Micro-grid energy storage optimal dispatching method based on absolute value linearization
By using absolute value linearization modeling and branch-and-bound algorithms, the nonlinear solution problem in microgrid energy storage optimization is solved, achieving a balance between economy and battery life, and improving solution efficiency and renewable energy utilization.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHONGQING UNIV OF POSTS & TELECOMM
- Filing Date
- 2026-02-12
- Publication Date
- 2026-05-29
AI Technical Summary
Microgrid energy storage optimization scheduling faces challenges due to nonlinear characteristics, resulting in high difficulty in solving problems and long computation time. It is difficult to balance economic objectives with battery life objectives. Traditional methods cannot effectively track dynamic changes, and monthly or annual optimization problems are large in scale and difficult to obtain high-quality solutions within a reasonable time.
By employing absolute value linearization modeling, the nonlinear programming problem is transformed into a mixed-integer linear programming problem. Combining the branch-and-bound algorithm and adaptive heuristic strategy, and by introducing auxiliary variables and penalty terms, linear equations or inequalities are constructed to optimize the charging and discharging and grid interaction plans of the energy storage system.
It significantly improves the solution efficiency of microgrid energy storage optimization, extends battery life, improves the utilization rate of renewable energy, achieves a balance between economy and battery life, and shortens the calculation time.
Smart Images
Figure CN122118870A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of energy management and automatic control technology, and relates to a microgrid energy storage optimization scheduling method based on absolute value linearization. Background Technology
[0002] Optimal scheduling of microgrid energy storage systems is a key technology for ensuring the efficient utilization of renewable energy and the stable operation of the power system. In scenarios with high renewable energy penetration and complex electricity pricing mechanisms, the optimal scheduling of energy storage systems faces the following challenges: Microgrid energy storage optimization involves multiple nonlinear objectives and constraints, such as battery life, cycle depth, and power smoothing, resulting in a highly nonlinear optimization problem that is difficult to solve and computationally time-consuming; load demand, renewable energy output, and electricity price signals exhibit rapid time-varying characteristics, and existing traditional optimization algorithms for static or simple scenarios cannot effectively track these dynamic changes; there is an inherent contradiction between economic objectives and battery life objectives, and traditional methods often struggle to find the optimal balance between the two; monthly or annual-scale optimization scheduling problems are large-scale, involving thousands of variables and constraints, making it difficult for nonlinear models to obtain high-quality solutions within a reasonable timeframe. Summary of the Invention
[0003] In view of this, the purpose of this invention is to provide a microgrid energy storage optimization scheduling method based on absolute value linearization.
[0004] To achieve the above objectives, the present invention provides the following technical solution: A microgrid energy storage optimization scheduling method based on absolute value linearization includes the following steps: S1: System Modeling and Multi-Objective Transformation: Construct a microgrid hybrid integer nonlinear programming model that includes renewable energy output, load demand, energy storage system dynamics, and grid interaction constraints; transform the objectives of battery life protection, power smoothing, and renewable energy consumption into penalty terms with weighted coefficients and incorporate them into the objective function that minimizes total operating cost; S2: Linearization and Reconstruction of Nonlinear Problems: Using the absolute value linearization modeling method, for the nonlinear terms in the objective function and constraints, by introducing auxiliary variables and constructing linear equations or inequalities, the mixed integer nonlinear programming model is equivalently transformed into a mixed integer linear programming model. S3: Efficient solution of linear models: The transformed mixed integer linear programming model is solved using a branch and bound algorithm framework. During the solution process, an adaptive heuristic strategy is used to select variable branches and nodes to obtain the optimal charging and discharging power, grid interaction power, and renewable energy curtailment plan for the energy storage system.
[0005] Furthermore, the microgrid hybrid integer nonlinear programming model described in step S1 includes battery cycle depth, SOC deviation from the target value, power change rate, deep discharge penalty term, and overcharge penalty term, wherein: Battery cycle depth is defined as the difference between the daily maximum and minimum SOC, expressed as:
[0006] SOC deviates from the target value ; The deep discharge threshold is ; Overcharging threshold is .
[0007] Furthermore, (1) by modeling with absolute value linearization, the nonlinear cyclic depth constraint is transformed into a linear form, including the following steps: Introduce auxiliary variables to represent daily Maximum and minimum values:
[0008]
[0009] Define the loop depth variable:
[0010] Linearize the target cycle depth deviation and set the target value. :
[0011] in ; Add a penalty term to the objective function:
[0012] (2) By modeling with absolute value linearization, the nonlinearity is transformed. Transforming deviations from the target value constraint into a linear form includes the following steps: Introduce auxiliary variables:
[0013] Add linear constraints:
[0014] Add a penalty term to the objective function:
[0015] (3) By modeling with absolute value linearization, the nonlinear power change rate constraint is transformed into a linear form, including the following steps: Linearization of the rate of change of charging power:
[0016] in ; Add a rate of change limit:
[0017] in The maximum permissible rate of change; The rate of change of discharge power was processed using the same method; Add a smoothing penalty term to the objective function:
[0018] (4) The deep discharge penalty constraint is transformed into a linear form through absolute value linearization modeling, including the following steps: Introduce auxiliary variables:
[0019] Add linear constraints:
[0020] Add a penalty term to the objective function:
[0021] (5) Transform the overcharging penalty constraint into a linear form through absolute value linearization modeling, including the following steps: Introduce auxiliary variables:
[0022] Add linear constraints:
[0023] Add a penalty term to the objective function: .
[0024] Furthermore, in step S2, the absolute value linearization modeling method is used to equivalently transform the mixed-integer nonlinear programming model into a mixed-integer linear programming model, specifically including the following steps: make:
[0025]
[0026] in For charging power, For discharge power, The battery is in its state of charge. To purchase electricity from the grid, To sell electricity to the grid, For the curtailed power of photovoltaic power, This refers to the amount of wind power that has been abandoned. This is a binary variable representing the charging state. This is a binary variable representing the discharge state. Linear modeling is performed on battery cycle depth, SOC deviation from target value, power change rate, deep discharge penalty term, and overcharge penalty term, transforming the original nonlinear optimization problem into a mixed-integer linear programming problem, and defining the total state variable vector:
[0027] The linearized constraints are expressed as follows:
[0028]
[0029] The objective function is to minimize the weighted total cost, which includes economic costs and lifetime protection penalties.
[0030] The definitions of each item are as follows: Grid interaction costs:
[0031] Battery degradation costs:
[0032] Penalties for curtailing renewable energy:
[0033] Loop depth penalty:
[0034] SOC deviation penalty:
[0035] Power smoothing penalty:
[0036] Deep discharge penalty:
[0037] Overcharging penalty:
[0038] Economic incentives:
[0039] By linearizing the problem, the optimization problem is transformed into a standard mixed-integer linear programming model:
[0040]
[0041]
[0042]
[0043] .
[0044] Furthermore, step S3 involves solving the transformed mixed-integer linear programming model using a branch and bound algorithm framework, including the following steps: S31: Initialization: Solve the linear programming relaxation problem to obtain the initial lower bound; S32: Node selection: Select the node with the optimal lower bound for processing; S33: Branching operation: Select a fraction variable to branch and generate subproblems; S34: Bounding operation: Calculate the objective value of the subproblem and update the global bound; S35: Pruning operation: Eliminate subproblems that cannot contain the optimal solution; S36: Convergence Judgment: Check the convergence condition, repeat steps S32-S35 until convergence, output the optimization result, and apply the optimization result to the actual microgrid energy storage system scheduling.
[0045] Furthermore, the adaptive heuristic strategy described in step S3 includes: Variable selection heuristic: Selecting branch variables based on pseudo-cost information; Node selection heuristic: Use the best-bound-first strategy to select the processing node; Cutting plane generation: Automatically generates valid inequalities to tighten the feasible region; Preprocessing techniques: Reduce problem size by fixing variables and simplifying constraints.
[0046] The beneficial effects of this invention are as follows: This method achieves significant benefits in different scenarios. Through innovative linearization modeling and multi-objective coordination mechanism, this method successfully solves the core contradiction in microgrid optimization where it is difficult to balance economy, battery life and computational efficiency. Through innovative absolute value linearization framework and multi-objective coordination mechanism, this invention successfully solves the industry problem of difficulty in balancing economy and battery life and low efficiency in solving large-scale problems in microgrid energy storage optimization. It provides a solution with both theoretical innovation and significant engineering application value for the efficient and economical operation of microgrids with a high proportion of renewable energy access.
[0047] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description
[0048] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein: Figure 1 This is a diagram of the microgrid energy storage system optimization scheduling system architecture based on absolute value linearization in an embodiment of the present invention; Figure 2 This is a schematic diagram of the absolute value linearization principle in an embodiment of the present invention; where (a) is the original absolute value function, (b) is the auxiliary variable decomposition, and (c) is the linearization reconstruction result; Figure 3 This is a diagram of the linearization modeling of the loop depth in an embodiment of the present invention; where (a) is the original SOC curve and the loop depth, (b) is the linearization constraint condition, and (c) is the linearization modeling of the loop depth. Figure 4 This is a linearization diagram of SOC deviation from the target value in an embodiment of the present invention; where (a) is the SOC curve and the target value, (b) is the decomposition of positive and negative SOC deviation, and (c) is the linearization penalty mechanism; Figure 5 The diagram shows the linearization of the power change rate in this embodiment of the invention; where (a) is the original charge and discharge power curve, (b) is the power change rate analysis, and (c) is the linearization smoothing penalty. Figure 6 The following is a linearization diagram of deep discharge penalty in an embodiment of the present invention; where (a) is the SOC curve and the deep discharge region, (b) is the linearization of the deep discharge variable, and (c) is the deep discharge penalty mechanism; Figure 7 The following is a performance comparison chart in the embodiments of the present invention; where (a) is a comparison of monthly operating costs, (b) is a comparison of solution time, and (c) is a comparison of loop depth control. Detailed Implementation
[0049] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.
[0050] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.
[0051] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.
[0052] Example 1: like Figure 1 As shown, this invention provides a microgrid energy storage optimization scheduling method based on absolute value linearization. The Basis Expansion Model for Absolute Value Linearization (BEM-AVL) is used to describe complex nonlinear optimization problems, reconstructing the nonlinear optimization into a linear coefficient estimation problem. Through systematic linearization techniques, the complex nonlinear optimization problem is transformed into a mixed-integer linear programming problem, significantly improving solution efficiency and practicality. Simulations and application examples demonstrate that this method can effectively extend battery life and improve renewable energy utilization while ensuring economic efficiency, providing an effective solution for the optimized operation of microgrid energy storage systems. This method mainly includes: 1. Construct a mixed-integer nonlinear programming model for a microgrid system that includes renewable energy, loads, energy storage, and grid interaction; 2. Analyze how the multi-objective collaborative optimization problem involving battery life protection and power smoothing can be transformed into a weighted total cost minimization problem with a penalty term; 3. The nonlinear programming problem is transformed into a mixed-integer linear programming problem by using the absolute value linearization modeling method; 4. The branch and bound algorithm is used to solve the transformed linear programming model to obtain the globally optimal energy storage charging and discharging and grid interaction scheduling plan.
[0053] Finally, simulation experiments confirmed the comprehensive effectiveness of the proposed optimized scheduling method in terms of economy, battery life protection, and renewable energy consumption.
[0054] Assume the microgrid system includes the following parameters: Rated capacity of energy storage battery (kWh) Maximum charge / discharge power of the battery (kW) Charging efficiency ( ) Discharge efficiency ( ) Minimum permissible state of charge (%) of battery Maximum permissible state of charge (%) of battery Optimize the total duration of the scheduling cycle (in hours, typically 720 hours). Time resolution (typically 1 hour) Renewable energy parameters: : t Forecasted photovoltaic power generation at any given time (kW) : t Forecast wind power generation at any given time (kW) Load and grid parameters: : t Forecast load power at any given time (kW) : t Real-time electricity purchase price (RMB / kWh) : t Electricity price per kWh (RMB) Maximum grid interconnection power (kW) For each time point t (t=1,2,...,T), the following decision variables are defined: Charging power (kW), a continuous variable.
[0055] Discharge power (kW), a continuous variable.
[0056] Battery state of charge (%), a continuous variable.
[0057] Power purchased from the grid (kW), a continuous variable.
[0058] Power sold to the grid (kW), a continuous variable.
[0059] : Photovoltaic curtailment power (kW), a continuous variable
[0060] Wind power curtailment (kW), a continuous variable.
[0061] : Binary variable representing charging status, {0,1} : Binary variable representing discharge state, {0,1} The optimal scheduling problem of microgrid energy storage systems is essentially a complex optimization problem involving multiple nonlinear objective terms and constraints. Absolute value linearization modeling transforms the nonlinear optimization problem into a basic coefficient estimation problem, eliminating the need for prior information on complex nonlinear solvers and thus avoiding a complex nonlinear solution process.
[0062] make:
[0063]
[0064] By using absolute value linearization modeling, nonlinear expressions in optimization problems can be represented in linear form.
[0065] The linearization model of the absolute value expression is as follows: Absolute value linearization modeling transforms nonlinear optimization problems into linear coefficient estimation problems, eliminating the need for prior nonlinear solver information and thus avoiding complex nonlinear solution processes. For absolute value expressions... The linearization method is as follows: 1. Introduce two non-negative auxiliary variables:
[0066] 2. Add linear constraints:
[0067] 3. Use in the objective function Alternative
[0068] This linearization method is mathematically equivalent: when hour, Therefore ;when hour, Therefore .
[0069] Battery cycle depth is defined as the difference between the daily maximum and minimum SOC, expressed as:
[0070] By using absolute value linearization modeling, the nonlinear cyclic depth constraint is transformed into a linear form: The absolute value linearization model of the loop depth is as follows: 1. Introduce auxiliary variables to represent daily... Maximum and minimum values:
[0071]
[0072] 2. Define the loop depth variable:
[0073] 3. Linearize the target cycle depth deviation (set target value) ):
[0074] in
[0075] 4. Add a penalty term to the objective function:
[0076] The linearization modeling of SOC deviation from the target value is as follows: set up Target value The linearization deviation expression is: 1. Introduce auxiliary variables:
[0077] 2. Add linear constraints:
[0078] 3. Add a penalty term to the objective function:
[0079] The linearization model for the rate of change of power is as follows: To limit drastic fluctuations in charging and discharging power, the power change rate is modeled linearly: 1. Linearization of charging power change rate:
[0080] in
[0081] 2. Add a rate of change limit:
[0082] in Maximum allowable rate of change 3. The discharge power change rate is processed using the same method. 4. Add a smoothing penalty term to the objective function:
[0083] The linearization model for deep discharge penalty is as follows: Set deep discharge threshold The method for linearizing the penalty term is as follows: 1. Introduce auxiliary variables:
[0084] 2. Add linear constraints:
[0085] 3. Add a penalty term to the objective function:
[0086] The linearization model for the overcharging penalty is as follows: Set overcharge threshold The method for linearizing the penalty term is as follows: 1. Introduce auxiliary variables:
[0087] 2. Add linear constraints:
[0088] 3. Add a penalty term to the objective function:
[0089] Using the linearization method described above, the original nonlinear optimization problem is transformed into a mixed-integer linear programming problem. Define the total state variable vector:
[0090] The linearized constraints can then be expressed as:
[0091]
[0092] The optimization objective is to minimize the weighted total cost, which includes economic costs and lifetime protection penalties.
[0093] The definitions of each item are as follows: 1. Grid interaction costs:
[0094] 2. Battery degradation costs:
[0095] 3. Penalties for curtailing renewable energy:
[0096] 4. Loop depth penalty:
[0097] 5. SOC deviation penalty:
[0098] 6. Power smoothing penalty:
[0099] 7. Deep discharge penalty:
[0100] 8. Overcharging penalty:
[0101] 9. Economic incentives (to encourage discharge):
[0102] Through the linearization process described above, the optimization problem is transformed into a standard mixed-integer linear programming model:
[0103]
[0104]
[0105]
[0106]
[0107] In complex microgrid scenarios, the multivariable and nonlinear characteristics of system operating conditions pose significant challenges to optimal scheduling. This invention proposes an efficient branch-and-bound solution method based on absolute value linearization modeling. BEM-AVL first transforms the nonlinear optimization problem into a mixed-integer linear programming problem. Subsequently, the branch-and-bound method systematically searches the feasible solution space through branching, bounding, and pruning processes to find the global optimum.
[0108] The branch and bound algorithm process is as follows: (1) Initialization: Solve the linear programming relaxation problem to obtain the initial lower bound; (2) Node selection: Select the node with the optimal lower bound for processing; (3) Branch operation: Select the fractional variable to branch and generate subproblems; (4) Bounding operation: Calculate the target value of the subproblem and update the global bound; (5) Pruning operation: Eliminate subproblems that cannot contain the optimal solution; (6) Convergence judgment: Check the convergence condition and repeat steps (2)-(5) until convergence.
[0109] To improve solution efficiency, the following adaptive heuristic strategies are adopted: Variable selection heuristic: branch variables are selected based on pseudo-cost information; Node selection heuristic: the best bound priority strategy is used to select processing nodes; Cutting plane generation: effective inequalities are automatically generated to tighten the feasible region; Preprocessing techniques: the problem size is reduced through variable fixing, constraint simplification and other techniques.
[0110] The overall steps of this method are as follows: (1) Input system parameters, prediction data, and optimization parameters; (2) Construct a mixed-integer linear programming model based on BEM-AVL; (3) The branch and bound algorithm is used to solve the linear programming relaxation problem to obtain the initial bound; (4) Apply an adaptive heuristic strategy to generate and process branch nodes; (5) Perform bounding and pruning operations to update the global optimal solution; (6) Check the convergence condition. If it does not converge, repeat steps 4-5. (7) Output optimization results, including charging and discharging plans, grid interaction plans, etc.; (8) Apply the optimization results to the actual microgrid energy storage system scheduling.
[0111] The simulation parameters are shown in Table 1.
[0112] Table 1
[0113] The results system verifies the superior performance of the proposed absolute value linearization modeling method in the optimal scheduling of energy storage in microgrids. Figure 7As shown in (a)-(c), this method reduces monthly operating costs compared to traditional nonlinear optimization, while precisely controlling the average battery cycle depth within the ideal range of 41.2%, significantly extending battery life compared to traditional methods. In terms of solution efficiency, this method achieves an order-of-magnitude improvement, greatly reducing the solution time for the 720-hour monthly optimization problem.
[0114] Figure 2-6 (a)-(c) in the paper reveal the source of the performance advantage. Through systematic absolute value linearization technology, nonlinear constraints such as cycle depth penalty, SOC deviation, and power change rate are successfully transformed into mixed integer linear programming problems, which greatly improves computational feasibility while ensuring model accuracy.
[0115] Practical application results show that this method achieves significant benefits in various scenarios. This method, through innovative linearization modeling and a multi-objective coordination mechanism, successfully solves the core contradiction in microgrid optimization where it is difficult to balance economy, battery life, and computational efficiency. In summary, this invention, through an innovative absolute value linearization framework and a multi-objective coordination mechanism, successfully solves the industry problem of balancing economy and battery life, as well as low efficiency in solving large-scale problems, in microgrid energy storage optimization. It provides a solution with both theoretical innovation and significant engineering application value for the efficient and economical operation of microgrids with high proportions of renewable energy integration.
[0116] Example 2: An electronic device, comprising a memory and a processor; The memory is used to store computer programs; The processor is configured to implement the method described in Embodiment 1 when executing the computer program.
[0117] Example 3: A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in Embodiment 1.
[0118] Example 4: A computer program product includes a computer program that, when executed by a processor, implements the method described in Example 1.
[0119] In the above embodiments, the reference to "this embodiment" in the specification indicates that a specific feature, structure, or characteristic described in connection with the embodiment is included in at least some embodiments, but not necessarily all embodiments. Multiple appearances of "this embodiment" do not necessarily refer to the same embodiment.
[0120] In the above embodiments, although the invention has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory structures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed. The embodiments of the invention are intended to cover all such substitutions, modifications, and variations falling within the broad scope of the appended claims.
[0121] As will be understood by those skilled in the art, the computer-readable storage medium described in this embodiment allows for the implementation of all or part of the steps in the above method embodiments by computer program-related hardware. The aforementioned computer program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.
[0122] The electronic terminal provided in this embodiment includes a processor, a memory, a transceiver, and a communication interface. The memory and the communication interface are connected to the processor and the transceiver and complete communication between them. The memory is used to store computer programs, the communication interface is used to perform communication, and the processor and the transceiver are used to run the computer programs, so that the electronic terminal performs the various steps of the above method.
[0123] In this embodiment, the memory may include random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device.
[0124] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.
[0125] This invention can be used in a wide range of general-purpose or special-purpose computing system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices, etc.
[0126] This invention can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This invention can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.
[0127] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.
Claims
1. A microgrid energy storage optimization scheduling method based on absolute value linearization, characterized in that: Includes the following steps: S1: System Modeling and Multi-Objective Transformation: Construct a microgrid hybrid integer nonlinear programming model that includes renewable energy output, load demand, energy storage system dynamics, and grid interaction constraints; transform the objectives of battery life protection, power smoothing, and renewable energy consumption into penalty terms with weighted coefficients and incorporate them into the objective function that minimizes total operating cost; S2: Linearization and Reconstruction of Nonlinear Problems: Using the absolute value linearization modeling method, for the nonlinear terms in the objective function and constraints, by introducing auxiliary variables and constructing linear equations or inequalities, the mixed integer nonlinear programming model is equivalently transformed into a mixed integer linear programming model. S3: Efficient solution of linear models: The branch and bound algorithm framework is used to solve the transformed mixed integer linear programming model; During the solution process, an adaptive heuristic strategy is used to select variables and nodes in order to obtain the optimal charging and discharging power, grid interaction power, and renewable energy curtailment plan for the energy storage system.
2. The microgrid energy storage optimization scheduling method based on absolute value linearization according to claim 1, characterized in that: The microgrid hybrid integer nonlinear programming model described in step S1 includes battery cycle depth, SOC deviation from target value, power change rate, deep discharge penalty term, and overcharge penalty term, wherein: Battery cycle depth is defined as the difference between the daily maximum and minimum SOC, expressed as: SOC deviates from the target value ; The deep discharge threshold is ; Overcharging threshold is .
3. The microgrid energy storage optimization scheduling method based on absolute value linearization according to claim 1, characterized in that: (1) By modeling with absolute value linearization, the nonlinear cyclic depth constraint is transformed into a linear form, including the following steps: Introduce auxiliary variables to represent daily Maximum and minimum values: Define the loop depth variable: Linearize the target cycle depth deviation and set the target value. : in ; Add a penalty term to the objective function: (2) By modeling with absolute value linearization, the nonlinearity is transformed. Transforming deviations from the target value constraint into a linear form includes the following steps: Introduce auxiliary variables: Add linear constraints: Add a penalty term to the objective function: (3) By modeling with absolute value linearization, the nonlinear power change rate constraint is transformed into a linear form, including the following steps: Linearization of the rate of change of charging power: in ; Add a rate of change limit: in The maximum permissible rate of change; The rate of change of discharge power was processed using the same method; Add a smoothing penalty term to the objective function: (4) The deep discharge penalty constraint is transformed into a linear form through absolute value linearization modeling, including the following steps: Introduce auxiliary variables: Add linear constraints: Add a penalty term to the objective function: (5) Transform the overcharging penalty constraint into a linear form through absolute value linearization modeling, including the following steps: Introduce auxiliary variables: Add linear constraints: Add a penalty term to the objective function: 。 4. The microgrid energy storage optimization scheduling method based on absolute value linearization according to claim 3, characterized in that: In step S2, the absolute value linearization modeling method is used to equivalently transform the mixed-integer nonlinear programming model into a mixed-integer linear programming model. This specifically includes the following steps: make: in For charging power, For discharge power, The battery is in its state of charge. To purchase electricity from the grid, To sell electricity to the grid, For the curtailed power of photovoltaic power, This refers to the amount of wind power that has been abandoned. This is a binary variable representing the charging state. This is a binary variable representing the discharge state. Linear modeling is performed on battery cycle depth, SOC deviation from target value, power change rate, deep discharge penalty term, and overcharge penalty term, transforming the original nonlinear optimization problem into a mixed-integer linear programming problem, and defining the total state variable vector: The linearized constraints are expressed as follows: The objective function is to minimize the weighted total cost, which includes economic costs and lifetime protection penalties. The definitions of each item are as follows: Grid interaction costs: Battery degradation costs: Penalties for curtailing renewable energy: Loop depth penalty: SOC deviation penalty: Power smoothing penalty: Deep discharge penalty: Overcharging penalty: Economic incentives: By linearizing the problem, the optimization problem is transformed into a standard mixed-integer linear programming model: 。 5. The microgrid energy storage optimization scheduling method based on absolute value linearization according to claim 1, characterized in that: Step S3 describes solving the transformed mixed-integer linear programming model using a branch and bound algorithm framework, which includes the following steps: S31: Initialization: Solve the linear programming relaxation problem to obtain the initial lower bound; S32: Node selection: Select the node with the optimal lower bound for processing; S33: Branching operation: Select a fraction variable to branch and generate subproblems; S34: Bounding operation: Calculate the objective value of the subproblem and update the global bound; S35: Pruning operation: Eliminate subproblems that cannot contain the optimal solution; S36: Convergence Judgment: Check the convergence condition, repeat steps S32-S35 until convergence, output the optimization result, and apply the optimization result to the actual microgrid energy storage system scheduling.
6. The microgrid energy storage optimization scheduling method based on absolute value linearization according to claim 1, characterized in that: The adaptive heuristic strategy described in step S3 includes: Variable selection heuristic: Selecting branch variables based on pseudo-cost information; Node selection heuristic: Use the best-bound-first strategy to select the processing node; Cutting plane generation: Automatically generates valid inequalities to tighten the feasible region; Preprocessing techniques: Reduce problem size by fixing variables and simplifying constraints.
7. An electronic device, characterized in that, Including memory and processor; The memory is used to store computer programs; The processor is configured to, when executing the computer program, implement the microgrid energy storage optimization scheduling method based on absolute value linearization as described in any one of claims 1-6.
8. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the microgrid energy storage optimization scheduling method based on absolute value linearization as described in any one of claims 1-6.
9. A computer program product, characterized in that: The method includes a computer program that, when executed by a processor, implements the microgrid energy storage optimization scheduling method based on absolute value linearization as described in any one of claims 1-6.