Intelligent micro-grid energy storage optimization scheduling system

Abstract

The invention relates to the technical field of intelligent power grids, and discloses an intelligent micro-grid energy storage optimization scheduling system, which comprises a linear programming module used for converting a regulation and control process of energy storage equipment into a linear programming problem, dividing the regulation and control process of the energy storage equipment into N time points, and regulating and controlling the time points in sequence; the target setting module is used for inputting a target function regulated and controlled by the energy storage equipment; the condition constraint module is used for setting constraint conditions for regulation and control of the energy storage equipment; the solution configuration module is used for configuring parameters of a solver and inputting the target function, the decision variable and the constraint condition into the solver for solution; the instruction issuing module is used for generating a regulation and control strategy according to the solving result and issuing the regulation and control strategy to the energy storage equipment for execution; the condition constraint module sets continuous charging and discharging times of the energy storage equipment, and linear processing is carried out on charging and discharging states by introducing a large M variable and a constraint condition; the problems that an existing energy storage scheduling system is complex in operation, slow and lack of conciseness and flexibility are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart grids, and in particular to a smart microgrid energy storage optimization scheduling system. Background Art

[0002] With the transformation of the global energy structure and the development of smart grid technology, microgrids, as an important carrier for the access and efficient use of distributed energy, are increasingly receiving widespread attention. As an important part of microgrids, microgrid energy storage systems can not only effectively balance the contradiction between supply and demand and improve the stability and reliability of the power grid by storing and releasing electrical energy, but also promote the consumption and efficient use of renewable energy.

[0003] In the prior art, for example, Chinese patent application CN 111639866 A discloses a method for configuring the energy storage capacity of an energy storage charging station based on a genetic algorithm. This method achieves the optimal configuration of the energy storage system capacity by establishing a full life cycle cost model for energy storage, an energy storage life loss model, and a capacity configuration model based on a genetic algorithm. This patent uses a heuristic algorithm - a genetic algorithm to perform optimal configuration calculations and obtains the optimal configuration scheme for the charging station capacity. This scheme comprehensively considers the operating performance and economy of the energy storage system, conducts an in-depth analysis of the correctness and feasibility of the integrated system energy storage configuration model, and effectively improves the utilization rate of light energy and the economic benefits of the system.

[0004] However, the heuristic algorithm used still has certain limitations when defining the charging and discharging states of the energy storage system. Although the heuristic algorithm can comprehensively consider multiple factors, its complex constraints and operation logic lead to high computational costs, making it difficult to achieve rapid response in real-time scheduling.

[0005] In addition, with the gradual opening of the power market and the increasing complexity of the electricity price mechanism, especially the price fluctuations in the power market, microgrids need to be more flexible in responding to complex and changing electricity price structures to achieve cost-optimized power dispatch. Since the microgrid energy storage system not only needs to balance supply and demand, but also needs to adjust the charging and discharging strategy according to real-time electricity price signals to maximize economic benefits, traditional dispatch strategies based on fixed rules or simple heuristic algorithms often find it difficult to achieve timely and accurate regulation effects. Summary of the invention

[0006] The present invention aims to provide an intelligent microgrid energy storage optimization scheduling system to solve the problems of complex calculation, slowness, lack of simplicity and flexibility in existing energy storage scheduling systems.

[0007] To achieve the above object, the present invention adopts the following technical solution: a smart microgrid energy storage optimization dispatching system, comprising:

[0008] A linear programming module is used to convert the regulation process of the energy storage device into a linear programming problem, divide the regulation process of the energy storage device into N time points, and regulate each time point in turn;

[0009] A target setting module is used to input the target function of energy storage device regulation;

[0010] The conditional constraint module is used to set the constraint conditions for energy storage equipment regulation;

[0011] The solution configuration module is used to configure the solver parameters and input the objective function, decision variables and constraints into the solver for solving;

[0012] An instruction issuing module is used to generate a control strategy based on the solution results and issue the control strategy to the energy storage device for execution;

[0013] The conditional constraint module specifically sets the number of consecutive charges and discharges of the energy storage device, and linearizes the charge and discharge state by introducing large M variables and constraint conditions, specifically:

[0014] Tctime≤C;

[0015] Tdtime≤C;

[0016] Pc[t]-M*Pc1[t]<=0;

[0017] Pc[t]>=Pc1[t]*epsilon;

[0018] Wherein, Tctime represents the number of consecutive charges within N time points, Tdtime represents the number of consecutive discharges within N time points, C is a preset constant, M is a sufficiently large positive number, epsilon is a sufficiently small positive number, Pc[t] represents the charging or discharging power at time point t, and Pc1[t] represents whether time point t is in the charging or discharging state.

[0019] The principle and advantage of this solution is that in traditional optimization models, the constraints of the charging or discharging state of energy storage devices are usually handled by more direct methods that may be nonlinear or difficult to solve directly. These methods may include:

[0020] Direct nonlinear constraints: Use the charge and discharge states of the energy storage device directly as variables and set nonlinear constraints to limit the number of consecutive charges and discharges. For example, you can define binary variables for charge and discharge and set conditions to limit the number of changes in these variables in consecutive time periods. However, this approach usually results in a very complex model that is difficult to solve using standard linear programming or integer programming solvers.

[0021] Heuristic algorithm: Use heuristic search algorithms (such as genetic algorithms, simulated annealing, etc.) to find solutions that meet the constraints. These methods usually do not need to explicitly define the constraints on the number of consecutive charges and discharges, but automatically adjust the charging and discharging strategies during the search process to meet actual needs through algorithms. However, heuristic algorithms usually cannot guarantee that the global optimal solution will be found, and the calculation time may be long.

[0022] This solution transforms the original nonlinear constraints into linear constraints by introducing large M variables and constraints. With the help of the relationship between the previous and next variables and the auxiliary variables, the continuity constraints are accurately implemented, which improves the accuracy and rationality of the model. This makes the model easier to solve, and the optimal solution can be found using standard linear programming or integer programming solvers. By introducing auxiliary variables and corresponding logical relationships, the number of continuous charging and discharging can be expressed and calculated more flexibly. This method not only makes the model more accurate, but also better reflects the actual operation of the energy storage equipment. Due to the use of linearization processing and auxiliary variables, the system response speed is improved, the difficulty of calculation is simplified, and the system has sufficient spare capacity to cope with complex changes in electricity prices.

[0023] This solution takes full account of the actual operation of the energy storage equipment. By accurately calculating the number of continuous charges and discharges, the model can more accurately reflect the performance and behavior of the energy storage equipment. This accuracy is essential for developing effective control strategies. At the same time, this solution also provides a high degree of flexibility. By adjusting the objective function and constraints, we can easily adapt to different microgrid environments and electricity price structures to achieve personalized energy storage optimization scheduling. This solution effectively solves the problems of complex calculations, slowness, lack of simplicity and flexibility in the existing energy storage scheduling system; and improves the system's response speed and the effective implementation of the control strategy.

[0024] Preferably, as an improvement, the solution configuration module specifically randomly generates a plurality of different initial capacities within a preset range, inputs the different initial capacities into the SCIP solver for solving, obtains a plurality of optimal solutions, and aggregates the plurality of optimal solutions generated by the solver to obtain a global optimal solution.

[0025] The beneficial effect of this improvement is that since the actual operation of the energy storage system may be affected by many factors, such as electricity price fluctuations, load changes, etc., these factors may cause uncertainty and volatility in the initial conditions (such as initial capacity). By randomly generating multiple initial capacities and solving them, the impact of this uncertainty on the solution can be effectively reduced.

[0026] Traditional solution methods often rely on a single initial condition, which may cause the solution to fall into a local optimal solution. However, this improved solution can significantly increase the possibility of obtaining a global optimal solution by introducing multiple initial capacities and performing aggregation processing. This is because different initial conditions may guide the solver to explore different areas of the solution space, thereby increasing the chance of finding a global optimal solution.

[0027] Fine-tuning the initial capacity and aggregating the optimal solutions helps reduce the risk of unstable solutions due to fluctuations in initial conditions. This stability is critical for optimal energy storage scheduling in practical applications because it ensures that the system maintains stable performance under various operating conditions.

[0028] Preferably, as an improvement, the planning module divides the control process into 96 time points, each time point represents 15 minutes, and arranges these time points in time series; sets the charging power in segments, generates a power segment table, so that the energy storage device is charged at the charging time point according to the power demand using the corresponding segment value, and also defines the decision variable as an integer variable.

[0029] The beneficial effect of this improvement is that through precise time point division and power segmentation, the complex nonlinear control problem is transformed into a linear programming problem, which greatly simplifies the model structure and reduces the computational complexity.

[0030] Among them, for a single time point, if charging is not constrained, full charging or full discharging is allowed. This strategy is based on a deep understanding of the actual business, avoiding unnecessary calculations and complexity, significantly reducing the solution space, and improving the solution efficiency. Through reasonable power segmentation settings, more targeted optimization can be carried out, reducing the amount of calculation and speeding up the solution. The use of integer variables is also in line with the actual situation, improving the calculation efficiency and accuracy.

[0031] Preferably, as an improvement, the conditional constraint module specifically marks the user's maximum demand according to the historical power consumption, and sets a constraint condition for limiting the charging power of the energy storage device according to the user's maximum demand, and its expression is:

[0032] Pct*0.25+Ut≤Dmax;

[0033] Among them, Pct represents the charging power at time t, Dmax represents the maximum demand, and Ut represents the expected user power usage at time t.

[0034] The beneficial effect of this improvement is that the maximum charging power is limited in combination with the maximum demand during the charging process, which effectively avoids charging causing a new maximum demand point, thereby reducing the demand electricity cost.

[0035] Preferably, as an improvement, the conditional constraint module further includes:

[0036] Pct*Pdt=0;

[0037] Among them, Pct is the charging power at time t, and Pdt is the discharging power at time t. Charging and discharging cannot be done at the same time.

[0038] The beneficial effect of this improvement is to ensure that the energy storage device does not perform charging and discharging operations at the same time, avoid equipment damage and energy waste, and improve energy utilization efficiency.

[0039] Preferably, as an improvement, the conditional constraint module also includes setting the maximum charging power and the maximum discharging power of the energy storage device, and setting a specific time point for charging or discharging according to the electricity price information and the user's maximum demand.

[0040] The beneficial effect of this improvement is that the discharge operation is specified at the location with the maximum demand or high electricity price, effectively reducing the time-of-use electricity fee and demand electricity fee cost. At the same time, the model can specify the location to continue charging or discharging, meeting the actual business needs and improving the pertinence and practicality of the control strategy.

[0041] Preferably, as an improvement, the conditional constraint module further includes defining the capacity of the energy storage device as:

[0042] E0=Einit;

[0043] E t =E t-1 +0.25*Nc*Pct-0.25*Pdt / Nd,t=1,2,…,96;

[0044] Among them, Einit represents the initial capacity, E t It represents the capacity of energy storage equipment at time t, Nc represents the charging efficiency, and Nd represents the discharging efficiency.

[0045] The beneficial effects of this improvement are: ensuring that the energy storage device does not exceed its capacity limit during operation, protecting the device safety. At the same time, by considering the charging and discharging efficiency, the operating state of the energy storage device can be more accurately simulated and optimized.

[0046] Preferably, as an improvement, the objective function constructed by the objective setting module is:

[0047] C=C1+C2+…+Ct+…C96;

[0048] Ct=0.25*cb x1 *Pct-0.25*cb x2 *Pdt;

[0049] Among them, C represents the overall electricity cost, Ct represents the electricity cost at time t, cbx1 represents the unit price of electricity at time t, cb x2 Indicates the unit price of electricity purchased at the time of energy storage discharge.

[0050] The beneficial effect of this improvement is that by constructing the objective function, the goal and direction of the control strategy can be clarified, that is, minimizing the overall electricity cost. At the same time, considering factors such as the unit price of electricity purchased at the time of energy storage discharge can help to formulate a more reasonable control strategy and reduce electricity costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a schematic structural diagram of an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The following is further described in detail through specific implementation methods:

[0053] Example

[0054] Basically as attached Figure 1 As shown, the smart microgrid energy storage optimization dispatching system includes a linear programming module, a target setting module, a condition constraint module, a solution configuration module and an instruction issuing module.

[0055] The linear programming module is used to convert the regulation process of energy storage equipment into a linear programming problem for efficient solution. Through fine time division and power segmentation, complex nonlinear regulation problems are simplified into linear programming problems, thereby improving solution efficiency and accuracy.

[0056] The linear programming module includes a time division unit, a power segmentation unit, and an integer variable setting unit;

[0057] The time division unit divides the control process into 96 time points, each time point represents 15 minutes, and arranges the 96 time points in time series; in order to more finely simulate and optimize the operating status of the energy storage equipment.

[0058] The power segmentation unit sets the charging power segmentation and generates a power segmentation table, so that the energy storage device can be charged at the corresponding interval value or power value according to the power demand at the charging time, that is, based on a continuous floating interval or based on a discrete integer value. The power segmentation is performed at the time point where charging is required, which reduces the optimization space and speeds up the solution.

[0059] The integer variable setting unit defines the decision variable as an integer variable, and sets the step size and value range of the decision variable to avoid using floating-point numbers and improve calculation efficiency and accuracy.

[0060] As in this embodiment, let Et represent the capacity of the energy storage device at time t, Pct represent the charging power at time t, and Pdt represent the discharging power at time t; if charging is not constrained at a certain time point, full charging or full discharging is allowed.

[0061] The target setting module is used to input the target function of energy storage equipment regulation, that is, minimizing the overall cost. This module constructs the target function by comprehensively considering factors such as electricity purchase cost, energy storage discharge income, and possible maximum demand cost, so as to facilitate subsequent solution and optimization.

[0062] The target setting module includes a cost calculation unit and an objective function construction unit.

[0063] The cost calculation unit is used to obtain electricity price information, historical electricity consumption, etc., and calculate the electricity purchase cost, energy storage discharge income, and possible maximum demand cost, etc.

[0064] The objective function construction unit constructs an objective function for minimizing the overall cost according to the cost calculation result.

[0065] The goal is to minimize the overall cost, which includes electricity purchase costs, energy storage discharge income, and possible maximum demand costs. The overall cost can be expressed as:

[0066] C=C1+C2+…+Ct+…C96;

[0067] Ct=0.25*cb x1 *Pct-0.25*cb x2 *Pdt;

[0068] Among them, C represents the overall electricity cost, Ct represents the electricity cost at time t, cb x1 represents the unit price of electricity at time t, cb x2 Indicates the unit price of electricity purchased at the time of energy storage discharge.

[0069] The conditional constraint module is used to set the constraints for energy storage device regulation, including capacity constraints, power constraints, charge and discharge constraints, maximum demand constraints, continuity constraints, and specific location charge and discharge constraints. These constraints ensure the effectiveness and feasibility of the regulation strategy.

[0070] The conditional constraint module includes a capacity constraint unit, a power constraint unit, a charge and discharge constraint unit, a maximum demand constraint unit, a continuity constraint unit and a specific location charge and discharge constraint unit.

[0071] The capacity constraint unit is used to obtain device parameters and set constraints such as the initial capacity, charging efficiency and discharging efficiency of the energy storage device, so as to set the upper and lower limits of the energy storage device capacity and calculate the capacity change of the energy storage device.

[0072] The capacity constraint can be expressed as:

[0073] E0=Einit;

[0074] E t =E t-1 +0.25*Nc*Pct-0.25*Pdt / Nd,t=1,2,…,96;

[0075] Among them, Einit represents the initial capacity, E t It represents the capacity of energy storage equipment at time t, Nc represents the charging efficiency, and Nd represents the discharging efficiency.

[0076] The power constraint unit sets the maximum charging power and maximum discharging power of the energy storage device. It obtains the device power parameters, sets the upper and lower power limits, and calculates the power change.

[0077] The power constraint can be expressed as:

[0078] 0<=Pct<=Pcmax;

[0079] 0<=Pdt<=Pdmax;

[0080] Among them, Pcmax represents the maximum charging power, and Pdmax represents the maximum discharging power.

[0081] The charge and discharge constraint unit sets the constraint condition that charging and discharging cannot be performed at the same time, checks the charge and discharge status, and sets the charge and discharge mutual exclusion conditions.

[0082] The charge and discharge constraints can be expressed as:

[0083] Pct*Pdt=0;

[0084] That is, charging and discharging cannot be done at the same time.

[0085] The maximum demand constraint unit marks the user's maximum demand according to the historical power consumption obtained by the target setting module, and sets the constraint conditions for limiting the charging power of the energy storage device according to the user's maximum demand. Mark the user's maximum demand information, set the upper limit of the charging power, and calculate the demand fee.

[0086] The maximum demand constraint can be expressed as:

[0087] Pct*0.25+Ut≤Dmax;

[0088] Among them, Dmax represents the maximum demand, and Ut represents the power expected to be used by the user at time t.

[0089] The continuity constraint unit sets the constraint conditions for the number of continuous charging and discharging times of the energy storage device. It introduces large M variables and constraint conditions for linear processing, introduces auxiliary variables and logical relationships to implement continuity constraints, and calculates the number of continuous charging and discharging times.

[0090] The continuity constraint can be expressed as:

[0091] Tctime≤2;

[0092] Tdtime≤2;

[0093] Among them, Tctime represents the number of consecutive charges within 96 time points, and Tdtime represents the number of consecutive discharges within 96 time points.

[0094] When dealing with continuity constraints, linearization is performed by introducing large M variables and constraints to map values ​​greater than 0 to 1.

[0095] For example, for the continuous charging restriction, you can define a new variable to indicate whether it is in the charging state, and then add the constraint:

[0096] Pc[t]-M*Pc1[t]<=0;

[0097] Pc[t]>=Pc1[t]*epsilon;

[0098] Among them, M is a sufficiently large positive number, epsilon is a sufficiently small positive number, Pc[t] represents the charging power at time point t (positive value represents charging, negative value represents discharging), and Pc1[t] represents whether the time point t is in a charging state (1 represents charging, 0 represents not charging).

[0099] That is, if Pc[t]>0, then Pc1[t]=1; if Pc[t]=0, then Pc1[t]=0. The limitation of continuous discharge is the same.

[0100] By introducing auxiliary variables and corresponding logical relationships, the values ​​of the previous and next variables and the value of the third variable are used to calculate the number of "and" and "or", thereby realizing the continuity constraint. This embodiment is to realize the 2-charge 2-discharge continuity constraint, that is, by adding new variables to judge the constraints of the data in the group and the or constraint, the number of charges and the number of discharges are counted, and the constraint is less than or equal to 2 to be realized.

[0101] The specific location charge and discharge constraint unit sets the constraint conditions for charge and discharge at a specific time point, such as forced discharge at the time of maximum demand or high price. The target setting module obtains the electricity price information, calculates the electricity price at each time point, sets the charge and discharge conditions, and calculates the charge and discharge costs at a specific location. The charge and discharge constraints at a specific time point are set according to the user's maximum demand, green power consumption and electricity price, such as forced discharge at the time of maximum demand or high price.

[0102] Solving configuration module, the solving configuration module is responsible for configuring the solver parameters, inputting the objective function, decision variables and constraints into the solver, and solving. This module is also responsible for dealing with the problem that the initial capacity fluctuation may lead to the local optimal solution, by fine-tuning the initial capacity and aggregating multiple optimal solutions to try to obtain the global optimal solution.

[0103] The solution configuration module includes a solver initialization unit, a model input unit and an optimal solution aggregation unit.

[0104] Solver initialization unit initializes the solver, sets parameters such as solution time limit and accuracy, selects the SCIP solver, configures solver parameters and starts the solver.

[0105] The model input unit inputs the objective function, decision variables and constraints into the solver, builds the model data structure, converts the model format, and inputs the solver.

[0106] The initial capacity fine-tuning unit fine-tunes the initial capacity and generates multiple different initial capacity values ​​in order to perform multiple solutions, determine the fine-tuning range, generate the initial capacity sequence, and update the model parameters.

[0107] The optimal solution aggregation unit aggregates multiple optimal solutions generated by the solver, such as taking the average or selecting the best solutions for comprehensive analysis to obtain the global optimal solution. It collects optimal solution data, calculates aggregation results, and generates a global optimal solution report.

[0108] The instruction issuing module issues the control strategy to the energy storage device according to the solution result, analyzes the solution result, generates the control strategy and issues it to the device for execution. That is, at time t, the corresponding charging power or discharging power value is in the charging state or discharging state.

[0109] The implementation method of the smart microgrid energy storage optimization dispatching system is as follows:

[0110] Step 1: Define an array of length 96 by the linear programming module to store data at each time point; define a power segment table containing multiple power intervals and corresponding charging power values; use integer variables to represent the capacity, charging power, and discharging power of the energy storage device.

[0111] Step 2: The target setting module obtains electricity price information, historical electricity consumption and other data from the data source, and uses mathematical expressions to construct the target function, including items such as electricity purchase costs, energy storage discharge income and demand costs. The electricity purchase costs and energy storage discharge income are calculated based on the electricity price information and electricity consumption, and the demand costs are calculated based on the user's maximum demand.

[0112] Step 3: The conditional constraint module obtains device parameters, including initial capacity, charging efficiency, and discharging efficiency, etc., sets upper and lower limits of the energy storage device capacity according to the device parameters, and calculates the capacity change of the energy storage device to ensure that it is within the constraint range;

[0113] Obtain equipment power parameters, including maximum charging power and maximum discharging power, etc.; set upper and lower power limits to ensure that the charging and discharging power of the energy storage equipment is within the constraints;

[0114] Use logical judgment statements to check the charge and discharge status at the same time point, set the charge and discharge mutual exclusion conditions, and ensure that only charging or discharging operations can be performed at the same time point;

[0115] Mark the user's maximum demand information and set the upper limit of charging power to ensure that the charging power of the energy storage device does not exceed the user's maximum demand;

[0116] Introduce large M variables and constraints for linearization, use auxiliary variables and logical relationships to implement continuity constraints, calculate the number of continuous charges and discharges, and ensure that it is within the constraints;

[0117] The charging and discharging conditions at a specific time are set according to the electricity price information and the user's maximum demand, and the charging and discharging costs at a specific location are calculated to ensure that the energy storage equipment performs appropriate charging and discharging operations at a specific time.

[0118] Step 4: The solution configuration module constructs the model data structure and converts the objective function, decision variables and constraints into a format recognizable by the solver; determines the fine-tuning range and generates an initial capacity sequence; updates the model parameters and inputs different initial capacity values ​​into the solver for multiple solutions; collects multiple optimal solution data generated by the solver, calculates the aggregated results, and generates a global optimal solution.

[0119] Step 5: The instruction issuing module analyzes the solution result, generates a control strategy, and sends the control strategy to the energy storage device for execution, including the charging state, the discharging state, and the corresponding charging power or discharging power value.

[0120] The above is only an embodiment of the present invention, and the common knowledge such as the known specific technical solutions and / or characteristics in the solution is not described in detail here. It should be pointed out that for those skilled in the art, without departing from the technical solution of the present invention, several modifications and improvements can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. Smart microgrid energy storage optimization dispatching system, characterized by: include: A linear programming module is used to convert the regulation process of the energy storage device into a linear programming problem, divide the regulation process of the energy storage device into N time points, and regulate each time point in turn; A target setting module is used to input the target function of energy storage device regulation; The conditional constraint module is used to set the constraint conditions for energy storage equipment regulation; The solution configuration module is used to configure the solver parameters and input the objective function, decision variables and constraints into the solver for solving; An instruction issuing module is used to generate a control strategy based on the solution results and issue the control strategy to the energy storage device for execution; The conditional constraint module specifically sets the number of consecutive charges and discharges of the energy storage device, and linearizes the charge and discharge state by introducing large M variables and constraint conditions, specifically: Tctime≤C; Tdtime≤C; Pc[t]-M*Pc1[t]<=0; Pc[t]>=Pc1[t]*epsilon; Wherein, Tctime represents the number of consecutive charges within N time points, Tdtime represents the number of consecutive discharges within N time points, C is a preset constant, M is a sufficiently large positive number, epsilon is a sufficiently small positive number, Pc[t] represents the charging or discharging power at time point t, and Pc1[t] represents whether time point t is in the charging or discharging state.

2. The smart microgrid energy storage optimization dispatching system according to claim 1 is characterized in that: Specifically, the solution configuration module randomly generates a plurality of different initial capacities within a preset range, inputs the different initial capacities into the SCIP solver for solving, obtains a plurality of optimal solutions, and aggregates the plurality of optimal solutions generated by the solver to obtain a global optimal solution.

3. The smart microgrid energy storage optimization dispatching system according to claim 2 is characterized in that: The planning module divides the control process into 96 time points, each time point represents 15 minutes, and arranges these time points in time series; sets the charging power in segments and generates a power segment table so that the energy storage device is charged at the charging time point according to the power demand using the corresponding segment value, and also defines the decision variable as an integer variable.

4. The smart microgrid energy storage optimization dispatching system according to claim 3 is characterized in that: Specifically, the conditional constraint module marks the maximum demand of the user according to the historical power consumption, and sets the constraint condition for limiting the charging power of the energy storage device according to the maximum demand of the user, and its expression is: Pct*0.25+Ut≤Dmax; Among them, Pct represents the charging power at time t, Dmax represents the maximum demand, and Ut represents the expected user power usage at time t.

5. The smart microgrid energy storage optimization dispatching system according to claim 4 is characterized in that: The conditional constraint module also includes: Pct*Pdt=0; Among them, Pct is the charging power at time t, and Pdt is the discharging power at time t. Charging and discharging cannot be done at the same time.

6. The smart microgrid energy storage optimization dispatching system according to claim 5 is characterized in that: The conditional constraint module also includes setting the maximum charging power and the maximum discharging power of the energy storage device, and setting a specific time point for charging or discharging according to the electricity price information and the user's maximum demand.

7. The smart microgrid energy storage optimization dispatching system according to claim 6 is characterized in that: The conditional constraint module also includes defining the capacity of the energy storage device as: E0=Einit; IN t =E t-1 +0.25*Nc*Pct-0.25*Pdt / Nd,t=1,2,…,96; Among them, Einit represents the initial capacity, E t It represents the capacity of energy storage equipment at time t, Nc represents the charging efficiency, and Nd represents the discharging efficiency.

8. The smart microgrid energy storage optimization dispatching system according to claim 7 is characterized in that: The objective function constructed by the objective setting module is: C=C1+C2+…+Ct+…C96; Ct=0.25*cb x1 *Pct-0.25*cb x2 *Pdt; Among them, C represents the overall electricity cost, Ct represents the electricity cost at time t, cb x1 represents the unit price of electricity at time t, cb x2 Indicates the unit price of electricity purchased at the time of energy storage discharge.

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