A method for evaluating the new energy absorption capacity of active distribution networks based on the flexibility of source, grid, load and storage

Abstract

The present invention discloses a method for evaluating the new energy consumption capacity of an active distribution network based on the flexibility of source, grid, load and storage, and the steps include: 1) establishing a simulation model of the flexibility of source, grid, load and storage of an active distribution network; 2) establishing an evaluation model of the new energy consumption capacity of an active distribution network with the goal of maximizing the new energy consumption of the active distribution network and minimizing the operating cost of the active distribution network; 3) converting the evaluation model of the new energy consumption capacity of an active distribution network into a multi-objective mixed integer linear optimization model by using a linearization method; 4) solving the multi-objective mixed integer linear optimization model to obtain the new energy consumption capacity of the active distribution network. The present invention is applicable to an active distribution network with a high proportion of new energy access, and realizes the maximum consumption of new energy with the minimum operating cost through the optimized scheduling of the source, grid, load and storage of the active distribution network. The method fully considers the flexibility of the source, grid, load and storage of the active distribution network, improves the level of new energy consumption, and effectively evaluates the new energy consumption capacity of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy power systems, and in particular to a method for evaluating the new energy consumption capacity of an active distribution network based on source-grid-load-storage flexibility. Background Art

[0002] How to fully tap and utilize the flexible interaction between sources, grids, loads and storage of active distribution networks to formulate reasonable and effective new energy consumption strategies has become one of the key problems that need to be solved urgently.

[0003] However, most of the existing measures to improve the flexibility of new energy consumption are relatively simple, and therefore cannot meet the flexibility requirements of high-proportion new energy grid connection and consumption, and cannot maximize the potential of active distribution networks to consume new energy. At present, there is a lack of a method for evaluating the new energy consumption capacity of active distribution networks that comprehensively considers the flexibility of sources, grids, loads and storage. Summary of the invention

[0004] The purpose of the present invention is to provide a method for evaluating the new energy consumption capacity of an active distribution network based on the flexibility of source, grid, load and storage, comprising the following steps:

[0005] 1) Establishing an active distribution network source-grid-load-storage flexibility simulation model. The active distribution network source-grid-load-storage flexibility simulation model includes a power supply flexibility simulation model, a network flexibility simulation model, a load flexibility simulation model and an energy storage flexibility simulation model.

[0006] The power supply flexibility simulation model is as follows:

[0007]

[0008] In the formula, is the active power reduction of the new energy connected to the grid at node i. is the theoretically available active power of the new energy connected to the grid at node i. is the active power that can be actually consumed by the renewable energy connected to the grid at node i. t is the time period identifier. t=1,2,…,T. T is the total number of time periods. is the reactive power of the renewable energy connected to the grid at node i. is the power factor of the new energy connected to the grid at node i. and They are the allowable variation range of power factor respectively. is the capacity of the new energy inverter connected to the grid at node i.

[0009] The network end flexibility simulation model is as follows:

[0010]

[0011] In the formula, ΩB It is the set of branches with remote control switches in the network. The maximum number of operations allowed for the remote control switch of branch ij throughout the day. The maximum number of operations allowed for all remote switches in the system in a day. ij.t Indicates the number of actions of the remote control switch in the tth period. N and N s are the total number of nodes and substations in the network, respectively.

[0012] M l is the number of branches in the lth power supply loop. l=,2,…,L. L is the total number of power supply loops in the network. sw ij.l.t is the state of the branch ij in the lth power supply loop. Ω B.l It is the branch set corresponding to the lth power supply loop.

[0013] Among them, the number of actions of the remote control switch in the tth period Δsw ij.t As shown below:

[0014] Δsw ij.t =|sw ij.t -sw ij.t-1 | (6)

[0015] In the formula, sw ij.t is the remote control switch state of branch ij in the tth period. sw ij.t-1 is the remote control switch state of branch ij in the t-1 period. The switch state is expressed in binary, "1" means closed, and "0" means open.

[0016] The load end flexibility simulation model is as follows:

[0017]

[0018] In the formula, and are the original power values ​​of the controllable load at node i respectively. and are respectively the active / reactive power of node i after the controllable load flexibility regulation. It is the power regulation capability of the controllable load in each scheduling period. and They are the maximum adjustment capabilities that the controllable load of node i can increase and decrease compared to the original load at the current moment.

[0019] Δt is the unit time interval.

[0020] The energy storage flexibility simulation model is as follows:

[0021]

[0022] In the formula, and are the charging and discharging powers of the conventional energy storage at node i respectively. and is the charge / discharge efficiency of conventional energy storage. It is the rated capacity of conventional energy storage. SOC i.t+1 and SOC i.t They are the state of charge at time t+1 and time t respectively. SOC i.max and SOC i.min are the maximum and minimum values ​​of the state of charge during period t respectively. and are the maximum charging and discharging powers of the energy storage respectively. is the reactive power output by the energy storage at node i. Represents the inverter capacity of the energy storage installed at node i.

[0023] 2) With the goal of maximizing the new energy consumption of the active distribution network and minimizing the operating cost of the active distribution network, an evaluation model for the new energy consumption capacity of the active distribution network is established.

[0024] The objective functions of the evaluation model for the new energy absorption capacity of the active distribution network are shown in formula (14) and formula (15), namely:

[0025]

[0026] In the formula, F DG It is the amount of new energy consumed by the active distribution network throughout the day. Ω DG It is the node set corresponding to the new energy.

[0027]

[0028] In the formula, F EC is the operating cost of the active distribution network throughout the day. DG (t), C SW (t), C DR (t) and C ESS (t) are the operating costs of all new energy sources, remote switches, controllable loads and energy storage in period t. Grid (t) is the cost of electricity purchased by the distribution network from the upper power grid during period t. LOSS (t) is the network loss cost of the active distribution network during period t.

[0029] The operating costs of all new energy sources, remote switches, controllable loads and energy storage in period t are as follows:

[0030]

[0031] In the formula, and They are respectively the cost coefficients of new energy active and reactive power output and the penalty cost coefficient of active power reduction. sw is the cost coefficient of a single switch action. Ω DR and Ω ESS are the node sets corresponding to controllable load and energy storage respectively. DR is the unit dispatch cost of controllable load. It is the unit operating cost of charging and discharging active power of energy storage. is the reactive power output cost coefficient of energy storage.

[0032] The electricity purchase cost from the upper power grid and the network loss cost of the active distribution network in period t are as follows:

[0033]

[0034] In the formula, Ω Grid and Ω F They are the set of all root nodes and the set of branches respectively. R is the power purchased by the root node i from the upper power grid during period t. ij is the resistance value between branches ij. is the electricity purchase price in period t. Loss is the network loss cost. ij.t is the current value of branch ij during period t.

[0035] The constraints of the active distribution network new energy absorption capacity assessment model include branch flow constraints, node power balance constraints, node voltage constraints, branch current constraints and upper grid interaction power constraints. The branch flow constraints are as follows:

[0036]

[0037] Where P ij.t and Q ij.t are the active power and reactive power of branch ij in period t respectively. ij and b ij are the conductance and susceptance of branch ij respectively. ij.t is the voltage phase angle difference between nodes i and j in period t. i.t , U j.t are the voltage amplitudes of nodes i and j in time period t respectively.

[0038] The node power balance constraint is as follows:

[0039]

[0040] In the formula, and are the active power and reactive power injected into the distribution network from the root node i by the upper power grid during period t. ii and b ii are the conductance and susceptance of the grounded branch connected to node i. Ω i The set of branches connected to node i.

[0041] The node voltage constraints are as follows:

[0042] U i.min <U i.t <U i.max (twenty four)

[0043] Where U i.min and U i.max are the lower and upper limits of the voltage at node i, respectively.

[0044] The branch current constraints are as follows:

[0045]

[0046] In the formula, I ij.t I is the current value of branch ij during period t. ij,max is the maximum allowable current carrying capacity of branch ij.

[0047] The upper-level grid interaction power constraint is as follows:

[0048]

[0049] Where P i grid and are the active power and reactive power limits exchanged between the upper power grid and the balancing node i respectively.

[0050] 3) Using the linearization method, the active distribution network new energy absorption capacity assessment model is converted into a multi-objective mixed integer linear optimization model, the steps include:

[0051] 3.1) Record the square variable of the voltage amplitude of node i in time period t The square variable of the voltage amplitude at node j during period t

[0052] Linearizing the branch power flow constraint (22) yields:

[0053]

[0054] Among them, the constant coefficient They are as follows

[0055] As shown:

[0056]

[0057]

[0058] In the formula, and are the initial values ​​of the voltage amplitudes at nodes i and j in period t respectively. and are the initial value difference of voltage amplitude and the initial value difference of voltage phase angle at nodes i and j in time period t respectively.

[0059] Constant coefficient a 1.t and a 2.t As shown below:

[0060]

[0061] The node voltage constraint (24) is converted into the following equation:

[0062]

[0063] 3.2) Introducing auxiliary variables and auxiliary variables The Big-M method is used to transform the node power balance constraint (23) into:

[0064]

[0065] Where M 1.t and M 2.t Is a positive number.

[0066] Introducing binary variables, formula (11) is equivalently linearized to:

[0067]

[0068] In the formula, and All are 0-1 variables.

[0069] Introducing binary variables, the Big-M method is used to linearize formula (5) to:

[0070]

[0071] In the formula, δ ij.t.1 and δ ij.t.2 All are 0-1 variables. 3.t Is a positive number.

[0072] By introducing auxiliary variables and binary variables and combining with the Big-M method, formula (16) and formula (18)-(19) are equivalently transformed into:

[0073]

[0074] In the formula, and They are all auxiliary variables. and and and All are 0-1 variables. 4.t , M 5.t and M 6.t All are positive numbers.

[0075] 3.3) Set the branch current amplitude square variable Right now:

[0076]

[0077] The branch current constraint is linearized and formula (39) is transformed as follows:

[0078]

[0079] Among them, the constant coefficient As shown below:

[0080]

[0081] Formula (21) and formula (25) are transformed as follows:

[0082]

[0083] Setting auxiliary variables The Big-M method is used to linearize equations (42)-(43) to obtain:

[0084]

[0085] Where M 7.t Is a positive number.

[0086] 3.4) Linearizing the complex power constraint, the steps include:

[0087] 3.4.1) The complementary angle of the power factor angle of the new energy source is and Set the number of segments to N DG .

[0088] 3.4.2) The arc range is evenly divided into N DG Connect each point in turn to get N DG The line segments are denoted as Among them, any line segment is represented as follows:

[0089]

[0090] 3.4.3) Establish the constraints of formula (47), namely:

[0091]

[0092] In the formula, N is the coefficient of the nth segment expression of the new energy constraint; DG The number of segments for new energy;

[0093] 3.5) Linearize the energy storage complex power and obtain:

[0094]

[0095] Where n is the current segment number; is the coefficient of the nth piecewise line segment expression for the energy storage complex power constraint; and The number of segments for linearizing the complex power constraints of new energy and energy storage; It represents the linearization segmentation of complex power constraints of energy storage. The subscript charge represents the charging state, and discharge represents the discharging state.

[0096] 4) Solving the multi-objective mixed integer linear optimization model to obtain the new energy consumption capacity of the active distribution network, the steps include:

[0097] 4.1) Solve the multi-objective mixed integer linear optimization model to obtain the maximum renewable energy absorption capacity of the active distribution network and minimal operating costs

[0098] 4.2) Solve the multi-objective mixed integer linear optimization model to obtain the minimum operating cost The corresponding maximum renewable energy consumption capacity of the active distribution network

[0099] 4.3) The interval Defined as the range of Pareto optimal solution set, .

[0100] 4.4) Set the step size z to maximize the new energy consumption capacity F of the active distribution network DG The range of Perform equal division to serve as the outer traversal indicator.

[0101] 4.5) Take the outer layer index as a constraint, and optimize the inner layer to solve the inner layer objective function F corresponding to the outer layer index constraint ECThe optimal value, after traversing the outer indicator constraints, can obtain z groups of Pareto optimal solutions.

[0102] 4.6) Use formula (51) to evaluate the membership function value μ of each objective function of each Pareto optimal solution i , and according to formula (52), the Pareto optimal solution with the largest standardized satisfaction value μ is selected as the optimal compromise solution.

[0103]

[0104] In the formula, f i is the value of the i-th objective function. i.max and f i.min are the upper and lower limits of the objective function respectively. M is the number of objective functions.

[0105] The technical effect of the present invention is unquestionable. The present invention proposes a new energy consumption strategy for active distribution networks based on the flexible interactivity of source, grid, load and storage. Taking into account the new energy consumption and operating costs of the active distribution network, the flexible interactivity of source, grid, load and storage is simulated, and a consumption model considering the flexible interactivity of source, grid, load and storage is established. Then, the model is transformed into a mixed integer linear optimization problem through a linearization method, and a double-layer nested structure is designed for solving it. The present invention can guide the rational development of new energy. The present invention is suitable for active distribution networks with a high proportion of new energy access. Through the optimal scheduling of the source, grid, load and storage of the active distribution network, the maximum consumption of new energy is achieved at the minimum operating cost. This method fully considers the flexibility of the source, grid, load and storage of the active distribution network, improves the level of new energy consumption, and effectively evaluates the system's new energy consumption capacity. BRIEF DESCRIPTION OF THE DRAWINGS

[0106] Figure 1 It is a flow chart of the new energy consumption assessment method;

[0107] Figure 2 It is a modified IEEE 33-node system diagram;

[0108] Figure 3 It is a schematic diagram of piecewise linearization of new energy complex power. DETAILED DESCRIPTION

[0109] The present invention is further described below in conjunction with the embodiments, but it should not be understood that the above subject matter of the present invention is limited to the following embodiments. Without departing from the above technical ideas of the present invention, various substitutions and changes are made according to the common technical knowledge and customary means in the art, which should all be included in the protection scope of the present invention.

[0110] Embodiment 1:

[0111] See also Figures 1 to 3, a method for evaluating the new energy consumption capacity of an active distribution network based on the flexibility of source, grid, load and storage, comprising the following steps:

[0112] 1) Establishing an active distribution network source-grid-load-storage flexibility simulation model. The active distribution network source-grid-load-storage flexibility simulation model includes a power supply flexibility simulation model, a network flexibility simulation model, a load flexibility simulation model and an energy storage flexibility simulation model.

[0113] The power supply flexibility simulation model is as follows:

[0114]

[0115] In the formula, is the active power reduction of the new energy connected to the grid at node i. is the theoretically available active power of the new energy connected to the grid at node i. is the active power that can be actually consumed by the renewable energy connected to the grid at node i. t is the time period identifier. t=1,2,…,T. T is the total number of time periods. is the reactive power of the renewable energy connected to the grid at node i. is the power factor of the new energy connected to the grid at node i. and They are the allowable variation range of power factor respectively. is the capacity of the new energy inverter connected to the grid at node i.

[0116] The network end flexibility simulation model is as follows:

[0117]

[0118] In the formula, Ω B It is the set of branches with remote control switches in the network. The maximum number of operations allowed for the remote control switch of branch ij throughout the day. The maximum number of operations allowed for all remote switches in the system in a day. ij.t Indicates the number of actions of the remote control switch in the tth period. N and N s are the total number of nodes and substations in the network, respectively.

[0119] M l is the number of branches in the lth power supply loop. l=,2,…,L. L is the total number of power supply loops in the network. sw ij.l.t is the state of the branch ij in the lth power supply loop. Ω B.l It is the branch set corresponding to the lth power supply loop.

[0120] Among them, the number of actions of the remote control switch in the tth period Δsw ij.tAs shown below:

[0121] Δsw ij.t =|sw ij.t -sw ij.t-1 | (6)

[0122] In the formula, sw ij.t is the remote control switch state of branch ij in the tth period. sw ij.t-1 is the remote control switch state of branch ij in the t-1 period. The switch state is expressed in binary, "1" means closed, and "0" means open.

[0123] The load end flexibility simulation model is as follows:

[0124]

[0125] In the formula, and are the original power values ​​of the controllable load at node i respectively.

[0126] and are respectively the active / reactive power of node i after the controllable load flexibility regulation.

[0127] It is the power regulation capability of the controllable load in each scheduling period. and are the maximum adjustment capacity that the controllable load of node i can increase and decrease compared with the original load at the current moment. Δt is the unit time interval.

[0128] The energy storage flexibility simulation model is as follows:

[0129]

[0130] In the formula, and are the charging and discharging powers of the conventional energy storage at node i respectively. and is the charge / discharge efficiency of conventional energy storage. It is the rated capacity of conventional energy storage. SOC i.t+1 and SOC i.t They are the state of charge at time t+1 and time t respectively. SOC i.max and SOC i.min are the maximum and minimum values ​​of the state of charge during period t respectively. and are the maximum charging and discharging power of the energy storage respectively. is the reactive power output by the energy storage at node i. Represents the inverter capacity of the energy storage installed at node i.

[0131] 2) With the goal of maximizing the new energy consumption of the active distribution network and minimizing the operating cost of the active distribution network, an evaluation model for the new energy consumption capacity of the active distribution network is established. The objective functions of the model are shown in formulas (14) and (15), namely:

[0132]

[0133] In the formula, F DG It is the amount of new energy consumed by the active distribution network throughout the day. Ω DG It is the node set corresponding to the new energy.

[0134]

[0135] In the formula, F EC is the operating cost of the active distribution network throughout the day. DG (t), C SW (t), C DR (t) and C ESS (t) are the operating costs of all new energy sources, remote switches, controllable loads and energy storage in period t. Grid (t) is the cost of electricity purchased by the distribution network from the upper power grid during period t. LOSS (t) is the network loss cost of the active distribution network during period t.

[0136] The operating costs of all new energy sources, remote switches, controllable loads and energy storage in period t are as follows:

[0137]

[0138] In the formula, and They are respectively the cost coefficients of new energy active and reactive power output and the penalty cost coefficient of active power reduction. sw is the cost coefficient of a single switch action. Ω DR and Ω ESS are the node sets corresponding to controllable load and energy storage respectively. DR is the unit dispatch cost of controllable load. It is the unit operating cost of charging and discharging active power of energy storage. is the reactive power output cost coefficient of energy storage.

[0139] The electricity purchase cost from the upper power grid and the network loss cost of the active distribution network in period t are as follows:

[0140]

[0141] In the formula, Ω Grid and Ω FThey are the set of all root nodes and the set of branches respectively. R is the power purchased by the root node i from the upper power grid during period t. ij is the resistance value between branches ij. is the electricity purchase price in period t. Loss is the network loss cost. ij.t is the current value of branch ij during period t.

[0142] The constraints of the active distribution network new energy absorption capacity assessment model include branch flow constraints, node power balance constraints, node voltage constraints, branch current constraints and upper grid interaction power constraints. The branch flow constraints are as follows:

[0143]

[0144] Where P ij.t and Q ij.t are the active power and reactive power of branch ij in period t respectively. ij and b ij are the conductance and susceptance of branch ij respectively. ij.t is the voltage phase angle difference between nodes i and j in period t. i.t , U j.t are the voltage amplitudes of nodes i and j in time period t respectively.

[0145] The node power balance constraint is as follows:

[0146]

[0147] In the formula, and are the active power and reactive power injected into the distribution network from the root node i by the upper power grid during period t. ii and b ii are the conductance and susceptance of the grounded branch connected to node i. Ω i The set of branches connected to node i.

[0148] The node voltage constraints are as follows:

[0149] U i.min <U i.t <U i.max (twenty four)

[0150] Where U i.min and U i.max are the lower and upper limits of the voltage at node i, respectively.

[0151] The branch current constraints are as follows:

[0152]

[0153] In the formula, I ij.t I is the current value of branch ij during period t. ij,max is the maximum allowable current carrying capacity of branch ij.

[0154] The upper-level grid interaction power constraint is as follows:

[0155]

[0156] Where P i grid and are the active power and reactive power limits exchanged between the upper power grid and the balancing node i respectively.

[0157] 3) Using the linearization method, the active distribution network new energy absorption capacity assessment model is converted into a multi-objective mixed integer linear optimization model, the steps include:

[0158] 3.1) Record the square variable of the voltage amplitude of node i in time period t The square variable of the voltage amplitude at node j during period t

[0159] Linearizing the branch power flow constraint (22) yields:

[0160]

[0161] Among them, the constant coefficient They are as follows

[0162] As shown:

[0163]

[0164]

[0165] In the formula, and are the initial values ​​of the voltage amplitudes at nodes i and j in period t respectively. and are the initial value difference of voltage amplitude and the initial value difference of voltage phase angle at nodes i and j in time period t respectively.

[0166] Constant coefficient a 1.t and a 2.t As shown below:

[0167]

[0168] The node voltage constraint (24) is converted into the following equation:

[0169]

[0170] 3.2) Introducing auxiliary variables and auxiliary variables The Big-M method is used to transform the node power balance constraint (23) into:

[0171]

[0172] Where M 1.t and M 2.t Is a positive number.

[0173] Introducing binary variables, formula (11) is equivalently linearized to:

[0174]

[0175] In the formula, and All are 0-1 variables.

[0176] Introducing binary variables, the Big-M method is used to linearize formula (5) to:

[0177]

[0178] In the formula, δ ij.t.1 and δ ij.t.2 All are 0-1 variables. 3.t Is a positive number.

[0179] By introducing auxiliary variables and binary variables and combining with the Big-M method, formula (16) and formula (18)-(19) are equivalently transformed into:

[0180]

[0181] In the formula, and They are all auxiliary variables. and and and All are 0-1 variables. 4.t , M 5.t and M 6.t All are positive numbers.

[0182] 3.3) Set the branch current amplitude square variable Right now:

[0183]

[0184] The branch current constraint is linearized and formula (39) is transformed as follows:

[0185]

[0186] Among them, the constant coefficient As shown below:

[0187]

[0188] Formula (21) and formula (25) are transformed as follows:

[0189]

[0190] Setting auxiliary variables The Big-M method is used to linearize equations (42)-(43) to obtain:

[0191]

[0192] Where M 7.t Is a positive number.

[0193] 3.4) Linearizing the complex power constraint, the steps include:

[0194] 3.4.1) The complementary angle of the power factor angle of the new energy source is and Set the number of segments to N DG .

[0195] 3.4.2) The arc range is evenly divided into N DG Connect each point in turn to get N DG The line segments are denoted as Among them, any line segment is represented as follows:

[0196]

[0197] 3.4.3) Establish the constraints of formula (47), namely:

[0198]

[0199] In the formula, N is the coefficient of the nth segment expression of the new energy constraint; DG The number of segments for new energy;

[0200] 3.5) Linearize the energy storage complex power and obtain:

[0201]

[0202]

[0203] Where n is the current segment number; is the coefficient of the nth piecewise line segment expression for the energy storage complex power constraint; and The number of segments for linearizing the complex power constraints of new energy and energy storage; It represents the linearization segmentation of complex power constraints of energy storage. The subscript charge represents the charging state, and discharge represents the discharging state.

[0204] 4) Solving the multi-objective mixed integer linear optimization model to obtain the new energy consumption capacity of the active distribution network, the steps include:

[0205] 4.1) Solve the multi-objective mixed integer linear optimization model to obtain the maximum renewable energy absorption capacity of the active distribution network and minimal operating costs

[0206] 4.2) Solve the multi-objective mixed integer linear optimization model to obtain the minimum operating cost The corresponding maximum renewable energy consumption capacity of the active distribution network

[0207] 4.3) The interval Defined as the range of Pareto optimal solution set, .

[0208] 4.4) Set the step size z to maximize the new energy consumption capacity F of the active distribution network DG The range of Perform equal division to serve as the outer traversal indicator.

[0209] 4.5) Take the outer layer index as a constraint, and optimize the inner layer to solve the inner layer objective function F corresponding to the outer layer index constraint EC The optimal value, after traversing the outer indicator constraints, can obtain z groups of Pareto optimal solutions.

[0210] 4.6) Use formula (51) to evaluate the membership function value μ of each objective function of each Pareto optimal solution i , and according to formula (52), the Pareto optimal solution with the largest standardized satisfaction value μ is selected as the optimal compromise solution.

[0211]

[0212] In the formula, f i is the value of the i-th objective function. i.max and f i.min are the upper and lower limits of the objective function respectively. M is the number of objective functions.

[0213] Embodiment 2:

[0214] See also Figures 1 to 3 , a method for evaluating the new energy consumption capacity of an active distribution network based on the flexibility of source, grid, load and storage, comprising the following steps:

[0215] 1) Simulate the flexibility of active distribution network sources, grids, loads and storage. The main steps are as follows:

[0216] 1.1) Establish flexibility at the power supply end: Considering the problems of wind and solar power abandonment during the operation of new energy, the actual active power consumed by new energy is:

[0217]

[0218] In the formula, is the active power reduction of the new energy connected to the grid at node i; is the theoretically available active power of the renewable energy connected to the grid at node i; is the active power that can be actually absorbed by the renewable energy connected to the grid at node i, t is the time period identifier (t=1,2,…,T), T

[0219] The total number of time periods.

[0220] When performing flexible reactive power control, new energy inverters need to meet the limitations of inverter capacity and power factor. The relevant constraints are:

[0221]

[0222] In the formula, is the reactive power of the renewable energy connected to the grid at node i; is the power factor of the new energy source connected to the grid at node i; and They are the allowable variation range of power factor respectively; is the capacity of the new energy inverter connected to the grid at node i.

[0223] 1.2) Establish network flexibility: Too frequent switching actions during distribution network reconstruction will affect switch stability. The number of switch actions needs to be limited during the dynamic reconstruction of the distribution network. Therefore:

[0224]

[0225] In the formula, Ω B is a set of branches with remote control switches in the network; The maximum number of operations allowed for the remote control switch of branch ij in a whole day; Δsw is the maximum number of operations allowed for all remote switches in the system throughout the day; ij.t It represents the number of actions of the remote control switch in the tth time period, which can be expressed as:

[0226] Δsw ij.t=|sw ij.t -sw ij.t-1 | (5)

[0227] In the formula, sw ij.t is the remote control switch state of branch ij in period t; sw ij.t-1 is the remote control switch state of branch ij in the t-1th period; the switch state is expressed in binary, "1" means closed and "0" means open.

[0228] In addition, the requirements for the connected radial shape of the distribution network operation should be met before and after the reconstruction. The connected radial shape constraints for the reconstruction are established as follows:

[0229]

[0230] In the formula, N and N s are the total number of nodes and substations in the network respectively; M l is the number of branches in the lth power supply loop, l =, 2,…, L, L is the total number of power supply loops in the network; sw ij.l.t is the state of the branch ij in the lth power supply loop; Ω B.l It is the branch set corresponding to the lth power supply loop.

[0231] 1.3) Establish load-side flexibility: Model the flexibility of controllable loads as:

[0232]

[0233] In the formula, and are the original power values ​​of the controllable load of node i respectively; and are respectively the active / reactive power of node i after the controllable load flexibility regulation; It is the power regulation capability of the controllable load in each dispatching period; and are the maximum adjustment capabilities that the controllable load of node i can increase and decrease compared to the original load at the current moment;

[0234] In addition, the load side has the ability to flexibly adjust power. When the controllable load is flexibly optimized and scheduled, it is necessary to ensure the balance of overall power consumption within a period of time:

[0235]

[0236] Where Δt is the unit time interval.

[0237] 1.4) Establishing flexibility in energy storage;

[0238] The energy that conventional energy storage can obtain at time period t is called the state of charge, which can be expressed as follows:

[0239]

[0240] In the formula, and are the charging and discharging power of conventional energy storage at node i respectively; and is the charge / discharge efficiency of conventional energy storage; It is the rated capacity of conventional energy storage.

[0241] In order to protect the service life of conventional battery energy storage, the state of charge of conventional energy storage should also be within the minimum allowable SOC i.min and maximum SOC i.max In addition, considering that the final state of charge of conventional energy storage on the current day is its initial state of charge for optimal scheduling on the next day, in order to optimize the scheduling of conventional energy storage on the second day, within a daily scheduling cycle, conventional energy storage must also meet the state of charge balance constraint, which is:

[0242]

[0243] During operation, the charging and discharging states of energy storage cannot occur at the same time, and when inverters are used to optimize reactive power scheduling, they are limited by the inverter capacity and need to meet the following constraints:

[0244]

[0245] In the formula, and are the maximum charging and discharging power of the energy storage respectively; is the reactive power output by the energy storage at node i; Represents the inverter capacity of the energy storage installed at node i.

[0246] 2) With the goal of maximizing the new energy consumption of the active distribution network and minimizing the operating cost of the active distribution network, an objective function for the consumption capacity evaluation is established.

[0247] The objective function mainly includes maximizing the new energy consumption of the active distribution network and minimizing the operating cost of the active distribution network.

[0248] 2.1) Objective function 1: Active distribution network new energy consumption

[0249] The objective function is to maximize the amount of renewable energy consumed during all periods of the day, that is:

[0250]

[0251] In the formula, FDG is the amount of new energy consumed by the active distribution network throughout the day; Ω DG is the node set corresponding to the new energy; in this paper, Δt=1h, T=24.

[0252] 2.2) Objective function 2: Active distribution network operation cost

[0253]

[0254] In the formula, F EC is the operating cost of the active distribution network throughout the day; C DG (t), C SW (t), C DR (t) and C ESS (t) are the operating costs of all new energy sources, remote switches, controllable loads and energy storage in period t; C Grid (t) is the electricity purchase cost of the distribution network from the upper power grid during period t; C LOSS (t) is the network loss cost of the active distribution network during period t. The specific expressions of each item are as follows:

[0255] The operating cost of source-grid-load-storage flexibility resources in the active distribution network can be expressed as:

[0256]

[0257] In the formula, and They are respectively the cost coefficients of new energy active and reactive power output and the penalty cost coefficient of active power reduction. sw is the cost coefficient of a single switch action. Ω DR and Ω ESS are the node sets corresponding to controllable load and energy storage respectively; DR is the unit dispatch cost of controllable load; The unit operating cost of charging and discharging active power for energy storage; is the reactive power output cost coefficient of energy storage.

[0258] The power purchase cost and network loss cost of the active distribution network can be expressed as:

[0259]

[0260] In the formula, Ω Grid and Ω F are the set of all root nodes and the set of branches respectively; is the power purchased by the root node i from the upper power grid during period t; R ij is the resistance value between branches ij; is the electricity purchase price during period t; Loss The network loss cost.

[0261] 3) Consider the constraints of the active distribution network such as power flow constraints and safe operation, and model them. The main steps are:

[0262] 3.1) Branch flow constraints

[0263]

[0264] Where P ij.t and Q ij.t are respectively the active power and reactive power of branch ij in period t; G ij and B ij are the conductance and susceptance of branch ij respectively; θ ij.t is the voltage phase angle difference between nodes i and j in period t; U i.t , U j.t are the voltage amplitudes of nodes i and j in time period t respectively.

[0265] 3.2) Node power balance constraints

[0266]

[0267] In the formula, and are respectively the active power and reactive power injected into the distribution network from the root node i by the upper power grid in period t; ii and b ii are the conductance and susceptance of the grounding branch connected to node i respectively; Ω i The set of branches connected to node i.

[0268] 3.3) Node voltage constraints

[0269] U i.min <U i.t <U i.max (twenty four)

[0270] Where U i.min and U i.max are the lower and upper limits of the voltage at node i, respectively.

[0271] 3.4) Branch current constraints

[0272]

[0273] In the formula, I ij.t is the current value of branch ij during period t; I ij,max is the maximum allowable current carrying capacity of branch ij.

[0274] 3.5) Upper-level grid interaction power constraints

[0275]

[0276] Where P i grid and are the active power and reactive power limits exchanged between the upper power grid and the balancing node i respectively.

[0277] 4) A variety of linearization methods are used to transform the original model into an easy-to-solve multi-objective mixed integer linear optimization model.

[0278] The main steps of transforming a multi-objective mixed integer linear optimization model are as follows:

[0279] 4.1) Linearization of power flow constraints

[0280] The high nonlinearity of the traditional power flow model increases the difficulty of solving the model, while the low nonlinear power flow model based on the hot start environment has high accuracy and efficient calculation. Therefore, this paper adopts this method to linearize the branch power flow constraints.

[0281] Define the node voltage magnitude square variable and make and Formula (22) can be transformed into:

[0282]

[0283] In the formula, are all constant coefficients, which can be obtained through the initial values ​​of node voltage amplitude and phase angle under hot start condition, and are continuously updated iteratively during the solution process. The specific expressions are as follows:

[0284]

[0285] In the formula, and are the initial values ​​of the voltage amplitudes of nodes i and j in period t respectively; and are the initial value difference of voltage amplitude and voltage phase angle at nodes i and j in time period t respectively; a 1.t and a 2.t is a constant coefficient, and its calculation expression is as follows:

[0286]

[0287] At this time, the node voltage constraint formula (24) also becomes:

[0288]

[0289] 4.2) Introducing variables and equivalent model transformation: Due to the existence of product terms of binary variables and continuous variables, product terms of continuous variables and continuous variables, and taking absolute value functions, equation (23) is also a nonlinear expression. Therefore, the auxiliary variable method is introduced to linearize it. and And using the Big-M method, equation (23) can be equivalently transformed into:

[0290]

[0291] Where M 1.t and M 2.t are all large enough positive numbers.

[0292] By introducing binary variables, equation (11) can be equivalently linearized as follows:

[0293]

[0294] In the formula, and All are 0-1 variables.

[0295] Introducing binary variables, the Big-M method is used to equivalently process the absolute value function in equation (5), and the following form is obtained:

[0296]

[0297] In the formula, δ ij.t.1 and δ ij.t.2 All are 0-1 variables; M 3.t is a sufficiently large positive number.

[0298] By introducing auxiliary variables and binary variables and combining with the Big-M method, equation (16) and equations (18)-(19) can be equivalently transformed into:

[0299]

[0300] In the formula, and are auxiliary variables, and and and All are 0-1 variables; M 4.t , M 5.t and M 6.t are all large enough positive numbers.

[0301] 4.3) Linearization of branch current constraints

[0302] Define the branch current amplitude square variable make It can be further expressed as:

[0303]

[0304] At this time, a linearization method based on hot start is used to transform equation (39) into equation (40).

[0305]

[0306] In the formula, They are all constant coefficients, and their specific calculation expressions are as follows:

[0307]

[0308] At this time, equations (21) and (25) also become:

[0309]

[0310] Since the binary variable sw ij.t With continuous variables The product term of leads to the fact that equations (42)-(43) are still nonlinear constraints, and auxiliary variables can be introduced The Big-M method is used to convert the equivalent into:

[0311]

[0312] Where M 7.t is a sufficiently large positive number.

[0313] 4.4) Linearization of complex power constraints: Complex power constraints present nonlinear characteristics due to the existence of quadratic terms. Here, the idea of ​​piecewise linear approximation is adopted to approximate the second-order function to a first-order linear form. Taking the complex power constraint of new energy as an example, the piecewise linearization steps are as follows:

[0314] Step 1): Select parameters and N DG ,in, and They are the complementary angles of the new energy power factor angle;

[0315] Step 2): The arc range is evenly divided into N DG Connect each point in turn to get N DG Line segment, that is Any line segment can be represented as:

[0316]

[0317] Step 3): Based on the theory of analytic geometry, we can use NDG The linear constraint combination corresponding to the line segments can be approximated by constraint formula (47):

[0318]

[0319] The piecewise linearization method of the energy storage complex power constraint is the same as above, and the energy storage complex power is expressed as:

[0320]

[0321] Where n is the current segment number; N is the coefficient of the nth segment expression of the new energy and energy storage complex power constraint; DG , and They are the number of linearized segments for complex power constraints of new energy and energy storage, respectively.

[0322] 5) Design a double-layer nested optimization structure to efficiently find the Pareto optimal solution set, and use the fuzzy membership function to select the optimal compromise solution.

[0323] The main steps to find the Pareto optimal solution set and select the optimal compromise solution are as follows:

[0324] 5.1) In the model, only the objective function F is considered DG and F EC , optimize and solve the maximum renewable energy absorption capacity of active distribution network and minimal operating costs

[0325] 5.2) Only consider the objective function F in the model EC , and Add to the model constraints, optimize and solve AND at the minimum running cost The corresponding maximum new energy consumption capacity

[0326] 5.3) The interval Defined as the range of Pareto optimal solution sets, this is because if It is inevitable that That is, an invalid dominant solution is obtained.

[0327] 5.4) Set the step size z and transform the objective function F DG The range of Perform equal division to serve as the outer traversal indicator.

[0328] 5.5) Take the outer layer index as a constraint, and optimize the inner layer to solve the inner layer objective function F corresponding to the outer layer index constraint EC The optimal value, after traversing the outer indicator constraints, can obtain z groups of Pareto optimal solutions.

[0329] 5.6) Use equation (51) to evaluate the membership function value μ of each objective function of each solution i , and according to formula (52), the solution with the largest standardized satisfaction value μ is selected as the optimal compromise solution.

[0330]

[0331] In the formula, f i is the value of the i-th objective function; f i.max and f i.min are the upper and lower limits of the objective function respectively; M is the number of objective functions.

[0332] Embodiment 3:

[0333] An experiment to verify the evaluation method of the new energy absorption capacity of active distribution network based on source-grid-load-storage flexibility is carried out. The main steps are as follows:

[0334] 1) Obtain the source, grid, load and storage data of the active distribution network and simulate the source, grid, load and storage flexibility of the active distribution network. Figure 2 Taking the improved IEEE-33 node distribution system shown in the figure as an example, its voltage level is 12.66kV, the voltage allowable range of all nodes is 0.95 (pu) ~ 1.05 (pu), and the maximum current of the branch is 300A. The distribution system interacts with the upper power grid through node 1. The upper and lower limits of active power at the gateway are ±6MW, and the upper and lower limits of reactive power are ±3Mvar. The access location and capacity of new energy are shown in Table 1. The operating range of the power factor of new energy is -0.8 ~ 0.8, and the capacity of the new energy inverter is 1.1 times the installed capacity of new energy. The whole day is divided into 24 periods, Δt = 1h. The output and load information of new energy are based on the measured data of a typical day. The basic configuration parameters of energy storage are shown in Table 2. The operating range of the power factor of energy storage is -0.75 ~ 0.75, and the capacity of the energy storage inverter is 1.5 times the maximum output power of energy storage. The technical parameters of flexible interaction between source, grid, load and storage are shown in Table 3. The economic parameters of the model are shown in Table 4.

[0335] Table 1 Basic configuration parameters of new energy

[0336] Access Node 4 6 9 13 20 27 Capacity / kW 1800 1600 1800 1600 1600 1800

[0337] Table 2 Basic configuration parameters of energy storage

[0338]

[0339] Table 3 Technical parameters for flexible interaction between source, grid, load and storage

[0340]

[0341] Table 4 Model economic parameters

[0342]

[0343] 2) With the goal of maximizing the new energy consumption of the active distribution network and minimizing the operating cost of the active distribution network, an objective function for evaluating the new energy consumption capacity is established.

[0344] 3) Consider the constraints such as power flow constraints and safe operation of the active distribution network and model them.

[0345] 4) A variety of linearization methods are used to transform the original model into an easy-to-solve multi-objective mixed integer linear optimization model.

[0346] 5) Design a double-layer nested optimization structure to efficiently find the Pareto optimal solution set, and use the fuzzy membership function to select the optimal compromise solution.

[0347] According to the double-layer nested structure, the new energy consumption capacity of the active distribution network is evaluated, and the various operating costs are calculated as shown in Table 5-6. After calculation, the typical daily new energy consumption of the system is 116.91MW·h, and the total operating cost is 25,580 yuan. Among them, the cost of abandoned power is 6,400 yuan, the cost of flexible interactivity is 3,300 yuan, the network loss cost is 4,570 yuan, and the cost of purchasing electricity is 17,070 yuan.

[0348] Table 5 New energy consumption and operating costs

[0349] Consumption capacity / (MW·h) Operating cost / 10,000 yuan 116.91 2.558

[0350] Table 6 Operating costs of active distribution network (10,000 yuan)

[0351] Cost of curtailment Flexible Interactivity Cost Network loss cost Power purchase cost 0.064 0.330 0.457 1.707

Claims

1. A method for evaluating the new energy consumption capacity of an active distribution network based on the flexibility of source, grid, load and storage, characterized in that: The following steps are involved: 1) Establish a simulation model for the source, grid, load and storage flexibility of the active distribution network; 2) To maximize the new energy consumption of the active distribution network and minimize the operating cost of the active distribution network, an evaluation model for the new energy consumption capacity of the active distribution network is established; 3) Using the linearization method, the new energy absorption capacity assessment model of the active distribution network is converted into a multi-objective mixed integer linear optimization model; 4) Solve the multi-objective mixed integer linear optimization model to obtain the new energy absorption capacity of the active distribution network; The objective functions of the evaluation model for the new energy consumption capacity of the active distribution network are shown in formula (1) and formula (2), namely: In the formula, F DG is the amount of new energy consumed by the active distribution network throughout the day; Ω DG is the node set corresponding to the new energy; is the active power that can be actually absorbed by the renewable energy connected to the grid at node i; t is the time period identifier; t=1,2,…,T; T is the total number of time periods; In the formula, F EC is the operating cost of the active distribution network throughout the day; C DG (t), C SW (t), C DR (t) and C ESS (t) are the operating costs of all new energy sources, remote switches, controllable loads and energy storage in period t; C Grid (t) is the electricity purchase cost of the distribution network from the upper power grid during period t; C LOSS (t) is the network loss cost of the active distribution network during period t; The operating costs of all new energy sources, remote switches, controllable loads and energy storage in period t are as follows: In the formula, and are the cost coefficients of new energy active and reactive power output and the penalty cost coefficient of active power reduction respectively; sw is the cost coefficient of a single switch action; Ω DR and Ω ESS are the node sets corresponding to controllable load and energy storage respectively; DR is the unit dispatch cost of controllable load; The unit operating cost of charging and discharging active power for energy storage; is the reactive power output cost coefficient of energy storage; is the active power reduction of the new energy connected to the grid at node i; is the reactive power of the renewable energy connected to the grid at node i; Δsw ij.t Indicates the number of actions of the remote control switch in the tth period; Ω B is a set of branches with remote control switches in the network; is the original power value of the controllable load at node i; is the active power after the controllable load flexibility regulation of node i; and are the charging and discharging power of conventional energy storage at node i respectively; is the reactive power output by the energy storage at node i; The electricity purchase cost from the upper power grid and the network loss cost of the active distribution network in period t are as follows: In the formula, Ω Grid and Ω F are the set of all root nodes and the set of branches respectively; is the power purchased by the root node i from the upper power grid during period t; R ij is the resistance value between branches ij; is the electricity purchase price during period t; Loss I is the network loss cost; ij.t is the current value of branch ij during period t; sw ij.t is the remote control switch state of branch ij in the tth period.

2. According to claim 1, a method for evaluating the new energy consumption capacity of an active distribution network based on source-grid-load-storage flexibility is characterized in that: The active distribution network source-grid-load-storage flexibility simulation model includes a power supply end flexibility simulation model, a network end flexibility simulation model, a load end flexibility simulation model and an energy storage end flexibility simulation model; The power supply flexibility simulation model is as follows: In the formula, is the active power reduction of the new energy connected to the grid at node i; is the theoretically available active power of the renewable energy connected to the grid at node i; is the active power that can be actually absorbed by the renewable energy connected to the grid at node i; t is the time period identifier; t=1,2,…,T; T is the total number of time periods; is the reactive power of the renewable energy connected to the grid at node i; is the power factor of the new energy source connected to the grid at node i; and They are the allowable variation range of power factor respectively; is the capacity of the new energy inverter connected to the grid at node i; The network end flexibility simulation model is as follows: In the formula, Ω B is a set of branches with remote control switches in the network; The maximum number of operations allowed for the remote control switch of branch ij in a whole day; Δsw is the maximum number of operations allowed for all remote switches in the system throughout the day; ij.t represents the number of actions of the remote control switch in the tth period; N and N s are the total number of nodes and substations in the network, respectively; M l is the number of branches in the lth power supply loop; l=,2,…,L; L is the total number of power supply loops in the network; sw ij.l.t is the state of the branch ij in the lth power supply loop; Ω B.l is the branch set corresponding to the lth power supply loop; Among them, the number of actions of the remote control switch in the tth period Δsw ij.t As shown below: Δsw ij.t =|sw ij.t -sw ij.t-1 | (14) In the formula, sw ij.t is the remote control switch state of branch ij in period t; sw ij.t-1 is the remote control switch state of branch ij in the t-1th period; the switch state is represented by binary, "1" means closed, and "0" means open; The load end flexibility simulation model is as follows: In the formula, and are the original power values ​​of the controllable load of node i respectively; and are respectively the active / reactive power of node i after the controllable load flexibility regulation; It is the power regulation capability of the controllable load in each dispatching period; and are the maximum adjustment capacity of the controllable load of node i compared to the original load at the current moment; Δt is the unit time interval; The energy storage flexibility simulation model is as follows: In the formula, and are the charging and discharging power of conventional energy storage at node i respectively; and is the charge / discharge efficiency of conventional energy storage; is the rated capacity of conventional energy storage; SOC i.t+1 and SOC i.t are the state of charge at time t+1 and time t respectively; SOC i.max and SOC i.min are the maximum and minimum values ​​of the state of charge in period t respectively; and are the maximum charging and discharging power of the energy storage respectively; is the reactive power output by the energy storage at node i; Represents the inverter capacity of the energy storage installed at node i.

3. The method for evaluating the new energy consumption capacity of an active distribution network based on the flexibility of source, grid, load and storage according to claim 2 is characterized by: The constraints of the active distribution network new energy absorption capacity assessment model include branch flow constraints, node power balance constraints, node voltage constraints, branch current constraints and upper grid interaction power constraints; The branch power flow constraints are as follows: Where P ij.t and Q ij.t are the active power and reactive power of branch ij in period t respectively; g ij and b ij are the conductance and susceptance of branch ij respectively; θ ij.t is the voltage phase angle difference between nodes i and j in period t; U i.t , U j.t are the voltage amplitudes of nodes i and j in time period t respectively; The node power balance constraint is as follows: In the formula, and are respectively the active power and reactive power injected into the distribution network from the root node i by the upper power grid in period t; ii and b ii are the conductance and susceptance of the grounding branch connected to node i respectively; Ω i The set of branches connected to node i; The node voltage constraints are as follows: IN i.min <In i.t <In i.max (24) Where U i.min and U i.max are the lower and upper limits of the voltage at node i, respectively; The branch current constraints are as follows: In the formula, I ij.t is the current value of branch ij during period t; I ij,max is the maximum allowable current carrying capacity of branch ij; The upper-level grid interaction power constraint is as follows: Where P i grid and are the active power and reactive power limits exchanged between the upper power grid and the balancing node i respectively.

4. The method for evaluating the new energy consumption capacity of an active distribution network based on the flexibility of source, grid, load and storage according to claim 3 is characterized in that: The steps of converting the active distribution network new energy absorption capacity assessment model into a multi-objective mixed integer linear optimization model include: 1) Record the square variable of the voltage amplitude of node i in time period t The square variable of the voltage amplitude at node j during period t Linearizing the branch power flow constraint (22) yields: Among them, the constant coefficient They are as follows: In the formula, and are the initial values ​​of the voltage amplitudes of nodes i and j in period t respectively; and are the initial value difference of voltage amplitude and the initial value difference of voltage phase angle of nodes i and j in time period t respectively; Constant coefficient a 1.t and a 2.t As shown below: The node voltage constraint (24) is converted into the following equation: 2) Introducing auxiliary variables and auxiliary variables The Big-M method is used to transform the node power balance constraint (23) into: Where M 1.t and M 2.t is a positive number; Introducing binary variables, formula (19) is equivalently linearized to: In the formula, and All are 0-1 variables; Introducing binary variables, the Big-M method is used to linearize formula (13) to: In the formula, δ ij.t.1 and δ ij.t.2 All are 0-1 variables; M 3.t is a positive number; By introducing auxiliary variables and binary variables and combining with the Big-M method, formula (3) and formula (5)-(6) are equivalently transformed into: In the formula, and All are auxiliary variables; and and All are 0-1 variables; M 4.t , M 5.t and M 6.t All are positive numbers; 3) Set the branch current amplitude square variable Right now: The branch current constraint is linearized and formula (39) is transformed as follows: Among them, the constant coefficient As shown below: Formula (8) and formula (25) are transformed as follows: Setting auxiliary variables The Big-M method is used to linearize equations (42)-(43) to obtain: Where M 7.t is a positive number; 4) Linearizing the complex power constraint, the steps include: 4.1) The complementary angle of the power factor angle of the new energy source is and Set the number of segments to N DG ; 4.2) The arc range is evenly divided into N DG Connect each point in turn to get N DG The line segments are denoted as Among them, any line segment is represented as follows: 4.3) Establish the constraints of formula (47), namely: In the formula, N is the coefficient of the nth segment expression of the new energy constraint; DG The number of segments for new energy; 5) Linearize the energy storage complex power and obtain: Where n is the current segment number; is the coefficient of the nth piecewise line segment expression for the energy storage complex power constraint; and The number of segments for linearizing the complex power constraints of new energy and energy storage; It represents the linearization segmentation of complex power constraints of energy storage. The subscript charge represents the charging state, and discharge represents the discharging state.

5. According to claim 1, a method for evaluating the new energy consumption capacity of an active distribution network based on the flexibility of source, grid, load and storage, characterized in that: The steps to solve the multi-objective mixed integer linear optimization model include: 1) Solve the multi-objective mixed integer linear optimization model to obtain the maximum renewable energy absorption capacity of the active distribution network and minimal operating costs 2) Solve the multi-objective mixed integer linear optimization model to obtain the minimum operating cost The corresponding maximum renewable energy consumption capacity of the active distribution network 3) The interval Defined as the range of Pareto optimal solution set; 4) Set the step size z to maximize the new energy consumption capacity F of the active distribution network DG The range of Perform equal division to serve as the outer traversal indicator; 5) Take the outer layer index as a constraint, and optimize the inner layer to solve the inner layer objective function F corresponding to the outer layer index constraint EC The optimal value, after traversing the outer indicator constraints, can obtain z groups of Pareto optimal solutions; 6) Use formula (51) to evaluate the membership function value μ of each objective function of each Pareto optimal solution i , and according to formula (52), the Pareto optimal solution with the largest standardized satisfaction value μ is selected as the optimal compromise solution; In the formula, f i is the value of the i-th objective function; f i.max and f i.min are the upper and lower limits of the objective function respectively; M is the number of objective functions.

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