Electric power system energy storage cluster and new energy collaborative scheduling method and system

Abstract

The invention provides a power system energy storage cluster and new energy collaborative scheduling method and system, and the method comprises the steps: constructing an energy storage cluster and new energy collaborative scheduling model considering the nested section bearing capacity evaluation through nested section hierarchical division and nested section bearing capacity evaluation for a power system, and solving the model to obtain a specific collaborative scheduling strategy. Under the condition that the nested section power is not out of limit, by optimizing the output plans of the energy storage cluster, the new energy unit and the thermal power unit, the operation safety of the power transmission section of the power system is guaranteed, meanwhile, the situation of new energy power abandonment is reduced, power transmission network resources and renewable power generation resources are utilized to the maximum extent, and the energy utilization rate is increased. And the operation cost of the power system is reduced while maximization of new energy consumption is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric power engineering, and relates to a method and system for coordinated dispatching of an electric power system. Background Art

[0002] Although with the development of renewable energy, the development and utilization of new energy power generation resources can effectively improve the energy structure, promote environmental protection, and maintain economic and social sustainable development. However, with the continuous increase in the proportion of grid-connected new energy power generation resources in the power grid, the power grid flow section presents a multi-level and highly nested structural feature; at the same time, the randomness and volatility of new energy itself also threaten the safety of nested section transmission, which easily leads to the situation of section power exceeding the limit, making it difficult to ensure the safety of power transmission section operation of the power system, and the proportion of new energy consumption is low. At present, in the coordinated dispatching of power storage clusters and new energy in the power system, the control of the power sensitivity of the deterministic section is mainly based on the sensitivity of the unit node injection power to the line flow change, and the power of the traditional power source and load nodes is adjusted up and down in proportion to eliminate the transmission section operation risk under the expected fault. However, this dispatching method does not take into account the difference in the carrying capacity of each level of nested sections for the output of new energy power generation, and the resulting coordinated dispatching strategy cannot make full use of the section transmission capacity to promote the consumption of new energy. Summary of the invention

[0003] In order to solve the problems described in the background technology that the safety of power system transmission section operation is difficult to ensure, the accuracy is low, and the proportion of new energy consumption is low, the present invention provides a method and system for coordinated scheduling of power system energy storage clusters and new energy.

[0004] The method of the present invention comprises the following steps:

[0005] According to the equivalent electrical distance between the transmission section of the new energy power station in the power system and the power grid, the transmission network is divided from near to far to obtain nested sections of various levels, and the limit transmission capacity of each level of nested sections is calculated based on the thermal stability limit formula;

[0006] According to the given day-ahead predicted power load demand curve and day-ahead predicted renewable energy generation curve, the maximum carrying capacity of each level of nested sections for renewable energy generation output in the region is evaluated by using the entropy weight method through the limit transmission capacity of each level of nested sections.

[0007] Based on the distributed robust optimization model, constraints are set according to the maximum carrying capacity of each level of nested sections for the output of renewable energy power generation in the region. The coordinated scheduling model is constructed by taking the carrying capacity of each level of nested sections as the objective function to maximize the consumption of renewable energy.

[0008] The collaborative scheduling model is solved to obtain the optimal strategy for collaborative scheduling of energy storage clusters and new energy sources.

[0009] Furthermore, the calculation formula for the equivalent electrical distance between the transmission section of the new energy power station and the power grid is:

[0010]

[0011]

[0012] Where, L mn is the electrical distance between the nth transmission line and the grid in section m, L m is the equivalent electrical distance between section m and the power grid; is a cross-section collection, is the set of transmission lines contained in section m; U mn , r mn are the voltage level, connected active load, and line resistance of the nth transmission line in section m respectively; is the number of transmission lines contained in section m. Arrange the section hierarchical order in order from near to far according to the equivalent electrical distance.

[0013] Furthermore, the calculation formula of the limit transmission capacity of each level of nested sections is as follows:

[0014]

[0015] In the formula, is the thermal stability limit of section m, i.e. the ultimate power transmission capacity of section m; is the base state flow value of section m; and are the transmission capacity and base state power flow value of the kth transmission line that is most prone to overload when N-1 faults occur in section m; mn is the active power flow through the nth line The positive direction marker parameter of section m, λ mn =1 means the positive direction is the same, λ mn =-1 means the positive direction is opposite.

[0016] Furthermore, the evaluation method of the maximum carrying capacity of each level of nested sections for the output of renewable energy power generation in the region is as follows:

[0017] The entropy weight method is used to evaluate the maximum carrying capacity of each level of nested sections for the output of renewable energy power generation in the region. The calculation formula is as follows:

[0018] The extreme value method is used to standardize the evaluation indicators, including the installed capacity of power stations in each level of nested sections, the maximum transmission capacity of the section, the priority of new energy units in the section, and the hierarchical order of nested sections.

[0019] The calculation formula for the standardized processing of the evaluation index as a positive index is as follows:

[0020]

[0021] The calculation formula for the standardized treatment of negative evaluation indicators is as follows:

[0022]

[0023] Where η mij is the jth evaluation index of the i-th power station in the nested section m; and x mij are the maximum and minimum values ​​of the jth evaluation index of the i-th power station in the nested section m, respectively;

[0024] According to the standardized evaluation indicators, the entropy weight method is used to calculate the maximum output weight of each power station in the nested section m. The maximum carrying capacity of each level of nested sections for the output of renewable energy power generation in the region is evaluated according to the maximum output weight. The characteristic proportion p of power station i in nested section m under evaluation indicator j is mij The calculation formula is as follows:

[0025]

[0026] In the formula, is the number of power stations in the nested section m;

[0027] Information entropy value e mj The calculation formula is as follows:

[0028]

[0029] Coefficient of variation μ mj The calculation formula is as follows:

[0030] μ mj =1-e mj (9);

[0031] The calculation formula of the weight under the evaluation index j is as follows:

[0032]

[0033] In the formula, J is the number of evaluation indicators;

[0034] The calculation formula for the comprehensive index evaluation score is as follows:

[0035]

[0036] The maximum output limit of the wth renewable energy power station in the nested section m The calculation formula is as follows:

[0037]

[0038] In the formula, Predict the power generation of the wth renewable energy power station in the nested section m on the date of time period t;

[0039] The maximum output limit of the power station obtained is the maximum carrying capacity of each level of nested sections for new energy under the new energy power generation curve predicted on a given day, that is, the maximum amount of new energy that can be absorbed by each level of nested sections while ensuring that it does not exceed its maximum transmission capacity.

[0040] Furthermore, the collaborative scheduling model is constructed as follows:

[0041] Based on the distributed robust optimization model, the objective function of the collaborative scheduling model is to maximize the consumption of new energy with the carrying capacity of each level of nested sections. The objective function formula is to maximize the consumption of new energy with the carrying capacity of each level of nested sections to minimize the total cost of thermal power generation, wind abandonment penalty, and load shedding penalty during the scheduling period:

[0042]

[0043] Where N T is the number of scheduling periods; N G is the number of thermal power units, is the active output variable of thermal power unit g in period t, a g , b g 、c g N is the coefficient of the quadratic polynomial function of the g consumption characteristic of the thermal power unit; W is the number of wind turbines, is the wind power abandonment variable of wind turbine w in period t, N is the penalty for wind turbine w’s wind abandonment; D is the system load quantity, is the active load shedding variable of load node d in period t, is the load shedding penalty for load node d;

[0044] The constraints of the collaborative dispatch model include: thermal power unit start and stop constraints, thermal power unit output range constraints, thermal power unit ramp constraints, energy storage cluster operation constraints, wind power unit output range and wind abandonment constraints, node voltage and phase angle constraints, branch flow constraints, nested section power constraints, load shedding constraints, and node power balance constraints.

[0045] The start-stop constraints of the thermal power unit include the unit operation state conversion constraints and the minimum start-stop time constraints, and the formula is as follows:

[0046]

[0047] In the formula, is the start and stop state variable of thermal power unit g in period t, Indicates that the thermal power unit g is in the startup state during period t. Indicates that thermal power unit g is in shutdown state during period t; Indicates that the thermal power unit g changes from shutdown state to startup state in time period t; Indicates that the thermal power unit g changes from the startup state to the shutdown state in time period t; and are the minimum startup and shutdown durations of thermal power unit g respectively;

[0048] The output range constraints of the thermal power unit are as follows:

[0049]

[0050] In the formula, and are the upper and lower limits of the technical output of thermal power unit i respectively;

[0051] The climbing constraints of the thermal power unit are as follows:

[0052]

[0053] In the formula, RU g and RD g are the upward and downward ramp rates of thermal power unit i, respectively, and Δt is the time interval between adjacent time periods;

[0054] The energy storage cluster operation constraints include cluster power constraints, cluster charge and discharge efficiency constraints, cluster power capacity constraints, and cluster charge and discharge state constraints. The formula is as follows:

[0055]

[0056]

[0057]

[0058]

[0059]

[0060]

[0061]

[0062]

[0063] In the formula, is the charging state variable of the energy storage cluster in the nested section m at time period t, Indicates that the energy storage cluster is in charging state. Indicates that the energy storage cluster is not in charging state; is the discharge state variable of the energy storage cluster in the nested section m in period t, Indicates that the energy storage cluster is in a discharging state. Indicates that the energy storage cluster is not in a discharging state; is the number of energy storage units contained in the energy storage cluster of nested section m; is the charge variable of the energy storage unit in period t, is the discharge variable of the energy storage unit in time period t; and are the upper and lower limits of the charging power of the energy storage cluster of the nested section m respectively; and are the upper and lower limits of the discharge power of the energy storage cluster of the nested section m respectively; is the electric energy variable of the energy storage cluster in the nested section m in time period t; and are the upper and lower limits of the electric energy capacity of the energy storage cluster in the nested section m respectively; and is the charging efficiency and discharging efficiency of the energy storage cluster in the nested section m;

[0064] The wind turbine output range and wind abandonment constraints are as follows:

[0065]

[0066] In the formula, is the maximum allowable output value of wind turbine w in nested section m in time period t.

[0067] The node voltage and phase angle constraints are as follows:

[0068]

[0069] In the formula, v i,t is the voltage amplitude variable of node i in time period t, and v i are the upper and lower limits of the voltage amplitude at node i respectively; θ i,t The voltage phase angle variable of node i in period t, and θ i are the upper limit and lower limit of the voltage phase angle of node i respectively; is the voltage phase angle variable of the balance node;

[0070] The branch power flow constraints are as follows:

[0071]

[0072] In the formula, and are respectively the active power flow variable and reactive power flow variable flowing through branch ij at time period t; ij is the linearization parameter of the power flow formula; θ ij,t G is the voltage phase difference variable of the first and last nodes of branch ij in time period t; ii , B ii , G ij , B ij are the branch admittance matrix parameters, is the upper limit of apparent power of branch ij; and are the upper limits of active power flow and reactive power flow allowed to flow through branch ij respectively; is the maximum allowable active power flow loss value of branch ij;

[0073] The nested section power constraints are as follows:

[0074]

[0075] In the formula, is the set of all branches in the nested section m; ij is the positive direction marking parameter of branch ij in nested section m;

[0076] The load shedding constraints are as follows:

[0077]

[0078] In the formula, is the reactive load shedding variable of load node d in period t; and are the active and reactive loads connected to the load node d respectively.

[0079] The node power balance constraints are as follows:

[0080]

[0081] In the formula, is the set of all thermal power units on node i; is the set of all wind turbines on node i; is the set of all branches ij on node i; ε i is the set of all energy storage clusters on node i; is the set of all loads on node i.

[0082] Furthermore, the solution method of the collaborative scheduling model is:

[0083] The collaborative scheduling model based on distributed robust optimization is transformed into a mixed integer linear programming model using dual method and linearization technology;

[0084] In the matrix computing platform Matlab, the Yalmip modeling language is used to write the computational code of the mixed integer linear programming model;

[0085] Call the Gurobi solver to solve the written mixed integer linear programming model to obtain the optimal strategy for the coordinated dispatch of energy storage clusters and new energy sources;

[0086] The optimal strategy for coordinated scheduling of energy storage clusters and new energy sources includes: an operation plan for thermal power units within a predetermined scheduling period, including a startup and shutdown plan for thermal power units, and an active power and reactive power output plan for thermal power units; an operation plan for new energy units within a predetermined scheduling period, including an active power output plan for new energy units, and a wind power abandonment strategy for new energy units; an operation plan for energy storage clusters within a predetermined scheduling period, including a charging and discharging plan for energy storage clusters, and a charging power and discharging power strategy for energy storage clusters; a nested section power allocation plan within a predetermined scheduling period, including a power allocation strategy for each time period of the nested section, and a node load shedding strategy.

[0087] The present invention also proposes a coordinated dispatching system for power system energy storage clusters and new energy sources, including a limit transmission capacity calculation module, a maximum carrying capacity calculation module, a coordinated dispatching model construction module and a coordinated dispatching optimal strategy solving module.

[0088] The limit transmission capacity calculation module is used to divide the transmission network from near to far to obtain nested section levels according to the equivalent electrical distance between the transmission section of the new energy power generation station in the power system and the power grid, and calculate the limit transmission capacity of the nested sections at each level based on the thermal stability limit formula.

[0089] The maximum carrying capacity calculation module is used to evaluate the maximum carrying capacity of each level of nested sections for the output of renewable energy power generation in the region based on the given day-ahead predicted power load demand curve and the day-ahead predicted renewable energy power generation curve, through the limit transmission capacity of each level of nested sections, using the entropy weight method.

[0090] The collaborative scheduling model construction module is used to set constraints based on the distributed robust optimization model according to the maximum carrying capacity of each level of nested sections for the output of renewable energy power generation in the region, and to construct a collaborative scheduling model by utilizing the carrying capacity of each level of nested sections to maximize the consumption of renewable energy as the objective function.

[0091] The collaborative scheduling optimal strategy solving module is used to solve the collaborative scheduling model to obtain the optimal strategy for collaborative scheduling of the energy storage cluster and new energy.

[0092] The implementation method of each module in the system is consistent with that described in the above method and will not be repeated here.

[0093] The present invention also proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the method for coordinated scheduling of power system energy storage clusters and new energy sources as described above.

[0094] The present invention also proposes an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to implement the method for coordinated scheduling of power system energy storage clusters and new energy sources as described above.

[0095] Compared with the prior art, the present invention divides the nested sections into levels and evaluates the carrying capacity of the nested sections for the power system, takes into account the influence of the carrying capacity of the nested sections on the output level of the new energy generator sets, adopts the entropy weight method to evaluate the maximum carrying capacity of each level of the nested sections for the new energy power generation output in the region, and allocates the upper limit of the output of the new energy units in the region; introduces the energy storage cluster to deal with the randomness and volatility of new energy; based on the distributed blue bar optimization model, constructs a coordinated scheduling model of the energy storage cluster and new energy taking into account the evaluation of the carrying capacity of the nested sections, and thus, by solving the coordinated scheduling model, obtains the optimal strategy for the coordinated scheduling of the energy storage cluster and new energy while ensuring that the power of the nested sections does not exceed the limit, and the total active power flow flowing through each nested section does not exceed its limit transmission capacity. While ensuring the safety of the operation of the power transmission section of the power system, the occurrence of new energy abandonment is reduced, the transmission network resources and renewable power generation resources are maximized, and the maximum consumption of new energy is achieved while reducing the operating cost of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] Figure 1 The figure is a flow chart of the method of the present invention.

[0097] Figure 2 This is a system architecture diagram of the present invention.

[0098] Figure 3 It is the improved IEEE 14-node power system topology diagram including energy storage cluster and wind power in Example 5.

[0099] Figure 4 This is a schematic diagram of the power load demand curve predicted a day ago in Example 5.

[0100] Figure 5It is a schematic diagram of the power generation curve of the new energy wind turbine group predicted recently in Example 5.

[0101] Figure 6 It is the output plan diagram of each resource under the collaborative scheduling strategy in Example 5.

[0102] Figure 7 It is the cross-sectional power diagram with and without considering the cross-sectional bearing capacity in Example 5. DETAILED DESCRIPTION

[0103] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0104] Example 1

[0105] The coordinated dispatching method of power system energy storage cluster and new energy is shown in the flowchart Figure 1 As shown, the specific steps are described as follows.

[0106] Firstly, according to the equivalent electrical distance between the transmission section of the renewable energy power station in the power system and the power grid, the transmission network is divided from near to far to obtain the nested section level, and the maximum transmission capacity of each level of nested section is calculated based on the thermal stability limit formula.

[0107] Specifically, the calculation formula for the equivalent electrical distance between the transmission section of the new energy power station and the power grid is:

[0108]

[0109] Where, L mn is the electrical distance between the nth transmission line and the grid in section m, L m is the equivalent electrical distance between section m and the power grid; is a cross-section collection, is the set of transmission lines contained in section m; U mn , r mn are the voltage level, connected active load, and line resistance of the nth transmission line in section m respectively; is the number of transmission lines contained in section m. Arrange the section hierarchical order in order from near to far according to the equivalent electrical distance.

[0110] The calculation formula of the ultimate transmission capacity of each level of nested sections is as follows:

[0111]

[0112]

[0113] In the formula, is the thermal stability limit of section m, i.e. the ultimate power transmission capacity of section m; is the base state flow value of section m; and are the transmission capacity and base state power flow value of the kth transmission line that is most prone to overload when N-1 faults occur in section m; mn is the active power flow through the nth line The positive direction marker parameter of section m, λ mn =1 means the positive direction is the same, λ mn =-1 means the positive direction is opposite.

[0114] Then, based on the given day-ahead predicted electricity load demand curve and the day-ahead predicted renewable energy power generation curve, the entropy weight method is used to evaluate the maximum carrying capacity of each level of nested sections for renewable energy power generation output in the region through the limit transmission capacity of each level of nested sections.

[0115] Specifically, the evaluation method for the maximum carrying capacity of each level of nested sections for the output of renewable energy power generation in the region is as follows:

[0116] The entropy weight method is used to evaluate the maximum carrying capacity of each level of nested sections for the output of renewable energy power generation in the region. The calculation formula is as follows:

[0117] The extreme value method is used to standardize the evaluation indicators, including the installed capacity of power stations in each level of nested sections, the maximum transmission capacity of the section, the priority of new energy units in the section, and the hierarchical order of nested sections.

[0118] The calculation formula for the standardized processing of the evaluation index as a positive index is as follows:

[0119]

[0120] The calculation formula for the standardized treatment of negative evaluation indicators is as follows:

[0121]

[0122] Where η mij is the jth evaluation index of the i-th power station in the nested section m; and x mij are the maximum and minimum values ​​of the jth evaluation index of the i-th power station in the nested section m, respectively;

[0123] According to the standardized evaluation indicators, the entropy weight method is used to calculate the maximum output weight of each power station in the nested section m. The maximum carrying capacity of each level of nested sections for the output of renewable energy power generation in the region is evaluated according to the maximum output weight. The characteristic proportion p of power station i in nested section m under evaluation indicator j is mij The calculation formula is as follows:

[0124]

[0125] In the formula, is the number of power stations in the nested section m;

[0126] Information entropy value e mj The calculation formula is as follows:

[0127]

[0128] Coefficient of variation μ mj The calculation formula is as follows:

[0129] μ mj =1-e mj (9);

[0130] The calculation formula of the weight under the evaluation index j is as follows:

[0131]

[0132] In the formula, J is the number of evaluation indicators;

[0133] The calculation formula for the comprehensive index evaluation score is as follows:

[0134]

[0135] The maximum output limit of the wth renewable energy power station in the nested section m The calculation formula is as follows:

[0136]

[0137] In the formula, Predict the power generation of the wth renewable energy power station in the nested section m on the date of time period t;

[0138] The maximum output limit of the power station obtained is the maximum carrying capacity of each level of nested sections for new energy under the new energy power generation curve predicted on a given day, that is, the maximum amount of new energy that can be absorbed by each level of nested sections while ensuring that it does not exceed its maximum transmission capacity.

[0139] Then, based on the distributed robust optimization model, constraints are set according to the maximum carrying capacity of each level of nested sections for the output of renewable energy power generation in the region, and a collaborative scheduling model is constructed with the objective function of maximizing the carrying capacity of each level of nested sections to absorb renewable energy.

[0140] Specifically, the construction method of the collaborative scheduling model is:

[0141] Based on the distributed robust optimization model, the objective function of the collaborative scheduling model is to maximize the consumption of new energy with the carrying capacity of each level of nested sections. The objective function formula is to maximize the consumption of new energy with the carrying capacity of each level of nested sections to minimize the total cost of thermal power generation, wind abandonment penalty, and load shedding penalty during the scheduling period:

[0142]

[0143] Where N T is the number of scheduling periods; N G is the number of thermal power units, is the active output variable of thermal power unit g in period t, a g , b g 、c g N is the coefficient of the quadratic polynomial function of the g consumption characteristic of the thermal power unit; W is the number of wind turbines, is the wind power abandonment variable of wind turbine w in period t, N is the penalty for wind turbine w’s wind abandonment; D is the system load quantity, is the active load shedding variable of load node d in period t, is the load shedding penalty for load node d;

[0144] Based on the wind power output curve predicted on the day before, the uncertainty prediction error variable is introduced Uncertain output variables of wind turbines It is expressed as follows:

[0145]

[0146] Based on the moment information of the prediction error, a distributed robust optimization fuzzy set is constructed to specifically model the uncertainty of wind power output:

[0147]

[0148] In the formula, is the probability distribution; express The set of all probability distributions in ; represents the expectation operator; μ sis the given mean information; z(·) is the moment information of wind power prediction error uncertainty, δ s is the given upper limit of deviation; and are the upper and lower limits of the predicted deviation of the wind turbine output w, respectively;

[0149] According to the maximum carrying capacity of each level of nested sections for the output of renewable energy power generation in the region, the constraints of the coordinated scheduling model include: start and stop constraints of thermal power units, output range constraints of thermal power units, climbing constraints of thermal power units, operation constraints of energy storage clusters, output range and wind abandonment constraints of wind turbines, node voltage and phase angle constraints, branch flow constraints, nested section power constraints, load shedding constraints, and node power balance constraints.

[0150] The start-stop constraints of thermal power units include unit operation state conversion constraints and minimum start-stop time constraints. The formula is as follows:

[0151]

[0152] In the formula, is the start and stop state variable of thermal power unit g in period t, Indicates that the thermal power unit g is in the startup state during period t. Indicates that thermal power unit g is in shutdown state during period t; Indicates that the thermal power unit g changes from shutdown state to startup state in time period t; Indicates that the thermal power unit g changes from the startup state to the shutdown state in time period t; and are the minimum startup and shutdown durations of thermal power unit g respectively;

[0153] The output range constraints of thermal power units are as follows:

[0154]

[0155] In the formula, and are the upper and lower limits of the technical output of thermal power unit i respectively;

[0156] The climbing constraints of thermal power units are as follows:

[0157]

[0158] In the formula, RU g and RD g are the upward and downward ramp rates of thermal power unit i, respectively, and Δt is the time interval between adjacent time periods;

[0159] The energy storage cluster operation constraints include cluster power constraints, cluster charging and discharging efficiency constraints, cluster power capacity constraints, and cluster charging and discharging state constraints. The formula is as follows:

[0160]

[0161]

[0162]

[0163]

[0164]

[0165]

[0166]

[0167]

[0168] In the formula, is the charging state variable of the energy storage cluster in the nested section m at time period t, Indicates that the energy storage cluster is in charging state. Indicates that the energy storage cluster is not in charging state; is the discharge state variable of the energy storage cluster in the nested section m in period t, Indicates that the energy storage cluster is in a discharging state. Indicates that the energy storage cluster is not in a discharging state; is the number of energy storage units contained in the energy storage cluster of nested section m; is the charge variable of the energy storage unit in period t, is the discharge variable of the energy storage unit in time period t; and are the upper and lower limits of the charging power of the energy storage cluster of the nested section m respectively; and are the upper and lower limits of the discharge power of the energy storage cluster of the nested section m respectively; is the electric energy variable of the energy storage cluster in the nested section m in time period t; and are the upper and lower limits of the electric energy capacity of the energy storage cluster in the nested section m respectively; and is the charging efficiency and discharging efficiency of the energy storage cluster in the nested section m;

[0169] The output range of wind turbines and wind abandonment constraints are as follows:

[0170]

[0171] In the formula, is the maximum allowable output value of wind turbine w in nested section m in time period t.

[0172] The node voltage and phase angle constraints are as follows:

[0173]

[0174] In the formula, v i,t is the voltage amplitude variable of node i in time period t, and v i are the upper and lower limits of the voltage amplitude at node i respectively; θ i,t The voltage phase angle variable of node i in period t, and θ i are the upper limit and lower limit of the voltage phase angle of node i respectively; is the voltage phase angle variable of the balance node;

[0175] The branch power flow constraints are as follows:

[0176]

[0177] In the formula, and are respectively the active power flow variable and reactive power flow variable flowing through branch ij at time period t; ij is the linearization parameter of the power flow formula; θ ij,t G is the voltage phase difference variable of the first and last nodes of branch ij in time period t; ii , B ii , G ij , B ij are the branch admittance matrix parameters, is the upper limit of apparent power of branch ij; and are the upper limits of active power flow and reactive power flow allowed to flow through branch ij respectively; is the maximum allowable active power flow loss value of branch ij;

[0178] The nested section power constraints are as follows:

[0179]

[0180] In the formula, is the set of all branches in the nested section m; ij is the positive direction marking parameter of branch ij in nested section m;

[0181] The load shedding constraints are as follows:

[0182]

[0183] In the formula, is the reactive load shedding variable of load node d in period t; and are the active and reactive loads connected to the load node d respectively.

[0184] The node power balance constraints are as follows:

[0185]

[0186] In the formula, is the set of all thermal power units on node i; is the set of all wind turbines on node i; is the set of all branches ij on node i; ε i is the set of all energy storage clusters on node i; is the set of all loads on node i.

[0187] Finally, the collaborative scheduling model is solved to obtain the optimal strategy for collaborative scheduling of energy storage clusters and new energy sources.

[0188] Specifically, the solution method of the collaborative scheduling model is:

[0189] The collaborative scheduling model based on distributed robust optimization is transformed into a mixed integer linear programming model using dual method and linearization technology;

[0190] In the matrix computing platform Matlab, the Yalmip modeling language is used to write the computational code of the mixed integer linear programming model;

[0191] Call the Gurobi solver to solve the written mixed integer linear programming model to obtain the optimal strategy for the coordinated dispatch of energy storage clusters and new energy sources;

[0192] The optimal strategy for coordinated scheduling of energy storage clusters and new energy sources includes: an operation plan for thermal power units within a predetermined scheduling period, including a startup and shutdown plan for thermal power units, and an active power and reactive power output plan for thermal power units; an operation plan for new energy units within a predetermined scheduling period, including an active power output plan for new energy units, and a wind power abandonment strategy for new energy units; an operation plan for energy storage clusters within a predetermined scheduling period, including a charging and discharging plan for energy storage clusters, and a charging power and discharging power strategy for energy storage clusters; a nested section power allocation plan within a predetermined scheduling period, including a power allocation strategy for each time period of the nested section, and a node load shedding strategy.

[0193] Example 2

[0194] The power system energy storage cluster and new energy coordinated dispatching system, the architecture diagram is as follows Figure 2As shown, it consists of a limit transmission capacity calculation module, a maximum carrying capacity calculation module, a collaborative scheduling model construction module and a collaborative scheduling optimal strategy solution module.

[0195] The module for calculating the limit transmission capacity is used to divide the transmission network from near to far to obtain nested section levels according to the equivalent electrical distance between the transmission section of the new energy power generation station in the power system and the power grid, and calculate the limit transmission capacity of each level of nested sections based on the thermal stability limit formula.

[0196] Specifically, the calculation formula for the equivalent electrical distance between the transmission section of the new energy power station and the power grid is:

[0197]

[0198] Where, L mn is the electrical distance between the nth transmission line and the grid in section m, L m is the equivalent electrical distance between section m and the power grid; is a cross-section collection, is the set of transmission lines contained in section m; U mn , r mn are the voltage level, connected active load, and line resistance of the nth transmission line in section m respectively; is the number of transmission lines contained in section m. Arrange the section hierarchical order in order from near to far according to the equivalent electrical distance.

[0199] The calculation formula of the ultimate transmission capacity of each level of nested sections is as follows:

[0200]

[0201] In the formula, is the thermal stability limit of section m, i.e. the ultimate power transmission capacity of section m; is the base state flow value of section m; and are the transmission capacity and base state power flow value of the kth transmission line that is most prone to overload when N-1 faults occur in section m; mn is the active power flow through the nth line The positive direction marker parameter of section m, λ mn =1 means the positive direction is the same, λ mn =-1 means the positive direction is opposite.

[0202] The maximum carrying capacity calculation module is used to evaluate the maximum carrying capacity of each level of nested sections for the output of renewable energy power generation in the region based on the given day-ahead predicted power load demand curve and the day-ahead predicted renewable energy power generation curve, through the limit transmission capacity of each level of nested sections, and using the entropy weight method.

[0203] Specifically, the entropy weight method is used to evaluate the maximum carrying capacity of each level of nested sections for the output of renewable energy power generation in the region. The calculation formula is as follows:

[0204] The extreme value method is used to standardize the evaluation indicators, including the installed capacity of power stations in each level of nested sections, the maximum transmission capacity of the section, the priority of new energy units in the section, and the hierarchical order of nested sections.

[0205] The calculation formula for the standardized processing of the evaluation index as a positive index is as follows:

[0206]

[0207] The calculation formula for the standardized treatment of negative evaluation indicators is as follows:

[0208]

[0209] Where η mij is the jth index of the i-th power station in the nested section m; and x mij are the maximum and minimum values ​​of the jth index of the i-th power station in the nested section m, respectively;

[0210] Then, the entropy weight method is used to calculate the maximum output weight of each power station in the nested section m. The maximum carrying capacity of each level of nested sections for the renewable energy power generation output in the region is evaluated according to the maximum output weight. The characteristic proportion p of power station i in nested section m under evaluation index j is mij The calculation formula is as follows:

[0211]

[0212] In the formula, is the number of power stations in the nested section m;

[0213] Information entropy value e mj The calculation formula is as follows:

[0214]

[0215] Coefficient of variation μ mj The calculation formula is as follows:

[0216] μ mj =1-e mj(9);

[0217] The calculation formula of the weight under the evaluation index j is as follows:

[0218]

[0219] In the formula, J is the number of evaluation indicators;

[0220] The calculation formula for the comprehensive index evaluation score is as follows:

[0221]

[0222] Maximum output limit of the wth renewable energy power station in section m The calculation formula is as follows:

[0223]

[0224] In the formula, Predict the power generation of the wth renewable energy power station in the nested section m on the date of time period t;

[0225] The maximum output limit of the power station obtained is the maximum carrying capacity of each level of nested sections for new energy under the new energy power generation curve predicted on a given day, that is, the maximum amount of new energy that can be absorbed by each level of nested sections while ensuring that it does not exceed its maximum transmission capacity.

[0226] The collaborative scheduling model construction module is used to set constraints based on the distributed robust optimization model according to the maximum carrying capacity of each level of nested sections for the output of renewable energy power generation in the region, and to build a collaborative scheduling model with the objective function of maximizing the carrying capacity of each level of nested sections to absorb renewable energy.

[0227] Specifically, the construction method of the collaborative scheduling model is:

[0228] Based on the distributed robust optimization model, the objective function of the collaborative scheduling model is to maximize the consumption of new energy with the carrying capacity of each level of nested sections. The objective function formula is to maximize the consumption of new energy with the carrying capacity of each level of nested sections to minimize the total cost of thermal power generation, wind abandonment penalty, and load shedding penalty during the scheduling period:

[0229]

[0230] Where N T is the number of scheduling periods; N G is the number of thermal power units, is the active output variable of thermal power unit g in period t, a g , b g 、c gN is the coefficient of the quadratic polynomial function of the g consumption characteristic of the thermal power unit; W is the number of wind turbines, is the wind power abandonment variable of wind turbine w in period t, N is the penalty for wind turbine w’s wind abandonment; D is the system load quantity, is the active load shedding variable of load node d in period t, is the load shedding penalty for load node d;

[0231] Based on the wind power output curve predicted on the day before, the uncertainty prediction error variable is introduced Uncertain output variables of wind turbines It is expressed as follows:

[0232]

[0233] Based on the moment information of the prediction error, a distributed robust optimization fuzzy set is constructed to specifically model the uncertainty of wind power output:

[0234]

[0235] In the formula, is the probability distribution; express The set of all probability distributions in ; represents the expectation operator; μ s is the given mean information; z(·) is the moment information of wind power prediction error uncertainty, δ s is the given upper limit of deviation; and are the upper and lower limits of the predicted deviation of the wind turbine output w, respectively;

[0236] According to the maximum carrying capacity of each level of nested sections for the output of renewable energy power generation in the region, the constraints of the coordinated scheduling model include: start and stop constraints of thermal power units, output range constraints of thermal power units, climbing constraints of thermal power units, operation constraints of energy storage clusters, output range and wind abandonment constraints of wind turbines, node voltage and phase angle constraints, branch flow constraints, nested section power constraints, load shedding constraints, and node power balance constraints.

[0237] The start-stop constraints of thermal power units include unit operation state conversion constraints and minimum start-stop time constraints. The formula is as follows:

[0238]

[0239] In the formula, is the start and stop state variable of thermal power unit g in period t, Indicates that the thermal power unit g is in the startup state during period t. Indicates that thermal power unit g is in shutdown state during period t; Indicates that the thermal power unit g changes from shutdown state to startup state in time period t; Indicates that the thermal power unit g changes from the startup state to the shutdown state in time period t; and are the minimum startup and shutdown durations of thermal power unit g respectively;

[0240] The output range constraints of thermal power units are as follows:

[0241]

[0242] In the formula, and are the upper and lower limits of the technical output of thermal power unit i respectively;

[0243] The climbing constraints of thermal power units are as follows:

[0244]

[0245] In the formula, RU g and RD g are the upward and downward ramp rates of thermal power unit i, respectively, and Δt is the time interval between adjacent time periods;

[0246] The energy storage cluster operation constraints include cluster power constraints, cluster charging and discharging efficiency constraints, cluster power capacity constraints, and cluster charging and discharging state constraints. The formula is as follows:

[0247]

[0248]

[0249]

[0250]

[0251]

[0252]

[0253]

[0254]

[0255] In the formula, is the charging state variable of the energy storage cluster in the nested section m at time period t, Indicates that the energy storage cluster is in charging state. Indicates that the energy storage cluster is not in charging state; is the discharge state variable of the energy storage cluster in the nested section m in period t, Indicates that the energy storage cluster is in a discharging state. Indicates that the energy storage cluster is not in a discharging state; is the number of energy storage units contained in the energy storage cluster of nested section m; is the charge variable of the energy storage unit in period t, is the discharge variable of the energy storage unit in time period t; and are the upper and lower limits of the charging power of the energy storage cluster of the nested section m respectively; and are the upper and lower limits of the discharge power of the energy storage cluster of the nested section m respectively; is the electric energy variable of the energy storage cluster in the nested section m in time period t; and are the upper and lower limits of the electric energy capacity of the energy storage cluster in the nested section m respectively; and is the charging efficiency and discharging efficiency of the energy storage cluster in the nested section m;

[0256] The output range of wind turbines and wind abandonment constraints are as follows:

[0257]

[0258] In the formula, is the maximum allowable output value of wind turbine w in nested section m in time period t.

[0259] The node voltage and phase angle constraints are as follows:

[0260]

[0261] In the formula, v i,t is the voltage amplitude variable of node i in time period t, and v i are the upper and lower limits of the voltage amplitude at node i respectively; θ i,t The voltage phase angle variable of node i in period t, and θ i are the upper limit and lower limit of the voltage phase angle of node i respectively; is the voltage phase angle variable of the balance node;

[0262] The branch power flow constraints are as follows:

[0263]

[0264] In the formula, and are respectively the active power flow variable and reactive power flow variable flowing through branch ij at time period t; ij is the linearization parameter of the power flow formula; θ ij,t G is the voltage phase difference variable of the first and last nodes of branch ij in time period t; ii , B ii , G ij , B ij are the branch admittance matrix parameters, is the upper limit of apparent power of branch ij; and are the upper limits of active power flow and reactive power flow allowed to flow through branch ij respectively; is the maximum allowable active power flow loss value of branch ij;

[0265] The nested section power constraints are as follows:

[0266]

[0267] In the formula, is the set of all branches in the nested section m; ij is the positive direction marking parameter of branch ij in nested section m;

[0268] The load shedding constraints are as follows:

[0269]

[0270] In the formula, is the reactive load shedding variable of load node d in period t; and are the active and reactive loads connected to the load node d respectively.

[0271] The node power balance constraints are as follows:

[0272]

[0273] In the formula, is the set of all thermal power units on node i; is the set of all wind turbines on node i; is the set of all branches ij on node i; ε i is the set of all energy storage clusters on node i; is the set of all loads on node i.

[0274] The collaborative scheduling optimal strategy solving module is used to solve the collaborative scheduling model and obtain the optimal strategy for the collaborative scheduling of energy storage clusters and new energy.

[0275] Specifically, the solution method of the collaborative scheduling model is:

[0276] The collaborative scheduling model based on distributed robust optimization is transformed into a mixed integer linear programming model using dual method and linearization technology;

[0277] In the matrix computing platform Matlab, the Yalmip modeling language is used to write the computational code of the mixed integer linear programming model;

[0278] Call the Gurobi solver to solve the written mixed integer linear programming model to obtain the optimal strategy for the coordinated dispatch of energy storage clusters and new energy sources;

[0279] The optimal strategy for coordinated scheduling of energy storage clusters and new energy sources includes: an operation plan for thermal power units within a predetermined scheduling period, including a startup and shutdown plan for thermal power units, and an active power and reactive power output plan for thermal power units; an operation plan for new energy units within a predetermined scheduling period, including an active power output plan for new energy units, and a wind power abandonment strategy for new energy units; an operation plan for energy storage clusters within a predetermined scheduling period, including a charging and discharging plan for energy storage clusters, and a charging power and discharging power strategy for energy storage clusters; a nested section power allocation plan within a predetermined scheduling period, including a power allocation strategy for each time period of the nested section, and a node load shedding strategy.

[0280] Example 3

[0281] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the method for coordinated dispatching of a power system energy storage cluster and new energy as described in Example 1 and the system for coordinated dispatching of a power system energy storage cluster and new energy as described in Example 2.

[0282] Example 4

[0283] An electronic device comprises: a memory and a processor, the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor implements the coordinated dispatching method of a power system energy storage cluster and new energy as described in Example 1 and the coordinated dispatching system of a power system energy storage cluster and new energy as described in Example 2 by executing the computer instructions.

[0284] Example 5

[0285] The present invention uses the improved IEEE 14 node to demonstrate the coordinated dispatch of a power system containing energy storage clusters and wind power. The topology of the power system is shown in Figure 3 , including 4 150MW thermal power units with a total capacity of 600MW; 3 100MW wind turbines with a total capacity of 300MW; 3 40MW energy storage clusters with a total capacity of 120MW.

[0286] According to the calculation, the nested section classification results are as follows: Figure 3 As shown by the thick dotted line, the levels are marked as SC1, SC2, SC3, and SC4. In this embodiment, 24 hours is a scheduling cycle, and each scheduling period is 1 hour, so there are 24 periods in total. The power load demand curve predicted a day ago is as follows Figure 4 As shown in Figure 1, the active load power value within a dispatching cycle is used as the vertical axis and the 24 time periods are used as the horizontal axis to construct the rectangular coordinate system of power load demand. The solid line with equilateral triangle marks is used to draw the power system load demand curve predicted a day ago; the power generation curve of new energy wind turbines predicted a day ago is shown in Figure 1. Figure 5 As shown in the figure, the new energy output rectangular coordinate system is constructed with the active output power value of new energy in a dispatching cycle as the vertical axis and 24 time periods as the horizontal axis, and the power generation curve of new energy wind turbines predicted a day ago is drawn using a solid line with square marks; Figure 4 and Figure 5 It can be seen that the peak load is 686.48MW.

[0287] The optimal collaborative scheduling strategy obtained by solving the collaborative scheduling model is as follows: Figure 6 As shown in the figure, whether the cross-sectional power comparison of the carrying capacity is taken into account is as follows Figure 7 The wind curtailment and load shedding conditions are shown in Table 1.

[0288] Table 1 Comparison of total wind curtailment and load shedding

[0289]

[0290] Analysis Table 1 Figure 3-Figure 7 It can be seen that compared with the case that does not take into account the carrying capacity of the nested sections, the coordinated scheduling method of the present invention can avoid the occurrence of wind abandonment and load shedding while ensuring the power load demand in each time period, and the output of the new energy power generation station in the nested section is fully absorbed; at the same time, the total amount of active power flow flowing through each nested section does not exceed its maximum transmission capacity, ensuring the safety of the operation of the transmission section.

[0291] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the application can adopt the form of complete hardware embodiment, complete software embodiment, or the embodiment in combination with software and hardware. Moreover, the application can adopt the form of the computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiment of the present application can be implemented in various computer languages, for example, object-oriented programming language Java, C++, Python and literal scripting language JavaScript, etc.

[0292] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0293] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0294] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0295] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0296] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for coordinating the dispatch of energy storage clusters and new energy sources in a power system, characterized in that: The following steps are involved: According to the equivalent electrical distance between the transmission section of the new energy power station in the power system and the power grid, the transmission network is divided from near to far to obtain nested sections of various levels, and the limit transmission capacity of each level of nested sections is calculated based on the thermal stability limit formula; According to the given day-ahead predicted power load demand curve and day-ahead predicted renewable energy generation curve, the maximum carrying capacity of each level of nested sections for renewable energy generation output in the region is evaluated by using the entropy weight method through the limit transmission capacity of each level of nested sections. Based on the distributed robust optimization model, constraints are set according to the maximum carrying capacity of each level of nested sections for the output of renewable energy power generation in the region, and a collaborative scheduling model is constructed with the objective function of maximizing the carrying capacity of each level of nested sections to absorb renewable energy. The collaborative scheduling model is solved to obtain the optimal strategy for collaborative scheduling of energy storage clusters and new energy sources.

2. The method for coordinated dispatching of power system energy storage cluster and new energy according to claim 1, characterized in that: The calculation formula for the equivalent electrical distance between the transmission section of the new energy power station and the power grid is: Where, L mn is the electrical distance between the nth transmission line and the grid in section m, L m is the equivalent electrical distance between section m and the power grid; is a cross-section collection, is the set of transmission lines contained in section m; U mn , r mn are the voltage level, connected active load, and line resistance of the nth transmission line in section m respectively; is the number of transmission lines contained in section m. Arrange the section hierarchical order in order from near to far according to the equivalent electrical distance.

3. The method for coordinated dispatching of power system energy storage cluster and new energy according to claim 2, characterized in that: The calculation formula of the ultimate transmission capacity of each level of nested sections is as follows: In the formula, is the thermal stability limit of section m, i.e. the ultimate power transmission capacity of section m; is the base state flow value of section m; and are the transmission capacity and base state power flow value of the kth transmission line that is most prone to overload when N-1 faults occur in section m; mn is the active power flow through the nth line The positive direction marker parameter of section m, λ mn =1 means the positive direction is the same, λ mn =-1 means the positive direction is opposite.

4. The method for coordinated dispatching of power system energy storage cluster and new energy according to claim 3 is characterized in that: The evaluation method of the maximum carrying capacity of each level of nested sections for the output of renewable energy power generation in the region is as follows: The entropy weight method is used to evaluate the maximum carrying capacity of each level of nested sections for the output of renewable energy power generation in the region. The calculation formula is as follows: The extreme value method is used to standardize the evaluation indicators, including the installed capacity of power stations in each level of nested sections, the maximum transmission capacity of the section, the priority of new energy units in the section, and the hierarchical order of nested sections. The calculation formula for the standardized processing of the evaluation index as a positive index is as follows: The calculation formula for the standardized treatment of negative evaluation indicators is as follows: Where η mij is the jth evaluation index of the i-th power station in the nested section m; and x mij are the maximum and minimum values ​​of the jth evaluation index of the i-th power station in the nested section m, respectively; According to the standardized evaluation indicators, the entropy weight method is used to calculate the maximum output weight of each power station in the nested section m. The maximum carrying capacity of each level of nested sections for the output of renewable energy power generation in the region is evaluated according to the maximum output weight. The characteristic proportion p of power station i in nested section m under evaluation indicator j is mij The calculation formula is as follows: In the formula, is the number of power stations in the nested section m; Information entropy value e mj The calculation formula is as follows: Coefficient of variation μ mj The calculation formula is as follows: μ mj =1-e mj (9); The calculation formula of the weight under the evaluation index j is as follows: In the formula, J is the number of evaluation indicators; The calculation formula for the comprehensive index evaluation score is as follows: The maximum output limit of the wth renewable energy power station in the nested section m The calculation formula is as follows: In the formula, Predict the power generation of the wth renewable energy power station in the nested section m on the date of time period t; The maximum output limit of the power station obtained is the maximum carrying capacity of each level of nested sections for new energy under the new energy power generation curve predicted on a given day, that is, the maximum amount of new energy that can be absorbed by each level of nested sections while ensuring that it does not exceed its maximum transmission capacity.

5. The method for coordinated dispatching of power system energy storage cluster and new energy according to claim 4 is characterized in that: The construction method of the collaborative scheduling model is: Based on the distributed robust optimization model, the objective function of the collaborative scheduling model is to maximize the consumption of new energy with the carrying capacity of each level of nested sections. The objective function formula is to maximize the consumption of new energy with the carrying capacity of each level of nested sections to minimize the total cost of thermal power generation, wind abandonment penalty, and load shedding penalty during the scheduling period: Where N T is the number of scheduling periods; N G is the number of thermal power units, is the active output variable of thermal power unit g in period t, a g 、b g 、c g N is the coefficient of the quadratic polynomial function of the g consumption characteristic of the thermal power unit; W is the number of wind turbines, is the wind power abandonment variable of wind turbine w in period t, N is the penalty for wind turbine w’s wind abandonment; D is the system load quantity, is the active load shedding variable of load node d in period t, is the load shedding penalty for load node d; The constraints of the collaborative dispatch model include: thermal power unit start and stop constraints, thermal power unit output range constraints, thermal power unit ramp constraints, energy storage cluster operation constraints, wind power unit output range and wind abandonment constraints, node voltage and phase angle constraints, branch flow constraints, nested section power constraints, load shedding constraints, and node power balance constraints. The start-stop constraints of the thermal power unit include the unit operation state conversion constraints and the minimum start-stop time constraints, and the formula is as follows: In the formula, is the start-stop state variable of thermal power unit g in period t, Indicates that the thermal power unit g is in the startup state during period t. Indicates that thermal power unit g is in shutdown state during period t; Indicates that the thermal power unit g changes from shutdown state to startup state in time period t; Indicates that the thermal power unit g changes from the startup state to the shutdown state in time period t; and are the minimum startup and shutdown durations of thermal power unit g respectively; The output range constraints of the thermal power unit are as follows: In the formula, and are the upper and lower limits of the technical output of thermal power unit i respectively; The climbing constraints of the thermal power unit are as follows: In the formula, RU g and RD g are the upward and downward ramp rates of thermal power unit i, respectively, and Δt is the time interval between adjacent time periods; The energy storage cluster operation constraints include cluster power constraints, cluster charge and discharge efficiency constraints, cluster power capacity constraints, and cluster charge and discharge state constraints. The formula is as follows: In the formula, is the charging state variable of the energy storage cluster in the nested section m at time period t, Indicates that the energy storage cluster is in charging state. Indicates that the energy storage cluster is not in charging state; is the discharge state variable of the energy storage cluster in the nested section m in period t, Indicates that the energy storage cluster is in a discharging state. Indicates that the energy storage cluster is not in a discharging state; is the number of energy storage units contained in the energy storage cluster of nested section m; is the charge variable of the energy storage unit in period t, is the discharge variable of the energy storage unit in time period t; and are the upper and lower limits of the charging power of the energy storage cluster of the nested section m respectively; and are the upper and lower limits of the discharge power of the energy storage cluster of the nested section m respectively; is the electric energy variable of the energy storage cluster in the nested section m in time period t; and are the upper and lower limits of the electric energy capacity of the energy storage cluster in the nested section m respectively; and is the charging efficiency and discharging efficiency of the energy storage cluster in the nested section m; The wind turbine output range and wind abandonment constraints are as follows: In the formula, is the maximum allowable output value of wind turbine w in nested section m in time period t. The node voltage and phase angle constraints are as follows: In the formula, v i,t is the voltage amplitude variable of node i in time period t, and v i are the upper and lower limits of the voltage amplitude at node i respectively; θ i,t The voltage phase angle variable of node i in period t, and θ i are the upper limit and lower limit of the voltage phase angle of node i respectively; is the voltage phase angle variable of the balance node; The branch power flow constraints are as follows: In the formula, and are respectively the active power flow variable and reactive power flow variable flowing through branch ij at time period t; ij is the linearization parameter of the power flow formula; θ ij,t G is the voltage phase difference variable of the first and last nodes of branch ij in time period t; ii , B ii , G ij , B ij are the branch admittance matrix parameters, is the upper limit of apparent power of branch ij; and are the upper limits of active power flow and reactive power flow allowed to flow through branch ij respectively; is the maximum allowable active power flow loss value of branch ij; The nested section power constraints are as follows: In the formula, is the set of all branches in the nested section m; ij is the positive direction marking parameter of branch ij in nested section m; The load shedding constraints are as follows: In the formula, is the reactive load shedding variable of load node d in period t; and are the active and reactive loads connected to the load node d respectively. The node power balance constraints are as follows: In the formula, is the set of all thermal power units on node i; is the set of all wind turbines on node i; is the set of all branches ij on node i; ε i is the set of all energy storage clusters on node i; is the set of all loads on node i.

6. The method for coordinated dispatching of power system energy storage cluster and new energy according to claim 5 is characterized in that: The solution method of the collaborative scheduling model is: The collaborative scheduling model based on distributed robust optimization is transformed into a mixed integer linear programming model using dual method and linearization technology; In the matrix computing platform Matlab, the Yalmip modeling language is used to write the computational code of the mixed integer linear programming model; Call the Gurobi solver to solve the written mixed integer linear programming model to obtain the optimal strategy for the coordinated dispatch of energy storage clusters and new energy sources; The optimal strategy for coordinated scheduling of energy storage clusters and new energy sources includes: an operation plan for thermal power units within a predetermined scheduling period, including a startup and shutdown plan for thermal power units, and an active power and reactive power output plan for thermal power units; an operation plan for new energy units within a predetermined scheduling period, including an active power output plan for new energy units, and a wind power abandonment strategy for new energy units; an operation plan for energy storage clusters within a predetermined scheduling period, including a charging and discharging plan for energy storage clusters, and a charging power and discharging power strategy for energy storage clusters; a nested section power allocation plan within a predetermined scheduling period, including a power allocation strategy for each time period of the nested section, and a node load shedding strategy.

7. The power system energy storage cluster and new energy coordinated dispatching system is characterized by: It includes the module for calculating the limit transmission capacity, the module for calculating the maximum carrying capacity, the module for building the collaborative dispatching model and the module for solving the optimal collaborative dispatching strategy; The limit transmission capacity calculation module is used to divide the transmission network from near to far to obtain nested sections at various levels according to the equivalent electrical distance between the transmission section of the new energy power generation station in the power system and the power grid, and calculate the limit transmission capacity of the nested sections at various levels based on the thermal stability limit formula; The maximum carrying capacity calculation module is used to evaluate the maximum carrying capacity of each level of nested sections for the output of renewable energy power generation in the region by using the entropy weight method based on the given day-ahead predicted power load demand curve and the day-ahead predicted renewable energy power generation curve through the limit power transmission capacity of each level of nested sections; The collaborative scheduling model building module is used to set constraints based on the distributed robust optimization model according to the maximum carrying capacity of each level of nested sections for the output of renewable energy power generation in the region, and to build a collaborative scheduling model with the objective function of maximizing the carrying capacity of each level of nested sections to absorb renewable energy; The collaborative scheduling optimal strategy solving module is used to solve the collaborative scheduling model to obtain the optimal strategy for collaborative scheduling of the energy storage cluster and new energy.

8. The power system energy storage cluster and new energy coordinated dispatching system according to claim 7 is characterized by: In the limit transmission capacity calculation module, the calculation formula for the equivalent electrical distance between the transmission section of the new energy power station and the power grid is: Where, L mn is the electrical distance between the nth transmission line and the grid in section m, L m is the equivalent electrical distance between section m and the power grid; is a cross-section collection, is the set of transmission lines contained in section m; U mn , r mn are the voltage level, connected active load, and line resistance of the nth transmission line in section m respectively; is the number of transmission lines contained in section m. Arrange the section hierarchical order in order from near to far according to the equivalent electrical distance.

9. The power system energy storage cluster and new energy coordinated dispatching system according to claim 8, characterized in that: In the limit power transmission capacity calculation module, the calculation formula of the limit power transmission capacity of each level of nested sections is as follows: In the formula, is the thermal stability limit of section m, i.e. the ultimate power transmission capacity of section m; is the base state flow value of section m; and are the transmission capacity and base state power flow value of the kth transmission line that is most prone to overload when N-1 faults occur in section m; mn is the active power flow through the nth line The positive direction marker parameter of section m, λ mn =1 means the positive direction is the same, λ mn =-1 means the positive direction is opposite.

10. The power system energy storage cluster and new energy coordinated dispatching system according to claim 9, characterized in that: In the maximum carrying capacity calculation module, the entropy weight method is used to evaluate the maximum carrying capacity of each level of nested sections for the output of renewable energy power generation in the region. The calculation formula is as follows: The extreme value method is used to standardize the evaluation indicators, including the installed capacity of power stations in each level of nested sections, the maximum transmission capacity of the section, the priority of new energy units in the section, and the hierarchical order of nested sections. The calculation formula for the standardized processing of the evaluation index as a positive index is as follows: The calculation formula for the standardized treatment of negative evaluation indicators is as follows: Where η mij is the jth index of the i-th power station in the nested section m; and x mij are the maximum and minimum values ​​of the jth index of the i-th power station in the nested section m, respectively; Then, the entropy weight method is used to calculate the maximum output weight of each power station in the nested section m. The maximum carrying capacity of each level of nested sections for the renewable energy power generation output in the region is evaluated according to the maximum output weight. The characteristic proportion p of power station i in nested section m under evaluation index j is mij The calculation formula is as follows: In the formula, is the number of power stations in the nested section m; Information entropy value e mj The calculation formula is as follows: Coefficient of variation μ mj The calculation formula is as follows: μ mj =1-e mj (9); The calculation formula of the weight under the evaluation index j is as follows: In the formula, J is the number of evaluation indicators; The calculation formula for the comprehensive index evaluation score is as follows: Maximum output limit of the wth renewable energy power station in section m The calculation formula is as follows: In the formula, Predict the power generation of the wth renewable energy power station in the nested section m on the date of time period t; The maximum output limit of the power station obtained is the maximum carrying capacity of each level of nested sections for new energy under the new energy power generation curve predicted on a given day, that is, the maximum amount of new energy that can be absorbed by each level of nested sections while ensuring that it does not exceed its maximum transmission capacity.

11. The power system energy storage cluster and new energy coordinated dispatching system according to claim 10, characterized in that: In the collaborative scheduling model construction module, the collaborative scheduling model construction method is: Based on the distributed robust optimization model, the objective function of the collaborative scheduling model is to maximize the consumption of new energy with the carrying capacity of each level of nested sections. The objective function formula is to maximize the consumption of new energy with the carrying capacity of each level of nested sections to minimize the total cost of thermal power generation, wind abandonment penalty, and load shedding penalty during the scheduling period: Where N T is the number of scheduling periods; N G is the number of thermal power units, is the active output variable of thermal power unit g in period t, a g 、b g 、c g N is the coefficient of the quadratic polynomial function of the g consumption characteristic of the thermal power unit; W is the number of wind turbines, is the wind power abandonment variable of wind turbine w in period t, N is the penalty for wind turbine w’s wind abandonment; D is the system load quantity, is the active load shedding variable of load node d in period t, is the load shedding penalty for load node d; The constraints of the collaborative dispatch model include: thermal power unit start and stop constraints, thermal power unit output range constraints, thermal power unit ramp constraints, energy storage cluster operation constraints, wind power unit output range and wind abandonment constraints, node voltage and phase angle constraints, branch flow constraints, nested section power constraints, load shedding constraints, and node power balance constraints. The start-stop constraints of the thermal power unit include the unit operation state conversion constraints and the minimum start-stop time constraints, and the formula is as follows: In the formula, is the start-stop state variable of thermal power unit g in period t, Indicates that the thermal power unit g is in the startup state during period t. Indicates that thermal power unit g is in shutdown state during period t; Indicates that the thermal power unit g changes from shutdown state to startup state in time period t; Indicates that the thermal power unit g changes from the startup state to the shutdown state in time period t; and are the minimum startup and shutdown durations of thermal power unit g respectively; The output range constraints of the thermal power unit are as follows: In the formula, and are the upper and lower limits of the technical output of thermal power unit i respectively; The climbing constraints of the thermal power unit are as follows: In the formula, RU g and RD g are the upward and downward ramp rates of thermal power unit i, respectively, and Δt is the time interval between adjacent time periods; The energy storage cluster operation constraints include cluster power constraints, cluster charge and discharge efficiency constraints, cluster power capacity constraints, and cluster charge and discharge state constraints. The formula is as follows: In the formula, is the charging state variable of the energy storage cluster in the nested section m at time period t, Indicates that the energy storage cluster is in charging state. Indicates that the energy storage cluster is not in charging state; is the discharge state variable of the energy storage cluster in the nested section m in period t, Indicates that the energy storage cluster is in a discharging state. Indicates that the energy storage cluster is not in a discharging state; is the number of energy storage units contained in the energy storage cluster of nested section m; is the charge variable of the energy storage unit in period t, is the discharge variable of the energy storage unit in time period t; and are the upper and lower limits of the charging power of the energy storage cluster of the nested section m respectively; and are the upper and lower limits of the discharge power of the energy storage cluster of the nested section m respectively; is the electric energy variable of the energy storage cluster in the nested section m in time period t; and are the upper and lower limits of the electric energy capacity of the energy storage cluster in the nested section m respectively; and is the charging efficiency and discharging efficiency of the energy storage cluster in the nested section m; The wind turbine output range and wind abandonment constraints are as follows: In the formula, is the maximum allowable output value of wind turbine w in nested section m in time period t. The node voltage and phase angle constraints are as follows: In the formula, v i,t is the voltage amplitude variable of node i in time period t, and v i are the upper and lower limits of the voltage amplitude at node i respectively; θ i,t The voltage phase angle variable of node i in period t, and θ i are the upper limit and lower limit of the voltage phase angle of node i respectively; is the voltage phase angle variable of the balance node; The branch power flow constraints are as follows: In the formula, and are respectively the active power flow variable and reactive power flow variable flowing through branch ij at time period t; ij is the linearization parameter of the power flow formula; θ ij,t G is the voltage phase difference variable of the first and last nodes of branch ij in time period t; ii , B ii , G ij , B ij are the branch admittance matrix parameters, is the upper limit of apparent power of branch ij; and are the upper limits of active power flow and reactive power flow allowed to flow through branch ij respectively; is the maximum allowable active power flow loss value of branch ij; The nested section power constraints are as follows: In the formula, is the set of all branches in the nested section m; ij is the positive direction marking parameter of branch ij in nested section m; The load shedding constraints are as follows: In the formula, is the reactive load shedding variable of load node d in period t; and are the active and reactive loads connected to the load node d respectively. The node power balance constraints are as follows: In the formula, is the set of all thermal power units on node i; is the set of all wind turbines on node i; is the set of all branches ij on node i; ε i is the set of all energy storage clusters on node i; is the set of all loads on node i.

12. The power system energy storage cluster and new energy coordinated dispatching system according to claim 11, characterized in that: In the collaborative scheduling optimal strategy solving module, the solution method of the collaborative scheduling model is: The collaborative scheduling model based on distributed robust optimization is transformed into a mixed integer linear programming model using dual method and linearization technology; In the matrix computing platform Matlab, the Yalmip modeling language is used to write the computational code of the mixed integer linear programming model; Call the Gurobi solver to solve the written mixed integer linear programming model to obtain the optimal strategy for the coordinated dispatch of energy storage clusters and new energy sources; The optimal strategy for coordinated scheduling of energy storage clusters and new energy sources includes: an operation plan for thermal power units within a predetermined scheduling period, including a startup and shutdown plan for thermal power units, and an active power and reactive power output plan for thermal power units; an operation plan for new energy units within a predetermined scheduling period, including an active power output plan for new energy units, and a wind power abandonment strategy for new energy units; an operation plan for energy storage clusters within a predetermined scheduling period, including a charging and discharging plan for energy storage clusters, and a charging power and discharging power strategy for energy storage clusters; a nested section power allocation plan within a predetermined scheduling period, including a power allocation strategy for each time period of the nested section, and a node load shedding strategy.

13. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for coordinated dispatching of a power system energy storage cluster and new energy sources as described in any one of claims 1 to 6 is implemented.

14. An electronic device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor implements the method for coordinated dispatching of energy storage clusters and new energy sources in a power system as described in any one of claims 1 to 6 by executing the computer instructions.

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