Layered and partitioned electric power and electric quantity balancing method for sea-land flexible interconnection system based on double-layer optimization

Abstract

The invention discloses a sea-land flexible interconnection system layering and partitioning electric power and electric quantity balancing method based on double-layer optimization, and the method comprises the steps: building an upper-layer optimal partitioning model according to a power grid partitioning constraint condition and an electrical connection coefficient, and taking the minimum total cost of a system as an objective function; establishing a lower-layer optimal power and electric quantity balance model according to the power grid frame constraint and the flexibility resource constraint; in the solving stage, power and electric quantity balance data in the optimal operation state are obtained through the lower-layer optimal power and electric quantity balance model, then the electrical distance is calculated through the upper-layer optimal partitioning model according to the power and electric quantity balance data, and a partitioning scheme is obtained according to the updated electrical distance. The topological structure after optimization of the cross-regional line is returned to a lower-layer optimal power and electric quantity balance model, an optimal partitioning result is obtained through repeated iteration, power and electric quantity balance is achieved, and through simulation, the total cost of the system is reduced by 3.3% compared with a traditional scheme; and the electric power connection coefficient is reduced by 18.4%.

Description

Technical Field

[0001] This invention relates to a technology in the field of power grid control, specifically a hierarchical and zoned power balance method for a flexible interconnected land-sea system based on two-layer optimization. Background Technology

[0002] With the large-scale grid connection of new energy sources (wind power and photovoltaics), the randomness, volatility, and intermittency of the power grid are constantly increasing. In scenarios with a high proportion of new energy, the uncertainty is even more pronounced, and the impact on the power grid is also increasing. At the same time, the difficulty in absorbing new energy is also one of the major challenges currently facing the grid. As the installed capacity of offshore wind power continues to increase, many regions in China have already encountered problems with the inability to absorb and transmit offshore wind power. Coupled with issues such as insufficient transmission capacity in some sections of the power grid, shortages of power supply capacity in load centers, and differences in the temporal and spatial distribution of load and new energy, the pressure on the safe operation of the power grid is constantly increasing. Summary of the Invention

[0003] This invention addresses the shortcomings of existing technologies that fail to address the complex scenarios of land-sea interconnected systems, hindering the coordination of offshore wind power, onshore conventional power sources, and flexible loads, and thus failing to achieve integrated power balance across different regions and time periods. It proposes a layered, zoned power balance method for flexible land-sea interconnected systems based on dual-layer optimization. This method addresses the issue of main line congestion, overcomes the uncertainty of renewable energy output, and achieves cross-regional, multi-time-period, and large-scale power balance.

[0004] This invention is achieved through the following technical solution:

[0005] This invention relates to a hierarchical zoning power balance method for a flexible interconnected land-sea system based on two-layer optimization. First, an upper-level optimal zoning model is established based on grid zoning constraints and electrical connectivity coefficients. Then, a lower-level optimal power balance model is established with the goal of minimizing the total system cost, based on grid structure constraints and flexibility resource constraints. In the solution phase, the lower-level optimal power balance model first obtains power balance data under optimal operating conditions. Then, the upper-level optimal zoning model calculates electrical distances based on the power balance data, derives a zoning scheme based on the updated electrical distances, and returns the optimized topology of inter-regional lines to the lower-level optimal power balance model. Through repeated iterations, the optimal zoning result is obtained, achieving power balance.

[0006] Technical effect

[0007] This invention considers the influence of three factors—impedance, power flow, and power cross-entropy—in calculating electrical distance. The improved electrical distance allows for adjustment of the weights of these three factors based on requirements. A two-layer optimization method is employed in the hierarchical and zoned power balance of a flexible onshore-offshore interconnected system, considering the mutual influence between upper and lower layers. An optimal zoning scheme is derived through iterative methods between the upper and lower layers. The final zoning scheme not only meets the zoning conditions but also exhibits better adaptability to high-proportion renewable energy scenarios. 3. In the power grid zoning model, a post-verification approach is used to address the connectivity issue within the zones, thereby significantly reducing solution time and improving the computational efficiency of the two-layer optimization model. Compared to existing technologies, this invention, through its improved electrical distance, better reflects the strength of electrical connections between nodes in scenarios with high-proportion renewable energy integration, and can be more effectively applied to zoning determination. Attached Figure Description

[0008] Figure 1 This is a schematic diagram of the flexible land-sea interconnection system of the present invention;

[0009] Figure 2 This is a diagram illustrating the relationship between the two-layer optimization model in an example.

[0010] Figure 3 Here is a flowchart of the two-layer optimization solution for an example;

[0011] Figure 4 This is a schematic diagram of the partitioning results for an example. Detailed Implementation

[0012] like Figure 1 As shown in this embodiment, a hierarchical and zoned power balance method for a flexible land-sea interconnection system based on two-layer optimization is proposed, including:

[0013] Step 1, construct as follows Figure 1 The model of the flexible interconnected land and sea system shown includes: the IEEE 24-node system transmission network is connected to 16 offshore wind turbines W with different installed capacities through nodes 3 and 15. At the same time, the transmission network is equipped with load demand and large-scale new energy power plants, such as wind turbines W, photovoltaic power plants S, nuclear power plants N, as well as various flexible resources, such as pumped storage power plants P, battery energy storage systems B, and thermal power plants.

[0014] All equipment is distributed and connected to the transmission grid to jointly ensure the safety, stability, and economy of the power system under the high proportion of renewable energy access.

[0015] The aforementioned flexible land-sea interconnection system model employs flexible DC power transmission.

[0016] Step 2: Establish an upper-level optimal partitioning model based on the flexible land-sea interconnection system model in Step 1, specifically including:

[0017] 2.1) Define electrical distance: ,in: For nodes and Electrical distance between them For nodes and The impedance of the line between the two nodes is taken as the total impedance of the shortest path between the two nodes when the two nodes are not directly adjacent. Here, represents the line weighting coefficient, and represents the node weighting coefficient. and The ratio of active power flowing through the line to the line capacity. When the two nodes are not directly adjacent, the minimum value of the line weight coefficient in the shortest path is taken. For nodes and The power interaction entropy; , , These are the weighting coefficients for the three factors, which can be set according to the actual situation.

[0018] The power cross-entropy is specifically: ,in: The matrix is ​​the power transfer distribution factor matrix. For the first The power flow of line 1 affects the first line 2. Sensitivity of injected power at each node; For the number of lines, That is to The column containing the node has been normalized. The impact of injected power on the overall network line flow. and They are nodes right and nodes right Power cross-entropy, Finally, the power interaction entropy is derived based on symmetry. The larger the power interaction entropy, the greater the difference in power behavior patterns between the two nodes, and therefore the larger the electrical distance.

[0019] 2.2) Set the electrical connection indicators for the sub-area, specifically as follows: ,in: This represents the total number of nodes in the system. For nodes A set of nodes in the same region; The strength of electrical connections between nodes within the region; This represents the strength of the electrical connection between nodes within a region and nodes in other regions. Smaller values ​​indicate stronger electrical connections between nodes within the region and weaker electrical connections between nodes in other regions.

[0020] 2.3) Constructing the objective function of the optimal partitioning model: Assign weight coefficients to the two indicators and set the objective function. ,in: This is the weighting coefficient, with a value ranging from 0 to 1. A larger value indicates a more optimal solution that prioritizes strong connections between nodes within a region; a smaller value indicates a more optimal solution that prioritizes weaker connections between nodes within different regions. In practical systems, the optimal value can be adjusted as needed. The value is adjusted.

[0021] 2.4) Set constraints, including:

[0022] i) Node partitioning constraints: In the partitioning result, each node should belong to only one region, therefore, the following should be satisfied: ,in: Number of partitions; For the node partitioning matrix, when At that time, it is a node In the region In this context, the constraint is that the summation of each row of the partition matrix is ​​1, meaning that each node belongs to only one partition.

[0023] ii) Supply and demand balance constraints: In the regional optimization of a high-proportion renewable energy power system, due to the uncertainty of renewable energy output, it is necessary to ensure that the sum of the generating power of thermal power units in each sub-region is greater than the sum of a certain proportion of load demand, so as to avoid the randomness of renewable energy generation from affecting the normal operation of the power grid. ,in: For the region The number of nodes in; The power of the thermal power unit at each node (0 if there is no thermal power unit); The load for each node (0 if there is no load demand); This is a proportionality coefficient, which can be selected according to actual needs. A larger value indicates a stricter requirement for supply and demand balance.

[0024] Step 3: Establish a lower-level optimal power balance model based on grid structure constraints and flexibility resource constraints, specifically including:

[0025] 3.1) Set the objective function: ,in: , These are the coal consumption cost and load shedding cost of thermal power units, respectively. , , The first Coal consumption characteristic coefficient of a thermal power generating unit; , , respectively, are the output power and load shedding power of thermal power units; For nodes Unit load shedding cost coefficient at the location; , and represent the total number of nodes and the number of generators in the system, respectively.

[0026] 3.2) Set constraints, including: flexibility resource constraints and grid operation constraints, wherein: flexibility resource constraints include: thermal power unit constraints, battery energy storage constraints and pumped storage unit constraints; grid operation constraints include: phase angle constraints, load shedding constraints, power flow constraints and node power balance constraints.

[0027] The aforementioned constraints on thermal power units refer to the fact that thermal power units can adjust their output or start and stop to cope with changes in load over a long period of time in order to meet the power balance during peak and off-peak periods. Accordingly, they need to meet unit output constraints, unit ramp-up constraints, and unit start and stop constraints.

[0028] The aforementioned unit output constraint refers to the fact that, due to the physical characteristics of thermal power units, their output must be within a certain required range. Therefore: ,in: For thermal power units During the period Binary decision variables for start-stop states. To shut down the thermal power unit Start-up of thermal power units; For thermal power units During the period contribution; , thermal power units Maximum and minimum output limits.

[0029] The aforementioned unit ramp-up constraint refers to: ,in: , thermal power units The uphill and downhill ramp rates. When the thermal power unit has its minimum starting output... Greater than the uphill climbing rate At that time, the above formula would prevent all shut-down units from starting, therefore the constraint condition is rewritten as: , ,in: , thermal power units The maximum startup ramp rate and maximum shutdown ramp rate. For simplicity, both the maximum startup ramp rate and maximum shutdown ramp rate are taken as: .

[0030] The aforementioned unit start-up and shutdown constraints refer to the following: In order to extend the service life of thermal power units as much as possible, the start-up and shutdown of thermal power units must be maintained for a certain period of time to prevent damage caused by frequent start-ups and shutdowns. Thermal power units need to meet the following start-up and shutdown constraints: , ,in: , These are the minimum shutdown and startup times for thermal power units. In this context, k is just one parameter, and... (The meanings are the same).

[0031] The aforementioned battery energy storage constraints refer to the fact that, due to battery capacity limitations, the state of charge (SOC) of a battery energy storage system must be strictly controlled within a certain range to avoid overcharging or over-discharging. Therefore, during the operation of a battery energy storage system, constraints related to SOC, battery charge / discharge, power balance, power flow, and load shedding must be met.

[0032] The aforementioned state of charge constraint refers to the ratio of the remaining capacity after a period of use to its capacity when fully charged. , ,in: For batteries During the period The state of charge; , These are the battery's charging and discharging efficiencies, respectively. , Batteries During the period The charging power and discharging power; For batteries Rated capacity; This refers to the interval time. , These represent the maximum and minimum values ​​of the battery's state of charge (SOC). The formula gives the relationship between the battery's SOC changes in adjacent time periods. This constraint requires that the current SOC value must be equal to the previous SOC plus the charging gain (charging power × efficiency) minus the discharging loss (discharging power / efficiency), and takes into account the conversion relationship between the time step and the rated capacity.

[0033] The aforementioned battery charge / discharge constraint refers to limiting the charge / discharge power of the battery energy storage system to avoid excessively rapid charge / discharge. Specifically, the battery energy storage system can only operate in one of three states: charging, off, or discharging. The charging and discharging power of the battery within a certain period of time must not exceed the maximum value. , ,in: , Batteries During the period Binary variables representing the charging and discharging states; , Batteries Maximum charging and discharging power.

[0034] The power balance constraint mentioned above refers to: ,in: The node number; , , , , , These are the node sets for wind turbines, photovoltaic systems, thermal power units, batteries, pumped storage units, and loads, respectively. , Transmission lines The starting point and the ending point; , Time periods The actual power generation of wind and solar power units; For time period The power generation of thermal power units; and Time periods Battery discharge and charge levels; and Indicates time period The electricity generated and stored by pumped storage power stations; For the line During the period Electricity flow; For nodes During the period The load; For nodes During the period Shear load.

[0035] The power flow constraint mentioned above refers to the transmission power of the line. The transmission power of the line is limited by the maximum transmission capacity. ,in: For the line During the period Traffic; For transmission lines Admittance; , Transmission lines During the period The phase angle at the start and end points; For the line Maximum capacity.

[0036] The aforementioned load shedding constraint refers to ensuring that the system can maintain stability and security by reducing some load during power supply and demand imbalances or emergencies. The magnitude of the load shedding is limited and must meet certain conditions. ,in: For nodes During the period The amount of load shedding; For nodes During the period The load capacity. Limits were placed on the amount of load shedding at nodes.

[0037] The phase angle constraint mentioned above refers to: , ,in: For nodes During the period The voltage phase angle; This is a reference node.

[0038] The aforementioned constraints on pumped storage units refer to the need to meet the demands of the power system while avoiding economic losses or equipment damage due to over-operation or resource waste. Specifically, these constraints include: pumped power generation operating conditions, pumped storage unit output constraints, and reservoir capacity constraints.

[0039] The aforementioned pumped-power generation operating condition constraints refer to the constraints on the four basic operating condition transition processes: pumping-shutdown, power generation-shutdown, shutdown-pumping, and shutdown-power generation. ,in: , Pumped storage units During the period Pumping and power generation status ( The pumped storage unit is in pumping mode. The pumped storage unit is in power generation mode. (The pumped storage unit is in a shutdown condition).

[0040] The aforementioned output constraint of pumped storage units refers to the fact that, regardless of whether they operate in turbine or pump mode, the output regulation characteristics of pumped storage units are similar to those of conventional hydroelectric units, and there is a minimum output limit: both the power generation and pumping power of pumped storage units are subject to maximum and minimum output limits. , ,in: , Pumped storage units During the period The pumping and power generation status; , Pumped storage units During the period Pumping and power generation capacity; , , and Pumped storage units The minimum and maximum pumping power and the minimum and maximum generating power of a pumped storage unit. In practice, the maximum and minimum generating and pumping power of a pumped storage unit are usually related to the rated installed capacity of the unit.

[0041] The reservoir capacity constraint mentioned above refers to the following: Under the daily regulation operation mode, a capacity configuration scheme of "small upper reservoir, large lower reservoir" is adopted, therefore the capacity of the upper reservoir meets the following requirements: , , , ,in: , Upper Reservoir Lower and upper limits of storage capacity; , They are respectively the lower reservoir Lower and upper limits of storage capacity; , , , These are the initial and final water storage capacities of the upper and lower reservoirs, respectively. The length of a unit of time period; , The conversion factor between pumped storage power generation and flow rate. Conversion coefficient between pumping power and flow rate , , The power generation efficiency and pumping efficiency of the reversible pump-turbine generator set are respectively taken as... and ; Let be the average density of water, and take . ; Let be the acceleration due to gravity, and take . ; This represents the average head difference between the upper and lower reservoirs.

[0042] Step 4, as follows Figure 3As shown, in the solution phase, the lower-level optimal power balance model first obtains the power balance data under optimal operating conditions. Then, the upper-level optimal partitioning model calculates the electrical distance based on the power balance data, derives the partitioning scheme based on the updated electrical distance, and returns the optimized cross-regional line topology to the lower-level optimal power balance model. After repeated iterations, the optimal partitioning result is obtained to achieve power balance. Specifically, this includes:

[0043] 4.1 Set the iteration termination condition as follows: the optimal value of the objective function in the lower-level optimal power balance model converges.

[0044] 4.2 First, the optimal power balance is obtained from the lower-level optimal power balance model, specifically including:

[0045] i) Establish the objective function to minimize the total system cost;

[0046] ii) Solve based on power grid constraints and flexibility resource constraints;

[0047] iii) The power flow parameters of each line in the system when the optimal solution is obtained are passed to the upper-level optimal partition model.

[0048] 4.3 After obtaining the optimal power balance at the upper level, the electrical distance matrix is ​​calculated. Then, the partitioning scheme is solved with the minimum electrical connectivity index as the objective function. Specifically, this includes:

[0049] i) Calculate the electrical distance between nodes based on the power flow parameters of each line obtained from the lower layer;

[0050] ii) Establish the objective function with the goal of minimizing the electrical connection coefficient;

[0051] iii) After obtaining the optimal partitioning result based on the partitioning constraints, optimize the cross-regional lines based on the power flow data, and pass the new topology to the lower-level optimal power balance model.

[0052] 4.4 Determine whether the stopping iteration condition is met. If not, continue to optimize the cross-regional lines based on the power balance data, update the topology, and then pass it to the next layer for iteration.

[0053] Preferably, after the upper-level optimal partitioning model obtains the partitioning scheme, it selects to disconnect the line with smaller power flow in the cross-regional line to reduce power flow interaction between regions (no disconnection is performed when there is only one cross-regional line). After the disconnection, the new line parameters are passed to the lower-level optimal power balance model. The lower-level optimal power balance model solves the optimal power balance again, and the upper-level optimal partitioning model can obtain the new power balance data to update the electrical matrix and partition again. This process continues until the iteration termination condition is met, and then the final optimal partitioning result is output.

[0054] Through practical application scenario experiments, the simulation environment was set to MATLAB 2023b version under a 64-bit Windows operating system, and the following was adopted: Figure 1 The "24-node + 18-node offshore wind turbine" flexible interconnection system shown verifies the scheme. Nodes S1 and S2 are existing substation nodes in the system, while nodes S3 and S4 are substation nodes to be constructed. Nodes 25-40 are connected to offshore wind turbines of different capacities, and nodes 41 and 42 are offshore collection stations, connected to nodes 3 and 15 of the 24-node system, respectively. The 24-node system integrates a high proportion of new energy power plants, including onshore wind turbines, photovoltaic power plants, pumped storage power plants, battery energy storage, and nuclear power plants. Specifically, nodes 3 and 15 are connected to nuclear power plants; nodes 3, 5, 7, 16, 21, and 23 are connected to onshore wind turbines; nodes 2, 9, 13, 18, and 22 are connected to battery energy storage; nodes 2, 8, 15, and 19 are connected to photovoltaic power plants; and nodes 7, 12, and 20 are connected to pumped storage power plants. Running the hierarchical and partitioned power balance method for flexible land-sea interconnection systems based on two-layer optimization in this invention using the CPLEX solver and YALMIP yields the following results: Figure 4 The result shown is that the entire system was divided into four sub-regions and three cross-region lines were cut off.

[0055] like Figure 4 As shown, disconnecting inter-regional lines reduces power exchange between regions, which is beneficial for ensuring the safety and stability of the power grid (this disconnection is not a physical break, but rather a reduction in the amount of active power flowing through the line). When a serious fault occurs in a region, the protection device can quickly disconnect the interconnection lines between regions, disconnecting the faulty region from the main grid. Furthermore, zoning promotes the consumption of renewable energy. Wind and solar power outputs are spatially and temporally complementary; by optimizing the power of interconnection lines between regions, cross-regional resource optimization can be achieved. Therefore, zoning a power grid with a high proportion of renewable energy sources helps the grid cope with the uncertainty of renewable energy output, which is of great significance for ensuring the reliability of power supply in sub-regional grids; and it can also achieve power self-balancing within the region as much as possible, reducing losses caused by power transmission.

[0056] Compared with existing technologies, this method reduces the total system cost by 3.3% compared with traditional solutions; it also reduces the power interconnection coefficient by 18.4% and achieves cross-regional, multi-time period, and large-scale power balance.

[0057] The above-described specific implementations can be partially adjusted by those skilled in the art in different ways without departing from the principles and purpose of the present invention. The scope of protection of the present invention is defined by the claims and is not limited to the above-described specific implementations. All implementation schemes within the scope of the claims are bound by the present invention.

Claims

1. A hierarchical and zoned power balance method for a flexible land-sea interconnection system based on two-layer optimization, characterized in that, First, an upper-level optimal partitioning model is established based on the power grid partitioning constraints and electrical connection coefficients. Then, with the minimum total system cost as the objective function, a lower-level optimal power balance model is established based on the power grid structure constraints and flexibility resource constraints. In the solution phase, the power balance data under the optimal operating state is first obtained from the lower-level optimal power balance model. Then, the upper-level optimal partitioning model calculates the electrical distance based on the power balance data. Based on the updated electrical distance, the partitioning scheme is derived, and the topology after optimizing the inter-regional lines is returned to the lower-level optimal power balance model. After repeated iterations, the optimal partitioning result is obtained, and power balance is achieved.

2. The hierarchical and zoned power balance method for flexible land-sea interconnection systems based on dual-layer optimization as described in claim 1, characterized in that, The aforementioned optimal partitioning model is constructed in the following way: 2.1) Define electrical distance: ,in: For nodes and Electrical distance between them For nodes and The impedance of the line between the two nodes is taken as the total impedance of the shortest path between the two nodes when the two nodes are not directly adjacent. Here, represents the line weighting coefficient, and represents the node weighting coefficient. and The ratio of active power flowing through the line to the line capacity. When the two nodes are not directly adjacent, the minimum value of the line weight coefficient in the shortest path is taken. For nodes and The power interaction entropy; , , These are the weighting coefficients for the three factors, which can be set according to the actual situation. 2.2) Set the electrical connection indicators for the sub-area, specifically as follows: ,in: This represents the total number of nodes in the system. For nodes A set of nodes in the same region; The strength of electrical connections between nodes within the region; The values ​​represent the strength of electrical connections between nodes within a region and nodes in other regions. The smaller the values, the stronger the electrical connections between nodes within the region and nodes in other regions. 2.3) Constructing the objective function of the optimal partitioning model: Assign weight coefficients to the two indicators and set the objective function. ,in: This is the weighting coefficient, with a value ranging from 0 to 1. A larger value indicates a more optimal solution that prioritizes strong connections between nodes within a region; a smaller value indicates a more optimal solution that prioritizes weaker connections between nodes between regions. In practical systems, the optimal value is determined based on specific needs. Adjust the value accordingly; 2.4) Set constraints, including: i) Node partitioning constraints: In the partitioning result, each node should belong to only one region, therefore, the following should be satisfied: ,in: Number of partitions; For the node partitioning matrix, when At that time, it is a node In the region In this context, the constraint is that the summation of each row of the partition matrix is ​​1, meaning that each node belongs to only one partition. ii) Supply and demand balance constraints: In the regional optimization of a high-proportion renewable energy power system, due to the uncertainty of renewable energy output, it is necessary to ensure that the sum of the generating power of thermal power units in each sub-region is greater than the sum of a certain proportion of load demand, so as to avoid the randomness of renewable energy generation from affecting the normal operation of the power grid. ,in: For the region The number of nodes in; The power of the thermal power unit at each node (0 if there is no thermal power unit); The load for each node (0 if there is no load demand); This is a proportionality coefficient, and its value is determined based on actual needs. A larger value indicates a stricter requirement for supply and demand balance.

3. The hierarchical and zoned power balance method for flexible land-sea interconnection systems based on dual-layer optimization as described in claim 2, characterized in that, The power cross-entropy is specifically as follows: ,in: The matrix is ​​the power transfer distribution factor matrix. For the first The power flow of line 1 affects the first line 2. Sensitivity of injected power at each node; For the number of lines, That is to The column containing the node has been normalized. The impact of injected power on the overall network power flow. and They are nodes right and nodes right Power cross-entropy, Finally, based on symmetry, the final power interaction entropy is obtained. The larger the power interaction entropy, the greater the difference in power behavior patterns between the two nodes, and therefore the larger the electrical distance.

4. The hierarchical and zoned power balance method for flexible land-sea interconnection systems based on dual-layer optimization as described in claim 1, characterized in that, The lower-level optimal power balance model is constructed in the following way: 3.1) Set the objective function: ,in: , These are the coal consumption cost and load shedding cost of thermal power units, respectively. , , The first Coal consumption characteristic coefficient of a thermal power generating unit; , , respectively, are the output power and load shedding power of the thermal power unit; For nodes Unit load shedding cost coefficient at the location; , and represent the total number of nodes and the number of generators in the system, respectively; 3.2) Set constraints, including: flexibility resource constraints and grid operation constraints, wherein: flexibility resource constraints include: thermal power unit constraints, battery energy storage constraints and pumped storage unit constraints; grid operation constraints include: phase angle constraints, load shedding constraints, power flow constraints and node power balance constraints.

5. The hierarchical and zoned power balance method for flexible land-sea interconnection systems based on dual-layer optimization as described in claim 4, characterized in that, The aforementioned constraints of thermal power units refer to the fact that thermal power units need to adjust their output or start and stop to cope with changes in load over a long period of time in order to meet the power balance during peak and off-peak periods. Accordingly, they need to meet unit output constraints, unit ramping constraints, and unit start and stop constraints. The aforementioned unit output constraint refers to the fact that, due to the physical characteristics of thermal power units, their output must be within a certain required range. Therefore: ,in: For thermal power units During the period Binary decision variables for start-stop states. To shut down the thermal power unit Start-up of thermal power units; For thermal power units During the period contribution; , thermal power units Maximum and minimum output limits; The aforementioned unit ramp-up constraint refers to: ,in: , thermal power units The uphill and downhill ramp rates, when the thermal power unit has its minimum starting output. Greater than the uphill climbing rate At that time, the above formula would prevent all shut-down units from starting, therefore the constraint condition is rewritten as: , ,in: , thermal power units For simplicity, the maximum startup ramp rate and maximum shutdown ramp rate are both set to: ; The aforementioned unit start-up and shutdown constraints refer to the following: In order to extend the service life of thermal power units as much as possible, the start-up and shutdown of thermal power units must be maintained for a certain period of time to prevent damage caused by frequent start-ups and shutdowns. Thermal power units need to meet the following start-up and shutdown constraints: , ,in: , These are the minimum shutdown and startup times for thermal power units. In this context, k is just one parameter, and... (The meaning is the same) The aforementioned battery energy storage constraints refer to the fact that, due to the limitation of battery capacity, the state of charge of the battery energy storage system must be strictly controlled within a certain range to avoid overcharging or over-discharging. Therefore, during the operation of the battery energy storage system, it is necessary to meet the constraints of state of charge, battery charge and discharge, power balance, power flow, and load shedding. The aforementioned state of charge constraint refers to the ratio of the remaining capacity after a period of use to its capacity when fully charged. , ,in: For batteries During the period The state of charge; , These are the battery's charging and discharging efficiencies, respectively. , Batteries During the period The charging power and discharging power; For batteries Rated capacity; This refers to the interval time. , These are the maximum and minimum values ​​of the battery's state of charge (SOC), respectively. The formula gives the relationship between the battery's SOC changes in adjacent time periods. This constraint requires that the SOC value at the current moment must be equal to the SOC at the previous moment plus the charging gain (charging power × efficiency) minus the discharge loss (discharge power / efficiency), and takes into account the conversion relationship between the time step and the rated capacity. The aforementioned battery charge / discharge constraint refers to limiting the charge / discharge power of the battery energy storage system to avoid excessively rapid charge / discharge. Specifically, the battery energy storage system can only operate in one of three states: charging, off, or discharging. The charging and discharging power of the battery within a certain period of time must not exceed the maximum value. , ,in: , Batteries During the period Binary variables representing the charging and discharging states; , Batteries Maximum charging and discharging power; The power balance constraint mentioned above refers to: ,in: The node number; , , , , , These are the node sets for wind turbines, photovoltaic systems, thermal power units, batteries, pumped storage units, and loads, respectively. , Transmission lines The starting point and the ending point; , Time periods The actual power generation of wind and solar power units; For time period The power generation of thermal power units; and Time periods Battery discharge and charge levels; and Indicates time period The electricity generated and stored by pumped-storage hydroelectric power stations; For the line During the period Electricity flow; For nodes During the period The load; For nodes During the period Shear load; The power flow constraint mentioned above refers to the transmission power of the line. The transmission power of the line is limited by the maximum transmission capacity. ,in: For the line During the period Traffic; For transmission lines Admittance; , Transmission lines During the period The phase angle at the starting and ending points; For the line Maximum capacity; The aforementioned load shedding constraint refers to ensuring that the system can maintain stability and security by reducing some load during power supply and demand imbalances or emergencies. The magnitude of the load shedding is limited and must meet certain conditions. ,in: For nodes During the period The amount of load shedding; For nodes During the period The load capacity limits the magnitude of node load shedding. The phase angle constraint mentioned above refers to: , ,in: For nodes During the period The voltage phase angle; Use as a reference node; The aforementioned constraints on pumped storage units refer to the need to meet the demands of the power system while avoiding economic losses or equipment damage due to over-operation or resource waste. Specifically, these constraints include: pumped power generation operating conditions, pumped storage unit output constraints, and reservoir capacity constraints. The aforementioned pumped-power generation operating condition constraints refer to the constraints on the four basic operating condition transition processes: pumping-shutdown, power generation-shutdown, shutdown-pumping, and shutdown-power generation. ,in: , Pumped storage units During the period Pumping and power generation status ( The pumped storage unit is in pumping mode. The pumped storage unit is in power generation mode. (This refers to the pumped storage unit being in shutdown condition); The aforementioned output constraint of pumped storage units refers to the fact that, regardless of whether they operate in turbine or pump mode, the output regulation characteristics of pumped storage units are similar to those of conventional hydroelectric units, and there is a minimum output limit: both the power generation and pumping power of pumped storage units are subject to maximum and minimum output limits. , ,in: , Pumped storage units During the period The pumping and power generation status; , Pumped storage units During the period Pumping and power generation capacity; , , and Pumped storage units The minimum and maximum pumping power and the minimum and maximum generating power of pumped storage units are usually related to the rated installed capacity of the unit in practice. The reservoir capacity constraint mentioned above refers to the following: Under the daily regulation operation mode, a capacity configuration scheme of "small upper reservoir, large lower reservoir" is adopted, therefore the capacity of the upper reservoir meets the following requirements: , , , ,in: , Upper Reservoir Lower and upper limits of storage capacity; , They are respectively the lower reservoir Lower and upper limits of storage capacity; , , , These are the initial and final water storage capacities of the upper and lower reservoirs, respectively. The length of a unit of time period; , The conversion factor between pumped storage power generation and flow rate. Conversion coefficient between pumping power and flow rate , , The power generation efficiency and pumping efficiency of the reversible pump-turbine generator set are respectively taken as... and ; Let be the average density of water, and take . ; Let be the acceleration due to gravity, and take . ; This represents the average head difference between the upper and lower reservoirs.

6. The hierarchical and zoned power balance method for flexible land-sea interconnection systems based on two-layer optimization as described in claim 1, characterized in that, First, the lower-level optimal power balance model obtains power balance data under optimal operating conditions. Then, the upper-level optimal partitioning model calculates electrical distances based on the power balance data, derives a partitioning scheme based on the updated electrical distances, and returns the optimized topology of cross-regional lines to the lower-level optimal power balance model. Through repeated iterations, the optimal partitioning result is obtained to achieve power balance. Specifically, this includes: 4.1 The iteration termination condition is set as follows: the optimal value of the objective function in the lower-level optimal power balance model converges; 4.2 First, the optimal power balance is obtained from the lower-level optimal power balance model, specifically including: i) Establish the objective function to minimize the total system cost; ii) Solve based on power grid structure constraints and flexibility resource constraints; iii) The power flow parameters of each line in the system when the optimal solution is obtained are passed to the upper-level optimal partition model; 4.3 After obtaining the optimal power balance at the upper level, the electrical distance matrix is ​​calculated. Then, the partitioning scheme is solved with the minimum electrical connectivity index as the objective function. Specifically, this includes: i) Calculate the electrical distance between nodes based on the power flow parameters of each line obtained from the lower layer; ii) Establish the objective function with the goal of minimizing the electrical connection coefficient; iii) After obtaining the optimal partitioning result based on the partitioning constraints, optimize the cross-regional lines based on the power flow data, and pass the new topology to the lower-level optimal power balance model. 4.4 Determine whether the stopping iteration condition is met. If not, continue to optimize the cross-regional lines based on the power balance data, update the topology, and then pass it to the next layer for iteration.

7. The hierarchical and zoned power balance method for flexible land-sea interconnection systems based on dual-layer optimization as described in claim 6, characterized in that, After the upper-level optimal partitioning model obtains the partitioning scheme, it chooses to disconnect the line with smaller power flow in the cross-regional line to reduce the power flow interaction between regions (no disconnection is performed when there is only one cross-regional line). After the disconnection, the new line parameters are passed to the lower-level optimal power balance model. The lower-level optimal power balance model solves the optimal power balance again, and the upper-level optimal partitioning model obtains the new power balance data to update the electrical matrix and partition again. This process continues until the iteration termination condition is met, and then the final optimal partitioning result is output.

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