A source-network collaborative optimization control method and system for green electricity tracing

Abstract

The application belongs to the technical field of power system collaborative control, and relates to a source-grid collaborative optimization control method and system for green electricity tracing, which comprises the following steps: acquiring power grid topology data, new energy power source access node information, typical day source load data, power flow data and power grid real-time operation state data of a power system; constructing a power distribution coefficient matrix to determine the distribution proportion relationship between the new energy power source and the node and calculate the path priority coefficient of the new energy power source; constructing a direct current power flow model to determine the adjusted line power flow; calculating the node green electricity component corrected by the comprehensive network loss rate of the whole network; constructing a source-grid-load-storage collaborative optimization model according to the relevant data; and obtaining the source-grid-load-storage collaborative optimization control strategy in the dispatching period according to the source-grid-load-storage collaborative optimization model. The application solves the dilemma of "abandoning green and preserving stability" in traditional regulation and control, and realizes the win-win of the safe and clean transformation of the regional power grid in the new energy area.

Description

Technical Field

[0001] This application belongs to the field of power system collaborative control technology, and in particular relates to a source-grid collaborative optimization control method and system for green electricity traceability. Background Technology

[0002] Driven by both "dual-carbon" goals and the construction of a new power system, the installed capacity and penetration rate of new energy sources such as wind power and photovoltaics continue to increase, and the power system is accelerating its deep transformation towards a clean and low-carbon direction. The National Development and Reform Commission and the National Energy Administration have successively issued relevant policies, explicitly proposing to build an efficient and coordinated new energy consumption and control system to address the challenges to grid safety and consumption caused by the randomness and volatility of new energy output. Against this backdrop, maximizing the consumption of local green electricity and ensuring the safe operation of power flow at key sections have become the core objectives of regional power grid operation and control. The synergistic optimization between these two aspects constitutes a current research focus and technological bottleneck in the energy and power sector.

[0003] In existing research, green electricity source tracing technology enables the precise allocation of new energy power output at each load node, providing a quantitative basis for the precise consumption of green electricity; while power flow sensitivity analysis, as an efficient tool for rapid and precise control of power flow in the power grid, has also yielded rich research results in the coordinated application of control resources.

[0004] The two technologies mentioned above remain relatively disconnected in their application. Green electricity source tracing research has not yet deeply integrated the obtained path characteristics and nodal green electricity components with real-time grid control strategies, making it difficult to effectively translate the goal of "prioritizing the absorption of green electricity from high-proportion renewable energy paths" into concrete, adjustable control measures. Meanwhile, power flow sensitivity analysis, in its application, fails to set differentiated absorption priorities based on the path characteristics revealed by green electricity source tracing, easily leading to insufficient absorption of green electricity from high-renewable energy paths. This disconnect makes it difficult for existing methods to simultaneously achieve the synergistic optimization of "precise green electricity absorption" and "grid power flow stability," constituting a decoupling between source tracing and control. Summary of the Invention

[0005] The purpose of this application is to provide a source-grid coordinated optimization control method and system for green electricity traceability, which can improve the green electricity consumption level while ensuring the safety and stability of power grid flow, and achieve deep integration of green electricity traceability results and real-time control.

[0006] To achieve the above objectives, this application employs the following technical solution:

[0007] Firstly, this application provides a source-grid coordinated optimization control method for green electricity traceability, including:

[0008] Acquire power system topology data, new energy power source access node information, typical daily source-load data, power flow data, and real-time power grid operation status data;

[0009] Based on the power grid topology data, construct a power allocation coefficient matrix;

[0010] Based on the new energy power access node information and the power allocation coefficient matrix, the allocation ratio between new energy power sources and grid nodes is determined;

[0011] Based on the new energy power access node information, calculate the path priority coefficient of the new energy power.

[0012] Based on the power grid topology data and the power flow data, a DC power flow model is constructed, and based on the DC power flow model, the adjusted line power flow is determined;

[0013] Based on the typical daily source-load data and the distribution ratio, the node green electricity component after correction for the overall network loss rate is calculated;

[0014] Based on the real-time operation status data of the power grid, the allocation ratio, the path priority coefficient, the line power flow and the green electricity component of the nodes, a source-grid-load-storage collaborative optimization model is constructed with the optimization objectives of minimizing power flow fluctuations and maximizing green electricity consumption.

[0015] Based on the source-grid-load-storage collaborative optimization model, a source-grid-load-storage collaborative optimization control strategy is generated within the scheduling period.

[0016] Furthermore, constructing the power allocation coefficient matrix based on the power grid topology data includes:

[0017] Based on the power grid topology data, construct the branch power flow diagonal matrix;

[0018] Based on the branch power flow diagonal matrix, calculate the nodal outflow power of each node:

[0019]

[0020]

[0021]

[0022] In the formula, This represents the total number of power grid branches. Represented as a power grid branch, The dimension representing the power grid branch takes the value of ; The total number of power grid nodes. Represented as a power grid node, The dimension representing a power grid node takes the value of ; This is a node-branch association matrix, where the elements are... , Indicates power grid branch With power grid nodes Starting from the power grid node, the power flow begins. Outflow, Indicates power grid branch With power grid nodes As the endpoint, the power flow originates from the grid node. Inflow, Indicates power grid branch and power grid nodes No direct connection; The dimension is The diagonal matrix is ​​used to characterize the real-time active power flow magnitude of each branch. diagonal matrix The elements are defined as the real-time active power flow value of power grid branch e, and the power flow direction is related to the node-branch correlation matrix. Consistent; Representing the node-branch association matrix A matrix composed of the absolute values ​​of its elements; Node-branch association matrix A submatrix of positive elements; The dimension is The whole vector; The total active power flowing from each node to the downstream branches;

[0023] Calculate the node-branch power allocation coefficient matrix based on the node outflow power:

[0024]

[0025]

[0026]

[0027] In the formula, For power grid nodes via power grid branch Outflow power; The node-branch power allocation coefficient matrix. For matrix The elements in the array are used to represent the elements from the power grid nodes. Inflow to power grid branch The power, accounting for the power of the power grid branch The proportion of total active power; Indicates from the power grid node Inflow to power grid branch The power components, Indicates power grid branch The total amount of active power flow;

[0028] Calculate the power allocation coefficient matrix based on the node-branch power allocation coefficient matrix:

[0029]

[0030]

[0031]

[0032] In the formula, As the starting node, It is the terminal node; This is the set of outflow branches of a power grid node; Starting node Flowing to the terminal node Outflow power; This is the power distribution coefficient matrix; For matrix The element represents the starting node. Flowing out and flowing into the terminal node The power of the starting node The proportion of total outflow power; Node-branch association matrix The transpose of .

[0033] Further, determining the allocation ratio between the new energy source and the grid node based on the new energy source access node information and the power allocation coefficient matrix includes:

[0034] Based on the new energy power access node information, define the power node unit injection vector:

[0035]

[0036] In the formula, Numbering new energy power sources; Indicates new energy power supply Location of the power grid node; To track new energy power sources Flow to grid nodes The unit injection value;

[0037] Based on the power allocation coefficient matrix and the unit injection vector, an iterative formula for power allocation ratio is constructed, and the initial value and convergence condition of the iteration are set.

[0038] Based on the iterative formula, initial values, and convergence conditions, iterative calculations are performed to determine the allocation ratio coefficient between the new energy source and the grid node:

[0039]

[0040]

[0041]

[0042] In the formula, For the number of iterations, Dimensions for characterizing new energy power sources; For the first The allocation ratio coefficient matrix between new energy sources and grid nodes after the next iteration For the first The allocation ratio coefficient matrix between new energy sources and grid nodes after the next iteration; For matrix The element in represents the first element. After the next iteration, new energy power supply Unit power distribution to grid nodes Power ratio; For matrix The element in represents the first element. After the next iteration, new energy power supply Unit power distribution to grid nodes Power ratio; Power allocation coefficient matrix The transpose of the matrix; For new energy power sources Unit power allocation to nodes The initial value of the power ratio; Defined as the convergence accuracy.

[0043] Further, the step of calculating the path priority coefficient of the new energy power source based on the new energy power source access node information includes:

[0044] Based on the new energy power access node information, determine the power supply path of each new energy power source;

[0045] The proportion of renewable energy in the power supply path at each time point is calculated to obtain the original priority coefficient sequence:

[0046]

[0047] In the formula, For new energy power sources Power supply path at time The strength of the green attribute ranges from [0,1]. The larger the value, the higher the proportion of new energy in the path and the higher the priority of green electricity consumption. A collection of new energy power sources within the regional power grid; For new energy power sources A subset of new energy power sources within a dedicated power supply path; Indicates the power supply number within the dedicated power supply path; This refers to the collection of all power sources within a dedicated power supply path, including new energy power sources and thermal power units; Power supply within a dedicated power path The moment Actual effort;

[0048] The original priority coefficient sequence is smoothed using a long period to obtain the path priority coefficients for new energy power sources.

[0049]

[0050] In the formula, For smoothed new energy power sources Path priority coefficient; This represents the total number of time periods.

[0051] Furthermore, the DC power flow model includes network-wide power flow equations and nodal power balance equations, the form of which is as follows:

[0052]

[0053] In the formula, Let be the active power flow vector of the power grid branch, where the elements are . , This represents the diagonal matrix of branch admittances. The node voltage phase angle vector;

[0054] The nodal power balance equations are in the following form:

[0055]

[0056] In the formula, The injected power vector for the grid node, with elements as follows: , The admittance matrix of the power grid nodes;

[0057] Based on the network-wide power flow equation and the node power balance equation, the equation relating node injected power to line power flow is obtained:

[0058] .

[0059] Furthermore, determining the adjusted line power flow based on the DC power flow model includes:

[0060] Based on the aforementioned DC power flow model, a DC power flow sensitivity model is constructed to determine the linear relationship between the adjustment of node injected power and the change in line power flow, and the adjusted line power flow is calculated.

[0061]

[0062]

[0063]

[0064]

[0065]

[0066] In the formula, Time scale; This is the DC power flow sensitivity matrix, where the elements are... Used to represent power grid nodes Power grid branch DC power flow sensitivity coefficient; For power grid nodes At any moment The amount of injection power adjustment; For power grid nodes time The adjustment amount of the energy storage system output; For power grid nodes At any moment The adjustment amount of the output of the thermal power unit; For power grid branch At any moment Branch flow adjustment amount; For the regulated power grid branches At any moment The branch trend; For power grid branch At any moment The trend of branch roads.

[0067] Further, the step of calculating the node green electricity component after correction for the overall network loss rate based on the typical daily source-load data and the allocation ratio includes:

[0068] Based on the typical daily source-load data and the allocation ratio, the output of each new energy source is allocated to the corresponding load node to obtain the uncorrected node green electricity component:

[0069]

[0070] In the formula, A set of load nodes within a regional power grid; For at any time power grid nodes Green electricity component; Defined as a new energy power source At any moment contribution; For new energy power sources Unit power distribution to grid nodes The power ratio;

[0071] The uncorrected node green electricity component is corrected using the overall network loss rate to obtain the corrected node green electricity component:

[0072]

[0073] In the formula, Defined as the overall network loss rate.

[0074] Furthermore, the source-grid-load-storage collaborative optimization model, which aims to minimize power flow fluctuations and maximize green energy consumption, has the following objective function:

[0075]

[0076]

[0077]

[0078] In the formula, These are the weighting coefficients for minimizing power flow fluctuations; Weighting coefficients for maximizing green energy consumption; Minimize the sub-objective value of power flow fluctuation; To maximize the sub-target value of green energy consumption; To optimize the objective function value; This is the line penalty factor; For power grid branch Trend reference value; For power grid nodes At any moment Energy storage output, For power grid nodes At any moment Energy storage output, For power grid nodes At any moment The actual load value;

[0079] The optimization constraints of the source-grid-load-storage collaborative optimization model include thermal power unit constraints, energy storage constraints, and power flow thermal stability limit constraints. The thermal power unit constraints are as follows:

[0080]

[0081] In the formula, For at the power grid node The maximum output of the thermal power unit, For at the power grid node The minimum output of thermal power units; For power grid nodes At any moment -1 energy storage output To optimize the time step;

[0082] The energy storage constraint takes the following form:

[0083]

[0084]

[0085] In the formula, For at the power grid node The upper limit of energy storage charging and discharging power, For at the power grid node The lower limit of energy storage charging and discharging power; For power grid nodes At any moment The stored energy state of the current, For power grid nodes At any moment The stored energy level is as follows: This is the upper limit of the energy storage capacity. This represents the lower limit of the energy storage capacity. For energy storage charging efficiency, For energy storage discharge efficiency;

[0086] The form of the power flow thermal stability limit constraint is as follows:

[0087]

[0088] In the formula, This is the maximum permissible active power flow value for the branch.

[0089] Further, the step of generating a source-grid-load-storage coordinated optimization control strategy within the scheduling period based on the source-grid-load-storage coordinated optimization model includes:

[0090] An optimization solver is used to solve the source-grid-load-storage collaborative optimization model for the current time period to obtain the source-grid-load-storage collaborative optimization control strategy for the current time period.

[0091] The optimized control strategy is then distributed to the thermal power units and energy storage system for execution.

[0092] Collect the actual operating status of the power grid for the next time period, and use this operating status as the initial condition for optimization in the next time period to enter the next optimization cycle;

[0093] Repeat the above steps until the optimization of all time periods within the scheduling period is completed, and output the source-grid-load-storage collaborative optimization control strategy within the scheduling period.

[0094] Secondly, this application provides a source-grid coordinated optimization control system for green electricity traceability, the system comprising:

[0095] The data acquisition module is used to acquire power grid topology data, new energy power source access node information, typical daily source-load data, power flow data, and real-time power grid operation status data of the power system.

[0096] The allocation coefficient matrix construction module is used to construct a power allocation coefficient matrix based on the power grid topology data.

[0097] The allocation ratio determination module is used to determine the allocation ratio between new energy sources and grid nodes based on the new energy power access node information and the power allocation coefficient matrix.

[0098] The path priority coefficient module is used to calculate the path priority coefficient of the new energy power source based on the new energy power source access node information.

[0099] The line power flow module is used to construct a DC power flow model based on the power grid topology data and the power flow data, and to determine the adjusted line power flow based on the DC power flow model.

[0100] The node green electricity component module is used to calculate the node green electricity component after correction of the overall network loss rate based on the typical daily source-load data and the distribution ratio.

[0101] The optimization model construction module is used to construct a source-grid-load-storage collaborative optimization model with the optimization objectives of minimizing power flow fluctuations and maximizing green energy consumption, based on the real-time operation status data of the power grid, the allocation ratio relationship, the path priority coefficient, the line power flow and the green energy component of the nodes.

[0102] The strategy generation module is used to obtain the source-grid-load-storage collaborative optimization control strategy within the scheduling period based on the source-grid-load-storage collaborative optimization model.

[0103] Compared with the prior art, the beneficial effects achieved by this application are as follows:

[0104] The source-grid coordinated optimization control method for green electricity traceability disclosed in this application, based on the allocation ratio between renewable energy sources and nodes, and combined with the renewable energy proportion of renewable energy supply paths to set path priority coefficients, constructs a source-grid-load-storage coordinated optimization model. Under the premise of meeting grid operation safety constraints, it achieves priority consumption of green electricity while suppressing power flow fluctuations, thus improving the transparency of green electricity consumption and the safety of grid operation. This application solves the technical problems in existing technologies, such as the inability to quantitatively assess the actual level of green electricity consumption and the difficulty in distinguishing different green electricity consumption priorities, through quantitative tracking of green electricity components. It can support the priority scheduling of power supply paths with a high proportion of renewable energy during source-grid coordinated optimization, effectively improving the green electricity consumption of the regional power grid. Attached Figure Description

[0105] Figure 1 A schematic flowchart of the source-grid coordinated optimization control method for green electricity traceability provided in this application embodiment;

[0106] Figure 2 This is a schematic diagram of the IEEE 11-node test system in an embodiment of this application;

[0107] Figure 3 This application provides a schematic diagram of green energy path priority curves under a focused critical section for embodiments of the present application;

[0108] Figure 4 A time-series simulation diagram of source-grid-load-storage coordination on a typical daily key section provided in this application embodiment. Detailed Implementation

[0109] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit this application or its application or use. Example 1

[0110] Please see Figure 1 This embodiment provides a source-grid coordinated optimization control method for green electricity traceability, including the following steps:

[0111] Step 1: Obtain the power grid topology data, new energy power source access node information, typical daily source-load data, power flow data, and real-time power grid operation status data of the power system;

[0112] In this embodiment, the power grid topology data includes the total number and number of nodes, the total number of branches, and the node-branch correlation matrix. Real-time power grid operating status data is obtained at the current moment, including real-time output of new energy sources, actual load values ​​of each node, initial power flow of lines, real-time energy storage status and power, real-time energy storage output, and real-time output of thermal power units.

[0113] Step 2: Construct a power distribution coefficient matrix based on the power grid topology data;

[0114] In this embodiment, a branch power flow diagonal matrix is ​​constructed based on the power grid topology data;

[0115] Based on the branch power flow diagonal matrix, calculate the nodal outflow power of each node:

[0116]

[0117]

[0118]

[0119] In the formula, This represents the total number of power grid branches. Represented as a power grid branch, To characterize the dimension of a power grid branch, the value is taken as... ; The total number of power grid nodes. Represented as a power grid node, To characterize the dimension of a power grid node, the value is taken as... ; This is a node-branch association matrix, where the elements are... , Indicates power grid branch With power grid nodes Starting from the power grid node, the power flow begins. Outflow, Indicates power grid branch With power grid nodes As the endpoint, the power flow originates from the grid node. Inflow, Indicates power grid branch and power grid nodes No direct connection; The dimension is The diagonal matrix is ​​used to characterize the real-time active power flow magnitude of each branch. diagonal matrix The elements are defined as the real-time active power flow value of power grid branch e, and the power flow direction is related to the node-branch correlation matrix. Consistent; express A matrix composed of the absolute values ​​of its elements; Node-branch association matrix A submatrix of positive elements; The dimension is The whole vector; The total active power flowing from each node to the downstream branches;

[0120] Calculate the node-branch power allocation coefficient matrix based on the node outflow power:

[0121]

[0122]

[0123]

[0124] In the formula, For power grid nodes via power grid branch Outflow power; The node-branch power allocation coefficient matrix. For matrix The elements in the array are used to represent the elements from the power grid nodes. Inflow to power grid branch The power, accounting for the power of the power grid branch The proportion of total active power; Indicates from the power grid node Inflow to power grid branch The power components, Indicates power grid branch The total amount of active power flow;

[0125] Calculate the power allocation coefficient matrix based on the node-branch power allocation coefficient matrix:

[0126]

[0127]

[0128]

[0129] In the formula, As the starting node, It is the terminal node; This is the set of outflow branches of a power grid node; Starting node Flowing to the terminal node Outflow power; This is the power distribution coefficient matrix; For matrix The element represents the starting node. Flowing out and flowing into the terminal node The power of the starting node The proportion of total outflow power; Node-branch association matrix The transpose of .

[0130] Step 3: Determine the allocation ratio between the new energy power source and the grid node based on the new energy power access node information and the power allocation coefficient matrix;

[0131] In this embodiment, based on the new energy power access node information, a power node unit injection vector is defined:

[0132]

[0133] In the formula, Numbering new energy power sources; Indicates new energy power supply Location of the power grid node; To track new energy power sources Flow to grid nodes The unit injection value;

[0134] Based on the power allocation coefficient matrix and the unit injection vector, an iterative formula for power allocation ratio is constructed, and the initial value and convergence condition of the iteration are set.

[0135] Based on the iterative formula, initial values, and convergence conditions, iterative calculations are performed to determine the allocation ratio coefficient between the new energy source and the grid node:

[0136]

[0137]

[0138]

[0139] In the formula, For the number of iterations, Dimensions for characterizing new energy power sources; For the first The allocation ratio coefficient matrix between new energy sources and grid nodes after the next iteration For the first The allocation ratio coefficient matrix between new energy sources and grid nodes after the next iteration; For matrix The element in represents the first element. After the next iteration, new energy power supply Unit power distribution to grid nodes Power ratio; For matrix The element in represents the first element. After the next iteration, new energy power supply Unit power distribution to grid nodes Power ratio; Power allocation coefficient matrix The transpose of the matrix; For new energy power sources Unit power allocation to nodes The initial value of the power ratio; Defined as the convergence accuracy.

[0140] Step 4: Calculate the path priority coefficient of the new energy power source based on the new energy power source access node information;

[0141] In this embodiment, the power supply path of each new energy source is determined based on the new energy source access node information;

[0142] The proportion of renewable energy in the power supply path at each time point is calculated to obtain the original priority coefficient sequence:

[0143]

[0144] In the formula, For new energy power sources Power supply path at time The strength of the green attribute ranges from [0,1]. The larger the value, the higher the proportion of new energy in the path and the higher the priority of green electricity consumption. A collection of new energy power sources within the regional power grid; For new energy power sources A subset of new energy power sources within a dedicated power supply path; Indicates the power supply number within the dedicated power supply path; This refers to the collection of all power sources within a dedicated power supply path, including new energy power sources and thermal power units; Power supply within a dedicated power path The moment Actual effort;

[0145] The original priority coefficient sequence is smoothed using a long period to obtain the path priority coefficients for new energy power sources.

[0146]

[0147] In the formula, For smoothed new energy power sources Path priority coefficient; This represents the total number of time periods.

[0148] Step 5: Based on the power grid topology data and the power flow data, construct a DC power flow model, and based on the DC power flow model, determine the adjusted line power flow;

[0149] In this embodiment, the DC power flow model includes a network-wide power flow equation and nodal power balance equations. The network-wide power flow equations are in the following form:

[0150]

[0151] In the formula, Let be the active power flow vector of the power grid branch, where the elements are . , This represents the diagonal matrix of branch admittances. The node voltage phase angle vector;

[0152] The nodal power balance equations are in the following form:

[0153]

[0154] In the formula, The injected power vector for the grid node, with elements as follows: , The admittance matrix of the power grid nodes;

[0155] Based on the network-wide power flow equation and the node power balance equation, the equation relating node injected power to line power flow is obtained:

[0156] .

[0157] Based on the aforementioned DC power flow model, a DC power flow sensitivity model is constructed to determine the linear relationship between the adjustment of node injected power and the change in line power flow, and the adjusted line power flow is calculated.

[0158]

[0159]

[0160]

[0161]

[0162]

[0163] In the formula, Time scale; This is the DC power flow sensitivity matrix, where the elements are... Used to represent power grid nodes Power grid branch DC power flow sensitivity coefficient; For power grid nodes At any moment The amount of injection power adjustment; For power grid nodes time The adjustment amount of the energy storage system output; For power grid nodes At any moment The adjustment amount of the output of the thermal power unit; For power grid branch At any moment Branch flow adjustment amount; For the regulated power grid branches At any moment The branch trend; For power grid branch At any moment The trend of branch roads.

[0164] Step Six: Based on the typical daily source-load data and the allocation ratio, calculate the node green electricity component after correction for the overall network loss rate;

[0165] In this embodiment, based on the typical daily source-load data and the allocation ratio, the output of each new energy power source is allocated to the corresponding load node to obtain the uncorrected node green electricity component:

[0166]

[0167] In the formula, A set of load nodes within a regional power grid; For at any time power grid nodes Green electricity component; Defined as a new energy power source At any moment contribution; For new energy power sources Unit power distribution to grid nodes The power ratio;

[0168] The uncorrected node green electricity component is corrected using the overall network loss rate to obtain the corrected node green electricity component:

[0169]

[0170] In the formula, Defined as the overall network loss rate.

[0171] Step 7: Based on the real-time operation status data of the power grid, the allocation ratio, the path priority coefficient, the line power flow and the green electricity component of the nodes, construct a source-grid-load-storage collaborative optimization model with the optimization objectives of minimizing power flow fluctuations and maximizing green electricity consumption;

[0172] In this embodiment, the objective function of the source-grid-load-storage collaborative optimization model is expressed as:

[0173]

[0174]

[0175]

[0176] In the formula, These are the weighting coefficients for minimizing power flow fluctuations; Weighting coefficients for maximizing green energy consumption; Minimize the sub-objective value of power flow fluctuation; To maximize the sub-target value of green energy consumption; To optimize the objective function value; This is the line penalty factor; For power grid branch Trend reference value; For power grid nodes At any moment Energy storage output, For power grid nodes At any moment Energy storage output, For power grid nodes At any moment The actual load value;

[0177] The optimization constraints of the source-grid-load-storage collaborative optimization model include thermal power unit constraints, energy storage constraints, and power flow thermal stability limit constraints. The thermal power unit constraints are as follows:

[0178]

[0179] In the formula, For at the power grid node The maximum output of the thermal power unit, For at the power grid node The minimum output of thermal power units; For power grid nodes At any moment -1 energy storage output To optimize the time step;

[0180] The energy storage constraint takes the following form:

[0181]

[0182]

[0183] In the formula, For at the power grid node The upper limit of energy storage charging and discharging power, For at the power grid node The lower limit of energy storage charging and discharging power; For power grid nodes At any moment The stored energy state of the current, For power grid nodes At any moment The stored energy level is as follows: This is the upper limit of the energy storage capacity. This represents the lower limit of the energy storage capacity. For energy storage charging efficiency, For energy storage discharge efficiency;

[0184] The form of the power flow thermal stability limit constraint is as follows:

[0185]

[0186] In the formula, This is the maximum permissible active power flow value for the branch.

[0187] Step 8: Based on the source-grid-load-storage collaborative optimization model, generate a source-grid-load-storage collaborative optimization control strategy for the scheduling period.

[0188] In this embodiment, an optimization solver is used to solve the source-grid-load-storage collaborative optimization model for the current time period to obtain the source-grid-load-storage collaborative optimization control strategy for the current time period.

[0189] The optimized control strategy is then distributed to the thermal power units and energy storage system for execution.

[0190] Collect the actual operating status of the power grid for the next time period, and use this operating status as the initial condition for optimization in the next time period to enter the next optimization cycle;

[0191] Repeat the above steps until the optimization of all time periods within the scheduling period is completed, and output the source-grid-load-storage collaborative optimization control strategy within the scheduling period. Example 2

[0192] Based on Example 1, this example provides an IEEE 11-node test system that includes new energy access. For example... Figure 2As shown, the system is configured as follows: Node BUS1 is set as the balancing node; Node BUS2 is connected to a 50MW thermal power unit; Node BUS7 is connected to a 100MW wind power generation unit; and Node BUS10 is connected to a 100MW photovoltaic power generation unit.

[0193] Furthermore, based on the preset allocation ratio between new energy power sources and nodes, the green electricity path priority coefficients for nodes BUS5 and BUS6 under several preset typical scenarios are calculated respectively. The calculation results are as follows: Figure 3 As shown. Figure 3 This represents a curve showing the relationship between the priority coefficient of the green electricity path and the fluctuation of renewable energy output. Specifically, the priority coefficient exhibits a predetermined correlation with the fluctuation of renewable energy output, meaning it changes accordingly with changes in renewable energy output.

[0194] By connecting the energy storage system to node BUS5 or node BUS9, the dual optimization objectives are "minimizing power flow fluctuations at critical sections" and "maximizing local green energy consumption considering path priorities," with the weighting coefficient for minimizing power flow fluctuations being determined. And the weighted coefficient system for maximizing green energy consumption Set to 0.5. Introduce a path priority coefficient and achieve precise source-load mapping from renewable energy sources to load nodes based on the power allocation coefficient matrix.

[0195] Using the minimum average volatility of key branches within the region as the optimization criterion, the time-series output of thermal power units and energy storage systems was calculated over the entire time period, yielding the time-series output sequence values ​​for thermal power and energy storage. The test period was 24 hours, with a sampling interval of 1 hour. Power flow data at key sections before and after optimization, as well as output data from energy storage Bus5 and thermal power Bus2, were collected. The results are as follows: Figure 4 As shown.

[0196] from Figure 4 It can be seen that without the control strategy of this invention, the power flow at the key section changes significantly with the temporal fluctuations of new energy output and load. Figure 4 The curve before optimization is shown in the figure. As can be seen from the curve, the peak value of the power flow at the critical section occurs in the early stage of the test (t=0h), which is about 10MW; the valley value occurs in the later stage of the test (t=20h), which is about -30MW. The peak-valley difference of the power flow at the critical section reaches 40MW. The power flow at the section fluctuates violently, which can easily lead to line overload or reverse power exceeding the limit, and poses a hidden danger to operational safety.

[0197] Using the control strategy of this invention, an energy storage system is connected at Bus 5 to coordinate the power output of the thermal power unit at Bus 2. The "Power Output of Energy Storage Bus 5" curve reflects the charging and discharging state of the energy storage system, where positive values ​​represent discharge power and negative values ​​represent charging power. The "Power Output of Thermal Power Bus 2" curve reflects the adjustment process of the thermal power unit, whose output increased to about 50MW in the later stage of the test, working together with the energy storage system to smooth out cross-sectional power flow fluctuations.

[0198] After adopting the control strategy of this invention, the power flow at the critical section is effectively suppressed, corresponding to Figure 4 The "optimized" curve is shown in the figure. As can be seen from the curve, the peak value of the power flow at the key section after optimization is about 2MW, the valley value is about -12MW, and the peak-valley difference is suppressed to 14MW. The power flow fluctuation suppression rate of the section is 65%, which achieves the minimization of power flow fluctuation and the maximization of local green power consumption, thus verifying the effectiveness of the proposed method. Example 3

[0199] This embodiment provides a source-grid coordinated optimization control system for green electricity traceability, the system comprising:

[0200] The data acquisition module is used to acquire power grid topology data, new energy power source access node information, typical daily source-load data, power flow data, and real-time power grid operation status data of the power system.

[0201] The allocation coefficient matrix construction module is used to construct a power allocation coefficient matrix based on the power grid topology data.

[0202] The allocation ratio determination module is used to determine the allocation ratio between new energy sources and grid nodes based on the new energy power access node information and the power allocation coefficient matrix.

[0203] The path priority coefficient module is used to calculate the path priority coefficient of the new energy power source based on the new energy power source access node information.

[0204] The line power flow module is used to construct a DC power flow model based on the power grid topology data and the power flow data, and to determine the adjusted line power flow based on the DC power flow model.

[0205] The node green electricity component module is used to calculate the node green electricity component after correction of the overall network loss rate based on the typical daily source-load data and the distribution ratio.

[0206] The optimization model construction module is used to construct a source-grid-load-storage collaborative optimization model with the optimization objectives of minimizing power flow fluctuations and maximizing green energy consumption, based on the real-time operation status data of the power grid, the allocation ratio relationship, the path priority coefficient, the line power flow and the green energy component of the nodes.

[0207] The strategy generation module is used to obtain the source-grid-load-storage collaborative optimization control strategy within the scheduling period based on the source-grid-load-storage collaborative optimization model.

[0208] The specific implementation process of each module function in this embodiment can be found in Embodiment 1, which has the same technical effect as the method provided in Embodiment 1, and will not be described in detail here.

[0209] Those skilled in the art will understand that the embodiments of this application can be provided as methods or systems. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects.

[0210] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems) according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A system that specifies functions in one or more boxes.

[0211] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including an instruction set implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0212] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0213] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims. All of these forms are within the protection scope of this application.

Claims

1. A source-grid coordinated optimization control method for green electricity traceability, characterized in that, include: Acquire power system topology data, new energy power source access node information, typical daily source-load data, power flow data, and real-time power grid operation status data; Based on the power grid topology data, construct a power allocation coefficient matrix; Based on the new energy power access node information and the power allocation coefficient matrix, the allocation ratio between new energy power sources and grid nodes is determined; Based on the new energy power access node information, calculate the path priority coefficient of the new energy power. Based on the power grid topology data and the power flow data, a DC power flow model is constructed, and based on the DC power flow model, the adjusted line power flow is determined; Based on the typical daily source-load data and the distribution ratio, the node green electricity component after correction for the overall network loss rate is calculated; Based on the real-time operation status data of the power grid, the allocation ratio, the path priority coefficient, the line power flow and the green electricity component of the nodes, a source-grid-load-storage collaborative optimization model is constructed with the optimization objectives of minimizing power flow fluctuations and maximizing green electricity consumption. Based on the source-grid-load-storage collaborative optimization model, a source-grid-load-storage collaborative optimization control strategy is generated within the scheduling period.

2. The source-grid coordinated optimization control method for green electricity traceability according to claim 1, characterized in that, The step of constructing a power allocation coefficient matrix based on the power grid topology data includes: Based on the power grid topology data, construct the branch power flow diagonal matrix; Based on the branch power flow diagonal matrix, calculate the nodal outflow power of each node: In the formula, This represents the total number of power grid branches. Represented as a power grid branch, To characterize the dimension of a power grid branch, the value is taken as... ; The total number of power grid nodes. Represented as a power grid node, To characterize the dimension of a power grid node, the value is taken as... ; This is a node-branch association matrix, where the elements are... , Indicates power grid branch With power grid nodes Starting from the power grid node, the power flow begins. Outflow, Indicates power grid branch With power grid nodes As the endpoint, the power flow originates from the grid node. Inflow, Indicates power grid branch and power grid nodes No direct connection; The dimension is The diagonal matrix is ​​used to characterize the real-time active power flow magnitude of each branch. diagonal matrix The elements are defined as the real-time active power flow value of power grid branch e, and the power flow direction is related to the node-branch correlation matrix. Consistent; express A matrix composed of the absolute values ​​of its elements; Node-branch association matrix A submatrix of positive elements; The dimension is The whole vector; The total active power flowing from each node to the downstream branches; Calculate the node-branch power allocation coefficient matrix based on the node outflow power: In the formula, For power grid nodes via power grid branch Outflow power; The node-branch power allocation coefficient matrix. For matrix The elements in the array are used to represent the elements from the power grid nodes. Inflow to power grid branch The power, accounting for the power of the power grid branch The proportion of total active power; Indicates from the power grid node Inflow to power grid branch The power components, Indicates power grid branch The total amount of active power flow; Calculate the power allocation coefficient matrix based on the node-branch power allocation coefficient matrix: In the formula, As the starting node, It is the terminal node; This is the set of outflow branches of a power grid node; Starting node Flowing to the terminal node Outflow power; This is the power distribution coefficient matrix; For matrix The element represents the starting node. Flowing out and flowing into the terminal node The power of the starting node The proportion of total outflow power; Node-branch association matrix The transpose of .

3. The source-grid coordinated optimization control method for green electricity traceability according to claim 2, characterized in that, The step of determining the allocation ratio between new energy sources and grid nodes based on the new energy power access node information and the power allocation coefficient matrix includes: Based on the new energy power access node information, define the power node unit injection vector: In the formula, Numbering new energy power sources; Indicates new energy power supply Location of the power grid node; To track new energy power sources Flow to grid nodes The unit injection value; Based on the power allocation coefficient matrix and the unit injection vector, an iterative formula for power allocation ratio is constructed, and the initial value and convergence condition of the iteration are set. Based on the iterative formula, initial values, and convergence conditions, iterative calculations are performed to determine the allocation ratio coefficient between the new energy source and the grid node: In the formula, For the number of iterations, Dimensions for characterizing new energy power sources; For the first The allocation ratio coefficient matrix between new energy sources and grid nodes after the next iteration For the first The allocation ratio coefficient matrix between new energy sources and grid nodes after the next iteration; For matrix The element in represents the first element. After the next iteration, new energy power supply Unit power distribution to grid nodes Power ratio; For matrix The element in represents the first element. After the next iteration, new energy power supply Unit power distribution to grid nodes Power ratio; Power allocation coefficient matrix The transpose of the matrix; For new energy power sources Unit power allocation to nodes The initial value of the power ratio; Defined as the convergence accuracy.

4. The source-grid coordinated optimization control method for green electricity traceability according to claim 3, characterized in that, The step of calculating the path priority coefficient of the new energy power source based on the new energy power source access node information includes: Based on the new energy power access node information, determine the power supply path of each new energy power source; The proportion of renewable energy in the power supply path at each time point is calculated to obtain the original priority coefficient sequence: In the formula, For new energy power sources Power supply path at time The strength of the green attribute ranges from [0,1]. The larger the value, the higher the proportion of new energy in the path and the higher the priority of green electricity consumption. It is a collection of new energy power sources within the regional power grid; For new energy power sources A subset of new energy power sources within a dedicated power supply path; Indicates the power supply number within the dedicated power supply path; This refers to the collection of all power sources within a dedicated power supply path, including new energy power sources and thermal power units; Power supply within a dedicated power path At any moment Actual output; The original priority coefficient sequence is smoothed using a long period to obtain the path priority coefficients for new energy power sources. In the formula, For smoothed new energy power sources Path priority coefficient; This represents the total number of time periods.

5. The source-grid coordinated optimization control method for green electricity traceability according to claim 4, characterized in that, The DC power flow model includes the overall network power flow equations and nodal power balance equations. The overall network power flow equations are in the following form: In the formula, Let be the active power flow vector of the power grid branch, where the elements are . , This represents the diagonal matrix of branch admittances. The node voltage phase angle vector; The nodal power balance equations are in the following form: In the formula, The injected power vector for the grid node, with elements as follows: , The admittance matrix of the power grid nodes; Based on the network-wide power flow equation and the node power balance equation, the equation relating node injected power to line power flow is obtained: 。 6. The source-grid coordinated optimization control method for green electricity traceability according to claim 5, characterized in that, The determination of the adjusted line power flow based on the DC power flow model includes: Based on the aforementioned DC power flow model, a DC power flow sensitivity model is constructed to determine the linear relationship between the adjustment of node injected power and the change in line power flow, and the adjusted line power flow is calculated. In the formula, Time scale; This is the DC power flow sensitivity matrix, where the elements are... Used to represent power grid nodes Power grid branch DC power flow sensitivity coefficient; For power grid nodes At any moment The amount of injection power adjustment; For power grid nodes time The adjustment amount of the energy storage system output; For power grid nodes At any moment The adjustment amount of the output of thermal power units; For power grid branch At any moment Branch flow adjustment amount; For the regulated power grid branches At any moment The branch trend; For power grid branch At any moment The trend of branch roads.

7. The source-grid coordinated optimization control method for green electricity traceability according to claim 6, characterized in that, The step of calculating the node green electricity component after correction for the overall network loss rate based on the typical daily source-load data and the allocation ratio includes: Based on the typical daily source-load data and the allocation ratio, the output of each new energy source is allocated to the corresponding load node to obtain the uncorrected node green electricity component: In the formula, A set of load nodes within a regional power grid; For at any time Grid nodes Green electricity component; Defined as a new energy power source At any moment contribution; For new energy power sources Unit power distribution to grid nodes The power ratio; The uncorrected node green electricity component is corrected using the overall network loss rate to obtain the corrected node green electricity component: In the formula, Defined as the overall network loss rate.

8. The source-grid coordinated optimization control method for green electricity traceability according to claim 7, characterized in that, The source-grid-load-storage collaborative optimization model, which aims to minimize power flow fluctuations and maximize green energy consumption, has the following objective function: In the formula, These are the weighting coefficients for minimizing power flow fluctuations; Weighting coefficients for maximizing green energy consumption; Minimize the sub-objective value of power flow fluctuation; To maximize the sub-target value of green energy consumption; To optimize the objective function value; This is the line penalty factor; For power grid branch Trend reference value; For power grid nodes At any moment Energy storage output, For power grid nodes At any moment Energy storage output, For power grid nodes At any moment The actual load value; The optimization constraints of the source-grid-load-storage collaborative optimization model include thermal power unit constraints, energy storage constraints, and power flow thermal stability limit constraints. The thermal power unit constraints are as follows: In the formula, For at the power grid node The maximum output of the thermal power unit, For at the power grid node The minimum output of thermal power units; For power grid nodes At any moment -1 energy storage output To optimize the time step; The energy storage constraint takes the following form: In the formula, For at the power grid node The upper limit of energy storage charging and discharging power, For at the power grid node The lower limit of energy storage charging and discharging power; For power grid nodes At any moment The energy storage state of the device is as follows: For power grid nodes At any moment The stored energy level is as follows: This is the upper limit of the energy storage capacity. This represents the lower limit of the energy storage capacity. For energy storage charging efficiency, For energy storage discharge efficiency; The form of the power flow thermal stability limit constraint is as follows: In the formula, This is the maximum permissible active power flow value for the branch.

9. The source-grid coordinated optimization control method for green electricity traceability according to claim 1, characterized in that, The step of generating a source-grid-load-storage coordinated optimization control strategy within the scheduling period based on the source-grid-load-storage coordinated optimization model includes: An optimization solver is used to solve the source-grid-load-storage collaborative optimization model for the current time period to obtain the source-grid-load-storage collaborative optimization control strategy for the current time period. The optimized control strategy is then distributed to the thermal power units and energy storage system for execution. Collect the actual operating status of the power grid for the next time period, and use this operating status as the initial condition for optimization in the next time period to enter the next optimization cycle; Repeat the above steps until the optimization of all time periods within the scheduling period is completed, and output the source-grid-load-storage collaborative optimization control strategy within the scheduling period.

10. A source-grid coordinated optimization control system for green electricity traceability, characterized in that, The system includes: The data acquisition module is used to acquire power grid topology data, new energy power source access node information, typical daily source-load data, power flow data, and real-time power grid operation status data of the power system. The allocation coefficient matrix construction module is used to construct a power allocation coefficient matrix based on the power grid topology data. The allocation ratio determination module is used to determine the allocation ratio between new energy sources and grid nodes based on the new energy power access node information and the power allocation coefficient matrix. The path priority coefficient module is used to calculate the path priority coefficient of the new energy power source based on the new energy power source access node information. The line power flow module is used to construct a DC power flow model based on the power grid topology data and the power flow data, and to determine the adjusted line power flow based on the DC power flow model. The node green electricity component module is used to calculate the node green electricity component after correction of the overall network loss rate based on the typical daily source-load data and the distribution ratio. The optimization model construction module is used to construct a source-grid-load-storage collaborative optimization model with the optimization objectives of minimizing power flow fluctuations and maximizing green energy consumption, based on the real-time operation status data of the power grid, the allocation ratio relationship, the path priority coefficient, the line power flow and the green energy component of the nodes. The strategy generation module is used to obtain the source-grid-load-storage collaborative optimization control strategy within the scheduling period based on the source-grid-load-storage collaborative optimization model.

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