Gridding power distribution network double-layer optimization method considering operation flexibility

Abstract

The invention discloses a gridding power distribution network double-layer optimization method considering operation flexibility, and the method comprises the steps: S1, carrying out the quantitative analysis of the flexibility of a power distribution network, analyzing the flexibility demand and adjustment capability of the power distribution network from a demand side and a supply side, and quantifying the flexibility resources of the power distribution network; s2, establishing a flexibility index of the power distribution network, and establishing a flexibility evaluation index according to the adjustment capability and the transmission capability of the flexibility resource of the power distribution network; s3, establishing a power distribution network flexibility upper-layer model by taking the grid division strategy of the power distribution network as a decision-making main body and taking the optimal power grid branch flexibility index as a target function; s4, establishing a lower-layer optimization model by taking supply and demand side flexibility adjustment resource output as a decision-making main body and taking distribution network operation cost and node flexibility index optimization as an objective function according to the power distribution network flexibility lower-layer model; and S5, establishing a power distribution network flexibility double-layer model structure, and performing lower-layer calculation based on an upper-layer optimization result so as to calculate an optimal grid division scheme.

Description

Technical Field

[0001] The present invention relates to the field of active power distribution networks, and in particular to a double-layer optimization method for gridded power distribution networks taking operational flexibility into consideration. Background Art

[0002] With the development of modern power systems, grids are expanding in size and increasing in complexity. The construction of new power systems, primarily based on renewable energy, is accelerating. In this context, achieving efficient consumption of a high proportion of renewable energy presents both a major opportunity and a major challenge for optimizing power system dispatch.

[0003] Considering that with the increasing number of distributed power sources such as wind power, photovoltaics, micro gas turbines, and electric vehicles connected to the distribution network, the uncertainty on both the source and load sides increases, and the grid operation status and controllable quantity dimensions increase, the existing methods for optimizing the flexibility of distribution network operation still have shortcomings.

[0004] At the same time, the large-scale integration of distributed power sources is also driving the development of distribution networks toward grid-based operation and control. With the advancement of projects such as county-wide photovoltaic systems, the overall distribution network is beginning to show a trend toward regionalization and gridding.

[0005] Hierarchical and zoned gridding technology allows for the efficient mobilization of large-scale distributed resources, including adjustable power sources, energy storage, and flexible loads, within a grid-based distribution network. This allows for enhanced clean energy consumption, flexible regulation, guaranteed power generation, and efficient operations and maintenance. Through intra-grid autonomy and inter-grid collaboration, system operational flexibility is enhanced, leveraging the unique characteristics of different regions.

[0006] However, current research on grid-based distribution networks has largely focused on the planning level. When optimizing and dispatching, the assumption is that individual grids in the distribution network remain static, lacking flexibility. However, when optimizing and dispatching, given the high uncertainty and volatility of renewable energy resources like wind and solar power, as well as uncontrollable loads, fixed grid division schemes often fail to meet the demands of real-time fluctuating power flows. Further research is needed to dynamically adjust the grid in real time based on system operating conditions to optimize grid operation.

[0007] Therefore, studying a two-layer optimization method for grid distribution networks that considers operational flexibility has important theoretical value and practical significance. Summary of the Invention

[0008] In order to overcome the deficiencies in the prior art, the present invention provides a two-layer optimization method for a grid distribution network that takes operational flexibility into consideration. By optimizing the flexible operational capability of the grid distribution network, the flexible and economical operation of the grid distribution network is achieved, thereby improving the power supply quality and reliability of the power grid.

[0009] In order to achieve the above-mentioned purpose of the invention, the technical solutions adopted to solve the technical problems are as follows:

[0010] A two-layer optimization method for a grid distribution network considering operational flexibility includes the following steps:

[0011] Step S1: Quantitative analysis of distribution network flexibility, based on analyzing the flexibility demand and regulation capacity of the distribution network from both the demand side and the supply side, to quantify the flexibility resources of the distribution network;

[0012] Step S2: Establishing the flexibility index of the distribution network, and establishing the flexibility evaluation index according to the regulation capability and transmission capability of the distribution network flexibility resources;

[0013] Step S3: The upper-level model of distribution network flexibility takes the grid division strategy of the distribution network as the decision-making subject and takes the optimal flexibility index of the power grid branch as the objective function to establish the upper-level optimization model;

[0014] Step S4: The lower-level model of distribution network flexibility is established, with the flexibility adjustment resource output on the supply and demand sides as the decision-making subject, and the optimal distribution network operation cost and node flexibility index as the objective function to establish the lower-level optimization model;

[0015] Step S5: A two-layer model structure of the distribution network flexibility is established, and the lower layer calculation is performed based on the upper layer optimization results to calculate the optimal grid division scheme.

[0016] Furthermore, in step S1, based on the analysis of the flexibility requirements and regulation capabilities of the distribution network from both the demand side and the supply side, a two-layer optimization scheduling model of upper centralized and lower distributed is constructed. Through dynamic mutual assistance between the grids, the flexibility supply and demand balance of each grid is achieved, and ultimately the flexibility within the distribution network is effectively improved to quantify the flexibility resources of the distribution network. The flexibility resources of the distribution network come from the power supply side and the load side, among which the flexibility resources on the power supply side mainly include the upper main grid interconnection line, gas turbines and energy storage devices, while the flexibility resources on the load side mainly come from the controllable load that can be provided by the user.

[0017] Furthermore, in step S1, the flexibility demand of the distribution network is quantified based on the uncertainty of wind and solar resources and the random fluctuation of load. The system flexibility demand analysis is shown in the following formula:

[0018]

[0019] Where: are the predicted values ​​of load, photovoltaic and wind turbine at time t respectively; and are the upper and lower limits of load, photovoltaic and wind turbine fluctuations at time t, respectively;

[0020] exist

[0021] Where: and are the upward and downward flexibility demands generated by the net load of the c-th grid at time t, respectively;

[0022] The flexibility of the distribution network is quantified based on the controllability of energy storage, the ramping capability of distributed power sources and the large power grid, and the adjustable capacity. Energy storage devices can provide upward / downward flexibility adjustment capabilities through discharge / charge, as shown in the formula:

[0023]

[0024] Where: are the upward and downward flexibility supply capabilities provided by the ESS at node i to the distribution network at time t; are the charging and discharging power of ESS at node i at time t; P i ch,max 、P i dis,max are the maximum charging and discharging powers of the ESS at node i, respectively; are the maximum and minimum storage capacity of ESS at node i, respectively; is the amount of electricity stored in the ESS at node i at time t; η ch ,η dis is the charge and discharge efficiency of ESS;

[0025] When the system output is moderate, its flexibility is only affected by the ramp capacity. When the system output is sufficiently adjusted upward or downward, its flexibility is affected by both the ramp capacity and the regulation capacity. When the system output is at its limit, the system only has the flexibility to adjust upward or downward.

[0026]

[0027] Where: are the upward and downward flexibility regulation capabilities provided by the substation at node i to the distribution network at time t; is the power transmitted from the substation at node i to the distribution network at time t; are the upper and lower limits of the ramp power of the substation at node i; P i sub,max is the maximum value of power transmitted from the substation to the distribution network at node i;

[0028] By integrating the flexibility supply capabilities of various resources within a single grid of the distribution network, we can obtain:

[0029]

[0030] Where: and are the upward and downward flexibility supplies at time t under the cth grid, respectively; are the installation node sets of substations, energy storage, gas turbines, and transferable loads in the cth grid respectively.

[0031] Furthermore, in step S2, flexibility evaluation indicators are established based on the regulation and transmission capabilities of the distribution network's flexibility resources. Through the transmission channels provided by the distribution network branch grid, a diversified topological combination of node flexibility resources is achieved, thereby ensuring that the flexibility supply and demand of the distribution network are balanced. The indicators include a flexibility deficit, which reflects the ratio between the flexibility deficit and the flexibility demand within the grid. When the flexibility margin is less than 0, a flexibility deficit occurs, that is:

[0032] and

[0033] Where: and are the upward and downward flexibility deficits of the cth grid at time t, respectively, and the grid flexibility deficit is:

[0034]

[0035] Where: is the flexibility deficit of the cth grid, reflecting the ratio between the flexibility deficit and the flexibility demand within the grid. Its value is between 0 and 1, and the closer it is to 0, the greater the overall flexibility margin within the grid, the smaller the flexibility deficit, and the more adaptable to the uncertain changes in net load. In addition to having sufficient node flexibility resources to provide flexibility supply, there are also certain requirements for network flexibility. Branch flexibility margin is needed to cope with random fluctuations in net load. The grid branch flexibility margin index is defined as To reflect the average load rate of the network branch, The larger the value, the greater the load margin of each branch in grid c, and the stronger the ability to cope with the uncertainty of flexibility demand:

[0036]

[0037] Where: is the branch set of grid c; N b is the number of branches in the grid; is the load rate of branch l in the cth grid at time t; L l,t is the current flowing through branch l at time t; is the maximum value of the current allowed to flow through branch 1.

[0038] Furthermore, in step S3, the branch flexibility index and network loss are optimized. In order to eliminate the dimensional influence between different objectives, a linear normalization method is used to perform data normalization processing, as shown in the formula:

[0039]

[0040] Where: N c is the number of all grids in the distribution network; F BF C is the flexibility margin of the distribution network branch; loss is the network loss cost of the distribution network, P t loss is the power loss on the distribution network line at time t, c loss Network loss costs;

[0041] The relevant constraints of the upper-level model include improved disflow constraints that take into account changes in network topology, system security constraints, substation injection power constraints, and distribution system connectivity and radiation constraints. The grid division strategy of the distribution network is used as the decision-making body, and the optimal grid branch flexibility index is used as the objective function to establish the upper-level optimization model.

[0042] Furthermore, in step S4, the first-stage min optimization problem uses the output of each flexible resource on the power supply side as the decision variable to minimize the operating cost. The second-stage max-min problem seeks the worst-case scenario probability distribution to minimize the demand-side resource call cost, the wind and solar curtailment penalty cost, and the grid flexibility shortfall penalty cost. The expression is as follows:

[0043]

[0044] f1(x)=C sub +C GT +C ESS

[0045] f2(y)=C TL +C PV +C WT +C flex

[0046] Where: x is the first-stage variable; y is the second-stage variable; K is the number of scenes after the sample is discretized after reduction; ξ k is the kth scene; p k is the probability of the kth scene occurring; Ω is the comprehensive norm fuzzy set; C sub 、C GT 、C ESS 、C TL 、CPW 、C PV 、C flex They are the main grid electricity purchase cost, GT operating cost, ESS operating cost, transferable load response cost, wind curtailment cost, solar curtailment cost and flexibility loss cost;

[0047] The relevant constraints of the lower-level model include flow balance constraints, gas turbine constraints, energy storage operation constraints, transferable load constraints, photovoltaic / wind turbine constraints, and the establishment of uncertain sets; the lower-level optimization model is established with the supply and demand side flexibility adjustment resource output as the decision-making body and the optimal distribution network operation cost and node flexibility index as the objective function.

[0048] Furthermore, in step S5, the lower-level calculation is performed based on the upper-level optimization results. The lower-level model is based on the grid topology structure solved in the upper-level model. An operation plan is formulated with the goal of optimizing the resource call cost on the supply and demand side of the distribution network, the cost of curtailing wind and solar power, and the penalty cost of grid flexibility deficiency. The uncertainty of wind and solar power resources is taken into account. In order to seek the optimal operation cost under the probability of the worst wind and solar power output scenario, the following formula is shown:

[0049]

[0050]

[0051] Where: c GT 、c ESS 、c TL 、c pw 、c pv 、c flex are the unit price of electricity purchased from the upper power grid at time t, the unit price of gas turbine power generation, the unit price of energy storage operation and maintenance, the unit price of transferable load response, the unit price of wind power curtailment, the unit price of solar power curtailment, and the cost coefficient of flexibility loss.

[0052] The lower layer of the model is the optimization of the operation of flexible resources on the source and load side, taking into account the economic efficiency and flexibility shortage losses. It aims to coordinate the scheduling of adjustable resources on the supply side based on the grid topology structure obtained by the upper layer optimization, and to reduce the risk of system flexibility shortage. The output results of the supply and demand side resources at this time are fed back to the upper layer. The upper layer model will then find the optimal grid division scheme again based on this, realize mutual iterative optimization between the upper and lower layers, and calculate the optimal grid division scheme.

[0053] Due to the adoption of the above technical solution, the present invention has the following advantages and positive effects compared with the prior art:

[0054] This invention proposes a two-tiered optimization method for grid-based distribution networks that takes operational flexibility into account. Dynamically partitioning the grid based on the network's operating status ensures that power flow constraints are met in all operating states, ensuring stable and secure grid operation. Compared to traditional methods, this method offers advantages in reducing costs and increasing distribution network flexibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following is a brief introduction to the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without inventive work. In the drawings:

[0056] Figure 1 It is a flow chart of a two-layer optimization method for a grid distribution network considering operational flexibility according to the present invention;

[0057] Figure 2 is a schematic diagram of a system flexibility analysis method according to an embodiment of the present invention;

[0058] Figure 3 Schematic diagram of a double-layer model structure according to an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0060] like Figure 1 As shown, this embodiment discloses a two-layer optimization method for a grid-based distribution network that takes into account operational flexibility. The method first comprehensively considers the flexible characteristics of various source-load side resources in the distribution network and establishes a flexibility evaluation index. Secondly, a dynamic grid two-layer optimization model for the distribution network is established that takes into account both economy and flexibility. The grid division strategy of the distribution network is considered in the upper-layer model; the output of the supply-demand side flexibility adjustment resources is considered in the lower-layer model to achieve optimization of the operation of the active distribution network. Specifically, the following steps are included:

[0061] Step S1: Quantitative analysis of distribution network flexibility, based on analyzing the flexibility demand and regulation capacity of the distribution network from both the demand side and the supply side, to quantify the flexibility resources of the distribution network;

[0062] Specifically, in step S1, based on the analysis of the flexibility requirements and regulation capabilities of the distribution network from both the demand side and the supply side, a two-layer optimization scheduling model of upper-layer centralized and lower-layer distributed is constructed. Through dynamic mutual assistance between the grids, the flexibility supply and demand balance of each grid is achieved, and ultimately the flexibility within the distribution network is effectively improved to quantify the flexibility resources of the distribution network. The flexibility resources of the distribution network come from the power supply side and the load side, among which the flexibility resources on the power supply side mainly include the upper main grid interconnection line, gas turbines and energy storage devices, while the flexibility resources on the load side mainly come from the controllable load that can be provided by the user.

[0063] Furthermore, in step S1, the flexibility demand of the distribution network is quantified based on the random fluctuations of uncertain resources such as wind and solar power and load. The system flexibility demand analysis is shown in the following formula:

[0064]

[0065]

[0066] Where: are the predicted values ​​of load, photovoltaic and wind turbine at time t respectively; and are the upper and lower limits of load, photovoltaic and wind turbine fluctuations at time t, respectively;

[0067] exist

[0068] Where: and are the upward and downward flexibility demands generated by the net load of the c-th grid at time t, respectively;

[0069] The flexibility of the distribution network is quantified based on the ramping capability and adjustable capacity of controllable distributed power sources such as energy storage and the large power grid. Energy storage devices can provide upward / downward flexibility adjustment capabilities through discharge / charge, as shown in the formula:

[0070]

[0071] Where: are the upward and downward flexibility supply capabilities provided by the ESS at node i to the distribution network at time t; are the charging and discharging power of ESS at node i at time t; P i ch,max 、P i dis,max are the maximum charging and discharging powers of the ESS at node i, respectively; are the maximum and minimum storage capacity of ESS at node i, respectively; is the amount of electricity stored in the ESS at node i at time t; η ch ,η dis is the charge and discharge efficiency of ESS;

[0072] When the system output is moderate, its flexibility is only affected by the ramp capacity. When the system output is sufficiently adjusted upward or downward, its flexibility is affected by both the ramp capacity and the regulation capacity. When the system output is at its limit, the system only has the flexibility to adjust upward or downward.

[0073]

[0074] Where: are the upward and downward flexibility regulation capabilities provided by the substation at node i to the distribution network at time t; is the power transmitted from the substation at node i to the distribution network at time t; are the upper and lower limits of the ramp power of the substation at node i; P i sub,max is the maximum value of power transmitted from the substation to the distribution network at node i;

[0075] See Figure 2 When the system's output is moderate (e.g., time t1), its flexibility is affected only by ramping capacity. When the system's output is sufficiently adjustable (e.g., time t2), its flexibility is affected by both ramping capacity and adjustable capacity. And when the system's output is at its limit (e.g., time t3), the system only has upward or downward flexibility. Therefore, the greater the ramping capacity and adjustable capacity of the adjustable resources, the greater the flexibility provided to the system.

[0076] By integrating the flexibility supply capabilities of various resources within a single grid of the distribution network, we can obtain:

[0077]

[0078] Where: and are the upward and downward flexibility supplies at time t under the cth grid, respectively; are the installation node sets of substations, energy storage, gas turbines, and transferable loads in the cth grid respectively.

[0079] Step S2: Establishing the flexibility index of the distribution network, and establishing the flexibility evaluation index according to the regulation capability and transmission capability of the distribution network flexibility resources;

[0080] Specifically, in step S2, flexibility evaluation indicators are established based on the regulation and transmission capabilities of the distribution network's flexibility resources. Through the transmission channels provided by the distribution network branch grid, a diversified topological combination of node flexibility resources is achieved, thereby ensuring that the flexibility supply and demand of the distribution network are balanced. The indicators include flexibility deficit, which reflects the ratio between the flexibility deficit and the flexibility demand within the grid. When the flexibility margin is less than 0, a flexibility deficit occurs, that is:

[0081] and

[0082] Where: and are the upward and downward flexibility deficits of the cth grid at time t, respectively, and the grid flexibility deficit is:

[0083]

[0084] Where: is the flexibility deficit of the cth grid, reflecting the ratio between the flexibility deficit and the flexibility demand within the grid. Its value is between 0 and 1, and the closer it is to 0, the greater the overall flexibility margin within the grid, the smaller the flexibility deficit, and the more adaptable to the uncertain changes in net load. In addition to having sufficient node flexibility resources to provide flexibility supply, there are also certain requirements for network flexibility. Branch flexibility margin is needed to cope with random fluctuations in net load. The grid branch flexibility margin index is defined as To reflect the average load rate of the network branch, The larger the value, the greater the load margin of each branch in grid c, and the stronger the ability to cope with the uncertainty of flexibility demand:

[0085]

[0086] Where: is the branch set of grid c; N b is the number of branches in the grid; is the load rate of branch l in the cth grid at time t; L l,t is the current flowing through branch l at time t; is the maximum value of the current allowed to flow through branch 1.

[0087] Step S3: The upper-level model of distribution network flexibility takes the grid division strategy of the distribution network as the decision-making subject and takes the optimal flexibility index of the power grid branch as the objective function to establish the upper-level optimization model;

[0088] Specifically, in step S3, the branch flexibility index and network loss are optimized. In order to eliminate the dimensional influence between different objectives, a linear normalization method is used to perform data normalization processing, as shown in the formula:

[0089]

[0090] Where: N c is the number of all grids in the distribution network; F BF C is the flexibility margin of the distribution network branch; loss is the network loss cost of the distribution network, P t loss is the power loss on the distribution network line at time t, c loss Network loss costs;

[0091] The relevant constraints of the upper-level model include improved disflow constraints that take into account changes in network topology, system security constraints, substation injection power constraints, and distribution system connectivity and radiation constraints. The grid division strategy of the distribution network is used as the decision-making body, and the optimal grid branch flexibility index is used as the objective function to establish the upper-level optimization model.

[0092] Step S4: The lower-level model of distribution network flexibility is established, with the flexibility adjustment resource output on the supply and demand sides as the decision-making subject, and the optimal distribution network operation cost and node flexibility index as the objective function to establish the lower-level optimization model;

[0093] Specifically, in step S4, the first-stage min optimization problem uses the output of each flexible resource on the power supply side as the decision variable to minimize the operating cost. The second-stage max-min problem seeks the worst-case scenario probability distribution to minimize the demand-side resource call cost, the wind and solar curtailment penalty cost, and the grid flexibility shortfall penalty cost. The expression is as follows:

[0094]

[0095] f1(x)=C sub +C GT +C ESS

[0096] f2(y)=C TL +C PV +C WT +C flex

[0097] Where: x is the first-stage variable; y is the second-stage variable; K is the number of scenes after the sample is discretized after reduction; ξ k is the kth scene; p k is the probability of the kth scene occurring; Ω is the comprehensive norm fuzzy set; C sub 、CGT 、C ESS 、C TL 、C PW 、C PV 、C flex They are the main grid electricity purchase cost, GT operating cost, ESS operating cost, transferable load response cost, wind curtailment cost, solar curtailment cost and flexibility loss cost;

[0098] The relevant constraints of the lower-level model include flow balance constraints, gas turbine constraints, energy storage operation constraints, transferable load constraints, photovoltaic / wind turbine constraints, and the establishment of uncertain sets; the lower-level optimization model is established with the supply and demand side flexibility adjustment resource output as the decision-making body and the optimal distribution network operation cost and node flexibility index as the objective function.

[0099] Step S5: A two-layer model structure of the distribution network flexibility is established, and the lower layer calculation is performed based on the upper layer optimization results to calculate the optimal grid division scheme.

[0100] Specifically, in step S5, the lower-level calculation is performed based on the upper-level optimization result. The lower-level model is based on the grid topology structure obtained by solving the upper-level model. The operation plan is formulated with the goal of optimizing the resource call cost on the supply and demand side of the distribution network, the cost of curtailing wind and solar power, and the penalty cost of grid flexibility deficiency. The uncertainty factors of resources such as wind and solar power are taken into consideration. In order to seek the optimal operation cost under the probability of the worst wind and solar power output scenario, the following formula is shown:

[0101]

[0102] Where: c GT 、c ESS 、c TL 、c pw 、c pv 、c flex are the unit price of electricity purchased from the upper power grid at time t, the unit price of gas turbine power generation, the unit price of energy storage operation and maintenance, the unit price of transferable load response, the unit price of wind power curtailment, the unit price of solar power curtailment, and the cost coefficient of flexibility loss.

[0103] The lower layer of the model is the optimization of the operation of flexible resources on the source and load side, taking into account the economic efficiency and flexibility shortage losses. It aims to coordinate the scheduling of adjustable resources on the supply side based on the grid topology structure obtained by the upper layer optimization, and to reduce the risk of system flexibility shortage. The output results of the supply and demand side resources at this time are fed back to the upper layer. The upper layer model will then find the optimal grid division scheme again based on this, realize mutual iterative optimization between the upper and lower layers, and calculate the optimal grid division scheme.

[0104] See Figure 3The upper layer determines a reasonable distribution network grid division strategy based on the branch flexibility index, and passes the obtained grid topology structure to the lower layer model. The lower layer optimizes the cost of supply and demand side flexibility resources based on the grid topology structure obtained by the upper layer, and feeds the results back to the upper layer. The upper layer model will then find the optimal grid division scheme again to achieve mutual iterative optimization between the upper and lower layers.

[0105] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A two-layer optimization method for grid distribution network considering operational flexibility, characterized in that: The following steps are involved: Step S1: Quantitative analysis of distribution network flexibility, based on analyzing the flexibility demand and regulation capacity of the distribution network from both the demand side and the supply side, to quantify the flexibility resources of the distribution network; Step S2: Establishing the flexibility index of the distribution network, and establishing the flexibility evaluation index according to the regulation capability and transmission capability of the distribution network flexibility resources; Step S3: The upper-level model of distribution network flexibility takes the grid division strategy of the distribution network as the decision-making subject and takes the optimal flexibility index of the power grid branch as the objective function to establish the upper-level optimization model; Step S4: The lower-level model of distribution network flexibility is established, with the flexibility adjustment resource output on the supply and demand sides as the decision-making subject, and the optimal distribution network operation cost and node flexibility index as the objective function to establish the lower-level optimization model; Step S5: A two-layer model structure of the distribution network flexibility is established, and the lower layer calculation is performed based on the upper layer optimization results to calculate the optimal grid division scheme.

2. A two-layer optimization method for grid distribution network considering operational flexibility according to claim 1, characterized in that: In step S1, based on the analysis of the flexibility requirements and regulation capabilities of the distribution network from both the demand side and the supply side, a two-layer optimization scheduling model with upper-layer centralized and lower-layer distributed is constructed. Through dynamic mutual assistance between the grids, the flexibility supply and demand balance of each grid is achieved, and ultimately the flexibility within the distribution network is effectively improved to quantify the flexibility resources of the distribution network. The flexibility resources of the distribution network come from the power supply side and the load side, among which the flexibility resources on the power supply side mainly include the upper main grid interconnection line, gas turbines and energy storage devices, while the flexibility resources on the load side mainly come from the controllable load that can be provided by the user.

3. A two-layer optimization method for grid distribution network considering operation flexibility according to claim 2, characterized in that: In step S1, the flexibility demand of the distribution network is quantified based on the uncertainty of wind and solar resources and the random fluctuation of load. The system flexibility demand analysis is shown in the following formula: Where: are the predicted values ​​of load, photovoltaic and wind turbine at time t respectively; and are the upper and lower limits of load, photovoltaic and wind turbine fluctuations at time t, respectively; Where: and are the upward and downward flexibility demands generated by the net load of the c-th grid at time t, respectively; The flexibility of the distribution network is quantified based on the controllability of energy storage, the ramping capability of distributed power sources and the large power grid, and the adjustable capacity. Energy storage devices can provide upward / downward flexibility through discharge / charge, as shown in the formula: Where: are the upward and downward flexibility supply capabilities provided by the ESS at node i to the distribution network at time t; are the charging and discharging power of ESS at node i at time t; P i ch,max 、P i dis,max are the maximum charging and discharging powers of the ESS at node i, respectively; are the maximum and minimum storage capacity of ESS at node i, respectively; is the amount of electricity stored in the ESS at node i at time t; η ch ,η dis is the charge and discharge efficiency of ESS; When the system output is moderate, its flexibility is only affected by the ramp capacity. When the system output is sufficiently adjusted upward or downward, its flexibility is affected by both the ramp capacity and the regulation capacity. When the system output is at its limit, the system only has the flexibility to adjust upward or downward. Where: are the upward and downward flexibility regulation capabilities provided by the substation at node i to the distribution network at time t; is the power transmitted from the substation at node i to the distribution network at time t; are the upper and lower limits of the ramp power of the substation at node i; P i sub,max is the maximum value of power transmitted from the substation to the distribution network at node i; By integrating the flexibility supply capabilities of various resources within a single grid of the distribution network, we can obtain: Where: and are the upward and downward flexibility supplies at time t under the cth grid, respectively; are the installation node sets of substations, energy storage, gas turbines, and transferable loads in the cth grid respectively.

4. A two-layer optimization method for grid distribution network considering operation flexibility according to claim 1, characterized in that: In step S2, flexibility evaluation indicators are established based on the regulation and transmission capabilities of the distribution network's flexibility resources. Through the transmission channels provided by the distribution network branch grid, a diversified topological combination of node flexibility resources is achieved, thereby ensuring that the flexibility supply and demand of the distribution network are balanced. The indicators include flexibility margin, which reflects the ratio between the flexibility margin and the flexibility demand within the grid. When the flexibility margin is less than 0, a flexibility margin will occur, that is: Where: and are the upward and downward flexibility deficits of the cth grid at time t, respectively, and the grid flexibility deficit is: Where: is the flexibility deficit of the cth grid, reflecting the ratio between the flexibility deficit and the flexibility demand within the grid. Its value is between 0 and 1, and the closer it is to 0, the greater the overall flexibility margin within the grid, the smaller the flexibility deficit, and the more adaptable to the uncertain changes in net load. In addition to having sufficient node flexibility resources to provide flexibility supply, there are also certain requirements for network flexibility. Branch flexibility margin is needed to cope with random fluctuations in net load. The grid branch flexibility margin index is defined as To reflect the average load rate of the network branch, The larger the value, the greater the load margin of each branch in grid c, and the stronger the ability to cope with the uncertainty of flexibility demand: Where: is the branch set of grid c; N b is the number of branches in the grid; is the load rate of branch l in the cth grid at time t; L l,t is the current flowing through branch l at time t; is the maximum value of the current allowed to flow through branch 1.

5. The grid distribution network double-layer optimization method considering operation flexibility according to claim 1 is characterized in that: In step S3, the branch flexibility index and network loss are optimized. In order to eliminate the dimensional influence between different objectives, the linear normalization method is used to perform data standardization, as shown in the formula: Where: N c is the number of all grids in the distribution network; F BF C is the flexibility margin of the distribution network branch; loss is the network loss cost of the distribution network, P t loss is the power loss on the distribution network line at time t, c loss Network loss costs; The relevant constraints of the upper-level model include improved disflow constraints that take into account changes in network topology, system security constraints, substation injection power constraints, and distribution system connectivity and radiation constraints. The grid division strategy of the distribution network is used as the decision-making body, and the optimal grid branch flexibility index is used as the objective function to establish the upper-level optimization model.

6. A grid distribution network double-layer optimization method considering operational flexibility according to claim 1, characterized in that: In step S4, the first-stage min optimization problem uses the output of each flexible resource on the power supply side as the decision variable to minimize the operating cost. The second-stage max-min problem seeks the worst-case scenario probability distribution to minimize the demand-side resource call cost, wind and solar curtailment penalty cost, and grid flexibility shortfall penalty cost. The expression is as follows: f1(x)=C sub +C GT +C ESS f2(y)=C TL +C PV +C WT +C flex Where: x is the first-stage variable; y is the second-stage variable; K is the number of scenes after the sample is discretized after reduction; ξ k is the kth scene; p k is the probability of the kth scene occurring; Ω is the comprehensive norm fuzzy set; C sub 、C GT 、C ESS 、C TL 、C PW 、C PV 、C flex They are the main grid electricity purchase cost, GT operating cost, ESS operating cost, transferable load response cost, wind curtailment cost, solar curtailment cost and flexibility loss cost; The relevant constraints of the lower-level model include flow balance constraints, gas turbine constraints, energy storage operation constraints, transferable load constraints, photovoltaic / wind turbine constraints, and the establishment of uncertain sets; the lower-level optimization model is established with the supply and demand side flexibility adjustment resource output as the decision-making body and the optimal distribution network operation cost and node flexibility index as the objective function.

7. A two-layer optimization method for grid distribution network considering operation flexibility according to claim 1, characterized in that: In step S5, the lower-level calculation is performed based on the upper-level optimization results. The lower-level model is based on the grid topology structure solved in the upper-level model. An operation plan is formulated with the goal of optimizing the resource call cost on the supply and demand side of the distribution network, the cost of curtailing wind and solar power, and the penalty cost of grid flexibility deficiency. The uncertainty of wind and solar power resources is taken into account. In order to seek the optimal operation cost under the probability of the worst wind and solar power output scenario, the following formula is shown: Where: c GT 、c ESS 、c TL 、c pw 、c pv 、c flex are the unit price of electricity purchased from the upper power grid at time t, the unit price of gas turbine power generation, the unit price of energy storage operation and maintenance, the unit price of transferable load response, the unit price of wind power curtailment, the unit price of solar power curtailment, and the cost coefficient of flexibility loss. The lower layer of the model is the optimization of the operation of flexible resources on the source and load side, taking into account the economic efficiency and flexibility shortage losses. It aims to coordinate the scheduling of adjustable resources on the supply side based on the grid topology structure obtained by the upper layer optimization, and to reduce the risk of system flexibility shortage. The output results of the supply and demand side resources at this time are fed back to the upper layer. The upper layer model will then find the optimal grid division scheme again based on this, realize mutual iterative optimization between the upper and lower layers, and calculate the optimal grid division scheme.

Images