Method and system for long-term time-series operation simulation in reconfigured power distribution network with embedded network

Abstract

The application belongs to the technical field of electrical engineering, and discloses a power distribution network medium and long-term time sequence operation simulation method and system embedded with network reconstruction. The simulation method adopts a power distribution network time sequence operation simulation model and a soft constraint risk diagnosis model. The constraint condition of the power distribution network time sequence operation simulation model is a hard constraint strictly meeting the normal operation requirements of the power grid. The soft constraint risk diagnosis model relaxes the hard constraint, and increases a load shedding penalty term, a branch power flow out-of-limit penalty term and a node voltage out-of-limit penalty term in the objective function. First, the soft constraint risk diagnosis model is solved to identify the out-of-limit window, and the power distribution network time sequence operation simulation model is called after network topology reconstruction for the out-of-limit window to generate the simulation operation result of the corresponding window. After the current window ends, the next window is entered, and the simulation is sequentially executed window by window until the simulation of all time sequences is completed. The above method can improve the authenticity of the annual operation simulation.

Description

Technical Field

[0001] This invention belongs to the technical field of electrical engineering, and more specifically, relates to a long-term time-series operation simulation method and system for power distribution networks with embedded network reconfiguration. Background Technology

[0002] With the continuous growth of new energy power generation such as wind power and photovoltaics, a large number of new resources such as distributed new energy, energy storage, electric vehicles, and flexible loads are being connected to the distribution network, causing the traditional distribution network to gradually evolve into an active distribution network. Compared with the traditional distribution network, which is mainly based on unidirectional power flow and passive power supply, the spatiotemporal coupling relationship between sources, loads, and storage in the active distribution network is more complex. Wind and solar power output is significantly affected by weather, seasons, and intraday fluctuations, load levels are significantly affected by user behavior and meteorological conditions, and energy storage devices will adjust their charging and discharging at different times. Therefore, the operation status of the distribution network is no longer a static, single-moment problem, but a medium- to long-term continuous operation problem with obvious time-series characteristics. In order to accurately evaluate the operational safety, new energy absorption capacity, power supply reliability, and energy storage operation effect of the distribution network throughout the year, it is necessary to construct a time-series operation simulation model covering an annual time scale of 8760 hours or longer to continuously simulate operating variables such as load, wind power, photovoltaics, energy storage, main grid power purchase, line power flow, and node voltage.

[0003] Existing long-term time-series simulation methods for distribution networks typically employ a fixed network topology for year-round operational verification. This means that the distribution network maintains its original wiring configuration throughout the year, and the operational optimization problem for each time period or rolling window is solved sequentially under this topology. While this method is relatively simple in its modeling process, it fails to adequately reflect the switching operation capabilities of actual distribution network dispatching. In real-world distribution networks, when issues such as line overload, localized low voltage, limited distributed renewable energy absorption, or increased load transfer pressure occur during a given period, dispatchers typically do not immediately determine system infeasibility or directly disconnect loads. Instead, they prioritize altering the network topology through coordinated operations of tie switches and sectionalizing switches to achieve power flow transfer, load transfer, and voltage improvement. Therefore, if a fixed topology is consistently used in year-round time-series simulations, it is easy to misjudge some scenarios that could be recovered through network reconfiguration as system infeasibility scenarios. This leads to deviations from actual assessments of distribution network operational risks, renewable energy absorption capacity, and energy storage demand.

[0004] Therefore, how to achieve accurate simulation of long-term time-series operation of the distribution network is a technical problem that urgently needs to be solved. Summary of the Invention

[0005] In view of the above-mentioned defects or improvement needs of the existing technology, the present invention provides a method and system for simulating the long-term time-series operation of a distribution network embedded in network reconfiguration, the purpose of which is to achieve accurate simulation of the long-term time-series operation of the distribution network.

[0006] According to a first aspect of the present invention, a long-term time-series operation simulation method for distribution networks embedded in network reconfiguration is provided. The method employs a distribution network time-series operation simulation model and a soft-constraint risk diagnosis model. The distribution network time-series operation simulation model has a first objective function including a cost term and hard constraints constructed based on network operation limitations. The hard constraints include node voltage limit exceedance constraints, line active power flow constraints, and load shedding constraints. The soft-constraint risk diagnosis model has a second objective function and relaxation constraints. The relaxation constraints include node voltage limit exceedance soft constraints, line active power flow soft constraints, and load shedding soft constraints. The node voltage limit exceedance soft constraints are implemented within the constraints of node voltage limit exceedance. The first objective function is to introduce non-negative voltage relaxation variables to expand the constraint range; the second objective function is to introduce non-negative active power flow over-limit relaxation variables to expand the constraint range; the third objective function is to introduce non-negative load demand relaxation variables to expand the constraint range; the fourth objective function is to add load shedding penalty terms, branch power flow over-limit penalty terms, and node voltage over-limit penalty terms to the first objective function. The load shedding penalty term is the spatiotemporal accumulation of load demand relaxation variables, the branch power flow over-limit penalty term is the spatiotemporal accumulation of active power flow over-limit relaxation variables, and the fifth objective function is the spatiotemporal accumulation of voltage relaxation variables. The method includes: S1. Divide the entire time period to be simulated into sub-time periods, each sub-time period being a time window. Take the first time window as the current time window and jump to S2. S2. For the current time window, determine whether there is a risk of exceeding the limit in the original topology of the power grid in the current time window by solving the soft constraint risk diagnosis model. If the soft constraint risk diagnosis model has no solution in the current time window or any of the solved load shedding penalty term, branch power flow limit exceeding penalty term, and node voltage limit exceeding penalty term is greater than zero, it indicates that there is a risk of exceeding the limit in the current time window, and jump to S3. Otherwise, use the solved operating parameters as the operating simulation result of the current time window and jump to S4. S3. By changing the state of the line switch to change the original topology of the distribution network, a set of candidate reconfiguration topologies is obtained. For each candidate reconfiguration topology in the set, the operating parameters and the first objective function value of the corresponding candidate reconfiguration topology under the current time window are obtained by solving the distribution network time-series operation simulation model. The candidate reconfiguration topology with the smallest first objective function value is taken as the final reconfiguration topology and its corresponding operating parameters are taken as the operation simulation result of the current time window. S4. Determine if this is the last time window. If not, proceed to the next time window as the current time window and jump to S2. Otherwise, end the simulation and summarize the simulation results for the entire time period.

[0007] According to a second aspect of the present invention, a long-term time-series operation simulation system for a distribution network embedded in network reconfiguration is provided, comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.

[0008] According to a third aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0009] According to a fourth aspect of the present invention, a computer program product is provided, comprising a computer program or instructions that, when executed by a processor, implement the steps of the method described above.

[0010] In summary, compared with the prior art, the technical solutions conceived in this invention have the following main advantages: To address the shortcomings of existing long-term time-series operation simulations of distribution networks, which often employ fixed network topologies and fail to reflect actual network reconfiguration operations during dispatching, and which easily misjudge voltage exceedance, branch power flow exceedance, and load shedding scenarios as system infeasibility, this invention proposes a long-term time-series operation simulation method for distribution networks that embeds network reconfiguration. This method employs a distribution network time-series operation simulation model and a soft-constraint risk diagnosis model. The constraints of the distribution network time-series operation simulation model are hard constraints that strictly meet the requirements for normal grid operation. The soft-constraint risk diagnosis model relaxes the voltage exceedance constraints, line active power flow constraints, and load shedding constraints of the distribution network time-series operation simulation model and adds a load shedding penalty term to the objective function. The method for implementing branch power flow and node voltage exceedance penalties involves first identifying exceedance windows by solving a soft-constraint risk diagnosis model. Then, for each exceedance window, network topology reconstruction is performed, and the distribution network time-series operation simulation model is invoked to generate simulation results for the corresponding window. After the current window ends, the next window begins, and the risk assessment of the original network topology is re-evaluated. This process is repeated window by window until all time-series simulations are completed. This method forms a closed-loop simulation mechanism that prioritizes the operation of the original topology, automatically triggers reconstruction for abnormal windows, and restores the normal topology after risk mitigation. This improves the realism of the annual operation simulation and provides a more accurate operational basis for subsequent energy storage planning, new energy consumption assessment, and distribution network operation safety analysis. Attached Figure Description

[0011] Figure 1This is a flowchart of the steps of a long-term time-series operation simulation method for a distribution network with embedded network reconfiguration, according to an embodiment of the present invention.

[0012] Figure 2 This is a time-window simulation operation diagram in one embodiment of the present invention.

[0013] Figure 3 This is a schematic diagram of time window division in one embodiment of the present invention.

[0014] Figure 4 This is a schematic diagram of the segmented solution of the time-series simulation model in one embodiment of the present invention.

[0015] Figure 5 This is a schematic diagram of an IEEE 33-node distribution network system according to an embodiment of the present invention.

[0016] Figure 6 This is a schematic diagram of a basic ring matrix in one embodiment of the present invention.

[0017] Figure 7 This is a schematic diagram comparing the topological state before and after reconstruction in one embodiment of the present invention.

[0018] Figure 8 This is a schematic diagram of the operation status of the year-round scrolling window reconstruction in one embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0020] Example 1 This invention provides a long-term time-series operation simulation method for distribution networks embedded in network reconfiguration. This technical solution utilizes two models: a distribution network time-series operation simulation model and a soft-constraint risk diagnosis model obtained by adjusting the distribution network time-series operation simulation model. To understand this invention, the distribution network time-series operation simulation model will be introduced first.

[0021] Understandably, before modeling, it is necessary to obtain the basic network parameters of the distribution network, load time series data, wind power output time series data, photovoltaic power output time series data, energy storage parameters, and main grid power purchase parameters.

[0022] The time-series operation simulation model of the power distribution network includes a first objective function and hard constraints.

[0023] The first objective function includes system operating cost items, specifically including grid power purchase costs, wind curtailment costs, solar curtailment costs, line loss costs, and energy storage operating costs. The optimization objective is to minimize the system operating cost, which can be expressed as: (1); In the formula, For time index, This represents the total number of simulation moments. for The punishment of abandoning light at all times for The penalty of abandoning the wind at any moment for Line loss penalty at any time, for The cost of purchasing electricity for thermal power units at all times.

[0024] , , and The calculation expression is as follows: (2); (3); (4); (5); In the formula, For a moment The power of abandoned light, For a moment wind curtailment power, For a moment The system network loss power, For a moment The active power output of the equivalent power source of the upstream power grid For a moment The light abandonment penalty coefficient, For a moment The wind curtailment penalty coefficient, This is the network loss penalty coefficient. The cost per unit of electricity purchased.

[0025] Among them, time System network loss power The calculation formula is: (6); In the formula, For route indexing, For a moment No. The square of the current flowing through the transmission line The number of transmission lines in the system. For the first The resistance of the line; and For node indexing, for Nodes and Branches between nodes (referred to as branches) The square of the current flowing through the branch. for The square of the active power of the branch circuit. for The square of the reactive power of the branch circuit, For nodes The square of the voltage value.

[0026] The above active power P, reactive power Q, current I, and voltage V are all operating parameters to be decided.

[0027] This embodiment designs constraints to ensure that the established time-series operation simulation model can accurately simulate the actual time-series operation of the distribution network. The constraints include node power balance constraints, Ohm's law constraints, second-order cone power constraints at the headend, node voltage exceeding limits constraints, node current exceeding limits constraints, line active power flow constraints, wind and solar power output constraints, energy storage operation constraints, tie-line power constraints, load shedding constraints, equivalent power output constraints from the upstream grid, and wind and solar curtailment constraints. The constraints designed in this embodiment are explained below. For brevity, time periods will be omitted in some subsequent formulas. Annotation.

[0028] Constraint 1: Node power balance constraint.

[0029] Node power balance constraints are used to ensure that the injected power at each node in the power grid is equal to the algebraic sum of the power flowing out of / into the branches of that node, in order to satisfy the energy conservation requirement of Kirchhoff's Current Law. Specifically: (7); in, , , For the index of the node, the set This represents the set of all nodes in a power distribution network. For Let be the set of branch end nodes of the first node. For nodes Active power injection, For the node Flow to Node active power, For the node Flow to Node active power, For Let be the set of the starting nodes of the branches of the terminal nodes. For the node Flow to Node The square of the current value, For the node To the node The resistance value, For nodes Earth conductivity, For nodes The square of the voltage value; For nodes reactive power injection For the node Flow to Node reactive power, For the node Flow to Node reactive power, For the node To the node The reactance value, For nodes The susceptivity to ground.

[0030] Constraint 2: Ohm's Law constraint.

[0031] Ohm's law constraint states that the voltage phasor difference between the two ends of each branch is equal to the complex impedance of that branch multiplied by the complex current flowing through the branch. It is a constraint condition obtained by coupling voltage drop and branch power / current according to Ohm's law based on the branch power flow model constructed by Kirchhoff's laws.

[0032] First, construct a branch power flow model based on Kirchhoff's laws. Then, combine this model with Ohm's law to derive the Ohm's law constraint conditions as shown in the following equation: (8); in, Represents the set of all branches in the system. For nodes The square of the voltage value.

[0033] From Ohm's law, we can obtain: (9); Both represent vectors. For the node To the node The impedance value.

[0034] Power at the beginning of the line As shown in equation (10): (10); In the formula, for The conjugate value of , substituting equation (10) into equation (9) yields: (11); In the formula, for The conjugate value, for The conjugate value of is obtained by multiplying both sides of the equation by its own conjugate, resulting in equation (12): (12); Equation (12) above is the Ohm's law constraint directly derived from the formula.

[0035] Constraint 3: Second-order cone constraint for power at the head end.

[0036] The head-end power constraint can be expressed as follows: (13); Equation (13) shows the relationship between node voltage, branch current and branch power.

[0037] Constraint 4: Node voltage over-limit constraint.

[0038] Node voltage over-limit constraints refer to the requirement that the operating voltage amplitude of each node in a distribution network must be limited within its specified lower and upper safety thresholds to ensure system power quality and safe equipment operation. Specifically, this can be expressed as: (14); in, Represents a node The minimum value taken by the square of the voltage. For nodes The maximum value taken by the square of the voltage. Set the variable to This can reduce the dimensionality of variables, thereby simplifying the calculation process of the model.

[0039] Constraint 5: Node current over-limit constraint.

[0040] Node current over-limit constraints refer to the requirement that the current amplitude flowing through each branch in a power system must not exceed the maximum allowable current carrying capacity of its conductors, in order to prevent equipment overheating and damage or safety accidents. Specifically, this can be expressed as: (15); in, For the line The minimum value taken by the square of the per-unit current. For the line The maximum value taken by the square of the per-unit value of the current. represent Similarly, set the variable to It can effectively reduce the dimensionality of variables, thereby simplifying the model calculation process.

[0041] Constraint 6: Active power flow constraint of the line.

[0042] Active power flow constraint refers to the requirement that the active power transmitted by each line in a power system must be limited to its maximum permissible range to prevent line overload from causing tripping or system cascading failures. Specifically, it can be expressed as: (16); In the formula, For the line Maximum permissible active power transmission value.

[0043] Constraint 7: Wind and solar power output constraints.

[0044] Wind and solar power output constraints refer to the fact that the actual active power output of both is limited at any given time by the upper limit of available resources such as wind speed and solar radiation, with the lower limit typically set to zero. Specifically, this can be expressed as: (17); In the formula, For the first The wind farm in the first Actual output at any moment For the first The wind farm in the first Predicting output at any given moment For the first The photovoltaic power station in the first Actual output at any moment For the first The photovoltaic power station in the first Predicting output at any given moment.

[0045] Constraint 8: Energy storage operation constraints.

[0046] Energy storage operation constraints refer to the requirement that the charging and discharging power and real-time state of charge (SOC) of energy storage at any given time must be limited to the maximum charging and discharging capacity and the upper and lower limits of the safe SOC, respectively, while simultaneously satisfying the SOC balance or given interval requirements at the beginning and end of a scheduling cycle, and that charging and discharging operations cannot be performed simultaneously within the same time period. Specifically, these constraints include charging power constraint formula (18), discharging power constraint formula (19), energy storage charging and discharging state constraint formula (20), energy storage state of charge constraint formula (21), and energy storage capacity constraint formula (22). (18); (19); (20); (twenty one); (twenty two); In the formula, For the first One energy storage power station The charging status is indicated by "1", which means that the device is currently charging. For the first One energy storage power station The discharge state is indicated by "1", which means that the device is in a discharge state at this moment. , The first The maximum and minimum charging power of each energy storage power station. , The first The maximum and minimum discharge power of each energy storage power station. Representing the Energy storage power station Energy storage at all times , These represent the charging and discharging efficiencies of the energy storage power station, respectively. , Representing the first The maximum and minimum energy storage capacity of each energy storage power station.

[0047] Constraint 9: Tie line power constraint.

[0048] Tie-line power constraints refer to the requirement that the active power exchanged between low-voltage systems and the main power grid via tie-lines must be limited to the tie-line thermal stability limits, transient stability requirements, and the maximum transmission power specified in the dispatching agreement to ensure the safe and stable operation of the interconnected system. Specifically, this can be expressed as: (twenty three); In the formula, for The power transmitted outward by the time system This is the upper limit of the transmission power of the tie line.

[0049] Constraint 10: Load reduction constraint.

[0050] Load shedding constraints prevent load shedding during normal operation. Specifically, this can be expressed as: (twenty four); In the formula This indicates the amount of load shedding.

[0051] Constraint 11: Output constraint of equivalent power source from the upstream power grid.

[0052] The equivalent power output constraint of the upper-level power grid refers to the requirement that the equivalent active and reactive power output of the upper-level power grid injected into the local power grid through the interconnection section must be limited by the adjustable capacity of the upper-level power grid's own units, the transmission limits of the tie lines, and the upper and lower limits of the equivalent output determined by safety verification, in order to ensure the power balance and safe and stable operation of the upper and lower-level power grids. Specifically, this can be expressed as: (25); In the formula, , The first Upper and lower limits of active power output of equivalent power sources of the upstream power grid; For the first Equivalent power source of the upstream power grid The status at any given time: "0" represents power off, and "1" represents power on. (26); In the formula, , The first Upper and lower limits of reactive power output of equivalent power source of the upstream power grid.

[0053] Constraint 12: Curtailment of wind and solar power.

[0054] Wind and solar curtailment constraints refer to the maximum permissible curtailment limit determined by a combination of factors, including grid connection agreements, renewable energy consumption responsibility weights, and operational economics, when wind and solar power cannot be fully absorbed due to limited system regulation capacity. Specifically, this can be expressed as: (27); (28); In the formula, This represents the wind curtailment power during time period t. This is the upper limit of wind curtailment power. This represents the amount of light discarded during time period t. This is the upper limit of the power to be discarded.

[0055] In one embodiment, considering that the first-end power second-order cone constraint of equation (13) is a non-convex constraint, the existence of this constraint causes the entire model to exhibit non-convex characteristics. To make the model convex, relaxation processing can be performed, and the second-order cone constraint of equation (13) can be relaxed to the following inequality (29): (29); Based on equation (29), the following derivation steps can be performed: (30); (31); (32); (33); in, This represents the L2 norm.

[0056] Through the above derivation, the second-order cone constraint of the head power is relaxed from equation (13) and transformed into equation (33), giving the model better convexity and numerical solvability. Then, it can be solved numerically efficiently using commercial optimization solvers such as CPLEX and Gurobi.

[0057] Based on the above, a time-series operation simulation model of the power distribution network can be constructed.

[0058] A soft-constraint risk diagnosis model is obtained by adjusting the distribution network time-series operation simulation model. The soft-constraint risk diagnosis model includes a second objective function and relaxation constraints. The second objective function includes system operating cost, load shedding penalty, branch power flow exceeding limit penalty, and node voltage exceeding limit penalty. The relaxation constraints include node voltage exceeding limit soft constraints, line active power flow soft constraints, and load reduction soft constraints. The node voltage exceeding limit soft constraint introduces a non-negative voltage relaxation variable to expand the constraint range when constructing the node voltage range constraint. The line active power flow soft constraint introduces a non-negative active power flow exceeding limit relaxation variable to expand the constraint range when constructing the line active power range constraint. The load reduction soft constraint introduces a non-negative load demand relaxation variable to allow load shedding within the range not exceeding the load demand relaxation variable. The load shedding penalty is the spatiotemporal accumulation of the load demand relaxation variable, the branch power flow exceeding limit penalty is the spatiotemporal accumulation of the active power flow exceeding limit relaxation variable, and the node voltage exceeding limit penalty is the spatiotemporal accumulation of the voltage relaxation variable.

[0059] Specifically, based on the time-series operation simulation model introduced above, a soft constraint diagnosis mechanism for identifying operational risks is introduced. That is, load shedding penalty term, branch power flow over-limit penalty term, and node voltage over-limit penalty term are added to the objective function. At the same time, some of the original hard constraints are transformed into soft constraints with relaxation variables, including line active power flow soft constraints, node voltage over-limit soft constraints, and load reduction soft constraints.

[0060] Specifically, the first objective function in the time-series simulation model is adjusted by adding load shedding penalty terms, branch power flow over-limit penalty terms, and node voltage over-limit penalty terms, resulting in a second objective function. This second objective function includes system operating cost terms, load shedding penalty terms, branch power flow over-limit penalty terms, and node voltage over-limit penalty terms, and can be expressed as follows: (34); In the formula, As a load shedding penalty, Penalties for exceeding the limit on side streets Penalty for exceeding voltage limits.

[0061] Specifically, the node voltage over-limit constraints, line active power flow constraints, and load reduction constraints in the time-series simulation model are relaxed to obtain the corresponding node voltage over-limit soft constraints, line active power flow soft constraints, and load reduction soft constraints.

[0062] The active power flow constraint of the line is to introduce an active power flow over-limit relaxation variable on the basis of the original active power flow constraint (16): (35); The soft constraint of active power flow on the line can be expressed as: (36); In the formula, Indicates the line The active power flow exceeding the limit relaxation variable in time period t reflects the degree of active power flow exceeding the limit.

[0063] The node voltage over-limit soft constraint is based on the original node voltage over-limit constraint (14) by introducing a voltage relaxation variable: (37); The node voltage over-limit soft constraint can be expressed as: (38); In the formula, This represents the low-voltage over-limit relaxation amount of node j in time period t. This represents the overvoltage relaxation amount at node j during time period t.

[0064] The load reduction soft constraint is to introduce a load demand slack variable based on the original load reduction constraint (10): (39) The soft constraint for load reduction can be expressed as: (40) In the formula, This represents the load reduction amount during time period t. This represents the load demand slack variable introduced during time period t. Therefore, this constraint means that the load reduction amount cannot be negative, nor can it exceed the total load demand at that time.

[0065] In the objective function It can be expressed as the following formula: (41); In the formula, The penalty coefficient for branch road exceeding the limit. This is the voltage over-limit penalty coefficient. This is the baseline capacity.

[0066] In the objective function The calculation formula is: (42); In the formula, This is the penalty coefficient for voltage exceeding the limit.

[0067] In the objective function It can be expressed as the following formula: (43); In the formula, This is the load shedding penalty coefficient.

[0068] In one embodiment, the soft-constraint risk diagnosis model performs relaxation operations on node voltage over-limit constraints, line active power flow constraints, and load shedding constraints, while other constraints are the same as those in the distribution network time-series operation simulation model.

[0069] This invention achieves accurate simulation of power grid operation by combining a power distribution network timing simulation model and a soft-constraint risk diagnosis model. For example... Figure 1 The diagram shown is a flowchart illustrating the steps of a long-term time-series operation simulation method for a distribution network embedded in network reconfiguration according to an embodiment of the present invention. Figure 2 The diagram shown is a time-window simulation operation diagram according to an embodiment of the present invention. The following is in conjunction with... Figure 1 and Figure 2 This method is described in detail.

[0070] The specific execution steps are as follows.

[0071] S1. Divide the entire time period to be simulated into sub-time periods, each sub-time period being a time window. Take the first time window as the current time window and jump to S2.

[0072] Specifically, considering the complexity of solving medium- and long-term time-series simulations and the difficulty of direct solutions, a piecewise solution method is adopted, such as... Figure 3 As shown, the entire year is divided into several short segments for sequential solving. The result of the last segment of the previous sub-segment is used as the starting state of the next sub-segment, thus satisfying the time-segment coupling constraint and ensuring a complete and feasible solution. Figure 4 As shown, by solving the problems sequentially and then summarizing the results of each sub-period, a complete annual time-series simulation result can be obtained. Sub-periods are usually set to a day or a week, which is acceptable in terms of computational complexity, but the simulation accuracy will be affected by too many sub-periods. However, agricultural power systems have fewer lines and fewer generating units, so longer sub-periods can be set to improve the accuracy of the solution and reduce the number of decompositions, provided that the computing power allows.

[0073] In one embodiment, the time-series operation of the distribution network throughout the year (8760 hours) can be simulated, which is divided into 48 time windows.

[0074] S2. For the current time window, determine whether there is an over-limit risk in the original topology of the power grid in the current time window by solving the soft constraint risk diagnosis model. If the soft constraint risk diagnosis model has no solution in the current time window or any of the solved load shedding penalty term, branch power flow over-limit penalty term, and node voltage over-limit penalty term is greater than zero, it indicates that there is an over-limit risk in the current time window, and jump to S3. Otherwise, use the solved operating parameters as the operating simulation result of the current time window and jump to S4.

[0075] Specifically, if the solution result shows a load shedding penalty term greater than zero, it indicates that load shedding has occurred. If the solution result shows a branch power flow limit violation penalty term greater than zero, it indicates that branch power flow has exceeded its limit. If the solution result shows a node voltage limit violation penalty term greater than zero, it indicates that node voltage has exceeded its limit. If the original topology cannot be solved within the current rolling window, or if at least one of the following operational risks occurs: load shedding, branch power flow limit violation, or node voltage limit violation, it indicates that there is a risk of exceeding the limit in the current time window, and the network reconfiguration mechanism needs to be triggered.

[0076] S3. By changing the state of the line switches to change the original topology of the distribution network, a set of candidate reconfiguration topologies is obtained. For each candidate reconfiguration topology in the set, the operating parameters and the first objective function value of the corresponding candidate reconfiguration topology under the current time window are obtained by solving the distribution network time-series operation simulation model. The candidate reconfiguration topology with the smallest first objective function value is taken as the final reconfiguration topology, and its corresponding operating parameters are taken as the operation simulation result of the current time window.

[0077] Specifically, the distribution network is equipped with sectionalizing switches and tie switches. In the original distribution network topology, the sectionalizing switches are normally closed, and the tie switches are normally open. When a risk of exceeding limits is identified in the current time window, it is necessary to restructure the network topology. This involves changing the original connectivity and power distribution paths of the power grid through switching operations, transferring the power flow of branches exceeding limits (such as overloaded lines and links associated with voltage-limited nodes) to redundant or lightly loaded lines. This eliminates or mitigates risks such as thermal instability and voltage limits, while maintaining system connectivity and power supply reliability.

[0078] In one embodiment, the distribution network is a radial distribution network. A radial distribution network refers to a distribution network in which, during normal operation, all branches form a loop-free tree-like interconnected structure, power is transmitted unidirectionally from the power source to each load node along a single path, and any non-power source node is powered by only one upstream branch. For example... Figure 5 As shown in the diagram, solid lines between each node are equipped with sectionalizing switches, while dashed lines represent tie switches. There are five tie switches; when a tie switch is closed, an additional branch will appear at the dashed line. However, for a radial distribution network, closing a tie switch creates a basic loop in the network, thus disrupting the characteristics of the radial distribution network. Therefore, it is necessary to disconnect a suitable branch in this basic loop by operating normally closed sectionalizing switches, thereby ensuring that the reconfigured distribution network still meets the requirements for radial operation.

[0079] In one embodiment, when constructing a candidate reconfiguration topology set for a radial distribution network, a basic ring matrix can be constructed first based on the topological relationship between tie switches and original branches, and then a candidate disconnected branch set corresponding to each ring can be generated based on the basic ring matrix. For example... Figure 6 As shown, in the basic ring matrix, the rows correspond to each independent ring, and the columns correspond to all the switch branches (including sectionalizing switches and tie switches) in the ring. For each ring, the switch branches that simultaneously belong to that ring are grouped into a candidate disconnection branch set corresponding to that ring. During reconstruction, each ring is only allowed to select one branch from its branch set to disconnect, thus eliminating the ring and restoring its radial shape, resulting in a candidate reconstruction topology, as shown below. Figure 7 The diagram shows a comparison of the network topology before and after reconstruction. All candidate reconstruction topologies constitute the candidate reconstruction topology set.

[0080] After obtaining the set of candidate reconstruction topologies, it is necessary to determine the optimal candidate reconstruction topology and use the simulation results of the optimal candidate reconstruction topology as the simulation results for the current time window.

[0081] In practice, for each candidate reconfiguration topology in the candidate reconfiguration topology set, a distribution network time-series operation simulation model is used. This model's constraints include prohibiting load shedding and ensuring that node voltage constraints and branch power flow constraints are both hard constraints. If the distribution network time-series operation simulation model has a solution, it indicates that the corresponding candidate reconfiguration topology can meet the strict operational constraints, and it is considered a feasible reconfiguration topology. If the distribution network time-series operation simulation model has no solution, it indicates that the corresponding candidate reconfiguration topology cannot meet the strict operational constraints, and this candidate reconfiguration topology is unlikely to solve the branch over-limit or voltage over-limit problems. If multiple feasible reconfiguration topologies exist, it means that multiple reconfiguration schemes can solve the branch over-limit or voltage over-limit problems. In this case, the solution results of each feasible reconfiguration topology can be compared, and the reconfiguration topology with the smallest first objective function value is selected as the final reconfiguration topology, with its corresponding operating parameters used as the operation simulation result for the current time window.

[0082] In one embodiment, if the distribution network time-series operation simulation model under each candidate reconfiguration topology in the set has no solution in the current time window, it indicates that it is difficult to solve the problem of branch over-limit or voltage over-limit by reconfiguring the topology. At this time, the emergency operation mode is entered, and the load reduction constraints in the distribution network time-series operation simulation model are relaxed (e.g., the original hard constraint is that load reduction is not allowed, while the emergency operation mode allows load reduction within a set range). Then, for each candidate reconfiguration topology in the set, the operation parameters and the first objective function value of the corresponding candidate reconfiguration topology in the current time window are obtained by solving the distribution network time-series operation simulation model with relaxed load reduction constraints. The candidate reconfiguration topology with the smallest first objective function value is taken as the final reconfiguration topology, and its corresponding operation parameters are taken as the operation simulation result of the current time window.

[0083] S4. Determine if this is the last time window. If not, proceed to the next time window as the current time window and jump to S2. Otherwise, end the simulation and summarize the simulation results for the entire time period.

[0084] Specifically, when entering the next time window, the original power grid topology is used again for verification, and steps S2 to S3 are executed to complete the simulation of the next time window. This achieves a year-round closed-loop operation simulation with the original topology running first, automatic reconfiguration triggered by infeasible scenarios, and restoration of the original topology after risk mitigation.

[0085] The annual window action plan should include at least: window number, window start and end times, original topology feasibility assessment results, whether reconstruction was triggered, the reason for triggering reconstruction, reconstruction operation mode, set of closed tie switches, set of disconnected original branches, set of tie switches that remain disconnected, whether to enter emergency operation mode, whether to restore the original topology, restoration action time, window operation status, and corresponding timing optimization results. The final generated annual rolling window status diagram is as follows: Figure 8As shown.

[0086] Through the above methods, the present invention can extend the network reconstruction scheme obtained within a single window to the medium- and long-term time-series operation simulation process throughout the year. While ensuring the continuous transmission of energy storage status and operation results between windows, it generates a rolling window reconstruction action plan for the whole year that can be used for scheduling execution and operation analysis. This avoids directly misjudging over-limit windows or load shedding windows that can be restored through network reconstruction as infeasible scenarios for the system.

[0087] 1) Improve the realism of year-round time-series operation simulation. This method introduces a network reconfiguration mechanism in the year-round rolling window simulation, enabling the model to simulate the operation process of "normal topology operation, abnormal scenario-triggered reconfiguration, and restoration of normal topology after risk mitigation" in the actual distribution network, avoiding the problem that traditional fixed topology simulation cannot reflect the operation of dispatching switches.

[0088] 2) Avoid misjudging infeasible scenarios. For windows in the original topology that have no solution, load shedding, voltage exceeding limits, or branch exceeding limits, this method will not directly determine that the system is infeasible. Instead, it will further determine whether power flow transfer and load transfer can be achieved by closing the tie switch and disconnecting the original branch, thereby more accurately distinguishing between "real infeasible scenarios" and "transient risk scenarios that can be recovered through network reconfiguration".

[0089] 3) Provides more precise operating boundaries for subsequent energy storage planning.

[0090] Since energy storage planning is highly dependent on annual load, renewable energy output, voltage overruns, line congestion, and load shedding, using only fixed topology simulations can easily overestimate or underestimate energy storage demand. This method considers the operational improvement capabilities after network reconfiguration, and can more accurately reflect the charging and discharging status of energy storage in different windows, its peak shaving and valley filling effects, its role in mitigating overruns, and its role in replacing load shedding. This makes subsequent energy storage capacity configuration and site selection results more consistent with actual operating conditions.

[0091] 4) Improve the accuracy of renewable energy consumption and operational safety assessments. After power flow redistribution is achieved through network reconfiguration, some issues related to wind and solar power curtailment and load shedding caused by line congestion or voltage constraints can be alleviated. Therefore, this method can more accurately assess the distribution network's capacity to accommodate distributed renewable energy sources such as wind and solar power, providing a basis for subsequent renewable energy integration planning, energy storage co-configuration, and distribution network expansion and upgrades.

[0092] In summary, this invention embeds a network reconfiguration mechanism into the long-term time-series simulation process of distribution networks, constructing a complete simulation framework that includes soft constraint risk diagnosis, candidate reconfiguration topology generation, and full-time rolling window reconfiguration action planning. By identifying risk scenarios such as load shedding, voltage overruns, and branch power flow overruns, and automatically triggering network reconfiguration when the original topology is infeasible, this invention effectively avoids the misjudgment of abnormal scenarios that can be recovered through reconfiguration as system infeasibility by traditional fixed topology simulation methods. It focuses on refined time-series production simulation of distribution networks, with a particular emphasis on identifying infeasible operating conditions, and then uses topology optimization to better simulate the actual situation of the distribution network to evaluate its operation. This provides a more accurate, continuous, and logically consistent data foundation for subsequent energy storage planning, new energy absorption capacity assessment, distribution automation switch configuration, and distribution network expansion and renovation, possessing significant engineering application value.

[0093] Example 2 The present invention also relates to a long-term time-series operation simulation system for a distribution network with embedded network reconfiguration, comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.

[0094] This long-term timing simulation system for embedded network reconfiguration in power distribution networks can be installed on computing devices such as desktop computers, laptops, handheld computers, and cloud servers. The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The memory can be used to store computer programs and / or modules. The processor implements various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory, and by accessing data stored in the memory.

[0095] Example 3 The present invention also relates to a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0096] Specifically, the memory may include high-speed random access memory, as well as non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart media cards (SMC), secure digital (SD) cards, flash cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0097] Example 4 This invention provides a computer program product or computer program that includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the steps of the method described in the above embodiments of this invention.

[0098] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification. It should be noted that the terms "in one embodiment," "for example," and "again" are intended to illustrate the present invention and are not intended to limit the present invention.

[0099] The embodiments described above are merely examples of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention.

Claims

1. A long-term time-series operation simulation method for distribution networks embedded in network reconfiguration, characterized in that, The method employs a distribution network time-series operation simulation model and a soft-constraint risk diagnosis model. The distribution network time-series operation simulation model has a first objective function including a cost term and hard constraints constructed based on network operation limitations. The hard constraints include node voltage over-limit constraints, line active power flow constraints, and load shedding constraints. The soft-constraint risk diagnosis model has a second objective function and relaxation constraints. The relaxation constraints include node voltage over-limit soft constraints, line active power flow soft constraints, and load shedding soft constraints. The node voltage over-limit soft constraints are based on the node voltage over-limit constraints, which introduce non-negative voltage relaxation variables to expand the constraint range. The line active power flow soft constraint is to introduce non-negative active power flow over-limit relaxation variables to expand the constraint range on the basis of the line active power flow constraint; the load reduction soft constraint is to introduce non-negative load demand relaxation variables to expand the constraint range on the basis of the load reduction constraint; the second objective function is to add load shedding penalty term, branch power flow over-limit penalty term and node voltage over-limit penalty term on the basis of the first objective function. The load shedding penalty term is the spatiotemporal accumulation of load demand relaxation variables, the branch power flow over-limit penalty term is the spatiotemporal accumulation of active power flow over-limit relaxation variables, and the node voltage over-limit penalty term is the spatiotemporal accumulation of voltage relaxation variables. The method includes: S1. Divide the entire time period to be simulated into sub-time periods, each sub-time period being a time window. Take the first time window as the current time window and jump to S2. S2. For the current time window, determine whether there is a risk of exceeding the limit in the original topology of the power grid in the current time window by solving the soft constraint risk diagnosis model. If the soft constraint risk diagnosis model has no solution in the current time window or any of the solved load shedding penalty term, branch power flow limit exceeding penalty term, and node voltage limit exceeding penalty term is greater than zero, it indicates that there is a risk of exceeding the limit in the current time window, and jump to S3. Otherwise, use the solved operating parameters as the operating simulation result of the current time window and jump to S4. S3. By changing the state of the line switch to change the original topology of the distribution network, a set of candidate reconfiguration topologies is obtained. For each candidate reconfiguration topology in the set, the operating parameters and the first objective function value of the corresponding candidate reconfiguration topology under the current time window are obtained by solving the distribution network time-series operation simulation model. The candidate reconfiguration topology with the smallest first objective function value is taken as the final reconfiguration topology and its corresponding operating parameters are taken as the operation simulation result of the current time window. S4. Determine if this is the last time window. If not, proceed to the next time window as the current time window and jump to S2. Otherwise, end the simulation and summarize the simulation results for the entire time period.

2. The long-term time-series operation simulation method for distribution networks as described in claim 1, characterized in that, The active power flow constraint of the line is: ； The active power flow soft constraint of the line is: ； ； In the formula, i and j are both node indices, and t is the time period index. For time period t, from node Flow to Node active power, For the line Maximum permissible active power transmission value, Indicates the line The active power flow exceeds the slack variable in time period t; The node voltage over-limit constraint is: ； The node voltage over-limit soft constraint is: ； ； In the formula, Represents a node Voltage at time t The square of, Represents a node The minimum value taken by the square of the voltage. For nodes The maximum value taken by the square of the voltage. This represents the low-voltage over-limit relaxation amount of node j in time period t. This represents the overvoltage relaxation amount at node j during time period t; The load reduction constraint is: ； The soft constraint for load reduction can be expressed as: ； In the formula, This represents the load reduction amount during time period t. This represents the load demand slack variable introduced during time period t.

3. The long-term time-series operation simulation method for distribution networks as described in claim 2, characterized in that, Branch road flow exceeding the limit penalty item The calculation formula is: ； In the formula, The penalty coefficient for branch road exceeding the limit. Base capacity; Node voltage over-limit penalty The calculation formula is: ； In the formula, This is the penalty coefficient for voltage exceeding the limit; Load shedding penalty The calculation formula is: ； In the formula, This is the load shedding penalty coefficient.

4. The long-term time-series operation simulation method for distribution networks as described in claim 1, characterized in that, Both the distribution network time-series operation simulation model and the soft-constraint risk diagnosis model include a second-order cone constraint on the head-end power: ； In the formula, For time period t, from node Flow to Node active power, For time period t, from node Flow to Node reactive power, For time period t, from node Flow to Node The square of the current value, For nodes The square of the voltage value during time period t.

5. The long-term time-series operation simulation method for distribution networks as described in claim 1, characterized in that, The distribution network is a radial distribution network. The process of constructing a set of candidate reconfiguration topologies for the radial distribution network includes: Construct the basic ring matrix of the distribution network. Different rows in the basic ring matrix represent different independent loops in the distribution network, and the elements in each row represent the switch branches in the corresponding loop. The candidate reconstructed topology set is determined based on the basic ring matrix. Each candidate reconstructed topology is obtained by selecting and disconnecting a branch from each row of the basic ring matrix.

6. The long-term time-series operation simulation method for distribution networks as described in claim 1, characterized in that, If the distribution network time-series operation simulation model under each candidate reconfiguration topology in the set has no solution in the current time window, then the emergency operation mode is entered, the load reduction constraint in the distribution network time-series operation simulation model is relaxed, and then for each candidate reconfiguration topology in the set, the operation parameters and the first objective function value of the corresponding candidate reconfiguration topology in the current time window are obtained by solving the distribution network time-series operation simulation model with relaxed load reduction constraints. The candidate reconfiguration topology with the smallest first objective function value is taken as the final reconfiguration topology, and its corresponding operation parameters are taken as the operation simulation result of the current time window.

7. The long-term time-series operation simulation method for distribution networks as described in claim 1, characterized in that, The distribution network time-series operation simulation model and the soft-constraint risk diagnosis model also include node power balance constraints, Ohm's law constraints, node current over-limit constraints, wind and solar power output constraints, energy storage operation constraints, tie line power constraints, equivalent power output constraints of the upstream grid, and wind and solar curtailment constraints.

8. A long-term time-series operation simulation system for a distribution network embedded in network reconfiguration, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the long-term timing operation simulation method for power distribution networks as described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the long-term timing operation simulation method for power distribution networks as described in any one of claims 1 to 7.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by the processor, they implement the steps of the long-term timing operation simulation method for power distribution networks as described in any one of claims 1 to 7.

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