Hierarchical Time-Series Production Simulation Method for Primary and Distribution Micro-Integration Based on Partition Aggregation

Abstract

The present invention relates to a hierarchical time-series production simulation method for integrated main, distribution, and micro-grid based on partition aggregation, which comprises the following steps: Step S1: Define the aggregation partitions of the main, distribution, and micro-grid; Step S2: Establish the upper-layer time-series production model; Step S3: Establish the lower-layer time-series production model; Step S4: Set the constraint conditions; Step S5: Based on Steps S2-S4, use Cplex for solution to obtain the time-series production simulation process. The present invention improves the power supply reliability and new energy utilization rate of the main, distribution, and micro-grid on the basis of optimizing the power balance of the main, distribution, and micro-grid, and ensures the safe and efficient operation of the main, distribution, and micro-grid.

Description

Technical Field

[0001] The present invention relates to the technical field of main and distribution microgrids, and in particular to a hierarchical time-series production simulation method for main and distribution microgrids based on partition aggregation. Background Art

[0002] Traditional main and distribution microgrids face severe challenges when dealing with the increasing proportion of new energy and complex load demands. With the rapid development of new energy such as wind power and solar energy, as well as the wide popularization of new types of loads such as electric vehicles and electric heating, the power grid structure has become more complex, and traditional production simulation and power dispatching methods are difficult to adapt to this dynamic, fluctuating, and decentralized energy pattern. Existing production simulation methods have limitations in the dispatching optimization of large power systems and cannot accurately reflect the power balance and power flow characteristics of each region in the main and distribution microgrids.

[0003] On the one hand, new energy has characteristics such as volatility, intermittency, and reverse peak shaving, which makes it difficult to maintain the balance between power supply and demand in the power grid and poses great challenges to the stability of the main and distribution microgrids. On the other hand, traditional main and distribution microgrids mainly rely on large centralized power plants, but a high proportion of distributed power sources are distributed everywhere, bringing more complexity to power dispatching. The technical constraints of the main and distribution microgrids (such as power loss, voltage support, line congestion, and unit start-stop, etc.) are coupled with the grid connection characteristics of new energy, further intensifying the impact on traditional dispatching methods.

[0004] In the prior art, for example, the application number "202310820841.6" proposes a two-layer optimal dispatching method for an active distribution network containing multiple microgrids. By the interaction between the top layer and the bottom layer models, the system operation cost is optimized and the power grid efficiency is improved. However, for the uncertainty modeling of wind and solar power generation, the heuristic moment matching method used is difficult to accurately capture the high randomness and volatility of wind and solar energy, which limits the accuracy and reliability of the model in actual power grid operations.

[0005] The application number "201510208209.1" proposes a time-series simulation method for the output of a photovoltaic power station based on the clear sky index. By obtaining geographical information and relevant parameters of the clear sky index, the direct irradiance is calculated, considering the tracking mode of the photovoltaic array, generating a spatio-temporal correlation normal distribution sequence of the clear sky index, and using these data to calculate the actual output of the photovoltaic power station to improve the accuracy of output prediction. Although this method can simulate the theoretical maximum output of the photovoltaic power station, it is insufficient to handle the volatility and uncertainty of photovoltaic output, especially the impact on the stability and reliability of the power grid in a multi-energy integrated system. Summary of the Invention

[0006] To overcome the deficiencies of the prior art, the present invention provides a hierarchical time-series production simulation method for integrated main, distribution, and microgrids based on partition aggregation, which improves the power and electricity balance analysis ability of the main, distribution, and microgrids through establishing a two-layer optimization model, and optimizes the consumption of new energy and the power supply reliability of the system.

[0007] The technical solution adopted by the present invention is as follows:

[0008] A hierarchical time-series production simulation method for integrated main, distribution, and microgrids based on partition aggregation includes the following steps:

[0009] Step S1: Define the aggregation partitions of the main, distribution, and microgrids;

[0010] Step S2: Establish an upper-layer time-series production model, mainly optimizing the start-stop states and output curves of conventional units in each partition to promote the consumption of new energy and reduce the power supply deficit;

[0011] Step S3: On the basis of the upper-layer time-series production model, construct a lower-layer time-series production model, mainly optimizing the economic dispatch of conventional units in each aggregation partition to minimize the operating cost of the system;

[0012] Step S4: Set the technical and economic constraint conditions for the upper and lower-layer time-series production models, including regional load balance, transmission power of section tie lines, start-stop of conventional units, and energy storage dispatch, etc.

[0013] In step S1, when defining the aggregation partitions of the main, distribution, and microgrids, the main, distribution, and microgrids are divided into multiple grid structure regions, and partition aggregation is carried out according to the actual geographical characteristics and structural complexity of the grid. In each aggregation partition, the detailed grid topology structure and power flow distribution are ignored, and each partition processes the power sources and loads within it.

[0014] In step S2, when establishing the upper-layer time-series production model, mainly optimizing the start-stop states and output curves of conventional units in each partition to promote the consumption of new energy and reduce the power supply deficit. The objective function of the upper-layer time-series production model:

[0015]

[0016] In the formula: N is the total number of aggregation partitions included in the system; n represents a certain aggregation partition; T represents the total simulation duration (unit: h); α is the weight coefficient ratio of the load shedding amount; t is the simulation time step; is the new energy curtailment amount in aggregation partition n at time step t; is the power supply deficit in aggregation partition n at time step t.

[0017] In step S3, a lower-layer time-series production model is established. Based on the upper-layer time-series production model, the lower-layer time-series production model is constructed. The lower-layer time-series production model optimizes the operating costs of conventional units in each partition, and independently optimizes and solves each partition. For the aggregated partition n. The objective function of the lower-layer time-series production model is:

[0018]

[0019] In the formula: D represents the total simulation duration (unit: days); J(n) is the total number of conventional units included in the system aggregated partition n; is the output of the conventional unit j included in the system aggregated partition n; d(t) represents the number of days corresponding to time period t; Y n,j (d) and Z n,j (d) are both binary variables, representing the unit startup and shutdown actions respectively. Y n,j (d) takes 1 when the unit j starts from the shutdown state to the operating state on day d, otherwise it takes 0; Z n,j (d) takes 1 when the unit j changes from the operating state to the shutdown state on day d, otherwise it takes 0; is the daily unit power generation cost of each conventional unit; and are the startup and shutdown costs of each conventional unit.

[0020] In step S4, constraint conditions are set, including the technical and economic constraint conditions of the upper-layer and lower-layer time-series production models, such as regional load balance, transmission power of section tie lines, startup and shutdown of conventional units, and energy storage scheduling. Constraint conditions of the upper-layer time-series production model:

[0021] (1) Regional load balance constraint:

[0022]

[0023] In the formula: is the power load of the aggregated partition n at the t-th time period; is the sum of the total powers of all conventional units in the aggregated partition n at the t-th time period; L k,n (t) is the transmission power from the aggregated partition k to the aggregated partition n. If there is no tie transmission line between the aggregated partitions k and n, then L k,n (t) takes 0; is the external power input to the aggregated partition n at the t-th time period; is the energy storage charging power of the aggregated partition n at the t-th time period; is the energy storage discharging power of the aggregated partition n at the t-th time period. is the total new energy output of the aggregated partition n at time period t.

[0024] (2) Section tie line transmission power constraint:

[0025]

[0026] L k,n I(t) = -L n,k I(t) (5)

[0027] Where: is the upper limit of the power transmission from aggregation zone k to aggregation zone n; the reference direction of the set current is: the positive direction is when flowing into aggregation zone n, and the negative direction is when flowing out of aggregation zone n.

[0028] (3) Total output constraint of conventional units:

[0029]

[0030] Where: is the maximum output power of conventional unit j; is the minimum output power of conventional unit j; X n,j I(d) is a binary variable representing the operating state of unit j in aggregation zone n on day d, taking 0 means the unit is shut down, and taking 1 means the unit is operating.

[0031] (4) New energy output constraint:

[0032]

[0033]

[0034]

[0035]

[0036] Wherein, and are the total theoretical maximum wind power output and the total theoretical maximum photovoltaic power output of aggregation zone n at time t respectively; and are the total wind power output and the total photovoltaic power output of aggregation zone n at time t respectively.

[0037] (5) System reserve constraint:

[0038]

[0039]

[0040] Where: R up is the positive spinning reserve; R down is the negative spinning reserve.

[0041] (6) Minimum start-up and shutdown time constraint of units:

[0042]

[0043] Where: T on and T off are the minimum continuous operation time and the minimum continuous shutdown time of the unit, respectively.

[0044] (7) Energy storage constraint:

[0045]

[0046]

[0047]

[0048]

[0049]

[0050]

[0051]

[0052] Where: and are both binary variables, representing the operating states of the aggregated energy storage in the aggregated partition n at time period t, respectively. Taking 1 means the aggregated energy storage is in the charging state, otherwise taking 0; Taking 1 means the aggregated energy storage is in the discharging state, otherwise taking 0; η ch and η dis represent the charging efficiency and discharging efficiency of the aggregated energy storage, respectively; SOC n (t) represents the state of charge of the aggregated energy storage in the aggregated partition n at time period t; and represent the upper and lower limits of the state of charge, respectively.

[0053] Constraints of the lower-layer time-series production model:

[0054] (1) Output constraint of conventional units:

[0055]

[0056]

[0057] (2) Utilization hours constraint of units:

[0058]

[0059] Where: and represent the upper and lower limits of the utilization hours of unit j in the aggregated partition n, respectively.

[0060] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0061] 1. The double-layer model of the present invention optimizes the power production and scheduling of each region through the method of zonal aggregation, making the start-stop plan of conventional units more coordinated with the output of new energy. The upper-layer time-series optimization model promotes the maximum consumption of new energy and reduces the power supply deficit; the lower-layer time-series production model optimizes the economic dispatch of conventional units while reducing the operating cost, improving the power supply reliability of the main distribution microgrid.

[0062] 2. The double-layer optimization strategy adopted by the present invention not only considers technical constraints such as power loss, voltage support, and line congestion, but also reduces the additional operating costs caused by grid overload or uneven resource allocation through zonal aggregation and precise time-series scheduling optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 is a schematic flow chart of the method of the present invention;

[0064] Figure 2 is a schematic diagram of the grid aggregation structure of the present invention;

[0065] Figure 3 is the double-layer model framework of the present invention;

[0066] Figure 4 is the load shedding situation of each partition of the present invention;

[0067] Figure 5 is the new energy unit tripping situation of each partition of the present invention;

[0068] Figure 6 is the total thermal power output curve of the southern region of the present invention;

[0069] Figure 7 is the charge-discharge (discharge direction) curve of the energy storage of the present invention;

[0070] Figure 8 is the power transmission curve of the section (southward transmission) between the central and southern regions of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0071] The technical solution of the present invention will be described more clearly and completely below by describing the preferred embodiments of the present invention in conjunction with the drawings.

[0072] As Figure 1-3 shown, the present invention is as follows:

[0073] Step S1: Define the aggregation partitions of the main distribution microgrid;

[0074] Step S2: Establish an upper-layer time-series production model, mainly optimizing the start-stop states and output curves of conventional units in each partition to promote the consumption of new energy and reduce the power supply deficit;

[0075] By optimizing the start-stop and output of conventional units, new energy generation can be better integrated to ensure the continuity and reliability of power grid supply. This model depends on the partition aggregation framework defined in Step S1 and adjusts the supply-demand balance of each aggregation partition through time-series analysis.

[0076] Step S3: Based on the upper-layer time-series production model, construct a lower-layer time-series production model, mainly optimizing the economic dispatch of conventional units in each aggregation partition to minimize the operating cost of the system;

[0077] Based on the results of the upper-layer time-series production model, further optimize the economic dispatch of conventional units in each aggregation partition. This step aims to minimize the operating cost and independently optimize and solve each aggregation partition. By finely adjusting the operation of the units, the cost is reduced while ensuring the stability and economy of the power grid.

[0078] Step S4: Set the technical and economic constraint conditions for the upper and lower-layer time-series production models, including regional load balance, transmission power of section tie lines, start-stop of conventional units, and energy storage dispatch, etc.

[0079] The upper and lower-layer time-series production models form a two-layer model;

[0080] Set the constraint conditions to ensure the practical applicability and executability of the model.

[0081] Step S5: Based on Steps S2 - S4, use Cplex for solution to obtain the time-series production simulation process.

[0082] In dealing with large-scale and geographically dispersed power grids, the present invention simplifies power grid management through aggregation partitions, and introduces time-series production simulation, enabling the method to more accurately predict and adjust the power system to cope with highly fluctuating load and production patterns. The present invention can significantly improve the adaptability of the power grid to new energy and the stability of the overall power system.

[0083] "Partition aggregation" in the present invention is a method of dividing the power grid into several regions or partitions, where each partition contains a group of power sources and loads, and these partitions are relatively independent but connected to each other.

[0084] Specifically as follows:

[0085] The present invention defines the aggregation partitions of the main and distribution microgrids, divides the main and distribution microgrids into multiple power grid structure regions, and performs partition aggregation according to the actual geographical characteristics and structural complexity of the power grid. In each aggregation partition, the detailed power grid topology structure and power flow distribution are ignored, and each partition processes the power sources and loads inside it.

[0086] Each aggregated partition is a subsystem of the power grid, having its own power source, load, and possibly energy storage devices. Within the aggregated partition, the grid operator can centrally manage the resources and loads in this area to optimize production and consumption. This approach allows for more efficient resource allocation and risk management, as each partition can be regulated independently according to its specific needs and conditions.

[0087] The present invention simplifies the complexity, allowing the omission of specific power grid structure details during simulation and concentrating on the power sources and loads within the partition, thereby reducing the computational cost while retaining the key information on the impact on the overall power grid operation.

[0088] Power grid structure: 1. Geographical location: The power grid is divided into different regions according to geographical location, such as the northern region, central region, and southern region; 2. Power source and load distribution: It is divided according to the distribution of power sources (such as power generation stations) and loads (power consumption demands) within the region, so that the power supply and demand within each partition are as balanced as possible.

[0089] In the application, it can also be divided according to functional requirements, where the functional requirements are: 1. Power supply reliability: It is divided according to the power supply reliability requirements of different regions. For example, some key industrial areas require higher power supply reliability; 2. New energy consumption: It is divided according to the distribution and consumption capacity of new energy (such as wind power, photovoltaic), so that each partition can maximize the consumption of new energy; 3. Economy: Consider the economy of power production and transmission, such as reducing transmission losses and lowering operating costs, etc. for division; 4. Load characteristics: It is divided according to the time characteristics (such as peak load, off-peak load) and types (such as residential load, industrial load) of the load.

[0090] As Figure 2 shown, the present invention establishes a partition aggregation structure of the main and distribution microgrids. According to the power grid structure and functional partitions, the provincial power grid is divided into several main grid and distribution microgrid aggregation partitions, retaining the power transmission constraints of the key sections between the partitions.

[0091] The present invention establishes an upper-layer time-series production model, mainly optimizing the start-stop states and output curves of conventional units within each partition to promote the consumption of new energy and reduce the power supply shortage.

[0092] It emphasizes the maximization of new energy consumption and the reduction of power supply shortage. Such a design takes into account the volatility and uncertainty of new energy such as wind power and solar energy, directly addressing this challenge through the optimization of the upper-layer time-series production model, enhancing the system's ability to absorb new energy, and reducing power shortages caused by new energy fluctuations.

[0093] Objective function of the upper-layer time-series production model:

[0094]

[0095] Where: N is the total number of aggregated partitions included in the system; n represents a certain aggregated partition; T represents the total simulation duration (unit: h); α is the ratio of the load shedding weight coefficient; t is the simulation time step; is the amount of new energy cut-off of the aggregated partition n at time period t; is the power supply shortage of the aggregated partition n at time period t.

[0096] The present invention establishes a lower-layer time-sequential production model. Based on the upper-layer time-sequential production model, a lower-layer time-sequential production model is constructed. The lower-layer time-sequential production model optimizes the operation cost of conventional units in each partition, and independently optimizes and solves each partition, aiming at the aggregated partition n.

[0097] The optimization at this layer helps the system to maximize cost-benefit while ensuring power supply.

[0098] Objective function of the lower-layer time-sequential production model:

[0099]

[0100] Where: D represents the total simulation duration (unit: days); J(n) is the total number of conventional units included in the system aggregated partition n; is the output of the conventional unit j included in the system aggregated partition n; d(t) represents the day corresponding to the time period t; Y n,j (d) and Z n,j (d) are both binary variables, representing the unit start-up and shutdown actions respectively. Y n,j (d) takes 1 indicating that the unit j starts from the shutdown state to the running state on the dth day, otherwise takes 0; Z n,j (d) takes 1 indicating that the unit j changes from the running state to the shutdown state on the dth day, otherwise takes 0; is the daily unit power generation cost of each conventional unit; and are the start-up and shutdown costs of each conventional unit.

[0101] The present invention sets constraint conditions, including technical and economic constraint conditions for the upper-layer and lower-layer time-sequential production models, such as regional load balance, transfer power of section tie lines, start-up and shutdown of conventional units, and energy storage scheduling. Constraint conditions of the upper-layer time-sequential production model:

[0102] (1) Regional load balance constraint:

[0103]

[0104] Where: is the power load of the aggregated partition n at the tth time period; is the sum of the total power of all conventional units in aggregation area n at time period t; L k,n L(t) is the transmission power flowing from aggregation area k to aggregation area n. If there is no connection transmission line between aggregation areas k and n, then L k,n (t) is taken as 0; is the external power input to aggregation area n at time period t; is the energy storage charging power of aggregation area n at time period t; is the energy storage discharging power of aggregation area n at time period t. is the total new energy output of aggregation area n at time period t.

[0105] (2) Transmission power constraint of section tie line:

[0106]

[0107] L k,n (t) = -L n,k (t) (5)

[0108] In the formula: is the upper limit of the transmission power flowing from aggregation area k to aggregation area n; The set current reference direction is: flowing into aggregation area n is the positive direction, and flowing out of aggregation area n is the negative direction.

[0109] (3) Total output constraint of conventional units:

[0110]

[0111] In the formula: is the maximum output power of conventional unit j; is the minimum output power of conventional unit j; X n,j X(d) is a binary variable representing the operating status of unit j in aggregation area n on day d. Taking 0 means the unit has stopped, and taking 1 means the unit is running.

[0112] (4) New energy output constraint:

[0113]

[0114]

[0115]

[0116]

[0117] In the formula, and are respectively the total theoretical maximum wind power output and the total theoretical maximum photovoltaic power output of aggregation area n at time period t; and They are the total wind power output and the total photovoltaic power output of the aggregated partition in period t respectively.

[0118] (5) System reserve constraint:

[0119]

[0120]

[0121] In the formula: R up is the positive spinning reserve; R down is the negative spinning reserve.

[0122] (6) Minimum start-up and shut-down time constraint of the unit:

[0123]

[0124] In the formula: T on and T off are the minimum continuous operation time and the minimum continuous shutdown time of the unit respectively, and i is the number of days.

[0125] (7) Energy storage constraint:

[0126]

[0127]

[0128]

[0129]

[0130]

[0131]

[0132]

[0133] In the formula: and are both binary variables, representing the operating states of the aggregated energy storage in the aggregated partition n at time t respectively. Taking 1 means the aggregated energy storage is in the charging state, otherwise taking 0; Taking 1 means the aggregated energy storage is in the discharging state, otherwise taking 0; η ch and η dis represent the charging efficiency and discharging efficiency of the aggregated energy storage respectively; SOC n (t) represents the state of charge of the aggregated energy storage in the aggregated partition n at time t; and represent the upper and lower limits of the state of charge respectively.

[0134] Lower - layer time - series production model constraint conditions:

[0135] (1) Conventional unit output constraint:

[0136]

[0137]

[0138] (2) Unit utilization hours constraint:

[0139]

[0140] In the formula: and respectively represent the upper and lower limits of the utilization hours of unit j in aggregation partition n.

[0141] The present invention conducts annual time - series production simulation based on the actual power grid structure and installed capacity of a certain province in 2023, builds a model in MATLAB, and calls the optimization software Cplex to solve it. Based on MATLAB 2016a programming, CplexV12.63 is called for solution. According to the set model parameters and conditions, annual time - series production simulation is executed. The simulation process includes power source scheduling at each time step and the accommodation situation of new energy.

[0142] The present invention considers the actual geographical characteristics, power grid structure, and distribution of key power flow sections of this province, divides the power grid of this province into three aggregation partitions: the northern region, the central region, and the southern region. The tie - lines between the three regions are equivalent to power transmission sections. The north - to - south power transmission limit of the tie - line between the northern and central regions of this province in 2023 is 2500 MW; the south - to - north power transmission limit is 2200 MW; the north - to - south power transmission limit of the tie - line between the central and southern regions is 3700 MW; the south - to - north power transmission limit is 6100 MW. The parameters of thermal power units are set as shown in Table 1. Since the minimum technical output of each thermal power unit in the province is different, the value range is given in Table 1.

[0143] Table 1 Thermal power unit parameters

[0144]

[0145] First, based on the principle of ensuring power supply first, the weight coefficient ratio α of the load shedding amount to the new - energy generator tripping amount is set to 10. Based on the optimization results of time - series production simulation, the load shedding amount is 323.05 MWh, and the new - energy generator tripping amount is 1282.30 MWh. The time distributions of load shedding and new - energy generator tripping in each partition are respectively as Figure 4 and Figure 5 shown.

[0146] Figures 6 to 8The total thermal power output curve of the southern region of the province, the charge and discharge curve of energy storage (with discharge being positive), and the power transmission curve of the section between the central and southern regions (in the southward transmission direction) are given respectively.

[0147] In terms of new energy accommodation, from Figures 5-7 it can be seen that during the low-load period of the second quarter, due to the limited energy storage capacity, even when the energy storage is in the charging state, it is still unable to provide sufficient new energy accommodation space; on the other hand, affected by the minimum start-stop time constraint, it is impossible to always deploy thermal power units with high peak shaving capacity to operate, and the relatively high minimum technical output level of thermal power squeezes the new energy accommodation space to a certain extent. The above factors together lead to the phenomenon of new energy generator tripping.

[0148] In terms of load guarantee, from Figure 6 it can be seen that in most periods of the third quarter, the thermal power units in the southern region have been in the maximum power output state (not fully loaded due to the setting of the thermal reserve coefficient); from Figure 8 it can be seen that during the summer peak period, the power transmission in the southward direction of the section between the central and southern regions of the province has reached the power limit during the load deficit period in the southern region. Therefore, the capacity limit of thermal power units in the southern region and the power constraint of the aggregated sectional power between intervals jointly lead to the power deficit during the summer peak load period in the southern region.

[0149] In the operation of the present invention, by reasonably dividing the power grid into zones, constructing upper and lower layer time-series production models, and setting relevant technical and economic constraint conditions, the production and dispatching processes of the main, distribution, and microgrids are accurately simulated, the power transmission and load balance relationships between regions are optimized, and the overall power supply reliability and new energy accommodation capacity of the system are improved.

[0150] The technical means disclosed in the solution of the present invention are not limited to the technical means disclosed in the above embodiments, but also include technical solutions composed of any combination of the above technical features. It should be noted that for those of ordinary skill in the art in this technical field, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements are also regarded as the protection scope of the present invention.

Claims

1. A hierarchical time-series production simulation method for main and distribution micro-integration based on partition aggregation, characterized in that: It includes the following steps: Step S1: Define the aggregation partitions of the main and distribution microgrids; Step S2: Based on the aggregation partitions, establish the upper-layer time-series production model; The objective function of the upper-layer time-series production model is: Where: N is the total number of aggregated partitions included in the system; n represents a certain aggregated partition; T represents the total simulation duration; α is the weight coefficient ratio of the load shedding amount; t is the simulation time step; is the new energy generator tripping amount of aggregated partition n at time period t; is the power supply deficit of aggregated partition n at time period t; Step S3: Based on the upper-layer time-series production model, establish the lower-layer time-series production model; The objective function of the lower-layer time-series production model is: Where: D represents the total simulation duration; J(n) is the total number of conventional units included in the system aggregation partition n; is the output of the conventional unit j included in the system aggregation partition n; d(t) represents the number of days corresponding to the time period t; Y n,j (d) and Z n,j (d) are both binary variables, representing the unit startup and shutdown actions respectively. Y n,j (d) takes 1 indicating that the unit j starts from the shutdown state to the operating state on day d, otherwise it takes 0; Z n,j (d) takes 1 indicating that the unit j changes from the operating state to the shutdown state on day d, otherwise it takes 0; is the daily unit power generation cost of each conventional unit; and are the startup and shutdown costs of each conventional unit; Step S4: Set the constraint conditions for the upper and lower-layer time-series production models; Step S5: Based on Steps S2 - S4, use Cplex for solution to obtain the time-series production simulation process.

2. The hierarchical time-series production simulation method for main and distribution micro-integration based on partition aggregation according to claim 1, characterized in that: In Step S1, The main and distribution microgrids are divided into multiple aggregation partitions according to the grid structure.

3. The hierarchical time-series production simulation method for the main and distribution micro-integration based on partition aggregation according to claim 1, wherein: In Step S4, The constraint conditions of the upper-layer time-series production model include regional load balance constraint, section tie-line transmission power constraint, total output constraint of conventional units, new energy output constraint, system reserve constraint, minimum start-stop time constraint of units, and energy storage constraint.

4. The method for hierarchical time-series production simulation of main and distribution micro-integration based on partition aggregation according to claim 3, characterized in that: The constraint conditions of the upper-layer time-series production model are specifically: (1) Regional load balance constraint: Wherein: is the power load of aggregation area n at the t-th time period; is the sum of the total powers of all conventional units in aggregation area n at the t-th time period; L k,n (t) is the transmission power flowing from aggregation area k to aggregation area n. If there is no connection transmission line between aggregation areas k and n, then L k,n (t) is taken as 0; is the external power into the area of aggregation area n at the t-th time period; is the energy storage charging power of aggregation area n at the t-th time period; is the energy storage discharging power of aggregation area n at the t-th time period; is the total new energy output of aggregation area n at time period t; (2) Section tie-line transmission power constraint: L k,n L(t) = -L n,k L(t) (5) Wherein: is the upper limit of the power transmission from the aggregation partition k to the aggregation partition n; (3) Total output constraint of conventional units: Where: is the maximum output power of the conventional unit j; is the minimum output power of the conventional unit j; X n,j (d) is a binary variable representing the operating status of unit j in aggregation partition n on day d, taking 0 indicates the unit is shut down, and taking 1 indicates the unit is in operation; (4) New energy output constraint: In the formula: and are respectively the total theoretical maximum wind power output and the total theoretical maximum photovoltaic power output of the aggregated partition in period t of hour n; and are respectively the total wind power output and the total photovoltaic power output of the aggregated partition in period t of hour n; (5) System reserve constraint: Where: R up is for positive rotation standby; R down is for negative rotation standby; (6) Minimum start-stop time constraint of units: Where: T on and T off are the minimum continuous operation time and the minimum continuous downtime of the unit, respectively; (7) Energy storage constraint: Wherein: and are both binary variables, respectively representing the operating states of the aggregated energy storage in the aggregated partition n at time t. Taking 1 indicates that the aggregated energy storage is in the charging state, otherwise taking 0; Taking 1 indicates that the aggregated energy storage is in the discharging state, otherwise taking 0; η ch and η dis respectively represent the charging efficiency and discharging efficiency of the aggregated energy storage; SOC n (t) represents the state of charge of the aggregated energy storage in the aggregated partition n at time t. and respectively represent the upper and lower limits of the state of charge.

5. The method for hierarchical time-series production simulation of main and distribution micro-integration based on partition aggregation according to claim 3, characterized in that: The constraint conditions of the lower-layer time-series production model include conventional unit output constraint and unit utilization hour constraint.

6. The method for hierarchical time-series production simulation of main and distribution micro-integration based on partition aggregation according to claim 5, characterized in that: The constraint conditions of the lower-layer time-series production model are specifically: (1) Conventional unit output constraint: (2) Unit utilization hour constraint: In the formula: and respectively represent the upper and lower limits of the utilization hours of unit j in aggregation partition n.

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