A novel sequential restoration optimization method for distribution system considering renewable energy output fluctuation

Abstract

The application discloses a novel power distribution network sequential restoration optimization method considering renewable energy output fluctuation, firstly, renewable energy output and load power demand models, power distribution network topology structure models in a sequential restoration process and source-grid-load-storage coordinated dispatching models in the sequential restoration process are respectively constructed, finally, linearization treatment is carried out on the constructed models and solutions are obtained. The technical scheme of the application considers renewable energy output fluctuation in the sequential restoration process, realizes reasonable dispatching of uncertain power sources such as wind power and photovoltaic power in the sequential restoration process of the novel power distribution network, and makes the most of local power generation resources, so that the sequential restoration capacity of the power-off system is improved while the safety of the power grid power supply restoration strategy is ensured.

Description

Technical Field

[0001] This invention belongs to the field of power grid technology, and specifically relates to a novel distribution network sequence recovery optimization method that takes into account the output fluctuations of renewable energy. Background Technology

[0002] Frequent extreme natural disasters pose significant challenges to the safe operation of power systems, especially distribution networks, which are highly vulnerable to large-scale power outages due to weak infrastructure and insufficient reserve capacity. To mitigate the social impact and economic losses caused by major power outages, emergency control of distribution networks under natural disasters has become a research hotspot. With the advancement of new energy power generation technologies and my country's dual-carbon goals in recent years, the construction of a new power system based on new energy sources is accelerating, with large-scale integration of distributed power sources such as wind power, energy storage, and mobile emergency power supplies into the distribution system. Against this backdrop, domestic and international scholars have proposed emergency control strategies based on "resilient distribution networks," utilizing distributed power sources and fault-free areas within the distribution network to form microgrids, maintaining continuous power supply to critical loads, thereby reducing the scope and duration of outages and improving power supply reliability.

[0003] Sequential restoration of distribution networks is one of the keys to improving their resilience under emergency control conditions. Existing sequential restoration research mainly utilizes deterministic power sources such as gas turbines and diesel generators to achieve sequential restoration of distribution networks. However, the large-scale integration of renewable energy sources such as wind power and photovoltaics makes existing sequential restoration methods based on deterministic power sources unsuitable for sequential restoration of distribution networks under new power system conditions. The uncertainty of renewable energy output can lead to the violation of safety constraints such as power balance and node voltage during sequential restoration, and in severe cases, may even trigger secondary power outages. Summary of the Invention

[0004] To address the uncertainties on both the supply and demand sides of new power systems, this invention proposes a novel distribution network sequential recovery optimization method that considers renewable energy output fluctuations. Based on a three-layer robust polarity optimization approach coordinating source-grid-load-storage, the method first models wind power output and load demand as a polygonal uncertain set. Then, a three-layer robust model is established to coordinate and schedule the source-grid-load-storage system. Furthermore, to effectively solve the proposed model, the original model is first relaxed to reduce the number of 0-1 variables. Subsequently, an extended column and constraint generation algorithm are used to iteratively solve the improved model. Simulation results show that the proposed method can guarantee the safety of microgrids under uncertain conditions, improve the overly conservative nature of traditional robust optimization methods, and increase the solution rate while maintaining model solution accuracy.

[0005] The specific technical solution for achieving the objective of this invention is as follows:

[0006] A novel distribution network sequence recovery optimization method that considers renewable energy output fluctuations includes the following steps:

[0007] Step 1: Construct a model for renewable energy output and load power demand;

[0008] Step 2: Construct a distribution network topology model during the sequential recovery process;

[0009] Step 3: Construct a coordinated scheduling model for the source-grid-load-storage process during sequential recovery;

[0010] Step 4: Linearize and solve the model constructed in Steps 1 to 3 to obtain the optimal power distribution network sequential recovery scheme.

[0011] Compared with the prior art, the significant advantages of this invention are:

[0012] The technical solution of this invention takes into account the output fluctuations of renewable energy during the sequential restoration process, realizes the rational scheduling of uncertain power sources such as wind power and photovoltaic power during the sequential restoration process of the new distribution network, makes greater use of local power generation resources, and improves the sequential restoration capability of the power outage system while ensuring the safety of the power grid power restoration strategy.

[0013] The present invention will now be described in further detail with reference to the accompanying drawings. Attached Figure Description

[0014] Figure 1 This is a flowchart of the steps in the novel power distribution network sequential recovery optimization method that takes into account the fluctuations in renewable energy output, as presented in this invention.

[0015] Figure 2 This is an IEEE 13 distribution network topology diagram in one embodiment of the present invention.

[0016] Figure 3 This is a schematic diagram of the output range of renewable energy prediction in one embodiment of the present invention.

[0017] Figure 4 This is a schematic diagram of the recovery path in two microgrids in one embodiment of the present invention.

[0018] Figure 5 This is a schematic diagram of the load recovery amount in a microgrid under the worst-case scenario in one embodiment of the present invention.

[0019] Figure 6 This is a schematic diagram of the active power dispatching of distributed generation in a microgrid under the worst-case scenario in one embodiment of the present invention. Detailed Implementation

[0020] Combination Figure 1A novel distribution network sequential recovery optimization method considering renewable energy output fluctuations includes the following steps:

[0021] Step 1: Construct a model for renewable energy output and load power demand, specifically as follows:

[0022] Step 1-1: Based on the obtained renewable energy forecast values, use the box uncertainty set to characterize the renewable energy output forecast error and construct a renewable energy output interval model that considers uncertainty:

[0023]

[0024] in, This represents the uncertain set of output from renewable energy units. This represents the set of nodes where renewable energy generating units are located. This represents the predicted output of renewable energy unit r at time t. This represents the uncertain output of renewable energy unit r at time t. This represents the predicted maximum output of renewable energy unit r at time t. This represents the predicted minimum output of renewable energy unit r at time t. This represents the upper boundary of the uncertain set of renewable energy output at time t. Let represent the lower boundary of the uncertain set of renewable energy output at time t;

[0025] Steps 1-2: Based on the obtained load demand forecast values, use the box uncertainty set to characterize the load demand forecast error and construct a load demand output range model that considers uncertainties.

[0026]

[0027] in, Represents the uncertain set of load power demand. This represents the set of nodes where the load is located; This represents the predicted power demand of the load at node l at time t. This represents the uncertain power demand of the load at node l at time t. This represents the maximum predicted power demand of the load at node l at time t. This represents the minimum predicted power demand of the load at node l at time t; This represents the upper boundary of the uncertain set of load power demand at node l at time t. This represents the lower boundary of the uncertain set of load power demand at node l at time t.

[0028] Step 2: Construct the distribution network topology model during the sequential recovery process, specifically as follows:

[0029] Step 2-1: To ensure that the power grid structure is gradually generated during the sequential restoration process, constraints are set on the topology of the power grid during the sequential restoration process:

[0030]

[0031] in, Let θ represent the power supply status of node i at time t, and h = θ(i) represent the set of parent nodes of node i in the distribution network topology.

[0032] Step 2-2: During the sequential restoration of the distribution network, the prerequisite for restoring power supply to loads, power sources, and other equipment is that the nodes where they are located are restored to power supply; the prerequisite for restoring power supply to a line is that the nodes at both ends of the line are restored to power supply. Therefore, constraints are set for the power supply status of loads, power sources, and lines during the sequential restoration of the distribution network:

[0033]

[0034]

[0035]

[0036]

[0037] in, This indicates the power supply recovery status of the load at node i at time t. This represents the operating state of the distributed power source g at time t. This indicates the closed state of the switch on line (h,i) at time t;

[0038] Steps 2-3: Set constraints on the distribution network topology. During the sequential restoration of the distribution network, a tree structure must be maintained, meaning that the system restoring power must satisfy the following constraints at each time step:

[0039]

[0040] Step 3: Construct a source-grid-load-storage coordinated scheduling model during the sequential recovery process, specifically as follows:

[0041] Step 3-1: Considering the uncertainty of renewable energy output, taking wind power as an example, and also taking into account the uncertainty of load power demand, establish a three-layer robust optimization model, with the maximum weighted load recovery during the fault duration as the recovery objective.

[0042]

[0043] Where T represents the set of sequential recovery time steps, This represents the weight coefficient of node i. Δt represents the active power scheduling value of node i at time t, and Δt represents the duration of an optimization step.

[0044] Furthermore, the constraints of the optimization model are:

[0045] (1) Distributed power generation output constraints, including power limit constraints, power ramp rate constraints, and power capacity constraints:

[0046]

[0047]

[0048]

[0049]

[0050] in, This represents the active power output of power source g at time t. This represents the reactive power output of power source g at time t. and Let G and V represent the minimum and maximum active power output of power source g, respectively. and These represent the minimum and maximum reactive power output of power source g, respectively. and These represent the downhill and uphill ramp rates of unit g within a unit time period, respectively. This indicates the capacity of unit g;

[0051] (2) Output constraints of energy storage devices, including power limit constraints and energy storage state of charge constraints:

[0052]

[0053]

[0054]

[0055]

[0056]

[0057]

[0058] in, This represents the charging state of the energy storage e at time t. This represents the discharge state of the stored energy e at time t. This represents the active charging power of energy storage e at time t. This represents the active discharge power of the stored energy e at time t. This indicates the minimum active charging power of energy storage device e. This represents the minimum active power of the energy storage device e. This indicates the maximum active charging power of the energy storage device e. This indicates the maximum active power of the energy storage device e. This represents the initial state of charge of the energy storage e. This represents the state of charge of the energy storage e at time t. This represents the minimum permissible state of charge of energy storage e. This represents the maximum permissible state of charge of energy storage e. This indicates the charging efficiency of energy storage e. This indicates the discharge efficiency of the stored energy e;

[0059] (3) The load demand constraints of controllable loads shall meet the following constraints:

[0060]

[0061]

[0062] in, This represents the maximum active power demand of load l at time t. This represents the minimum active power demand of load l at time t. This represents the reactive power dispatch value of load l at time t. The power demand factor representing load l. This indicates the power supply status of the load at node l at time t;

[0063] (4) Distribution network power flow constraints: The DistFlow power flow model commonly used in the power supply restoration process of the distribution network is adopted, and the power balance constraints and node voltage constraints are described as follows:

[0064]

[0065]

[0066]

[0067]

[0068]

[0069]

[0070]

[0071] in, Let represent the active power flowing through line (h,i) at time t. Let represent the reactive power flowing through line (h,i) at time t. Let represent the square of the voltage amplitude at node i at time t. This represents the maximum value of the square of the voltage magnitude at node i. This represents the minimum value of the square of the voltage magnitude at node i. This represents the maximum active power allowed to flow on line (h,i). This represents the minimum active power allowed to flow on line (h,i). This represents the maximum reactive power allowed to flow on line (h,i). This represents the minimum reactive power allowed to flow on line (h,i), where M is a very large constant.

[0072] Step 4: Linearize and solve the model constructed in Steps 1 to 3 to obtain the optimal distribution network sequential recovery scheme, specifically:

[0073] Step 4-1: Linearize the nonlinear constraints in the models constructed in Steps 1 to 3;

[0074] The power capacity constraint is a quadratic constraint, which can be approximated as a polygonal region using the inner polygon approximation method. When linearizing the quadratic constraint using the inner approximation method, a set of linear inequality constraints can be used to replace the quadratic constraint. Taking a regular dodecagon as an example, the reduced feasible region can be represented as the following set of inequality constraints:

[0075]

[0076] Step 4-2: Solve the model constructed in steps 1 to 3, specifically as follows:

[0077] The model constructed in steps 1 to 3 is a three-layer robust sequential recovery model, which can be represented in the following matrix form:

[0078]

[0079] The model is decomposed into a main problem and a subproblem using the EC&CG algorithm, and solved iteratively to obtain the optimized result, i.e., the optimal order recovery scheme under uncertainty conditions. The solution methods for the main problem and the subproblem are described below.

[0080] The main problem can be written in the following form:

[0081]

[0082] The subproblem in the (h+1)th iteration can be represented as:

[0083]

[0084] The inner problem in the above model is a mixed-integer linear programming problem, which makes it impossible to directly apply strong duality theory. Therefore, we first relax the 0-1 variables in the subproblem, and then perform an equivalent transformation. The specific steps for solving the subproblem are as follows:

[0085] 1) Obtaining the optimal solution to the main problem Then, by relaxing the 0-1 variables in the subproblem into continuous variables, we can obtain the corresponding linear programming relaxation problem:

[0086]

[0087] 2) By applying strong duality theory, the two-level "min-max" problem can be transformed into an equivalent single-level "min" problem, which can then be solved to obtain the worst-case scenario of wind power output and load demand fluctuation. .

[0088]

[0089] Due to the existence of bilinear terms It needs to be linearized using McCormick. Let = And the following four sets of constraints are introduced:

[0090]

[0091] 3) Optimize the solution of the main problem And obtained in step 2) As parameters, the optimized second-stage decision variables are obtained by solving a single-level deterministic mixed-integer linear programming model. and .

[0092]

[0093] 4) Fix the 0-1 variable z= The subproblem is transformed into a bi-level linear programming problem, and then further transformed into a single-level optimization problem using strong duality theory. McCormick's algorithm is then used to linearize the bilinear terms. The equivalent single-stage optimization problem can be expressed as:

[0094]

[0095] A novel distribution network sequential restoration system that takes into account fluctuations in renewable energy output includes the following modules:

[0096] Renewable Energy Output and Load Demand Model Construction Module: Used to construct renewable energy output and load demand models;

[0097] Distribution network topology model building module: used to build the distribution network topology model during sequential recovery;

[0098] Source-Grid-Load-Storage Coordinated Scheduling Model Construction Module: Used to construct the source-grid-load-storage coordinated scheduling model during sequential recovery;

[0099] Solver module: Used to linearize the constructed model and solve it to obtain the optimal power distribution network sequence recovery scheme.

[0100] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, performs the following steps:

[0101] Step 1: Construct a model for renewable energy output and load power demand;

[0102] Step 2: Construct a distribution network topology model during the sequential recovery process;

[0103] Step 3: Construct a coordinated scheduling model for the source-grid-load-storage process during sequential recovery;

[0104] Step 4: Linearize and solve the model constructed in Steps 1 to 3 to obtain the optimal power distribution network sequential recovery scheme.

[0105] A computer-storable medium having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0106] Step 1: Construct a model for renewable energy output and load power demand;

[0107] Step 2: Construct a distribution network topology model during the sequential recovery process;

[0108] Step 3: Construct a coordinated scheduling model for the source-grid-load-storage process during sequential recovery;

[0109] Step 4: Linearize and solve the model constructed in Steps 1 to 3 to obtain the optimal power distribution network sequential recovery scheme.

[0110] The present invention will be further described below with reference to the embodiments.

[0111] Example

[0112] The effectiveness of the proposed sequential recovery method was verified using an improved IEEE 13-bus distribution system, the topology of which is as follows: Figure 2 As shown.

[0113] Assuming the power distribution system is completely shut down due to an extreme natural disaster, and the main grid outage lasts approximately 30 minutes, what is the time interval between adjacent time steps during the sequential recovery process? The timeframe is set to 3 minutes. The distribution network contains three distributed generation sources, two energy storage devices, and two wind turbines. The parameters of the distributed generation sources are shown in Table 1, including location, status, and power capacity. The generator status reflects its black-start capability, where "1" indicates that the source has black-start capability; "0 / 1" indicates that it does not have black-start capability but can supply power when the starting power requirement is met; and "0" indicates that the distributed generation source has exited operation and does not participate in the sequential recovery process.

[0114] Table 1. Distributed power source parameters in the IEEE 13-node distribution system

[0115] Tab.1 Parameters of distributed generators in IEEE 13-bus system

[0116]

[0117] The energy storage device parameters are shown in Table 2, including the location, capacity, initial state of charge (SoC), allowable upper and lower limits of the SoC, maximum charge / discharge power, and charge / discharge efficiency. Additionally, nodes 632 and 671 are each connected to a wind turbine with rated capacities of 20kW and 30kW, respectively. The predicted wind power output range is as follows: Figure 3 As shown in Table 3, the switching operation times of each line in the system are shown in Table 4; the load parameters are shown in Table 5. In this embodiment, the wind turbine output prediction error is set to ±15%, and the load power demand prediction error is set to ±5%.

[0118] Table 2 Parameters of Energy Storage Devices in the IEEE 13-Node Distribution System

[0119] Tab.2 Parameters of energy storage systems in IEEE 13-bus system

[0120]

[0121] Table 3 Circuit switch operation time

[0122] Tab.3 Operation time of line switch

[0123]

[0124] Table 4 Load parameters in the IEEE 13-node power distribution system

[0125] Tab.4 Parameters of loads in IEEE 13-bus system

[0126]

[0127] First, two sets of comparative examples are used to verify the necessity of considering wind power participation in the sequential recovery process. In Example 1, wind turbines do not participate in the sequential recovery process; in Example 2, three distributed power sources, two energy storage systems, and two wind turbines all participate in the sequential recovery process. Both sets of examples consider the uncertainty of load demand.

[0128] Table 5 compares the optimization results, showing the objective function, number of iterations, and computation time for the two sets of examples. The table shows that the objective function in Example 2 is approximately 15% larger than that in Example 1, indicating that considering the participation of renewable energy in the sequential restoration process of resilient distribution networks can effectively improve the power restoration of critical loads.

[0129] Table 5 Comparison of results between the two examples

[0130] Tab.5 Companion of results in two cases

[0131] 1 1688.2 3 0.53s 2 1944.7 4 0.79s

[0132] Figure 4 The recovery paths within microgrids MG1 and MG2 in Example 2 are illustrated. Since the distribution network contains two black-start power sources, two microgrids are gradually generated, with each microgrid containing at least one black-start power source and multiple distributed power sources.

[0133] The optimized power supply and load restoration sequence is shown in Table 6. It can be seen that the sequential restoration process in Example 2 is completed earlier than that in Example 1. This is because in Example 2, the two wind turbines restore power in the second time step, so more load can be restored without rapidly increasing the power output.

[0134] Table 6. Power supply and load restoration sequence in the two sets of examples.

[0135] Tab.6 Restoration sequences of DGs and loads in two cases

[0136]

[0137] In Example 2, under the worst-case scenario of wind power output and load demand fluctuations, the load recovery at each time step in the two microgrids is as follows: Figure 5 As shown. Note that some loads (such as L4 and L13) were disconnected again after power was restored. This is because the capacity of distributed generation is limited, and restoring these loads would cause the power balance safety constraints in the distribution network to be exceeded. Therefore, considering the scarcity of distributed generation, in order to ensure the safe operation of the microgrid, it is necessary to disconnect some loads that have been restored but have small load weights, and restore them again when the power capacity within the microgrid is sufficient.

[0138] In Example 2, under the worst-case scenario of wind power output and load demand fluctuations, the active power dispatch output of the power equipment at each time step is as follows: Figure 6 As shown, the power supply equipment gradually increases its output to achieve the sequential restoration of the distribution network. From Figure 5 and Figure 6 It can be seen that the sum of the power output of the power supply equipment in the microgrid at each time step is equal to the active power demand of the load.

[0139] To illustrate the impact of uncertainty costs on the sequential recovery results, this example uses three uncertainty sets to describe the uncertainties in wind power output and load demand. The uncertainty costs in the three sets are shown in Table 7. From set 1 to set 3, the uncertainty costs gradually increase to represent larger prediction errors in renewable energy output and load demand. Furthermore, this example also tests a sequential recovery strategy based on deterministic methods to demonstrate the necessity of a robust optimization-based sequential recovery method.

[0140] Table 7. Sets with different uncertainties in cost

[0141] Tab.7 Different uncertainty budget set

[0142]

[0143] The feasibility of the generated sequential recovery schemes was further verified using Monte Carlo sampling. For each scenario set, 1000 wind power output and load demand scenarios were randomly generated. If no safety constraints were exceeded, the recovery scheme was considered feasible; otherwise, it was considered infeasible. From scenario set 1 to scenario set 3, the standard deviation of the normal distribution gradually increased to represent the increase in uncertainty. Table 8 shows the average load recovery amount of feasible sequential recovery schemes under the three sets of uncertain costs, as well as the proportion of infeasible scenarios when prediction errors actually occurred, based on the sequential recovery schemes obtained through robust and deterministic methods.

[0144] Table 8 Feasibility Verification of Sequential Recovery Strategies under Different Uncertain Sets

[0145] Tab.8 Feasibility check of SSR scheme under different uncertaintysets

[0146]

[0147] It can be seen that when the actual wind power output and load demand fluctuate relatively little, the proposed sequential recovery strategy based on robust optimization can fully guarantee the feasibility of the sequential recovery scheme. However, infeasible scenarios account for 2.3% of the sequential recovery schemes obtained through the deterministic model. As the degree of system uncertainty increases, the proportion of infeasible scenarios in the sequential recovery schemes obtained through the deterministic model increases rapidly. Simultaneously, infeasible scenarios also begin to appear in the schemes obtained through uncertainty sets 1 and 2, but the sequential recovery strategy obtained through uncertainty set 3 can fully guarantee its feasibility. Therefore, although the robust optimization method may result in a more conservative optimization outcome, it can better ensure the safety of the microgrid when prediction errors actually occur.

[0148] The above embodiments illustrate and describe the basic principles and main features of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the present invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed.

Claims

1. A novel distribution network sequence recovery optimization method considering renewable energy output fluctuations, characterized in that, Includes the following steps: Step 1: Construct a model for renewable energy output and load power demand. Step 1-1: Based on the obtained renewable energy forecasts, construct a renewable energy output range model that considers uncertainties: ； in, This represents the uncertain set of output from renewable energy units. This represents the set of nodes where renewable energy generating units are located. This represents the predicted output of renewable energy unit r at time t. This represents the uncertain output of renewable energy unit r at time t. This represents the predicted maximum output of renewable energy unit r at time t. This represents the predicted minimum output of renewable energy unit r at time t. This represents the upper boundary of the uncertain set of renewable energy output at time t. Let represent the lower boundary of the uncertain set of renewable energy output at time t; Step 1-2: Based on the obtained load power demand forecast values, construct a load power demand output range model that considers uncertainties: ； in, Represents the uncertain set of load power demand. This represents the set of nodes where the load is located; This represents the predicted power demand of the load at node l at time t. This represents the uncertain power demand of the load at node l at time t. This represents the maximum predicted power demand of the load at node l at time t. This represents the minimum predicted power demand of the load at node l at time t; This represents the upper boundary of the uncertain set of load power demand at node l at time t. This represents the lower boundary of the uncertain set of load power demand at node l at time t; Step 2: Construct a distribution network topology model during the sequential recovery process; Step 3: Construct a source-grid-load-storage coordinated scheduling model during the sequential recovery process: Step 3-1: Considering the uncertainty of renewable energy output, taking wind power as an example, and also taking into account the uncertainty of load power demand, establish a three-layer robust optimization model, with the maximum weighted load recovery during the fault duration as the order of recovery objectives: ； Where T represents the set of sequential recovery time steps, This represents the weight coefficient of node i. Δt represents the active power scheduling value of node i at time t, and Δt represents the duration of an optimization time step. Step 4: Linearize and solve the model constructed in Steps 1 to 3 to obtain the optimal power distribution network sequential recovery scheme: Step 4-1: Linearize the nonlinear constraints in the models constructed in Steps 1 to 3. Step 4-2: Solve the model constructed in steps 1 to 3: The model constructed in steps 1 to 3 is a three-layer robust sequential recovery model. The model is decomposed into a main problem and a sub-problem using the EC&CG algorithm, and the optimization result is obtained by iterative solution.

2. The novel distribution network sequence recovery optimization method considering renewable energy output fluctuations according to claim 1, characterized in that, The construction of the distribution network topology model in step 2 during the sequential recovery process is specifically as follows: Step 2-1: Set constraints on the topology during the sequential restoration process of the distribution network: ； in, Let θ represent the power supply status of node i at time t, and h = θ(i) represent the set of parent nodes of node i in the distribution network topology. Step 2-2: Set constraints on the power supply status of loads, power sources, and lines during the sequential restoration process of the distribution network as follows: ； ； ； ； in, This indicates the power supply recovery status of the load at node i at time t. This represents the operating state of the distributed power source g at time t. This indicates the closed state of the switch on line (h,i) at time t; Steps 2-3: Set constraints on the distribution network topology: 。 3. The novel distribution network sequence recovery optimization method considering renewable energy output fluctuations according to claim 2, characterized in that, The constraints of the optimization model are: (1) Output constraints of distributed power sources: ； ； ； ； in, This represents the active power output of power source g at time t. This represents the reactive power output of power source g at time t. and Let G and V represent the minimum and maximum active power output of power source g, respectively. and These represent the minimum and maximum reactive power output of power source g, respectively. and These represent the downhill and uphill ramp rates of unit g within a unit time period, respectively. This indicates the capacity of unit g; (2) Output constraints of energy storage equipment: ； ； ； ； ； ； in, This represents the charging state of the energy storage e at time t. This represents the discharge state of the stored energy e at time t. This represents the active charging power of energy storage e at time t. This represents the active discharge power of the stored energy e at time t. This indicates the minimum active charging power of energy storage device e. This represents the minimum active power of the energy storage device e. This indicates the maximum active charging power of the energy storage device e. This indicates the maximum active power of the energy storage device e. This represents the initial state of charge of energy storage e. This represents the state of charge of the energy storage e at time t. This represents the minimum permissible state of charge of energy storage e. This represents the maximum permissible state of charge of energy storage e. This indicates the charging efficiency of energy storage e. This indicates the discharge efficiency of the stored energy e; (3) Load demand constraints for controllable loads: ； ； in, This represents the maximum active power demand of load l at time t. This represents the minimum active power demand of load l at time t. This represents the reactive power dispatch value of load l at time t. The power demand factor representing load l. This indicates the power supply status of the load at node l at time t; (4) Distribution network power flow constraints: ； ； ； ； ； ； ； in, Let represent the active power flowing through line (h,i) at time t. Let represent the reactive power flowing through line (h,i) at time t. Let represent the square of the voltage amplitude at node i at time t. This represents the maximum value of the square of the voltage magnitude at node i. This represents the minimum value of the square of the voltage magnitude at node i. This represents the maximum active power allowed to flow on line (h,i). This represents the minimum active power allowed to flow on line (h,i). This represents the maximum reactive power allowed to flow on line (h,i). This represents the minimum reactive power allowed to flow on line (h,i), where M represents a constant.

4. A novel distribution network sequential restoration system considering renewable energy output fluctuations, used to execute the method of claim 1, characterized in that, Includes the following modules: Renewable Energy Output and Load Demand Model Construction Module: Used to construct renewable energy output and load demand models; Distribution network topology model building module: used to build the distribution network topology model during sequential recovery; Source-Grid-Load-Storage Coordinated Scheduling Model Construction Module: Used to construct the source-grid-load-storage coordinated scheduling model during sequential recovery; Solver module: Used to linearize the constructed model and solve it to obtain the optimal power distribution network sequence recovery scheme.

5. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1-3.

6. A computer-storable medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-3.

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