Micro-grid energy storage capacity configuration method through three-stage distribution robust optimization

Abstract

The invention discloses a micro-grid energy storage capacity configuration method through three-stage distributed robust optimization, and relates to the technical field of micro-grid control. According to the method, the configuration of the energy storage capacity of the micro-grid is divided into three stages, namely an energy storage capacity configuration stage, a pre-scheduling stage and a re-scheduling stage, a micro-grid energy storage capacity configuration model for three-stage micro-grid distribution robust optimization is constructed according to the three stages, and the energy storage investment cost, the energy storage system scheduling cost and the renewable energy consumption probability are considered in the model. Pre-dispatching output of the thermal power generating unit and various costs of the thermal power generating unit are reduced; constructing constraint conditions of optimization targets of three stages in the micro-grid energy storage capacity configuration model, frequency change constraint conditions of the micro-grid and a rotation kinetic energy relational expression of the micro-grid; and solving the microgrid energy storage capacity configuration model through a GUROBI solver according to the microgrid frequency change constraint condition and the microgrid rotation kinetic energy relational expression, and configuring the microgrid energy storage capacity according to the solving result.

Description

Technical Field

[0001] The present invention relates to a microgrid control technology, and in particular to a microgrid energy storage capacity configuration method through three-stage distributed blue rod optimization. Background Art

[0002] As the proportion of renewable energy (such as photovoltaic and wind power) in the energy structure continues to increase, the inertia of the system continues to weaken. In addition, the output of renewable energy has characteristics such as intermittent and volatile, which brings great challenges to the system's power generation and load to maintain power balance. Microgrids are the main carriers of renewable energy. Energy storage systems can enhance the regulation capabilities of microgrids and enhance the inertia and primary frequency regulation capabilities of microgrids by adopting strategies such as droop control. However, the investment in energy storage systems accounts for a large proportion of microgrid planning and construction. Determining the appropriate energy storage capacity configuration can reduce the energy consumption of microgrids and improve the economic efficiency of microgrid operation. However, there is currently no effective method for configuring microgrid energy storage capacity. Summary of the invention

[0003] In view of the defects existing in the above-mentioned prior art, the technical problem to be solved by the present invention is to provide a microgrid energy storage capacity configuration method through three-stage distributed robust optimization, which can effectively achieve both renewable energy consumption and operational economy.

[0004] In order to solve the above technical problems, the present invention provides a microgrid energy storage capacity configuration method through three-stage distributed blue rod optimization, characterized in that the specific steps are as follows:

[0005] Step 101: Divide each day into multiple dispatching periods, and divide the configuration of the microgrid energy storage capacity into three stages, wherein the first stage is the energy storage capacity configuration stage, the second stage is the pre-dispatching stage, and the third stage is the re-dispatching stage;

[0006] A three-stage microgrid distributed robust optimization microgrid energy storage capacity configuration model is constructed. The specific model formula is:

[0007] min C I +C D -δa

[0008]

[0009]

[0010] In the formula, min is the minimum value of the post-order formula, C I is the energy storage investment cost of the microgrid, C D is the dispatching cost of the energy storage system of the microgrid, a is the probability of renewable energy consumption of the microgrid, and δ is the weight coefficient of renewable energy consumption of the microgrid;

[0011] In the formula, S is the set of energy storage devices in the microgrid, s is the energy storage device in S, C SP is the power cost of the energy storage system in the microgrid, C SS is the electricity cost of the energy storage system in the microgrid, is the maximum power configured for the energy storage device s, is the maximum capacity of the energy storage device s, T s is the service life of the energy storage system in the microgrid, M is the average daily maintenance cost of the energy storage system in the microgrid;

[0012] In the formula, T is the set of daily dispatch periods, t is the dispatch period in T, G is the set of thermal power units in the microgrid, g is the thermal power unit in G, P gt is the pre-dispatched output of thermal power unit g in period t, F g (P gt ) is the fuel cost of thermal power unit g in period t, is the no-load cost of thermal power unit g, x gt is the switch state value of thermal power unit g in period t, is the startup cost of thermal power unit g, u gt is the startup state value of thermal power unit g in period t, is the shutdown cost of thermal power unit g, v gt is the shutdown state value of thermal power unit g in period t, C b is the price of electricity purchased by the microgrid from the upstream grid, C s is the price at which the microgrid sells electricity to the upstream grid, is the power amount purchased by the microgrid from the upstream power grid during period t, is the power sold by the microgrid to the upstream grid during period t;

[0013] Step 102: Constructing constraints for the three-stage optimization objectives in the microgrid energy storage capacity configuration model;

[0014] 102.1) The constraints in the energy storage capacity configuration phase are:

[0015]

[0016] In the formula, is the maximum power allowed by the energy storage device s, The maximum amount of electricity allowed by the energy storage device s;

[0017] 102.2) The constraints in the pre-dispatch phase include: microgrid balance constraints, thermal power unit constraints, energy storage device operation constraints, and PCC interactive power constraints;

[0018] 102.2.1) The microgrid balance constraints are:

[0019]

[0020] Where K is the set of wind turbines in the microgrid, k is the wind turbine in K, is the wind turbine output of wind turbine k in period t, is the charging power of energy storage device s in period t, is the discharge power of the energy storage device s in time period t, N is the set of nodes in the microgrid, the nodes in the microgrid refer to the thermal power units and wind power units in the microgrid, n is the node in N, d nt is the load demand of node n at time t;

[0021] 102.2.2) Constraints for thermal power units include start-stop constraints for thermal power units, output constraints for thermal power units, and ramp rate constraints for thermal power units;

[0022] The start and stop constraints of thermal power units are:

[0023]

[0024]

[0025] In the formula, x gt-1 is the switch state value of thermal power unit g in period t-1, x gi is the switch state value of thermal power unit g in period i, UT g is the minimum start time of thermal power unit g, DT g is the minimum shutdown time of thermal power unit g;

[0026] The output constraints of thermal power units are:

[0027]

[0028] In the formula, is the lower limit of the output of thermal power unit g, is the output upper limit of thermal power unit g;

[0029] The climbing rate constraint of thermal power unit is:

[0030]

[0031] In the formula, is the downward climbing capacity per unit time of thermal power unit g in shutdown state, is the upward climbing capacity per unit time of thermal power unit g in the startup state, is the downward climbing capacity per unit time of thermal power unit g in operation state, is the upward climbing capacity per unit time of thermal power unit g in operation state, Δ d is the scheduling interval length, P gt-1 is the pre-dispatched output of thermal power unit g in period t-1;

[0032] 102.2.3) The operating constraints of the energy storage device are:

[0033]

[0034]

[0035]

[0036]

[0037] S0=S T

[0038] In the formula, S st is the charge capacity of energy storage device s in period t, S s(t+1) is the charge amount of the energy storage device s in the period t+1, is the maximum allowable capacity of the energy storage device s in time period t, β is the self-discharge rate of the energy storage device, η c is the charging efficiency of the energy storage device, η d is the discharge efficiency of the energy storage device, Δ ds is the charging and discharging time of the energy storage device s, S0 is the power of the energy storage device at the beginning of the scheduling cycle, S T is the amount of electricity of the energy storage device at the end of the dispatch cycle;

[0039] 102.2.4) PCC Interaction Power Constraints

[0040]

[0041]

[0042] Where P PCC,max is the maximum transmission power of the tie line between the microgrid and the upstream grid;

[0043] 102.3) The constraints of the rescheduling phase are:

[0044]

[0045] a0≤a≤1

[0046]

[0047] In the formula, inf is the lower bound of the post-order formula, and P is ξ t The probability distribution function oft is the deviation of the actual output of the wind turbine in the microgrid relative to its predicted output during period t, is the lower bound of the fluctuation range of the wind turbine in the microgrid during period t, is the upper limit of the fluctuation range of the wind turbine in the microgrid during period t, a0 is the lower limit of a, D is the fuzzy set of P, Ep is the mathematical expectation, Var is the variance, ξ kt is the deviation of the actual output of wind turbine k in the microgrid relative to its predicted output during period t, μ kt For kt The mathematical expectation of For kt The variance of kt It's about μ kt Unimodal means that the probability distribution function P is in μ kt It was non-decreasing before, and in μ kt It is non-increasing afterwards; μ kt is the deviation of the actual output of wind turbine k in period t from its predicted output;

[0048] Step 103: The frequency change constraint condition of the microgrid is constructed as follows:

[0049] Δf min ≤Δf≤Δf max

[0050]

[0051] Where Δf min is the lower limit of the frequency deviation of the microgrid, Δf max is the upper limit of the frequency deviation of the microgrid, R ocof is the frequency change rate of the microgrid, is the lower limit of the frequency change rate of the microgrid, is the upper limit of the frequency change rate of the microgrid;

[0052] Step 104: Construct the rotational kinetic energy relationship of the microgrid as follows:

[0053]

[0054] In the formula, H eq，t is the equivalent inertia constant of the node in the microgrid during period t, E sys is the total installed capacity of the microgrid, H g is the inertia constant of the thermal power unit g in the microgrid, H s is the inertia constant of the energy storage device s in the microgrid;

[0055] Step 105: According to the frequency change constraint of the microgrid and the rotational kinetic energy relationship of the microgrid, the microgrid energy storage capacity configuration model is solved by the GUROBI solver, and the microgrid energy storage capacity is configured according to the solved microgrid energy storage capacity configuration model formula.

[0056] The present invention provides a method for configuring the energy storage capacity of a microgrid through three-stage distributed robust optimization, which constructs a probability distribution fuzzy set of random variables for renewable energy processing based on moment information, and jointly optimizes the energy storage capacity configuration, day-ahead scheduling and the renewable energy absorption capacity of the microgrid. The model solution takes into account the frequency change of the microgrid and the rotational kinetic energy inertia of the microgrid, and can ensure that the frequency changes within a normal range, solve the problems of power fluctuation and frequency stability, and can solve the energy storage capacity configuration result under the worst probability distribution of renewable energy output, effectively realizing the microgrid energy storage capacity configuration that takes into account both renewable energy absorption and operational economy. DETAILED DESCRIPTION

[0057] The technical solution of the present invention is further described in detail below in conjunction with specific embodiments, but this embodiment is not intended to limit the present invention. All similar structures and similar variations of the present invention should be included in the protection scope of the present invention. The commas in the present invention represent the relationship of and, and the English letters in the present invention are case-sensitive.

[0058] An embodiment of the present invention provides a method for configuring energy storage capacity of a microgrid through three-stage distributed blue stick optimization, characterized in that the specific steps are as follows:

[0059] Step 101: Divide each day into multiple scheduling periods (in this embodiment, each day is divided into 24 scheduling periods), and divide the configuration of the microgrid energy storage capacity into three stages, wherein the first stage is the energy storage capacity configuration stage, the second stage is the pre-scheduling stage, and the third stage is the re-scheduling stage;

[0060] A three-stage microgrid distributed robust optimization microgrid energy storage capacity configuration model is constructed. The specific model formula is:

[0061] min C I +C D -δa

[0062]

[0063]

[0064] In the formula, min is the minimum value of the post-order formula, C I is the energy storage investment cost of the microgrid, C Dis the dispatching cost of the energy storage system of the microgrid, a is the probability of renewable energy consumption in the microgrid, δ is the weight coefficient of renewable energy consumption in the microgrid, and the value of δ represents the economic cost required for renewable energy consumption in the microgrid;

[0065] In the formula, S is the set of energy storage devices in the microgrid, s is the energy storage device in S, C SP is the power cost of the energy storage system in the microgrid, C SS is the electricity cost of the energy storage system in the microgrid, is the maximum power configured for the energy storage device s, is the maximum capacity of the energy storage device s, T s is the service life of the energy storage system in the microgrid, M is the average daily maintenance cost of the energy storage system in the microgrid;

[0066] In the formula, T is the set of daily dispatch periods, t is the dispatch period in T, G is the set of thermal power units in the microgrid, g is the thermal power unit in G, P gt is the pre-dispatched output of thermal power unit g in period t, F g (P gt ) is the fuel cost of thermal power unit g in period t, is the no-load cost of thermal power unit g, x gt is the switch state value of thermal power unit g in period t (when thermal power unit g is in the on state, its value is 1, and when thermal power unit g is in the off state, its value is 0), is the startup cost of thermal power unit g (the startup cost of a thermal power unit refers to the cost of a thermal power unit from startup to reaching a stable operating state), u gt is the startup state value of thermal power unit g in period t (when thermal power unit g is in startup state, its value is 1, and when thermal power unit g is in non-startup state, its value is 0. The startup state of thermal power unit means that the thermal power unit has been started but has not reached the state of stable operation. The non-startup state of thermal power unit means that the thermal power unit is in stable operation or shutdown state). is the shutdown cost of thermal power unit g, v gt is the shutdown state value of thermal power unit g in period t (when thermal power unit g is in shutdown state, its value is 1, and when thermal power unit g is in non-shutdown state, its value is 0. The shutdown state of thermal power unit means that the thermal power unit is shut down and loses power), C b is the price of electricity purchased by the microgrid from the upstream grid, C s is the price at which the microgrid sells electricity to the upstream grid, is the power amount purchased by the microgrid from the upstream power grid during period t, is the power sold by the microgrid to the upstream grid during period t;

[0067] Step 102: Constructing constraints for the three-stage optimization objectives in the microgrid energy storage capacity configuration model;

[0068] 102.1) The constraints for the energy storage capacity configuration phase (first phase) are:

[0069]

[0070] In the formula, is the maximum power allowed by the energy storage device s, The maximum amount of electricity allowed by the energy storage device s;

[0071] 102.2) The constraints in the pre-dispatch phase (second phase) include: microgrid balance constraints, thermal power unit constraints, energy storage device operation constraints, and PCC interactive power constraints (PCC refers to the interconnection line between the microgrid and the upstream grid);

[0072] 102.2.1) The microgrid balance constraints are:

[0073]

[0074] Where K is the set of wind turbines in the microgrid, k is the wind turbine in K, is the wind turbine output of wind turbine k in period t, is the charging power of energy storage device s in period t, is the discharge power of the energy storage device s in time period t, N is the set of nodes in the microgrid, and the nodes in the microgrid refer to the thermal power units and wind power units in the microgrid. Each thermal power unit is a node, each wind power unit is a node, n is the node in N, and d nt is the load demand of node n at time t;

[0075] 102.2.2) Constraints for thermal power units include start-stop constraints for thermal power units, output constraints for thermal power units, and ramp rate constraints for thermal power units;

[0076] The start and stop constraints of thermal power units are:

[0077]

[0078]

[0079] In the formula, x gt-1 is the switch state value of thermal power unit g in period t-1, x gi is the switch state value of thermal power unit g in period i, UT g is the minimum start-up time of thermal power unit g (the start-up time refers to the length of time the thermal power unit is in the start-up state), DT g is the minimum shutdown time of thermal power unit g;

[0080] The output constraints of thermal power units are:

[0081]

[0082] In the formula, is the lower limit of the output of thermal power unit g, is the output upper limit of thermal power unit g;

[0083] The climbing rate constraint of thermal power unit is:

[0084]

[0085] In the formula, is the downward climbing capacity per unit time of thermal power unit g in shutdown state, is the upward climbing capacity per unit time of thermal power unit g in the startup state, is the downward climbing capacity per unit time of thermal power unit g in operation state, is the upward climbing capacity per unit time of thermal power unit g in operation state, Δ d is the scheduling interval length, P gt-1 is the pre-dispatched output of thermal power unit g in period t-1;

[0086] 102.2.3) The operating constraints of the energy storage device are:

[0087]

[0088]

[0089]

[0090]

[0091] S0=S T

[0092] In the formula, S st is the charge capacity of energy storage device s in period t, S s(t+1) is the charge amount of the energy storage device s in the period t+1, is the maximum allowable capacity of the energy storage device s in time period t, β is the self-discharge rate of the energy storage device, η c is the charging efficiency of the energy storage device, η d is the discharge efficiency of the energy storage device, Δ ds is the charging and discharging time of the energy storage device s, S0 is the power of the energy storage device at the beginning of the scheduling cycle, S T is the amount of electricity of the energy storage device at the end of the dispatch cycle;

[0093] 102.2.4) PCC Interaction Power Constraints

[0094]

[0095]

[0096] Where P PCC,max is the maximum transmission power of the tie line between the microgrid and the upstream grid;

[0097] 102.3) The redispatch phase (the third phase) is used to evaluate the system's ability to absorb renewable energy. The constraints of the redispatch phase are:

[0098]

[0099] a0≤a≤1

[0100]

[0101] In the formula, inf is the lower bound of the post-order formula, and P is ξ t The probability distribution function of t is the deviation of the actual output of the wind turbine in the microgrid relative to its predicted output during period t, is the lower bound of the fluctuation range of the wind turbine in the microgrid during period t, is the upper limit of the fluctuation range of the wind turbine in the microgrid during period t, a0 is the lower limit of a, D is the fuzzy set of P, Ep is the mathematical expectation, Var is the variance, ξ kt is the deviation of the actual output of wind turbine k in the microgrid relative to its predicted output during period t, μ kt For kt The mathematical expectation of For kt The variance of kt It's about μ kt Unimodal means that the probability distribution function P is in μ kt It was non-decreasing before, and in μ kt It is non-increasing afterwards; μ kt is the deviation of the actual output of wind turbine k in period t from its predicted output;

[0102] Step 103: The frequency change constraint condition of the microgrid is constructed as follows:

[0103] Δf min ≤Δf≤Δf max

[0104]

[0105] Where Δf minis the lower limit of the frequency deviation of the microgrid, Δf max is the upper limit of the frequency deviation of the microgrid, R ocof is the frequency change rate of the microgrid, R ocof is the lower limit of the frequency change rate of the microgrid, is the upper limit of the frequency change rate of the microgrid; R in this embodiment ocof The value is -0.8Hz, The value is 0.8Hz;

[0106] Step 104: Construct the rotational kinetic energy relationship of the microgrid as follows:

[0107]

[0108] In the formula, H eq，t is the equivalent inertia constant of the node (thermal power unit, wind power unit) in the microgrid during period t, E sys is the total installed capacity of the microgrid, H g is the inertia constant of the thermal power unit g in the microgrid, H s is the inertia constant of the energy storage device s in the microgrid;

[0109] Step 105: According to the frequency change constraint of the microgrid (constructed by step 103) and the rotational kinetic energy relationship of the microgrid (constructed by step 104), the microgrid energy storage capacity configuration model is solved by the GUROBI solver, and the microgrid energy storage capacity is configured according to the solved microgrid energy storage capacity configuration model formula;

[0110] The GUROBI solver is a prior art and is described in many patent documents, such as the Chinese patent document with publication number CN114882715B.

[0111] In order to obtain the optimal solution of the microgrid energy storage capacity configuration model formula, the embodiment of the present invention adopts three methods to solve the microgrid energy storage capacity configuration model, and the final results are shown in Table 1;

[0112] Method A solves the microgrid energy storage capacity configuration model by using the GUROBI solver without considering the microgrid frequency change constraint and the microgrid rotation kinetic energy relationship;

[0113] Method B is to solve the microgrid energy storage capacity configuration model by using the GUROBI solver while considering the microgrid frequency change constraint condition but not the microgrid rotation kinetic energy relation;

[0114] Method C is the method adopted by the embodiment of the present invention, that is, the microgrid energy storage capacity configuration model is solved by the GUROBI solver while considering the microgrid frequency change constraint condition and the microgrid rotational kinetic energy relationship.

[0115] Table 1

[0116] Method A Method B Method C Maximum energy storage power (MW) 58.20 80.04 59.68 Maximum energy storage capacity (MWh) 52.38 72.03 53.71 Energy storage investment cost ($) 60034.2 82558.5 61562.9 Scheduling cost ($) 138028.4 209767.9 140294.5 Total cost ($) 371394.1 386332.0 372656.5 Maximum power imbalance (MW) 60 30 60 Whether it exceeds the set threshold yes no no Actual absorption probability 0.9588 0.9547 0.9575

[0117] Table 1 lists the energy storage capacity configuration schemes (maximum energy storage power and maximum energy storage capacity), energy storage investment cost, dispatching cost and total cost under the three models, verifying the economic cost advantage of the microgrid energy storage capacity configuration model considering energy storage inertial support and frequency stability constraints.

[0118] By comparing the data of method C and method B in Table 1, it can be seen that the energy storage cost of method C is lower, which proves that the method of the embodiment of the present invention improves the economy of the microgrid, and the maximum unbalance allowed when a fault occurs is 60MW, which proves that the method of the embodiment of the present invention increases the frequency regulation capability of the system. In addition, by comparing the specific data of method A and method B, it can be seen that the energy storage investment cost of method B increases significantly (about 37.52%), and by comparing the specific data of method A and method C, it can be seen that the energy storage investment cost of method C increases slightly (about 2.55%), and by comparing the actual absorption probability, it can be seen that method C slightly reduces the absorption probability of the system. These results prove that the method of the embodiment of the present invention can respond to the occurrence of emergency faults (unplanned off-grid faults) and reduce economic costs while ensuring the safe operation of the system.

Claims

1. A method for configuring energy storage capacity of a microgrid through three-stage distributed blue stick optimization, characterized in that: The specific steps are as follows: Step 101: Divide each day into multiple scheduling periods, and divide the configuration of the microgrid energy storage capacity into three stages, wherein the first stage is the energy storage capacity configuration stage, the second stage is the pre-scheduling stage, and the third stage is the re-scheduling stage; A three-stage microgrid distributed robust optimization microgrid energy storage capacity configuration model is constructed. The specific model formula is: min C I +C D -δa In the formula, min is the minimum value of the post-order formula, C I is the energy storage investment cost of the microgrid, C D is the dispatching cost of the energy storage system of the microgrid, a is the probability of renewable energy consumption of the microgrid, and δ is the weight coefficient of renewable energy consumption of the microgrid; In the formula, S is the set of energy storage devices in the microgrid, s is the energy storage device in S, C SP is the power cost of the energy storage system in the microgrid, C SS is the electricity cost of the energy storage system in the microgrid, is the maximum power configured for the energy storage device s, is the maximum capacity of the energy storage device s, T s is the service life of the energy storage system in the microgrid, M is the average daily maintenance cost of the energy storage system in the microgrid; In the formula, T is the set of daily dispatch periods, t is the dispatch period in T, G is the set of thermal power units in the microgrid, g is the thermal power unit in G, P gt is the pre-dispatched output of thermal power unit g in period t, F g (P gt ) is the fuel cost of thermal power unit g in period t, is the no-load cost of thermal power unit g, x gt is the switch state value of thermal power unit g in period t, is the startup cost of thermal power unit g, u gt is the startup state value of thermal power unit g in period t, is the shutdown cost of thermal power unit g, v gt is the shutdown state value of thermal power unit g in period t, C b is the price of electricity purchased by the microgrid from the upstream grid, C s is the price at which the microgrid sells electricity to the upstream grid, is the power amount purchased by the microgrid from the upstream power grid during period t, is the power sold by the microgrid to the upstream grid during period t; Step 102: Constructing constraints for the three-stage optimization objectives in the microgrid energy storage capacity configuration model; 102.1) The constraints in the energy storage capacity configuration phase are: In the formula, is the maximum power allowed by the energy storage device s, The maximum amount of electricity allowed by the energy storage device s; 102.2) The constraints in the pre-dispatch phase include: microgrid balance constraints, thermal power unit constraints, energy storage device operation constraints, and PCC interactive power constraints; 102.2.1) The microgrid balance constraints are: Where K is the set of wind turbines in the microgrid, k is the wind turbine in K, is the wind turbine output of wind turbine k in period t, is the charging power of energy storage device s in period t, is the discharge power of the energy storage device s in time period t, N is the set of nodes in the microgrid, the nodes in the microgrid refer to the thermal power units and wind power units in the microgrid, n is the node in N, d nt is the load demand of node n at time t; 102.2.2) Constraints for thermal power units include start-stop constraints for thermal power units, output constraints for thermal power units, and ramp rate constraints for thermal power units; The start and stop constraints of thermal power units are: In the formula, x gt-1 is the switch state value of thermal power unit g in period t-1, x gi is the switch state value of thermal power unit g in period i, UT g is the minimum start time of thermal power unit g, DTg is the minimum shutdown time of thermal power unit g; The output constraints of thermal power units are: In the formula, is the lower limit of the output of thermal power unit g, is the output upper limit of thermal power unit g; The climbing rate constraint of thermal power unit is: In the formula, is the downward climbing capacity per unit time of thermal power unit g in shutdown state, is the upward climbing capacity per unit time of thermal power unit g in the startup state, is the downward climbing capacity per unit time of thermal power unit g in operation state, is the upward climbing capacity per unit time of thermal power unit g in operation state, Δ d is the scheduling interval length, P gt-1 is the pre-dispatched output of thermal power unit g in period t-1; 102.2.3) The operating constraints of the energy storage device are: S0=S T In the formula, S st is the charge amount of energy storage device s in period t, S s(t+1) is the charge amount of the energy storage device s in the period t+1, is the maximum allowable capacity of the energy storage device s in period t, β is the self-discharge rate of the energy storage device, η c is the charging efficiency of the energy storage device, η d is the discharge efficiency of the energy storage device, Δ ds is the charging and discharging time of the energy storage device s, S0 is the power of the energy storage device at the beginning of the scheduling cycle, S T is the amount of electricity of the energy storage device at the end of the dispatch cycle; 102.2.4) PCC Interaction Power Constraints Where P PCC,max is the maximum transmission power of the tie line between the microgrid and the upstream grid; 102.3) The constraints of the rescheduling phase are: a0≤a≤1 In the formula, inf is the lower bound of the post-order formula, and P is ξ t The probability distribution function of t is the deviation of the actual output of the wind turbine in the microgrid relative to its predicted output during period t, is the lower bound of the fluctuation range of the wind turbine in the microgrid during period t, is the upper limit of the fluctuation range of the wind turbine in the microgrid during period t, a0 is the lower limit of a, D is the fuzzy set of P, Ep is the mathematical expectation, Var is the variance, ξ kt is the deviation of the actual output of wind turbine k in the microgrid relative to its predicted output during period t, μ kt For kt The mathematical expectation of For kt The variance of kt It's about μ kt Unimodal means that the probability distribution function P is in μ kt is non-decreasing before, and in μ kt It is non-increasing afterwards; μ kt is the deviation of the actual output of wind turbine k in period t from its predicted output; Step 103: The frequency change constraint condition of the microgrid is constructed as follows: Δf min ≤Δf≤Δf max Where Δf min is the lower limit of the frequency deviation of the microgrid, Δf max is the upper limit of the frequency deviation of the microgrid, R ocof is the frequency change rate of the microgrid, R ocof is the lower limit of the frequency change rate of the microgrid, is the upper limit of the frequency change rate of the microgrid; Step 104: Construct the rotational kinetic energy relationship of the microgrid as follows: In the formula, H eq,t is the equivalent inertia constant of the node in the microgrid during period t, E sys is the total installed capacity of the microgrid, H g is the inertia constant of the thermal power unit g in the microgrid, H s is the inertia constant of the energy storage device s in the microgrid; Step 105: According to the frequency change constraint of the microgrid and the rotational kinetic energy relationship of the microgrid, the microgrid energy storage capacity configuration model is solved by the GUROBI solver, and the microgrid energy storage capacity is configured according to the solved microgrid energy storage capacity configuration model formula.

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