Virtual power plant collaborative power grid planning method and system

Abstract

The application provides a virtual power plant collaborative power grid planning method and system, and belongs to the field of power grid planning. The virtual power plant collaborative power grid planning method comprises the following steps: determining equivalent annual expansion coefficients of planning lines, equivalent annual planning coefficients of virtual power plants, annual abandoned wind coefficients and multi-infeed short-circuit ratio constraints based on original data of a power grid to be planned; determining virtual power plant constraints according to maximum rated storage capacities of batteries configured at each node in the power grid to be planned, maximum and minimum rated powers, maximum increase and interrupted load values of each node in each time period under each typical scenario; based on the multi-infeed short-circuit ratio constraints and the virtual power plant constraints, establishing a power grid planning model with the minimum sum of the equivalent annual expansion coefficients of the planning lines, the equivalent annual planning coefficients of the virtual power plants and the annual abandoned wind coefficients as the target, and solving the model to determine a power grid planning result. The optimal planning scheme is given under the premise that the power grid framework has sufficient system strength, and the capacity of the power grid to accept new energy power generation is improved.

Description

Technical Field

[0001] This invention relates to the field of power grid planning, and in particular to a virtual power plant collaborative power grid planning method for multi-DC feed-in systems. Background Technology

[0002] With the development of the power industry, a new type of power system based on new energy sources will gradually emerge in the future. Among them, the development of high-voltage direct current (HVDC) transmission technology has led to the formation of multiple HVDC feed-in systems in the transmission and receiving areas. With the large-scale integration of various clean energy systems with fluctuating output and uncertain loads such as electric vehicles into the receiving-end grid, power grid planning is showing new characteristics such as increased interaction between power sources, grids, loads, and storage, and diversification of planning elements.

[0003] A virtual power plant aggregates distributed renewable energy power sources, energy storage systems, and controllable loads within a specific area, allowing them to participate in grid operation as a whole. By coordinating and optimizing the dispatch of internal resources, virtual power plants can mitigate the randomness and volatility of renewable energy sources, reduce the difficulty of grid dispatch, improve the quality of renewable energy generation, and enhance the overall economic efficiency and environmental friendliness of the power grid.

[0004] The characteristics of power grid planning determine the effectiveness of incorporating virtual power plant elements. However, existing technologies primarily consider the economics and renewable energy absorption of virtual power plants only in operation and dispatch, without taking into account the characteristics of power grid planning, resulting in an unreasonable final power grid planning scheme. Summary of the Invention

[0005] The purpose of this invention is to provide a virtual power plant collaborative power grid planning method and system, which provides the optimal planning scheme while ensuring that the power grid structure has sufficient system strength, thereby improving the power grid's ability to accommodate new energy power generation.

[0006] To achieve the above objectives, the present invention provides the following solution:

[0007] A virtual power plant collaborative power grid planning method includes:

[0008] Obtain the raw data of the power grid to be planned within the preset planning time; the raw data includes the output parameters of thermal power units, load parameters, start-up and shutdown strategies of thermal power units, grid parameters, wind farm access points, wind farm capacity, DC system access points, rated transmission power, time-series data of the original load, and time-series data of the maximum output of wind turbine units;

[0009] Based on the original data, the equivalent annual expansion coefficient of the planned line, the equivalent annual planning coefficient of the virtual power plant, the annual wind curtailment coefficient, and the multi-infeed short-circuit ratio constraint are determined.

[0010] Based on the maximum rated storage capacity of the batteries configured at each node in the power grid to be planned, the maximum rated power of the batteries configured at each node, the minimum rated power of the batteries configured at each node, the maximum increase in load at each node in each typical scenario and time period, and the maximum interruption load at each node in each typical scenario and time period, the constraints of the virtual power plant are determined.

[0011] Based on the multi-infeed short-circuit ratio constraint and the virtual power plant constraint, a power grid planning model is established with the goal of minimizing the sum of the equivalent annual expansion coefficient of the planned line, the equivalent annual planning coefficient of the virtual power plant, and the annual wind curtailment coefficient.

[0012] The power grid planning model is solved to determine the power grid planning results; the power grid planning results include the optimal power grid line path and the battery capacity and controllable load output of the virtual power plant.

[0013] Optionally, the objective function of the power grid planning model is:

[0014] minf = f line +f vpp +f wind ;

[0015] Where f is the objective function value, f line f is the equivalent annual expansion coefficient for the planned route. vpp f is the equivalent annual planning coefficient for the virtual power plant. wind This represents the annual wind curtailment coefficient.

[0016] Optionally, the equivalent annual expansion coefficient of the planned route can be determined using the following formula:

[0017]

[0018] Among them, f line The equivalent annual expansion coefficient for the planned route is given by 'a', where 'a' is the equivalent annual conversion rate of the route expansion coefficient, and 'q' is the service life. Let c be the set of candidate branches in the power grid to be planned. ij The expansion weighting factor for the candidate branch ij, The number of branch roads to be constructed is ij.

[0019] Optionally, the equivalent annual planning coefficient of the virtual power plant can be determined using the following formula:

[0020]

[0021] Among them, f vpp f is the equivalent annual planning coefficient for the virtual power plant. vpp1 f is the equivalent annual planning coefficient for batteries in the virtual power plant. vpp2 Ω represents the equivalent annual planning coefficient for controllable loads in a virtual power plant.vpp To allow the configuration of a node set for the battery, c fvpp,k Let c be the fixed weighting factor for the battery at node k in the power grid to be planned. vvpp,k Let x be the variable weighting factor for the battery at node k in the power grid to be planned. vpp,k For configuring batteries at node k in the power grid to be planned, k is a 0-1 decision variable. de k is the annual depreciation weighting factor for the battery. oc k is the operating weighting factor for the battery. mc k is the maintenance weighting factor for the battery. dc c is the weighting factor for the disposal of batteries. p Pr vpp,k Let c be the unit power of the battery at node k in the power grid to be planned. e Er vpp,k Let Γ be the unit capacity coefficient of the battery at node k in the planned power grid, Γ be the typical set of annual load and wind power output, and d be the unit capacity coefficient of the battery at node k in the planned power grid. s Let be the number of samples in a typical scenario s, and T be the planning period. The unit compensation weighting factor for interruptible load within time period t. The unit compensation weighting factor for the incentivizing load during time period t. For the set of nodes participating in the incentive load contract, For the set of nodes participating in the interruptible load contract, This represents the increase in load for node m during time period t in scenario s. Let n be the interrupt load of node n in scenario s during time period t.

[0022] Alternatively, the annual wind curtailment coefficient can be determined using the following formula:

[0023]

[0024] Among them, f wind Γ represents the annual wind curtailment coefficient, Γ represents the typical set of annual load and wind power output scenarios, and d represents the annual wind curtailment coefficient. s Ω represents the number of samples in a typical scenario s. wind Let Ψ be the set of wind farms, and c be the set of optimization times for each typical scenario. wind As a unit wind curtailment weighting factor, P represents the maximum output of wind farm w during time period t in scenario s. wind,w,t,s This represents the actual power output of the wind farm w during time period t under scenario s.

[0025] Optionally, the constraints of the power grid planning model may also include: system power flow constraints, thermal power and wind power operation constraints, and system reserve constraints.

[0026] Optionally, the multi-feed short-circuit ratio constraint is:

[0027]

[0028]

[0029]

[0030]

[0031]

[0032]

[0033]

[0034]

[0035] Among them, K 0 Let I be the set of all branches in the power grid to be planned. jic Let I be the current flowing from node j to node i on the c-th branch between node j and node i. ijc Let b be the current flowing from node i to node j on the c-th branch between node i and node j. i Let N be the injected current at node i, and let N be the set of all nodes in the power grid to be planned. HVDC The set of DC feed-in nodes in the power grid to be planned. x is the per-unit value of the rated transmission power of node i. ijc V is the reactance of the c-th branch between node i and node j. i Let V be the voltage at node i. j Let z be the voltage at node j. ijc M is a 0-1 variable representing the actual operating state of branch ijc, and M is a set value.

[0036] Optionally, the virtual power plant constraints include battery constraints and controllable load constraints.

[0037] To achieve the above objectives, the present invention also provides the following solution:

[0038] A virtual power plant collaborative power grid planning system, comprising:

[0039] The data acquisition unit is used to acquire the raw data of the power grid to be planned within a preset planning time. The raw data includes the output parameters of thermal power units, load parameters, start-up and shutdown strategies of thermal power units, grid parameters, wind farm access points, wind farm capacity, DC system access points, rated transmission power, time-series data of the raw load, and time-series data of the maximum output of wind turbine units.

[0040] The target constraint determination unit, connected to the data acquisition unit, is used to determine the equivalent annual expansion coefficient of the planned line, the equivalent annual planning coefficient of the virtual power plant, the annual wind curtailment coefficient, and the multi-infeed short-circuit ratio constraint based on the original data.

[0041] The virtual power plant constraint determination unit is used to determine the virtual power plant constraints based on the maximum rated storage capacity of the batteries configured at each node in the power grid to be planned, the maximum rated power of the batteries configured at each node, the minimum rated power of the batteries configured at each node, the maximum increase in load at each node in each time period under each typical scenario, and the maximum interruption load at each node in each time period under each typical scenario.

[0042] The planning model establishment unit is connected to the target constraint determination unit and the virtual power plant constraint determination unit respectively. It is used to establish a power grid planning model based on the multi-infeed short-circuit ratio constraint and the virtual power plant constraint, with the goal of minimizing the sum of the equivalent annual expansion coefficient of the planned line, the equivalent annual planning coefficient of the virtual power plant and the annual wind curtailment coefficient.

[0043] The solution unit, connected to the planning model establishment unit, is used to solve the power grid planning model and determine the power grid planning results; the power grid planning results include the optimal power grid line path and the battery capacity and controllable load output of the virtual power plant.

[0044] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects: with the goal of minimizing the sum of the equivalent annual expansion coefficient of the planned line, the equivalent annual planning coefficient of the virtual power plant, and the annual wind curtailment coefficient, and taking into account the multi-infeed short-circuit ratio constraint and the virtual power plant constraint, the present invention jointly optimizes the planning of the power grid line path, the battery capacity of the virtual power plant, and the controllable load output, and provides the optimal planning scheme under the premise of ensuring that the power grid has sufficient system strength, thereby improving the power grid's ability to accept new energy power generation. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is a flowchart of the virtual power plant collaborative power grid planning method of the present invention;

[0047] Figure 2 This is a schematic diagram of the modules of the virtual power plant collaborative power grid planning system of the present invention;

[0048] Figure 3This is a schematic diagram of the IEEE 24-node system in Example 3;

[0049] Figure 4 This is a time-series data graph of the initial load and the maximum output of the wind farm in Example 3;

[0050] Figure 5 This is a schematic diagram of the active power output of the 138kV zoned virtual power plant in Case 4 of Example 3;

[0051] Figure 6 This is a schematic diagram of the active power output of a 230kV zoned virtual power plant in Case 4 of Example 3.

[0052] Symbol explanation:

[0053] Data acquisition unit-101, target constraint determination unit-102, virtual power plant constraint determination unit-103, planning model establishment unit-104, solution unit-105;

[0054] Node 1-1, Node 2-2, Node 3-3, Node 4-4, Node 5-5, Node 6-6, Node 7-7, Node 8-8, Node 9-9, Node 10-10, Node 11-11, Node 12-12, Node 13-13, Node 14-14, Node 15-15, Node 16-16, Node 17-17, Node 18-18, Node 19-19, Node 20-20, Node 21-21, Node 22-22, Node 23-23, Node 24-24. Detailed Implementation

[0055] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0057] Example 1

[0058] To ensure that new energy sources have reasonable absorption capacity and that the power grid has sufficient system strength, such as Figure 1 As shown, the virtual power plant collaborative power grid planning method provided by this invention includes:

[0059] S1: Obtain the raw data of the power grid to be planned within the preset planning time.

[0060] The raw data includes thermal power unit output parameters, load parameters, thermal power unit start-up and shutdown strategies, power grid parameters, wind farm connection points, wind farm capacity, DC system connection points, rated transmission power, time-series data of the original load, and time-series data of the maximum output of the wind turbine units.

[0061] Specifically, the output parameters of thermal power units include the node where the thermal power unit is located and the rated active power output of the thermal power unit.

[0062] Load parameters include the peak power of each node.

[0063] The parameters of the power grid include line reactance, line capacity, number of existing lines, number of lines that can be expanded, and line length.

[0064] The time-series data of the original load is the actual load power in each scenario.

[0065] The time-series data of the maximum output of the wind turbine is the active power of wind power converted from wind power in each scenario. It is the maximum value of the actual active power output of wind power to the system. The difference between this value and the actual output of wind power is the wind power that needs to be discarded.

[0066] The combination of various parameter data forms the basic situation of the power grid to be planned, providing a foundation for subsequent power grid planning.

[0067] In this embodiment, the virtual power plant is formed by the aggregation of batteries and controllable loads. The active power output of the virtual power plant is the sum of the active power output of the configured batteries and the active power output of the controllable loads.

[0068] S2: Based on the original data, determine the equivalent annual expansion coefficient of the planned line, the equivalent annual planning coefficient of the virtual power plant, the annual wind curtailment coefficient, and the multi-infeed short-circuit ratio constraint.

[0069] Specifically, the multi-feed short-circuit ratio constraint is determined based on the current in each branch, the reactance in each branch, the operating status of each branch, the injected current in each node, the voltage in each node, and the rated transmission power of each DC feed-in node.

[0070] This embodiment provides a practical theoretical derivation and simplification of the formula for the multi-infeed short-circuit ratio, using per-unit values:

[0071]

[0072] Among them, K i Z represents the multi-feed short-circuit ratio at node i. ij Let be the mutual impedance between node i and node j. Let be the per-unit value of the rated transmission power of node j, and h be the number of DC feed-in nodes in the planned power grid. In this formula, both node i and node j are DC feed-in nodes.

[0073] By referencing the method for solving the nodal impedance matrix, injecting a current equivalent to the per-unit value of the rated transmission power into the DC feed-in nodes in the power grid to be planned, while leaving other nodes open, and constructing a transient mapping network, an improved mixed-integer linear model of the multi-feed-in short-circuit ratio constraint can be obtained:

[0074]

[0075]

[0076]

[0077]

[0078]

[0079]

[0080]

[0081]

[0082] Among them, K 0 I represents the set of all branches in the power grid to be planned (including all branches in the network to be planned, including unit-to-ground branches). jic Let I be the current flowing from node j to node i on the c-th branch between node j and node i. ijc Let b be the current flowing from node i to node j on the c-th branch between node i and node j. i Let b be the injected current at node i, if and only if node i is a DC-feed node. i Numerically equal to the per-unit value of the rated transmission power of the DC system at that node, otherwise b i =0, where N is the set of all nodes in the power grid to be planned. HVDC The set of DC feed-in nodes in the power grid to be planned. x is the per-unit value of the rated transmission power of node i. ijc V is the reactance of the c-th branch between node i and node j. i Let V be the voltage at node i. j Let z be the voltage at node j. ijc M is a 0-1 variable representing the actual operating state of branch ijc, and M is a set value. In this embodiment, M is a very large number.

[0083] Formula (9) limits the short-circuit ratio of multiple feeders to be greater than or equal to 3, ensuring that the feeder system has sufficient system strength.

[0084] S3: Determine the virtual power plant constraints based on the maximum rated storage capacity of the batteries configured at each node in the power grid to be planned, the maximum rated power of the batteries configured at each node, the minimum rated power of the batteries configured at each node, the maximum increase in load at each node in each typical scenario and time period, and the maximum interruption load at each node in each typical scenario and time period.

[0085] First, the connection points and parameter constraints for the batteries and controllable loads under the virtual power plant control are set to determine the constraints of the virtual power plant. The parameter constraints mainly include: the maximum rated storage capacity of the batteries configured at node k. Maximum rated power of the battery configured at node k Minimum rated power of the battery configured at node k The maximum increase in load for node m during time period t under typical scenario s The maximum interruption load value of node m during time period t under typical scenario s

[0086] The settings for battery and controllable load connection points and parameter constraints directly affect the constraints of the virtual power plant and the planning results. For a given power grid to be planned, if there are no capacity or output limitations for batteries and controllable loads, and in the extreme case, no line expansion is considered, all wind power output is absorbed by batteries, and the load shortage and surplus caused by line capacity limitations in the power grid are balanced by controllable loads, then the power grid to be planned must have a solution. However, considering practical technical and economic issues, relatively strict virtual power plant parameter constraints are considered in the initial planning stage. If the model has no solution, the constraints are relaxed until the optimal solution is obtained.

[0087] In this embodiment, the virtual power plant constraints include battery constraints and controllable load constraints.

[0088] Specifically, the battery constraints are as follows:

[0089]

[0090]

[0091] -Pr vpp,k ≤P vpp,k,t,s ≤Pr vpp,k (12)

[0092] 0≤E vpp,k,t,s ≤Er vpp,k (13)

[0093] Evpp,k,1,s =E vpp,k,T,s +P vpp,k,1,s (14)

[0094]

[0095] Among them, Er vpp,k The rated storage capacity of the battery configured for node k, Pr vpp,k The rated discharge power of the battery configured for node k. The maximum rated storage capacity of the battery configured for node k. The minimum rated power of the battery configured for node k. P is the maximum rated power of the battery configured for node k. vpp,k,t,s E represents the discharge power of the battery configured for node k during time period t in scenario s. vpp,k,t,s E represents the stored energy of the battery configured for node k during time period t in scenario s. vpp,k,1,s The stored energy E of the battery configured for node k in the first time period of scenario s. vpp,kT,s P represents the stored energy of the battery configured for node k during the last time period in scenario s. vpp,k,1,s The power of the battery configured for node k in the first time period of scenario s.

[0096] Formulas (10)-(11) limit the rated storage capacity and rated power range of the configured battery, formula (12) limits the power limit of each charge and discharge of the battery, formula (13) ensures that the battery storage energy is within the rated storage capacity range, and formulas (14)-(15) determine the corresponding change relationship between the battery storage energy and its discharge power.

[0097] The controllable load constraint is:

[0098]

[0099]

[0100]

[0101]

[0102]

[0103] in, To increase the load on node m within time period t under typical scenario s, This represents the maximum increase in load for node m during time period t under typical scenario s. This represents the interruption load of node m during time period t in a typical scenario s. This represents the maximum interruption load value of node m during time period t under typical scenario s. Let represent the 0-1 variables representing the increased load of users who have signed controllable load agreements during time period t in typical scenario s for node m. Let represent the 0-1 variables of the interruption load of users who have signed controllable load agreements during time period t in typical scenario s for node m. To increase the maximum duration of load increase, This represents the longest continuous interruption time for an interruptible load.

[0104] Formulas (16)-(17) limit the power value of node m increasing or interrupting load in time period t under typical scenario s. Formula (18) ensures that node m does not increase or interrupt load at the same time. Formulas (19)-(20) limit the duration of node continuously increasing or interrupting load.

[0105] Formulas (10)-(20) together constitute the virtual power plant constraints. When the access points and parameter limits of the batteries and controllable loads under the control of the virtual power plant change, this part of the constraints will also change accordingly.

[0106] S4: Based on the multi-infeed short-circuit ratio constraint and the virtual power plant constraint, a power grid planning model is established with the goal of minimizing the sum of the equivalent annual expansion coefficient of the planned line, the equivalent annual planning coefficient of the virtual power plant, and the annual wind curtailment coefficient.

[0107] In this embodiment, the constraints of the power grid planning model also include conventional constraints such as system power flow constraints, thermal and wind power operation constraints, and system reserve constraints. The specific expressions of conventional constraints will not be elaborated further here.

[0108] Specifically, the objective function of the power grid planning model is:

[0109] minf = fline + fvpp + f wind (twenty one)

[0110] Where f is the objective function value, f line f is the equivalent annual expansion coefficient for the planned route. vpp f is the equivalent annual planning coefficient for the virtual power plant. wind The wind curtailment coefficient is the annual value. In this embodiment, the wind curtailment coefficient of the power grid planning model is based on the planning year, so the line expansion coefficient and the planning coefficient of the virtual power plant are converted into equal-year values.

[0111] The equivalent annual expansion coefficient of the planned route is determined using the following formula:

[0112]

[0113] Among them, f lineThe equivalent annual expansion coefficient for the planned route is given by 'a', where 'a' is the equivalent annual conversion rate of the route expansion coefficient, and 'q' is the service life. Let c be the set of candidate branches in the power grid to be planned. ij The expansion weighting factor for the candidate branch ij, The number of branch roads to be constructed is ij.

[0114] The equivalent annual planning coefficient for the virtual power plant is determined using the following formula:

[0115]

[0116] Among them, f vpp f is the equivalent annual planning coefficient for the virtual power plant. vpp1 f is the equivalent annual planning coefficient for batteries in the virtual power plant. vpp2 Ω represents the equivalent annual planning coefficient for controllable loads in a virtual power plant. vpp To allow the configuration of a node set for the battery, c fvpp,k Let c be the fixed weighting factor for the battery at node k in the power grid to be planned. vvpp,k Let x be the variable weighting factor for the battery at node k in the power grid to be planned. vpp,k For configuring batteries at node k in the power grid to be planned, k is a 0-1 decision variable. de k is the annual depreciation weighting factor for the battery. oc k is the operating weighting factor for the battery. mc k is the maintenance weighting factor for the battery. dc c is the weighting factor for the disposal of batteries. p Pr vpp,k Let c be the unit power of the battery at node k in the power grid to be planned. e Er vpp,k Let Γ be the unit capacity coefficient of the battery at node k in the planned power grid, Γ be the typical set of annual load and wind power output, and d be the unit capacity coefficient of the battery at node k in the planned power grid. s Let be the number of samples in a typical scenario s, and T be the planning period. The unit compensation weighting factor for interruptible load within time period t. The unit compensation weighting factor for the incentivizing load during time period t. For the set of nodes participating in the incentive load contract, For the set of nodes participating in the interruptible load contract, This represents the increase in load for node m during time period t in scenario s. Let n be the interrupt load of node n in scenario s during time period t.

[0117] The annual wind curtailment coefficient is determined using the following formula:

[0118]

[0119] Among them, f wind Γ represents the annual wind curtailment coefficient, Γ represents the typical set of annual load and wind power output scenarios, and d represents the annual wind curtailment coefficient. s Ω represents the number of samples in a typical scenario s. wind Let Ψ be the set of wind farms, and c be the set of optimization times for each typical scenario. wind As a unit wind curtailment weighting factor, P represents the maximum output of wind farm w during time period t in scenario s. wind,w,t,s This represents the actual power output of the wind farm w during time period t under scenario s.

[0120] S5: Solve the power grid planning model to determine the power grid planning results. The power grid planning results include the optimal power grid line paths and the battery capacity and controllable load output of the virtual power plant.

[0121] For the overall power grid planning model, the defined decision variables consist of integer and continuous variables, where the integer variables include the 0-1 variable z representing the actual operating state of branch ijc. ijc The 0-1 variable representing the increased load of users who signed controllable load agreements during the time period t in a typical scenario s for node m. The 0-1 variable of interrupted load of users who signed controllable load agreements during time period t in typical scenario s for node m. The planned line layout and the participation status of controllable loads were determined. The continuous variable is the current I flowing from node j to node i on the c-th branch between node j and node i, which is within the multi-infeed short-circuit ratio constraint. ijc Voltage V at node i i The discharge power P of the battery configured at node k in the virtual power plant constraint during time period t in scenario s. vpp,k,t,s The stored energy E of the battery configured at node k during time period t in scenario s. vpp,k,t,s Node m increases load within time period t under typical scenario s. The interrupt load of node m during time period t in a typical scenario s And the line active power flow p in conventional system power flow constraints, thermal power and wind power operation constraints, and system reserve constraints. ijc,t,s Active power p of thermal power plants g,t,s Active power p of wind farm w,t,s Node voltage phase angle θ i,t,s These measures are taken to ensure that the system can operate normally under various security constraints.

[0122] The power grid planning model in this embodiment is a mixed integer linear programming model. Therefore, Gurobi is called in the Matlab environment to solve the power grid planning model.

[0123] Further, step S5 specifically includes: calling Gurobi in the Matlab runtime environment to solve the power grid planning model, and determining whether a solution exists based on the solution results. If a solution exists, this solution is the power grid planning result; if no solution exists, the constraint range of the virtual power plant parameters is adjusted, and the process proceeds to step S3 to redetermine the virtual power plant constraints.

[0124] In this embodiment, four cases are set up. After the power grid planning model is built, the values ​​of sce are selected sequentially for planning. The results are compared and analyzed to distinguish the impact of whether or not virtual power plants are connected on the power grid planning. When sce is 1, flag_DSM=0 and flag_ESS=0, indicating that no virtual power plants are configured; when sce is 2, flag_DSM=1 and flag_ESS=0, indicating that only controllable loads are configured; when sce is 3, flag_DSM=0 and flag_ESS=1, indicating that only energy storage batteries are configured; when sce is 4, flag_DSM=1 and flag_ESS=1, indicating that a virtual power plant including batteries and controllable loads is configured. Through simulation analysis of the power grid planning model in the Matlab environment, the results show that the power grid planning model provided by this invention can effectively guarantee system strength and provide a comprehensive optimal power grid planning scheme.

[0125] This invention considers the participation of energy storage batteries and controllable loads under the control of virtual power plants in the process of absorbing new energy power, mainly wind power, in the conventional power grid planning model. At the same time, it introduces the limitation of multiple infeed short-circuit ratio to ensure sufficient system strength. It jointly optimizes the configuration capacity of virtual power plants with the power grid structure and incorporates the quantification of new energy absorption into the objective function. It can provide the optimal planning scheme under the premise of ensuring sufficient system strength of the power grid structure, ensuring the cleanliness and environmental protection of the power grid, and improving the power grid's ability to accept new energy power generation and its technical and economic efficiency.

[0126] Example 2

[0127] In order to implement the method corresponding to Embodiment 1 above and achieve the corresponding functions and technical effects, a virtual power plant collaborative power grid planning system is provided below.

[0128] like Figure 2 As shown, the virtual power plant collaborative power grid planning system provided in this embodiment includes: a data acquisition unit 101, an objective constraint determination unit 102, a virtual power plant constraint determination unit 103, a planning model establishment unit 104, and a solution unit 105.

[0129] The data acquisition unit 101 is used to acquire the raw data of the power grid to be planned within a preset planning time. The raw data includes the output parameters of thermal power units, load parameters, start-up and shutdown strategies of thermal power units, grid structure parameters, wind farm connection points, wind farm capacity, DC system connection points, rated transmission power, time-series data of the original load, and time-series data of the maximum output of wind turbine units.

[0130] The target constraint determination unit 102 is connected to the data acquisition unit 101. The target constraint determination unit 102 is used to determine the equivalent annual expansion coefficient of the planned line, the equivalent annual planning coefficient of the virtual power plant, the annual wind curtailment coefficient, and the multi-infeed short-circuit ratio constraint based on the original data.

[0131] The virtual power plant constraint determination unit 103 is used to determine the virtual power plant constraints based on the maximum rated storage capacity of the batteries configured at each node in the power grid to be planned, the maximum rated power of the batteries configured at each node, the minimum rated power of the batteries configured at each node, the maximum increase in load value of each node in each time period under each typical scenario, and the maximum interruption load value of each node in each time period under each typical scenario.

[0132] The planning model establishment unit 104 is connected to the target constraint determination unit 102 and the virtual power plant constraint determination unit 103 respectively. The planning model establishment unit 104 is used to establish a power grid planning model based on the multi-infeed short-circuit ratio constraint and the virtual power plant constraint, with the goal of minimizing the sum of the equivalent annual expansion coefficient of the planned line, the equivalent annual planning coefficient of the virtual power plant and the annual wind curtailment coefficient.

[0133] The solution unit 105 is connected to the planning model establishment unit 104. The solution unit 105 is used to solve the power grid planning model and determine the power grid planning results. The power grid planning results include the optimal power grid line path and the battery capacity and controllable load output of the virtual power plant.

[0134] Example 3

[0135] To verify the effectiveness and universality of the power grid planning models established in Embodiments 1 and 2 above, this embodiment uses the IEEE 24-bus system for example analysis, such as... Figure 3 The diagram illustrates the distribution of 24 nodes, wind farms, and DC feed-in systems. In addition to the thermal power unit parameters, load parameters, and basic grid parameters included in the example system, it is assumed that the total maximum predicted load increases to 8850MW in a certain planning year. There are 41 feasible transmission corridors for expansion, with each corridor capable of constructing a maximum of 3 new lines (including existing lines). The original load time-series data is shown below. Figure 4 As shown in Table 1, the parameters of the thermal power unit and the load parameters are as follows:

[0136] Table 1 Parameters and Load Parameters of Thermal Power Units

[0137]

[0138]

[0139] A power of 0 indicates that there are no thermal power units at this node or that it is a non-load node. The start-up and shutdown strategy of thermal power units is set according to the order of their nodes, numbered G1-G10, as follows: as the load time sequence value increases from 0 to the maximum value, they are put into operation in the order of G1, G3, G5, G6, G9, G10, G7, G2, G4, G8, and once put into operation, they must run continuously for at least 5 time periods.

[0140] The DC transmission points are at node 19 and node 20, both with a rated transmission capacity of 600MW. A 500MW wind farm is connected at node 3, and an 800MW wind farm is connected at node 23. The time-series data for maximum wind power output are as follows: Figure 4 As shown, batteries are configured at the wind farm grid connection point to smooth out random fluctuations in wind farm output power. The maximum rated storage capacity is limited to 4000MW, and the maximum and minimum rated power limits are ±1000MW. All load nodes in the system are set as addable loads, with a maximum addable load of 50% of the node's peak power. Nodes 3, 6, 9, 13, 15, and 18 are set as interruptible loads, with a maximum interruptible load of 25% of the node's peak power. A virtual power plant is configured in each of the 138kV and 230kV voltage zones. Both virtual power plants aggregate batteries and several controllable loads. The virtual power plants accept dispatch tasks and participate in operation, allocating corresponding active power adjustments to the batteries and controllable loads in their respective zones. Their output at each time period is the sum of the aggregated outputs.

[0141] The unit wind curtailment weight factor is 0.61 / kWh. The battery weight factor parameters in the virtual power plant are shown in Table 2, and the time-sharing compensation weight factor for controllable loads is shown in Table 3.

[0142] Table 2 Battery Weighting Factors

[0143] <![CDATA[r op ]]> <![CDATA[r de ]]> <![CDATA[c p ( / kW)]]> <![CDATA[c e ( / kWh)]]> 0.01 0.05 200 4000

[0144] Table 3 Time-sharing compensation weighting factors

[0145]

[0146] To demonstrate the advantages of combining virtual power plants with grid planning, four different case studies were set up for planning model solving. Case 1: Model solving without configuring virtual power plants; Case 2: Model solving with only controllable loads configured; Case 3: Model solving with only batteries configured; Case 4: Model solving with virtual power plants that include both batteries and controllable loads configured. Table 4 shows the planning results for different cases, and Table 5 shows the planning coefficients for different cases.

[0147] Table 4. Planning Results for Different Cases

[0148]

[0149] Table 5. Planning coefficients under different cases

[0150] Unit: million

[0151] Case <![CDATA[f line ]]> <![CDATA[f wind ]]> <![CDATA[f vpp1 ]]> <![CDATA[f vpp2 ]]> f Case 1 - - - - - Case 2 20.2709 237.2794 0 8.369636 265.92 Case 3 30.4064 3.031004 100.1521 0 133.5895 Case 4 20.2709 12.6336 25.927 5.91679 64.7484

[0152] Analyze the planning results in Tables 4 and 5:

[0153] Case 1 is the case where no virtual power plant is configured, and the result is no solution.

[0154] Cases 2, 3, and 4 all have solutions, indicating that adding energy storage batteries and controllable loads is of positive significance for system planning, and the multi-infeed short-circuit ratio in each case meets the requirements.

[0155] Case 2 is a case where the model is solved by simply configuring controllable load. The result shows a wind curtailment coefficient of 237.2794 Mb, plus a controllable load planning coefficient of 8.369636 Mb.

[0156] Case 3 presents a model solution based solely on battery configuration. Comparing the planning results of Case 2 and Case 3, it can be observed that the wind curtailment coefficient in Case 3 is reduced to 3.031004 Mb, despite a larger battery planning coefficient of 100.1521 Mb., the total annual coefficient is 132.3305 Mb smaller than in Case 2. This indicates that adding batteries to the wind farm side can not only smooth out wind power fluctuations but also improve system regulation capabilities and increase the system's ability to accommodate wind power. Meanwhile, the line expansion coefficient in Case 2 is lower than in Case 3, suggesting that increasing controllable load can reduce peak load on the load curve, thereby reducing the planned line expansion coefficient.

[0157] Comparing the planning results of Case 4 with Cases 2 and 3, it can be found that compared to Case 2, the controllable load planning coefficient in Case 4 is 2.452846 MWh lower; compared to Case 3, the battery planning coefficient in Case 4 is 74.2251 MWh lower. Although the wind curtailment coefficient is larger than in Case 3, the total annual coefficient is the smallest. This indicates that configuring a virtual power plant that includes both batteries and controllable loads has the most significant impact on the economic benefits of the plan, as shown in the active power output diagram. Figure 5 and Figure 6 As shown.

[0158] Based on the above analysis, it can be determined that compared with the planning model that only considers one factor (configuring batteries or controllable loads on the wind farm side), the virtual power plant configuration that comprehensively considers the cooperation between batteries and controllable loads can fully balance various planning coefficients. On the basis of ensuring the safe and reliable operation of the system, it can maximize the wind power absorption capacity, optimize the virtual power plant configuration capacity and line planning scheme, and thus achieve the overall optimal solution.

[0159] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0160] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A virtual power plant collaborative grid planning method, characterized in that, The virtual power plant collaborative power grid planning method comprises the following steps: Obtain original data of the power grid to be planned within a preset planning time; the original data comprises output parameters of thermal power units, load parameters, start-stop strategies of thermal power units, power grid network parameters, wind farm access points, wind farm capacities, direct current system access points, rated transmission power, time sequence data of original loads, and time sequence data of maximum output of wind power units; Based on the original data, determine equivalent annual expansion coefficients of planning lines, equivalent annual planning coefficients of virtual power plants, annual abandoned wind coefficients, and multi-infeed short circuit ratio constraints; Determine the equivalent annual expansion coefficients of the planning lines by using the following formula: wherein f line is the equivalent annual expansion coefficient of the planned line, a is the annual conversion rate of the line expansion coefficient, q is the service life, is the set of candidate branches in the to-be-planned power grid, c ij is the expansion weight factor of the candidate branch ij, is the number of constructions of the candidate branch ij; Determine the equivalent annual planning coefficients of the virtual power plants by using the following formula: wherein f vpp is the equivalent annual planning coefficient of the virtual power plant, f vpp1 is the equivalent annual planning coefficient of the battery in the virtual power plant, f vpp2 is the equivalent annual planning coefficient of the controllable load in the virtual power plant, Ω vpp is the set of nodes allowed to be configured with batteries, c fvpp,k is the fixed weight factor of the battery at node k in the power grid to be planned, c vvpp,k is the variable weight factor of the battery at node k in the power grid to be planned, x vpp,k is the 0-1 decision variable of configuring the battery at node k in the power grid to be planned, k de is the annual depreciation weight factor of the battery, k oc is the operation weight factor of the battery, k mc is the maintenance weight factor of the battery, k dc is the disposal weight factor of the battery, c p Pr vpp,k is the unit power of the battery at node k in the power grid to be planned, c e Er vpp,k is the unit capacity coefficient of the battery at node k in the power grid to be planned, Γ is the set of typical scenarios of annual load and wind power output, d s is the sample number of the typical scenario s, T is the planning period, is the unit compensation weight factor of the interruptible load in the t period, is the unit compensation weight factor of the incentive load in the t period, is the set of nodes participating in the incentive load contract, is the set of nodes participating in the interruptible load contract, is the amount of increased load of node m in the t period under scenario s, is the amount of interrupted load of node n in the t period under scenario s; Determine the annual abandoned wind coefficients by using the following formula: where f wind is the annual wind curtailment factor, Ω wind is the set of wind farms, Ψ is the set of optimization times for each typical scenario, c wind is the unit wind curtailment weight factor, is the maximum output of wind farm w in time period t under scenario s, P wind,w,t,s is the actual output of wind farm w in time period t under scenario s. The multi-infeed short circuit ratio constraint is: wherein K 0 is the set of all branches in the power grid to be planned, I jic is the current flowing from node j to node i through the cth branch between node j and node i, I ijc is the current flowing from node i to node j through the cth branch between node i and node j, b i is the injected current of node i, N is the set of all nodes in the power grid to be planned, N HVDC is the set of DC feed-in nodes in the power grid to be planned, is the normalized value of the rated transmission power of node i, x ijc is the reactance of the cth branch between node i and node j, V i is the voltage of node i, V j is the voltage of node j, z ijc is a 0-1 variable of the actual operating state of the branch i-j-c, and M is a set value; Determine virtual power plant constraints according to the maximum rated storage capacity of the batteries configured for each node in the power grid to be planned, the maximum rated power of the batteries configured for each node, the minimum rated power of the batteries configured for each node, the maximum increased load values of each node in each time period under each typical scenario, and the maximum interrupted load values of each node in each time period under each typical scenario; the virtual power plant constraints comprise battery constraints and controllable load constraints; The battery constraints are: - Pr vpp,k ≤ P vpp,k,t,s ≤ Pr vpp,k ; 0 < E vpp,k,t,s ≤ Er vpp,k ; E vpp,k,1,s = E vpp,k,T,s + P vpp,k,1,s ; wherein, Er vpp,k is the nominal storage capacity of the battery configured for node k, Pr vpp,k is the nominal discharge power of the battery configured for node k, is the maximum nominal storage capacity of the battery configured for node k, is the minimum nominal power of the battery configured for node k, is the maximum nominal power of the battery configured for node k, P vpp,k,t,s is the discharge power of the battery configured for node k at time period t under scenario s, E vpp,k,t,s is the storage energy of the battery configured for node k at time period t under scenario s, E vpp,k,1,s is the storage energy of the battery configured for node k at the first time period under scenario s, E vpp,kT,s is the storage energy of the battery configured for node k at the last time period under scenario s, P vpp,k,1,s is the power of the battery configured for node k at the first time period under scenario s; The controllable load constraints are: wherein, is the amount of load increase for node m in period t under typical scenario s, is the maximum amount of load increase for node m in period t under typical scenario s, is the amount of load interruption for node m in period t under typical scenario s, is the maximum amount of load interruption for node m in period t under typical scenario s, is a 0-1 variable for the amount of load increase for a controllable load agreement user of node m in period t under typical scenario s, is a 0-1 variable for the amount of load interruption for a controllable load agreement user of node m in period t under typical scenario s, is the maximum duration of load increase for controllable load agreement users, is the maximum duration of load interruption for controllable load agreement users; Based on the multi-infeed short circuit ratio constraints and the virtual power plant constraints, establish a power grid planning model with the minimum sum of the equivalent annual expansion coefficients of the planning lines, the equivalent annual planning coefficients of the virtual power plants, and the annual abandoned wind coefficients as the target; Solve the power grid planning model to determine a power grid planning result; the power grid planning result comprises an optimal power grid line path and battery capacities and controllable load outputs of the virtual power plant.

2. The virtual power plant collaborative grid planning method of claim 1, wherein, The objective function of the power grid planning model is: minf = f line + f vpp + f wind ; wherein f is the objective function value, f line is the equivalent annual expansion coefficient of the planned line, f vpp is the equivalent annual planning coefficient of the virtual power plant, f wind is the annual wind curtailment coefficient.

3. The virtual power plant collaborative grid planning method of claim 1, wherein, The constraint conditions of the power grid planning model further comprise system power flow constraints, thermal power and wind power operation constraints, and system reserve constraints.

4. A virtual power plant collaborative grid planning system, applied to the virtual power plant collaborative grid planning method of any one of claims 1-3, characterized in that, The virtual power plant collaborative power grid planning system comprises: A data acquisition unit configured to acquire original data of a power grid to be planned within a preset planning time; the original data comprises output parameters of thermal power units, load parameters, start-stop strategies of thermal power units, power grid network parameters, wind farm access points, wind farm capacities, direct current system access points, rated transmission power, time sequence data of original loads, and time sequence data of maximum output of wind power units; A target constraint determination unit connected to the data acquisition unit and configured to determine equivalent annual expansion coefficients of planning lines, equivalent annual planning coefficients of virtual power plants, annual abandoned wind coefficients, and multi-infeed short circuit ratio constraints based on the original data; A virtual power plant constraint determination unit configured to determine virtual power plant constraints according to the maximum rated storage capacity of the batteries configured for each node in the power grid to be planned, the maximum rated power of the batteries configured for each node, the minimum rated power of the batteries configured for each node, the maximum increased load values of each node in each time period under each typical scenario, and the maximum interrupted load values of each node in each time period under each typical scenario; the virtual power plant constraints comprise battery constraints and controllable load constraints; A planning model establishing unit, connected with the target constraint determining unit and the virtual power plant constraint determining unit respectively, is configured to establish a power grid planning model based on the multi-infeed short-circuit ratio constraint and the virtual power plant constraint, with the objective of minimizing the sum of the equivalent annual expansion coefficient of the line, the equivalent annual planning coefficient of the virtual power plant, and the annual curtailed wind power. A solving unit, connected with the planning model establishing unit, is configured to solve the power grid planning model to determine a power grid planning result, which includes the optimal power grid line path and the battery capacity and controllable load output of the virtual power plant.

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