-load model, as shown in formula (11), assuming that the prediction error obeys the normal distribution: (11) in, and They represent the actual output of the renewable energy generator set and load connected to the i-th node at time t (i.e., the generating power of the unit and the power consumption of the load); and They represent the power prediction values ​​of the renewable energy generator and load connected to the i-th node at time t respectively; and They represent the prediction errors of the renewable energy generator and load connected to the i-th node at time t; and They represent the standard deviation of the prediction error of the renewable energy generator set and load connected to the i-th node at time t.

[0046] As an example, the large M method can be used to construct the N-1 branch safety constraints of the target power system based on the N-1 expected accident set, which constrains the system to be safe when a fault occurs at any time t. For example, as shown in formula (12), Represents the constructed power system N-1 accident set; M is a large positive real number; is a quantity describing the branch state, When , it means branch ij is normally conducting. When , it means branch ij is disconnected; represents the susceptance value of branch ij; represents the phase angle of the i-th node at time t; represents the phase angle of the jth node at time t; represents the power transmitted by branch ij at time t; Indicates the full-load transmission power limit of branch ij.

[0047] (12) In step S103 , the site selection information and capacity information of each energy storage site are obtained by solving a two-layer optimization model including an upper-layer planning model (ie, system planning layer) and a lower-layer operation model (ie, system operation layer).

[0048] According to the exemplary embodiment of the present disclosure, taking into account the different time scales of system facilities and system operations, the following are designed: Figure 2 The hierarchical optimization model shown is divided into a system planning layer and a system operation layer. The system planning layer uses the annual time scale to determine the capacity and site selection of each energy storage site (e.g., power station). The objective function is to optimize the system's annual economic performance, with the maximum capacity of energy storage facilities that can be configured at a single site and the site selection as constraints. The system operation layer determines the internal energy flow distribution strategy and the output of each unit within a typical day. The objective function is to minimize the system's daily operating costs and safety indicators, with the operating characteristics of each system component and energy supply balance as constraints.

[0049] As an example, step S103 may include: converting the two-level optimization model into a mixed integer linear programming model by linearizing the safety indicators and supply and demand balance constraints; then, by solving the mixed integer linear programming model, obtaining the site selection information and capacity information of each energy storage site and the operation strategy of the target power system.

[0050] As an example, the safety index is linearized as shown in Equation (13), where: represents the power transmitted by branch ij at time t; 、 is a continuous variable introduced in the linearization process; represents the overload risk threshold of branch ij; Indicates a branch ij At the moment t Line overload severity function; Indicates a branch ij Full load transmission power limit; is a Boolean variable introduced in the linearization process, let , which is used to represent the charging power of the energy storage site k (i.e., the kth energy storage site among all energy storage sites) connected to the i-th node at time t, and The quadratic term of is linearized, and Equation (9) can be transformed into Equation (14). Similarly, the linearization of Equation (10) can be completed. In Equation (14), Indicates in i The charging power of the energy storage site connected to each node at time t; Indicates the k Energy storage sites at the time t Charging power; Indicates the maximum charging power of an energy storage site (for example, a power station).

[0051] (13) (14) The transformed system model is solved as a mixed integer linear programming problem. As an example, the planning layer can be solved using the particle swarm algorithm, and the operation layer can be solved using the solver gurobi.

[0052] According to an exemplary embodiment of the present disclosure, a power system energy storage planning method considering multivariate uncertainty and N-1 constraints is proposed, and a safety evaluation index and a linearized model that can optimize transmission redundancy during optimized operation are proposed.

[0053] Below, we take the modified IEEE33-node power system (such as Figure 3The power system example is based on the classic IEEE 33-node example, with branch E22-E2 changed to E22-E33. Node E1 is connected to the main grid; nodes E7, E14, and E28 are connected to renewable energy generators, with the power levels of these three nodes remaining consistent. The forecast errors for renewable energy generators and load are approximately 15% and 10%, respectively. Figure 3 The dotted lines in the figure represent the contact lines.

[0054] In one embodiment, when studying the impact of source-load uncertainty on power system configuration and optimization results, the number of storage stations configured for the power system is ≤4. Considering the actual situation of energy storage modularization, the unit capacity of the energy storage facility is set to 0.4MW·h. Table 1 shows the configuration of the planning scheme.

[0055] Table 1 Planning scheme configuration

[0056] A comparative analysis of the various configuration options in Table 2 reveals that, compared to Option 1, Option 3 significantly increases energy storage capacity and unit operating costs after adding the N-1 constraint. This indicates that to prevent the impact of N-1 events on the system, various units are operated more frequently, significantly improving safety indicators and placing greater pressure on the power system's energy supply lines. Comparing Options 2 and 3, the introduction of safety indicators and the rational allocation of energy storage capacity and location result in slightly higher costs and less stress on the system's lines. Values ​​in Table 2 are approximately zero when they are less than 10^(-4).

[0057] Table 2 Energy storage configuration results under multiple scenarios

[0058] According to the exemplary embodiment of the present disclosure, the total load of a typical day is 60.1677MW·h. Assuming that the power system must disconnect one branch at each moment, the total load is 60.1677MW·h. There are 37 representative accident scenarios, and the load shedding analysis is carried out. The selected scenario is the case where the same branch is disconnected at all times on a typical day. The load shedding amount under the typical day of the accident using configuration scheme 3 is as follows: Figure 4 As shown in Figure 2. Since power system branches 1, 22, and 37 are close to heavily loaded nodes, the load shedding required to disconnect these branches is significantly higher than that required in other accident scenarios. Figure 4 It can be seen that the energy storage configuration scheme proposed in this disclosure only requires a very small amount of load shedding to achieve N-1 safety.

[0059] Based on the configuration results shown in Table 2, multiple groups of operating scenarios are set, as shown in Table 3.

[0060] Table 3 Operation scenario settings

[0061] The typical day optimization results of scenarios 1 and 2 are shown in Table 4. Due to the limitation of energy storage capacity, the unit operating cost of scenario 2 is significantly lower than that of scenario 1. However, the load shedding amount of scenario 2 is much higher than that of scenario 1, and the safety index value is also significantly higher. The planning scheme that only considers the uncertainty of both the source and the load cannot meet the needs of dealing with N-1 accidents and accidents with uncertainty of both the source and the load.

[0062] Table 4 System operation layer optimization results of various configuration schemes under N-1 accident

[0063] Scenario 3 and Scenario 4 are mainly used to compare the impact of the security indicators proposed in this disclosure on the system optimization operation effect. Figure 5 It can be seen that the introduction of safety indicators will have a greater impact on the system optimization operation results and power flow distribution: there are fewer power system branches on the verge of full load, the system safety is significantly improved, and more redundant space is provided for the system's emergency regulation.

[0064] Figure 6 A structural block diagram of an energy storage planning device for a power system according to an exemplary embodiment of the present disclosure is shown.

[0065] like Figure 6 As shown, the energy storage planning device 600 for a power system according to an exemplary embodiment of the present disclosure includes: an upper-layer construction unit 601 , a lower-layer construction unit 602 , and a solution unit 603 .

[0066] Specifically, the upper-level construction unit 601 is configured to construct an upper-level planning model based on the structural information of the target power system and the number of energy storage sites planned for the target power system, wherein the decision variables of the upper-level planning model include the site selection information and capacity information of each energy storage site, and the optimization goal of the upper-level planning model is to minimize the objective function used to characterize the annualized total cost of the target power system.

[0067] The lower-layer construction unit 602 is configured to construct a lower-layer operation model based on the supply and demand information of the new energy power generation units and loads of the target power system, wherein the decision variables of the lower-layer operation model include the operation strategy of the target power system, and the optimization objective of the lower-layer operation model is to minimize the objective function based on the intraday operation cost and safety index of the target power system, and the safety index is used to characterize the severity of line overload of the target power system.

[0068] The solving unit 603 is configured to obtain the site selection information and capacity information of each energy storage site by solving a two-layer optimization model including the upper-layer planning model and the lower-layer operation model.

[0069] As an example, the safety index may be determined based on line overload severity indices at multiple times during a day; wherein the line overload severity index at each time may be determined based on the line overload severity of each branch of the target power system at that time.

[0070] As an example, if the power transmitted by any branch at time t is less than the overload risk threshold of the branch, the line overload severity of the branch at time t is 0; if the power transmitted by any branch at time t is greater than or equal to the overload risk threshold of the branch, the line overload severity of the branch at time t is determined based on the full load transmission power limit of the branch, the overload risk threshold and the power transmitted at time t; wherein, the overload risk threshold of a branch can be determined based on the full load transmission power limit of the branch.

[0071] As an example, the constraints of the upper-level planning model may include: positional relationship constraints between each energy storage site and each node of the target power system, and energy storage site capacity constraints.

[0072] As an example, the positional relationship constraints between the various energy storage sites and the various nodes of the target power system may include: the total number of elements with a value of 1 in the matrix used to describe the positional relationship is the number of the energy storage sites, the sum of the values ​​of the elements in each row of the matrix is ​​less than or equal to 1, and the sum of the values ​​of the elements in each column of the matrix is ​​equal to 1; wherein, when the value of the element located in the i-th row and k-th column is 1, it means that the i-th node is connected to the k-th energy storage site, and when the value of the element located in the i-th row and k-th column is 0, it means that the i-th node is not connected to the k-th energy storage site.

[0073] As an example, the constraints of the lower-level operation model may include: supply and demand balance constraints based on source and load uncertainty of the target power system, branch N-1 safety constraints of the target power system, equipment operation constraints of the target power system, and flow constraints of the target power system.

[0074] As an example, the equipment operation constraints of the target power system may include: operation constraints of each new energy generator set, output ramp constraints of each energy storage site, and storage capacity constraints of each energy storage site.

[0075] As an example, the supply and demand balance constraint based on the source and load uncertainty of the target power system can be constructed using a probability distribution method to describe the relationship constraint between the input power and output power of each node of the target power system considering the source and load uncertainty.

[0076] As an example, the branch N-1 security constraint of the target power system may be constructed using the Big M method based on the N-1 anticipated accident set.

[0077] As an example, the daily operating costs may include: transaction costs between the target power system and the external power grid, operating costs of each energy storage site, load shedding costs, and energy abandonment costs of each new energy generator set.

[0078] As an example, the annualized total cost of the target power system may include: an annualized investment cost of an energy storage site, and an annualized operating cost obtained based on the intraday operating cost.

[0079] As an example, the solving unit 603 can be configured to: convert the two-layer optimization model into a mixed integer linear programming model by linearizing the safety index and the supply and demand balance constraint; and obtain the site selection information and capacity information of each energy storage site and the operation strategy of the target power system by solving the mixed integer linear programming model.

[0080] It should be understood that the specific processing performed by the energy storage planning device of the power system according to the exemplary embodiment of the present disclosure has been referred to. Figures 1 to 5 The details are described in detail and will not be repeated here.

[0081] It should be understood that the various units in the energy storage planning apparatus for a power system according to the exemplary embodiments of the present disclosure may be implemented as hardware components and / or software components. Those skilled in the art may implement the various units using, for example, a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC), depending on the processing performed by the defined units.

[0082] An electronic device according to an exemplary embodiment of the present disclosure includes: at least one processor (not shown) and at least one memory (not shown), wherein the at least one memory stores a computer program, and when the computer program is executed by the at least one processor, it prompts the at least one processor to execute the energy storage planning method for the power system as described in the above exemplary embodiment.

[0083] As an example, the electronic device may be an electronic device with data processing capabilities, for example, a terminal (such as a personal notebook, desktop computer, etc.) or a server (such as an independent server, a server cluster, a cloud platform, etc.). The embodiments of the present disclosure are not limited to this.

[0084] According to an exemplary embodiment of the present disclosure, a computer-readable storage medium storing instructions may also be provided, wherein when the instructions are executed by at least one processor, the at least one processor is prompted to execute the energy storage planning method for the power system as described in the above exemplary embodiment. Examples of computer-readable storage media include: read-only memory (ROM), random access programmable read-only memory (PROM), electrically erasable programmable read-only memory (EEPROM), random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), flash memory, non-volatile memory, CD-ROM, CD-R, CD+R, CD-RW, CD+RW, DVD-ROM, DVD-R, DVD+R, DVD-RW, DVD+RW, DVD-RAM, BD-ROM, BD-R, BD-R LTH, BD-RE, Blu-ray or optical disk storage, hard disk drive (HDD), solid state drive (SSD), card storage (such as a multimedia card, secure digital (SD) card or extreme digital (XD) card), magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid state disk and any other device configured to store a computer program and any associated data, data files and data structures in a non-transitory manner and provide the computer program and any associated data, data files and data structures to a processor or computer so that the processor or computer can execute the computer program. The computer program in the above-mentioned computer-readable storage medium can be executed in an environment deployed in a computer device such as a client, a host, an agent device, a server, etc. In addition, in one example, the computer program and any associated data, data files and data structures are distributed on a networked computer system so that the computer program and any associated data, data files and data structures are stored, accessed and executed in a distributed manner by one or more processors or computers.

[0085] According to an exemplary embodiment of the present disclosure, a computer program product may be provided. Instructions in the computer program product may be executed by at least one processor to implement the energy storage planning method for the power system as described in the above exemplary embodiment.

[0086] Other embodiments of the present disclosure will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the claims.

[0087] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A method for energy storage planning of a power system, characterized in that: include: Based on the structural information of the target power system and the number of energy storage sites planned for the target power system, an upper-level planning model is constructed, wherein the decision variables of the upper-level planning model include the location information and capacity information of each energy storage site, and the optimization objective of the upper-level planning model is to minimize an objective function representing the annualized total cost of the target power system; Constructing a lower-level operation model based on supply and demand information of the target power system's renewable energy generators and loads, wherein the decision variables of the lower-level operation model include an operation strategy of the target power system, and the optimization objective of the lower-level operation model is to minimize an objective function based on the target power system's intraday operation cost and a safety index, wherein the safety index is used to characterize the severity of line overload in the target power system; By solving a two-layer optimization model including the upper-layer planning model and the lower-layer operation model, the site selection information and capacity information of each energy storage site are obtained.

2. The energy storage planning method according to claim 1, characterized in that: The safety index is determined based on the line overload severity index at multiple times during the day; The line overload severity index at each moment is determined based on the line overload severity of each branch of the target power system at that moment.

3. The energy storage planning method according to claim 2, characterized in that: If the power transmitted by any branch at time t is less than the overload risk threshold of the branch, the line overload severity of the branch at time t is 0; If the power transmitted by any branch at time t is greater than or equal to the overload risk threshold of the branch, the line overload severity of the branch at time t is determined based on the full load transmission power limit of the branch, the overload risk threshold, and the power transmitted at time t; The overload risk threshold of a branch is determined based on the full-load transmission power limit of the branch.

4. The energy storage planning method according to claim 1, characterized in that: The constraints of the upper-level planning model include: positional relationship constraints between each energy storage site and each node of the target power system, and energy storage site capacity constraints.

5. The energy storage planning method according to claim 4, characterized in that: The positional relationship constraints between each energy storage site and each node of the target power system include: the total number of elements with a value of 1 in a matrix used to describe the positional relationship is equal to the number of energy storage sites, the sum of the values ​​of each element in each row of the matrix is ​​less than or equal to 1, and the sum of the values ​​of each element in each column of the matrix is ​​equal to 1; Among them, when the value of the element located in the i-th row and k-th column is 1, it means that the i-th node is connected to the k-th energy storage site, and when the value of the element located in the i-th row and k-th column is 0, it means that the i-th node is not connected to the k-th energy storage site.

6. The energy storage planning method according to claim 1, characterized in that: The constraints of the lower-level operation model include: supply and demand balance constraints based on source and load uncertainty of the target power system, branch N-1 safety constraints of the target power system, equipment operation constraints of the target power system, and flow constraints of the target power system.

7. The energy storage planning method according to claim 6, characterized in that: The equipment operation constraints of the target power system include: operation constraints of each new energy generator set, output ramp constraints of each energy storage site, and storage capacity constraints of each energy storage site; And / or, the supply and demand balance constraint based on the source and load uncertainty of the target power system is constructed using a probability distribution method to describe the relationship constraint between the input power and output power of each node of the target power system considering the source and load uncertainty; And / or, the branch N-1 safety constraint of the target power system is constructed using the big M method based on the N-1 expected accident set.

8. The energy storage planning method according to claim 1, characterized in that: The daily operating costs include: transaction costs between the target power system and the external power grid, operating costs of each energy storage site, load shedding costs, and energy abandonment costs of each new energy generator set; And / or, the annualized total cost of the target power system includes: the annualized investment cost of the energy storage site and the annualized operating cost obtained based on the daily operating cost.

9. The energy storage planning method according to claim 6, characterized in that: The site selection information and capacity information of each energy storage site are obtained by solving the two-layer optimization model including the upper-layer planning model and the lower-layer operation model, including: By linearizing the safety index and the supply-demand balance constraint, the two-level optimization model is converted into a mixed integer linear programming model; By solving the mixed integer linear programming model, the site selection information and capacity information of each energy storage site and the operation strategy of the target power system are obtained.

10. An energy storage planning device for a power system, characterized in that: include: an upper-level construction unit configured to construct an upper-level planning model based on structural information of a target power system and the number of energy storage sites planned for the target power system, wherein decision variables of the upper-level planning model include location information and capacity information of each energy storage site, and an optimization objective of the upper-level planning model is to minimize an objective function representing an annualized total cost of the target power system; a lower-layer construction unit configured to construct a lower-layer operation model based on supply and demand information of the new energy power generation units and loads of the target power system, wherein the decision variables of the lower-layer operation model include the operation strategy of the target power system, and the optimization objective of the lower-layer operation model is to minimize an objective function based on the daily operation cost and safety index of the target power system, wherein the safety index is used to characterize the severity of line overload of the target power system; The solving unit is configured to obtain the site selection information and capacity information of each energy storage site by solving a two-layer optimization model including the upper-layer planning model and the lower-layer operation model.

11. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, it causes the processor to execute the energy storage planning method for a power system according to any one of claims 1 to 9.

12. An electronic device, characterized in that: The electronic device comprises: processor; A memory storing a computer program, which, when executed by a processor, prompts the processor to execute the energy storage planning method for a power system according to any one of claims 1 to 9.

13. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the energy storage planning method for the power system according to any one of claims 1 to 9 is implemented.

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