Distribution robust optimization method considering flexibility of novel power distribution network

Abstract

The invention belongs to the field of power distribution network dispatching optimization. The invention discloses a distribution robust optimization method considering flexibility of a novel power distribution network. The method is characterized by comprising the following steps: step 1, analyzing and modeling supply and demand balance of flexibility of the novel power distribution network; 2, taking the flexibility margin and the line capacity margin of the novel power distribution network as flexibility evaluation indexes of the novel power distribution network; 3, in order to adapt to uncertainty of new energy output, a three-layer two-stage distribution robust optimization method is adopted, and in the first stage, an optimal solution is searched under limited probability distribution; in the second stage, on the basis of obtaining the decision variable in the first stage, searching the worst scene probability distribution which enables the cost of the second stage to be minimum; a novel scheme with optimal flexibility and minimum operation cost of the power distribution network is found; and 4, decoupling an original problem into mutual iteration of a main problem and a sub-problem, and solving the two-stage distribution robust model by adopting a column and constraint generation algorithm. The method can improve the robustness and flexible operation capability of the power distribution network.

Description

Technical Field

[0001] This invention belongs to the field of distribution network dispatch optimization, and in particular relates to a sub-Bluerg bar optimization method that takes into account the flexibility of new distribution networks. Background Technology

[0002] Developing new energy sources is a crucial way to achieve the "dual carbon" goals. However, the randomness, volatility, and intermittency of new energy sources pose severe challenges to the supply-demand balance and stability of the power system, necessitating the development of diverse and flexible resources to ensure the safe and stable operation of the system. The increasing penetration rate of new energy sources such as wind and solar power in distribution networks has led to difficulties such as line overload and a surge in flexibility demands. The reduction in the proportion of conventional generating units makes the traditional method of relying on reserve capacity to cope with drastic fluctuations in net load costly and impractical.

[0003] In the research on the flexibility of new distribution networks, there are various interval optimization scheduling methods for flexibility resources. These methods utilize electric vehicles and energy storage as flexibility resources to improve the flexibility of new distribution networks. They also point out that the flexibility resources of new distribution networks are distributed on both the node and network sides. Furthermore, they introduce the structure, working principle, and mechanism of smart soft switches (SOPs) in improving the flexibility of new distribution networks. However, they do not consider the coordination between network-side and node-side flexibility resources. Currently, the main methods for studying the uncertainty of renewable energy output in distribution networks include stochastic optimization, robust optimization, and partial Bruker optimization. Stochastic optimization, which follows a specific probability distribution, is difficult to obtain; robust optimization results tend to be conservative; and existing partial Bruker optimization methods do not consider the correlation of wind and solar power output within a geographical area when constructing the fuzzy set of probability distribution. Summary of the Invention

[0004] The purpose of this invention is to provide a sub-bar optimization method that takes into account the flexibility of new distribution networks, which can improve the robustness and flexible operation capability of distribution networks.

[0005] The technical solution adopted in this invention is: a sub-bulb optimization method considering the flexibility of new distribution networks, characterized by the following steps: Step 1: To improve the flexible adjustment capability of the distribution network, and taking into account the coordinated optimization of multiple types of flexibility resources, an analysis and modeling of the supply and demand balance of flexibility in the new distribution network is conducted. Step 2: Use the flexibility margin and line capacity margin of the new distribution network as evaluation indicators for the flexibility of the new distribution network; Step 3: To adapt to the uncertainty of new energy output, a 3-layer, 2-stage distributed bar optimization method is adopted. In the first stage, the optimal solution is found under a finite probability distribution. In the second stage, based on the decision variables obtained in the first stage, the worst-case probability distribution that minimizes the cost of the second stage is found. The optimal solution for the new distribution network with the best flexibility and the lowest operating cost is found. Step 4: Decouple the original problem into the mutual iteration of the main problem and the subproblems, and use the column and constraint generation algorithm to solve the two-stage sub-Bruker model.

[0006] In step 1, the modeling of the supply and demand balance for the flexibility of the new distribution network uses net load to characterize the flexibility demand of the new distribution network at time t. ,when A value greater than 0 indicates upward flexibility requirements, represented as... ,when A value less than 0 indicates a downward flexibility requirement, expressed as... ; In the formula: Let t be the net load power at time t; , , These represent the load, wind power, and photovoltaic power at time t, respectively. In demand-side flexibility resources, interruptible loads and transferable loads provide flexibility to the system by adjusting their own electricity consumption behavior, and the distribution network provides upward flexibility at time t. and downward flexibility supply for: In the formula , , , These represent the number of gas turbines, interruptible loads, transferable loads, and energy storage units in the system, respectively. The overall upward flexibility of the system at time t. , , , They are respectively The upward flexibility provided to the system by the nth gas turbine, the kth interruptible load, the hth transferable load, and the fth energy storage at any given time; For segmented intervals; , These represent the upward and downward climbing capabilities of the nth gas turbine, respectively. , , They are respectively The output and upper and lower limits of the nth gas turbine at time n; , , They are respectively The interruption amount and upper and lower limits of the k-th interruptible load at time k; , , for The transfer amount and upper and lower limits of the h-th transferable load at time h; , They are respectively The output and maximum charging / discharging power of the f-th energy storage unit at time f; , , They are respectively The capacity and upper and lower limits of the f-th energy storage unit at time f; , To improve the charging and discharging efficiency of energy storage; , , , They are respectively The nth gas turbine, the kth interruptible load, the hth transferable load, and the fth energy storage provide the system with downside flexibility at any given time. , , These represent the downward flexibility provided by the h-th demand-side resource to the system at time t, the power state of the h-th demand-side resource at time t, and the lower power limit of the h-th demand-side resource, respectively.

[0007] In step 2, the flexibility margin and line capacity margin of the new distribution network are used as evaluation indicators for the flexibility of the new distribution network.

[0008] Flexibility margin is divided into upward flexibility margin and downward flexibility margin ; In the formula, This represents the system's upward flexibility demand at time t; when the system's upward flexibility supply is insufficient, the upward flexibility margin is less than zero, which will cause the risk of load shedding, and conversely, it will cause the risk of wind and solar power curtailment; the cost of flexibility deficit is expressed through this. The magnitude of the potential risks arising from the lack of flexibility in the quantification system; In the formula: , These are the costs of system uplink and downlink flexibility deficits, respectively. , These are the risk cost coefficients for load shedding and wind / solar curtailment, respectively. The scheduling cycle is 24 hours.

[0009] Line capacity margin is divided into ordinary branch lines and branch lines containing SOPs. In the formula: The scheduling cycle is 24 hours. This represents the active power transmitted on the i-th side of the z-th SOP at time t. This represents the active power transmitted on the j-th side of the z-th SOP at time t; For line capacity margin; , They are respectively The line capacity margin includes both SOP (Smart Soft Switch) branches and ordinary branches at all times; This refers to the number of branches in the distribution network. Number of SOPs; , They are respectively Timetable The current and the maximum current; For the first The maximum allowable transmission power for each SOP port.

[0010] In step 3, assigning weight coefficients transforms the multi-objective optimization problem into a single-objective optimization problem; the objective function is the comprehensive cost. for: In the formula: T is the scheduling period of 24 hours. , , These are the gas turbine, interruptible load, and transferable load in the system, respectively. This is the economic weighting coefficient; For flexibility weighting coefficients; , , , , , These represent the unit costs of the nth gas turbine, the kth interruptible load response, the hth transferable load response, the fth energy storage, the electricity purchased from the upstream grid, and grid losses, respectively. This indicates the cost of inefficiency. , These represent the charging and discharging power of the f-th energy storage unit at time t, respectively. for The output of the nth gas turbine at time n. for The interruption amount of the k-th interruptible load at time k. for The transfer amount of the h-th transferable load at time h; Let t be the active power transmitted from the upstream power grid to the distribution network. Let t be the active power loss of the line.

[0011] The constraints 0 and 1 for establishing the distributed bar optimal scheduling model are: Current constraints: In the formula: , Injecting nodes at time t The active and reactive power; , Injecting nodes t respectively The active and reactive loads; , Branch roads The electrical conductivity and susceptance; , The nodes at time t are respectively and nodes The voltage; This refers to the number of nodes in the distribution network. branch road Phase angle difference between voltages; Energy storage constraints: In the formula: Let f be the amount of electrical energy stored at time t+1. Let f be the amount of electrical energy stored at time t. Let f be the charging power of the f-th energy storage unit at time t. For energy storage charging efficiency, For energy storage and discharge efficiency, Let f be the discharge power of the f-th energy storage unit at time t. Let f be the net power of the f-th energy storage unit at time t. For time step, This represents the minimum allowable electrical energy storage capacity of the f-th energy storage device. This represents the maximum allowable electrical energy storage capacity of the f-th energy storage device. This represents the charging power of the f-th energy storage unit at time t. This represents the maximum charging power of the f-th energy storage device. This represents the maximum discharge power of the f-th energy storage unit; , These represent the initial and final energy storage capacities, respectively. , Let be the 0-1 variables representing the charging and discharging states of the f-th energy storage unit at time t, respectively. If 1 is taken, it represents the energy storage state; otherwise, it represents 0.

[0012] Upper-level power grid constraints: In the formula: This represents the active power transmitted from the upstream power grid to the distribution network at time t; Reactive power transmitted to the upper-level power grid; , , , These are the upper and lower limits of active and reactive power transmitted from the upper-level power grid to the distribution network, respectively. To adapt to the uncertainty of new energy output, a 3-layer, 2-stage distributed bar optimization model is established, as follows: In the formula: These are the decision variables for stage 1; This represents the total number of scenes; , The first Each scenario and probability; The set of probability distributions to be found is included in the fuzzy set. For the first Decision variables in stage 2 of each scenario; Indicates 1 group hour The feasible domain; This represents the costs of electricity purchase and gas turbine operation in Phase 1; Indicates the second stage In each scenario, the distribution network costs other than the costs in the first stage are as follows; Equation (12) corresponds to the equations, inequalities, and second-order cone constraints constructed in step 3. , , , , , , , , , , This is the corresponding coefficient matrix (the range of values ​​for the coefficient matrix is ​​determined based on actual power system operating constraints, equipment parameters, and other engineering conditions). It is a second-order cone constraint. The right-hand coefficient vector has elements whose values ​​are related to physical quantities in the power system that are associated with second-order cone constraints. The wind and solar power outputs are correlated within a geographical area. A joint distribution function of wind and solar power outputs is established using the binary Frank-Copula function of equation (13). M samples are obtained through sampling. The initial probability distribution of wind and solar power outputs is obtained through scene reduction. ; In the formula: It is a binary Frank-Copula function; The operator for natural logarithms; It is part of the internal structure of the Frank-Copula function, and is a constant term whose specific value is determined by the relevant parameters. The core function of this decision is to standardize the form of the function and ensure that the Copula function satisfies the basic property of joint distribution. For the distribution of wind power output, through indices and parameters The expression after the action is used to characterize the relevant features of wind power output when the Copula function constructs a joint distribution; For the indices and parameters related to photovoltaic power output distribution The expression after the action is used to characterize the relevant features of wind power output when the Copula function constructs a joint distribution; , For the distribution function of wind and solar power output; For relevant parameters, .

[0013] The magnitude of the KL divergence measures the initial probability distribution. Compared with actual distribution The similarity, and considering and We construct fuzzy sets by identifying all distribution functions whose KL divergence does not exceed the distance tolerance d. In the formula: for and KL divergence values ​​between them; , They are respectively and The probability of the s-th scenario; for Chi-square distribution of degrees of freedom Upper quantiles ensure that the true distribution is at least equal to or greater than 100%. The probability is contained in the fuzzy set D. For the total number of scenes, This indicates the actual distribution of wind and solar power output; Indicates distance tolerance.

[0014] In step 4, a column and constraint generation algorithm is used to solve the two-stage sub-Bruker model, decoupling the original problem into a main problem and sub-problems that are solved iteratively. Iteration stops when the difference between the lower bound obtained from solving the main problem and the upper bound obtained from solving the sub-problems meets the set error.

[0015] The beneficial effects of this invention are as follows: To address the problems encountered in the process of technological development, a fuzzy set of probability distribution is established using the Copula function and KL divergence to obtain results that balance economy and robustness. To address the problem of insufficient flexibility in new distribution networks caused by fluctuations in new energy sources, a sub-Blu-ray robust optimization scheduling model considering the flexibility and economy of new distribution networks is established, thereby improving the robustness and flexible operation capability of the distribution network. Attached Figure Description

[0016] Figure 1 The flowchart shows the sub-bulb optimization method for considering the flexibility of new power distribution networks in this invention.

[0017] Figure 2 This is a network topology diagram of the IEEE 33-node system of this invention.

[0018] Figure 3 This is a diagram of the active power transmitted in the SOP of this invention.

[0019] Figure 4 This is a diagram showing the voltage amplitude of node 3 throughout the day in this invention. Detailed Implementation

[0020] The present invention will be further described in detail below with reference to the embodiments and accompanying drawings, but the embodiments of the present invention are not limited thereto.

[0021] Figure 1 The flowchart of the sub-Bluer bar optimization method considering the flexibility of the new distribution network is as follows: Step 1: To improve the flexible adjustment capability of the distribution network, and taking into account the coordinated optimization of multiple types of flexible resources, an analysis and modeling of the supply and demand balance of flexibility in the new distribution network is conducted.

[0022] Step 2: Use the flexibility margin and line capacity margin of the new distribution network as evaluation indicators for the flexibility of the new distribution network.

[0023] Step 3: To adapt to the uncertainty of new energy output, a 3-layer, 2-stage distributed bar optimization method is adopted. In the first stage, the optimal solution is found under a finite probability distribution. In the second stage, based on the decision variables obtained in the first stage, the worst-case probability distribution that minimizes the cost of the second stage is found. The optimal solution for the new distribution network with the best flexibility and the lowest operating cost is found.

[0024] Step 4: Decouple the original problem into the mutual iteration of the main problem and the subproblems, and use the column and constraint generation algorithm to solve the two-stage sub-Bruker model.

[0025] In step 1, the modeling of the supply and demand balance for the flexibility of the new distribution network uses net load to characterize the flexibility demand of the new distribution network at time t. ,when A value greater than 0 indicates upward flexibility requirements, represented as... ,when A value less than 0 indicates a downward flexibility requirement, expressed as... . In the formula: Let t be the net load power at time t; , , These represent the load, wind power, and photovoltaic power at time t, respectively.

[0026] In demand-side flexibility resources, interruptible and transferable loads provide system flexibility by adjusting their own electricity consumption behavior. The distribution network provides upward flexibility at time t. and downward flexibility supply for: In the formula , , , These represent the number of gas turbines, interruptible loads, transferable loads, and energy storage units in the system, respectively. The overall upward flexibility of the system at time t. , , , They are respectively The upward flexibility provided to the system by the nth gas turbine, the kth interruptible load, the hth transferable load, and the fth energy storage at any given time; For segmented intervals; , These represent the upward and downward climbing capabilities of the nth gas turbine, respectively. , , They are respectively The output and upper and lower limits of the nth gas turbine at time n; , , They are respectively The interruption amount and upper and lower limits of the k-th interruptible load at time k; , , for The transfer amount and upper and lower limits of the h-th transferable load at time h; , They are respectively The output and maximum charging / discharging power of the f-th energy storage unit at time f; , , They are respectively The capacity, upper limit, and lower limit of the energy storage at time f; , To improve the charging and discharging efficiency of energy storage; , , , They are respectively The nth gas turbine, the kth interruptible load, the hth transferable load, and the fth energy storage provide the system with downside flexibility at any given time. , , These represent the downward flexibility provided by the h-th demand-side resource to the system at time t, the power state of the h-th demand-side resource at time t, and the lower power limit of the h-th demand-side resource, respectively.

[0027] In step 2, the flexibility margin and line capacity margin of the new distribution network are used as evaluation indicators for the flexibility of the new distribution network.

[0028] Flexibility margin is divided into upward flexibility margin and downward flexibility margin . In the formula: This represents the system's upward flexibility demand at time t. When the system's upward flexibility supply is insufficient, the upward flexibility margin is less than zero, which will cause the risk of load shedding; conversely, it will cause the risk of wind and solar power curtailment. The cost of flexibility deficit is quantified. The magnitude of the potential risks arising from the lack of flexibility in the quantification system. In the formula: , These are the costs of system uplink and downlink flexibility deficits, respectively. , These are the risk cost coefficients for load shedding and wind / solar curtailment, respectively. The scheduling cycle is 24 hours.

[0029] Line capacity margin is divided into ordinary branch lines and branch lines containing SOPs. In the formula: The scheduling cycle is 24 hours. This represents the active power transmitted on the i-th side of the z-th SOP at time t. This represents the active power transmitted on the j-th side of the z-th SOP at time t. For line capacity margin; , They are respectively The line capacity margin includes both SOP (Smart Soft Switch) branches and ordinary branches at all times; This refers to the number of branches in the distribution network. Number of SOPs; , They are respectively Timetable The current and the maximum current; For the first The maximum allowable transmission power for each SOP port.

[0030] In step 3, assigning weights transforms the multi-objective optimization problem into a single-objective optimization problem. The objective function is the overall cost. for: In the formula: T is the scheduling period of 24 hours. , , These are the gas turbine, interruptible load, and transferable load in the system, respectively. This is the economic weighting coefficient; For flexibility weighting coefficients; , , , , , These represent the unit costs of the nth gas turbine, the kth interruptible load response, the hth transferable load response, the fth energy storage, the electricity purchased from the upstream grid, and grid losses, respectively. This indicates the cost of inefficiency. , These represent the charging and discharging power of the f-th energy storage unit at time t, respectively. for The output of the nth gas turbine at time n. for The interruption amount of the k-th interruptible load at time k. for The amount of transferable load at time h. Let t be the active power transmitted from the upstream power grid to the distribution network. Let t be the active power loss of the line.

[0031] The constraints for establishing the distributed bar optimization scheduling model are: Current constraints: In the formula: , Injecting nodes at time t The active and reactive power; , Injecting nodes t respectively The active and reactive loads; , Branch roads The electrical conductivity and susceptance; , The nodes at time t are respectively and nodes The voltage; This refers to the number of nodes in the distribution network. branch road Phase angle difference between voltages.

[0032] Energy storage constraints: In the formula: Let f be the amount of electrical energy stored at time t+1. Let f be the amount of electrical energy stored at time t. Let f be the charging power of the f-th energy storage unit at time t. For energy storage charging efficiency, For energy storage and discharge efficiency, Let f be the discharge power of the f-th energy storage unit at time t. Let f be the net power of the f-th energy storage unit at time t. For time step, This represents the minimum allowable electrical energy storage capacity of the f-th energy storage device. This represents the maximum allowable electrical energy storage capacity of the f-th energy storage device. This represents the charging power of the f-th energy storage unit at time t. This represents the maximum charging power of the f-th energy storage device. This represents the maximum discharge power of the f-th energy storage unit; , These represent the initial and final energy storage capacities, respectively. , Let be the 0-1 variables representing the charging and discharging states of the f-th energy storage unit at time t, respectively. If 1 is taken, it represents the energy storage state; otherwise, it represents 0.

[0033] Upper-level power grid constraints: In the formula: This represents the active power transmitted from the upstream power grid to the distribution network at time t. Reactive power transmitted to the upper-level power grid; , , , These are the upper and lower limits of active and reactive power transmitted from the upper-level power grid to the distribution network, respectively.

[0034] To adapt to the uncertainty of new energy output, a 3-layer, 2-stage distributed bar optimization model is established, as follows: In the formula: These are the decision variables for stage 1; This represents the total number of scenes; , The first Each scenario and probability; The set of probability distributions to be found is included in the fuzzy set. For the first Decision variables in stage 2 of each scenario; Indicates 1 group hour The feasible domain; This represents the costs of electricity purchase and gas turbine operation in Phase 1; Indicates the second stage In each scenario, the distribution network costs other than the costs in the first stage are as follows; Equation (19) corresponds to the equations, inequalities, and second-order cone constraints constructed in step 3. , , , , , , , , , , This is the corresponding coefficient matrix. The range of values ​​for the coefficient matrix is ​​determined based on the actual operating constraints of the power system, equipment parameters, and other engineering conditions. It is a second-order cone constraint. The coefficient vector on the right side has elements whose values ​​are related to physical quantities in the power system that are associated with second-order cone constraints.

[0035] The wind and solar power outputs are correlated within a geographical area. A joint distribution function of wind and solar power outputs is established using the binary Frank-Copula function of equation (20). M samples are obtained through sampling. The initial probability distribution of wind and solar power outputs is obtained through scene reduction. ; In the formula: It is a binary Frank-Copula function. This is the natural logarithm operator. It is part of the internal construction of the Frank-Copula function and is a constant term. and The core function of Copula (variation) is to standardize the form of the function and ensure that the Copula function satisfies the basic property of joint distribution. , For the distribution function of wind and solar power output; For relevant parameters, .

[0036] The magnitude of the KL divergence measures the initial probability distribution. Compared with actual distribution The similarity, and considering and We construct fuzzy sets by identifying all distribution functions whose KL divergence does not exceed the distance tolerance d. In the formula: for and KL divergence values ​​between them; , They are respectively and The probability of the s-th scenario; for Chi-square distribution of degrees of freedom Upper quantiles ensure that the true distribution is at least equal to or greater than 100%. The probability is contained in the fuzzy set D. S is the total number of scenes. This indicates the actual distribution of wind and solar power output. Indicates distance tolerance.

[0037] In step 4, a column and constraint generation algorithm is used to solve the two-stage sub-Bruker model, decoupling the original problem into a main problem and sub-problems that are solved iteratively. Iteration stops when the difference between the lower bound obtained from solving the main problem and the upper bound obtained from solving the sub-problems meets the set error.

[0038] Figure 2 The network topology diagram for the IEEE 33-node system is shown. The voltage variation range of the AC and DC subnet nodes is [0.95, 1.05] pu, and the voltage of the root node is set to 1 pu. The upper limits of active and reactive power at the substation gate are 3.5MW and 2Mvar, respectively, and the lower limits are 0MW and 0Mvar, respectively. The current carrying capacity limit of the AC sub-region branch is 2.5MVA, and the current carrying capacity limit of the DC sub-region branch is 1MW. The voltage base value is 12.66kV, and the power base value is 1MVA. The rigid load of the DC sub-region is set to 1 / 4 of the total rigid load of the AC sub-region, and each DC node is set to the same load value for ease of calculation. When calculating the electricity price, the peak-to-valley ratio is selected as 5:1, and the step size is 0.15.

[0039] Figure 3 For the active power transmitted by SOP, each node has more paths to obtain power, which alleviates the loss in the power transmission process, enhances the flexibility of the node-side resource network and improves the flexibility of the distribution network.

[0040] Figure 4 The SOP represents the total voltage amplitude of node 3 throughout the day. As a network-side flexibility resource, it improves the power flow distribution of the system and increases the capacity margin of the distribution network lines. The more SOPs are connected, the greater the line capacity margin, which enhances the inter-network mutual support among the new distribution network's flexibility resources.

Claims

1. A sub-Brow bar optimization method considering the flexibility of new distribution networks, characterized in that... Includes the following steps: Step 1: To improve the flexible adjustment capability of the distribution network, and taking into account the coordinated optimization of multiple types of flexibility resources, an analysis and modeling of the supply and demand balance of flexibility in the new distribution network is conducted. Step 2: Use the flexibility margin and line capacity margin of the new distribution network as evaluation indicators for the flexibility of the new distribution network; Step 3: To adapt to the uncertainty of new energy output, a 3-layer, 2-stage distributed bar optimization method is adopted. In the first stage, the optimal solution is found under a finite probability distribution. In the second stage, based on the decision variables obtained in the first stage, the worst-case probability distribution that minimizes the cost of the second stage is found. The optimal solution for the new distribution network with the best flexibility and the lowest operating cost is found. Step 4: Decouple the original problem into the mutual iteration of the main problem and the subproblems, and use the column and constraint generation algorithm to solve the two-stage sub-Bruker model.

2. The sub-bulb optimization method considering the flexibility of new distribution networks according to claim 1, characterized in that... In step 1, the modeling of the supply and demand balance for the flexibility of the new distribution network uses net load to characterize the flexibility demand of the new distribution network at time t. ,when A value greater than 0 indicates upward flexibility requirements, represented as... ,when A value less than 0 indicates a downward flexibility requirement, expressed as... ; In the formula: Let t be the net load power at time t; , , These represent the load, wind power, and photovoltaic power at time t, respectively. In demand-side flexibility resources, interruptible loads and transferable loads provide flexibility to the system by adjusting their own electricity consumption behavior, and the distribution network provides upward flexibility at time t. and downward flexibility supply for: In the formula , , , These represent the number of gas turbines, interruptible loads, transferable loads, and energy storage units in the system, respectively. The overall upward flexibility of the system at time t. , , , They are respectively The upward flexibility provided to the system by the nth gas turbine, the kth interruptible load, the hth transferable load, and the fth energy storage at any given time; For segmented intervals; , These represent the upward and downward climbing capabilities of the nth gas turbine, respectively. , , They are respectively The output and upper and lower limits of the nth gas turbine at time n; , , They are respectively The interruption amount and upper and lower limits of the k-th interruptible load at time k; , , for The transfer amount and upper and lower limits of the h-th transferable load at time h; , They are respectively The output and maximum charging / discharging power of the f-th energy storage unit at time f; , , They are respectively The capacity and upper and lower limits of the f-th energy storage unit at time f; , To improve the charging and discharging efficiency of energy storage; , , , They are respectively The nth gas turbine, the kth interruptible load, the hth transferable load, and the fth energy storage provide the system with downside flexibility at any given time. , , These represent the downward flexibility provided by the h-th demand-side resource to the system at time t, the power state of the h-th demand-side resource at time t, and the lower power limit of the h-th demand-side resource, respectively.

3. The sub-bulb optimization method considering the flexibility of new distribution networks according to claim 1, characterized in that... In step 2, the flexibility margin and line capacity margin of the new distribution network are used as evaluation indicators for the flexibility of the new distribution network. Flexibility margin is divided into upward flexibility margin and downward flexibility margin ; In the formula, This represents the system's upward flexibility requirement at time t; When the system's upward flexibility is insufficient, the upward flexibility margin is less than zero, which will cause the risk of load shedding; conversely, it will cause the risk of wind and solar power curtailment. The cost of flexibility deficits is significant. The magnitude of the potential risks arising from the lack of flexibility in the quantification system; In the formula: , These are the costs of system uplink and downlink flexibility deficits, respectively. , These are the risk cost coefficients for load shedding and wind / solar curtailment, respectively. The scheduling cycle is 24 hours. Line capacity margin is divided into ordinary branch lines and line capacity margin including SOP branch lines; In the formula: The scheduling cycle is 24 hours. This represents the active power transmitted on the i-th side of the z-th SOP at time t. This represents the active power transmitted on the j-th side of the z-th SOP at time t; For line capacity margin; , They are respectively Line capacity margin including SOP branches and ordinary branches at all times; This refers to the number of branches in the distribution network. Number of SOPs; , They are respectively Timetable The current and the maximum current; For the first The maximum allowable transmission power for each SOP port.

4. The sub-bulb optimization method considering the flexibility of new distribution networks according to claim 1, characterized in that... In step 3, assigning weight coefficients transforms the multi-objective optimization problem into a single-objective optimization problem; the objective function is the comprehensive cost. for: In the formula: T is the scheduling period of 24 hours. , , These are the gas turbine, interruptible load, and transferable load in the system, respectively. This is the economic weighting coefficient; For flexibility weighting coefficients; , , , , , These represent the unit costs of the nth gas turbine, the kth interruptible load response, the hth transferable load response, the fth energy storage, the electricity purchased from the upstream grid, and grid losses, respectively. This indicates the cost of inefficiency. , These represent the charging and discharging power of the f-th energy storage unit at time t, respectively. for The output of the nth gas turbine at time n. for The interruption amount of the k-th interruptible load at time k. for The transfer amount of the h-th transferable load at time h; Let t be the active power transmitted from the upstream power grid to the distribution network. The active power loss of the line at time t; The constraints 0 and 1 for establishing the distributed bar optimal scheduling model are: Current constraints: In the formula: , Injecting nodes at time t The active and reactive power; , Injecting nodes t respectively The active and reactive loads; , Branch roads The electrical conductivity and susceptance; , The nodes at time t are respectively and nodes The voltage; This refers to the number of nodes in the distribution network. branch road Phase angle difference between voltages; Energy storage constraints: In the formula: Let f be the amount of electrical energy stored at time t+1. Let f be the amount of electrical energy stored at time t. Let f be the charging power of the f-th energy storage unit at time t. For energy storage charging efficiency, For energy storage and discharge efficiency, Let f be the discharge power of the f-th energy storage unit at time t. Let f be the net power of the f-th energy storage unit at time t. For time step, This represents the minimum allowable electrical energy storage capacity of the f-th energy storage device. This represents the maximum allowable electrical energy storage capacity of the f-th energy storage device. This represents the charging power of the f-th energy storage unit at time t. This represents the maximum charging power of the f-th energy storage device. This represents the maximum discharge power of the f-th energy storage unit; , These represent the initial and final energy storage capacities, respectively. , These are the 0-1 variables representing the charging and discharging states of the f-th energy storage unit at time t, respectively. A value of 1 indicates the energy storage state, and a value of 0 indicates otherwise. Upper-level power grid constraints: In the formula: This represents the active power transmitted from the upstream power grid to the distribution network at time t; Reactive power transmitted to the upper-level power grid; , , , These are the upper and lower limits of active and reactive power transmitted from the upper-level power grid to the distribution network, respectively. To adapt to the uncertainty of new energy output, a 3-layer, 2-stage distributed bar optimization model is established, as follows: In the formula: These are the decision variables for stage 1; This represents the total number of scenes; , The first Each scenario and probability; The set of probability distributions to be found is included in the fuzzy set. For the first Decision variables in stage 2 of each scenario; Indicates 1 group hour The feasible domain; This represents the costs of electricity purchase and gas turbine operation in Phase 1; Indicates the second stage In each scenario, the distribution network costs other than the costs in the first stage are as follows; Equation (12) corresponds to the equations, inequalities, and second-order cone constraints constructed in step 3. , , , , , , , , , , This is the corresponding coefficient matrix; It is a second-order cone constraint. The right-hand coefficient vector has elements whose values ​​are related to physical quantities in the power system that are associated with second-order cone constraints. The wind and solar power outputs are correlated within a geographical area. A joint distribution function of wind and solar power outputs is established using the binary Frank-Copula function of equation (13). M samples are obtained through sampling. The initial probability distribution of wind and solar power outputs is obtained through scene reduction. ; In the formula: It is a binary Frank-Copula function; The operator for natural logarithms; It is part of the internal structure of the Frank-Copula function, and is a constant term whose specific value is determined by the relevant parameters. The core function of this decision is to standardize the form of the function and ensure that the Copula function satisfies the basic property of joint distribution. For the distribution of wind power output, through indices and parameters The expression after the action is used to characterize the relevant features of wind power output when the Copula function constructs a joint distribution; For the indices and parameters related to photovoltaic power output distribution The expression after the action is used to characterize the relevant features of wind power output when the Copula function constructs a joint distribution; , For the distribution function of wind and solar power output; For relevant parameters, ; The magnitude of the KL divergence measures the initial probability distribution. Compared with actual distribution The similarity, and considering and The KL divergence of all distribution functions does not exceed the distance tolerance d, thus constructing a fuzzy set; In the formula: for and KL divergence values ​​between them; , They are respectively and The probability of the s-th scenario; for Chi-square distribution of degrees of freedom Upper quantiles ensure that the true distribution is at least equal to or greater than 100%. The probability is contained in the fuzzy set D. For the total number of scenes, This indicates the actual distribution of wind and solar power output; Indicates distance tolerance.

5. The sub-bulb optimization method considering the flexibility of new distribution networks according to claim 1, characterized in that... In step 4, the column and constraint generation algorithm is used to solve the two-stage sub-Bruker model, decoupling the original problem into a main problem and sub-problems for iterative solving; the iteration stops when the difference between the lower bound obtained from solving the main problem and the upper bound obtained from solving the sub-problems meets the set error.

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