Energy storage multi-objective optimization adjustment method and device, storage medium and electronic device

Abstract

The invention discloses an energy storage multi-objective optimization adjustment method and device, a storage medium and an electronic device, and the method comprises the steps: defining to-be-optimized parameters through a plurality of preset objective functions and decision variables corresponding to an energy storage system, and setting constraint conditions corresponding to the to-be-optimized parameters, the to-be-solved data at least comprises a plurality of objective functions, decision variables and constraint conditions, and the constraint conditions are used for coordinating the relation among the objective functions and indicating the value range of the decision variables; inputting the to-be-solved data into the solver to obtain a first solving result; and analyzing the first solution result, and adjusting a weight coefficient corresponding to each target function according to an analysis result to obtain a plurality of control functions conforming to the adjustment and optimization of the to-be-optimized parameters. The problems of low control efficiency and high control cost of the energy storage system are solved. And the optimal balance of the energy storage system among the adjustment cost, the energy efficiency and the system reliability is effectively realized.

Description

Technical Field

[0001] The present application relates to the field of energy storage technology, and in particular to an adjustment method and device for multi-objective optimization of energy storage, a storage medium and an electronic device. Background Art

[0002] With the global emphasis on sustainable development and the need to reduce greenhouse gas emissions, the use of renewable energy such as wind and solar energy has increased significantly. However, these energy sources are intermittent and unpredictable, which poses a challenge to the stable operation of the power system. The operation of the power system involves a large number of decision variables, both continuous variables (such as charging power) and discrete variables (such as the state of energy storage equipment). At present, the economic efficiency of the energy storage system mainly depends on the cost of electricity during the charging and discharging process. In the energy storage system, the energy storage device generally requires a small power density and is in a discharging state most of the time in the energy storage system. The discharge power is less than the charging and discharging power, which causes the energy storage device to operate in a low charging and discharging efficiency state for a long time, further resulting in low energy storage efficiency and high cost. In addition, the energy storage system control strategy mainly depends on the control of the charging and discharging power in the energy storage system. At this time, the charging and discharging power in the energy storage system mainly depends on the energy storage equipment in the energy storage system. The control ability of the energy storage equipment in the energy storage system is limited, which further leads to low energy storage efficiency and high cost. It is impossible to meet the balance between different goals in the energy storage system, which leads to the energy storage system being unable to meet the needs of power generation, transmission, distribution, and electricity consumption.

[0003] With regard to the problems of low control efficiency and high control cost of energy storage systems in related technologies, no effective solution has been proposed so far.

[0004] Therefore, it is necessary to improve the existing related technologies to overcome the above-mentioned defects in the related technologies and meet the needs of current actual production. Summary of the invention

[0005] The embodiments of the present application provide an adjustment method and device for multi-objective optimization of energy storage, a storage medium and an electronic device, so as to at least solve the problems of low control efficiency and high control cost of energy storage systems in related technologies.

[0006] According to one aspect of an embodiment of the present application, a method for adjusting multi-objective optimization of energy storage is provided, comprising: obtaining photovoltaic information of a target area where a photovoltaic power station is located, the photovoltaic information comprising: solar energy resource parameters of the target area, configuration information of photovoltaic components set in the target area, capacity ratio change information of the target area, and power restriction data; calculating the construction cost corresponding to the photovoltaic power station under different capacity ratios according to the photovoltaic information and a preset photovoltaic project cost database, and calculating the system efficiency of the photovoltaic power station under different capacity ratios according to the photovoltaic information; inputting the construction cost, system efficiency, and power restriction data into a trained kilowatt-hour cost model to obtain the kilowatt-hour costs corresponding to different capacity ratios and power restriction rates, and determining the linear change relationship between each capacity ratio in different capacity ratios and the kilowatt-hour cost; and when a power restriction rate threshold is set in the target area, evaluating the optimal capacity ratio value of the photovoltaic power station based on the linear change relationship.

[0007] In an exemplary embodiment, the parameters to be optimized are defined by multiple preset objective functions and decision variables corresponding to the energy storage system, including: obtaining a predefined comprehensive objective function of the current energy storage system, wherein the comprehensive objective function is determined by the following formula: F(x)=w1·C total +w2·μ eff +w3·A sys ; F(x) is the comprehensive objective function, w1 is the weight for minimizing the adjustment cost; C total is the objective function for minimizing the adjustment cost; w2 is the weight for maximizing energy efficiency; μ eff is the objective function of maximizing energy efficiency; w3 is the weight of system reliability; A sys is the system reliability objective function; obtains the predefined target decision of the current energy storage system, wherein the target decision at least includes: charging and discharging power, and device status, wherein the charging and discharging power is the charging and discharging power of the energy storage system at each time step, and the device status is the status of the energy storage device at each time step; defines the parameters to be optimized based on the comprehensive objective function and the target decision.

[0008] In an exemplary embodiment, after obtaining the predefined comprehensive objective function of the current energy storage system, the method further includes: determining a first subformula corresponding to the regulation cost minimization objective function, wherein the first subformula is: C total is the total adjustment cost; t is the index of the time step; T is the total time step in the time period; P t is the charge and discharge power; p t is the electricity price in the electricity market; μ is the charging and discharging efficiency of the energy storage device; c w is the loss cost of the equipment caused by each unit of charge and discharge; c fix is the fixed cost; c o Additional cost for each charge and discharge operation; Ncy is the number of charge and discharge cycles.

[0009] In an exemplary embodiment, after obtaining the predefined comprehensive objective function of the current energy storage system, the method further includes: determining a second sub-formula corresponding to the energy efficiency maximization objective function, wherein the second sub-formula is: μ eff is the energy efficiency, P cha (t) is the charging power; μ cha is the charging efficiency; T cha is the charging duration; μ dis is the discharge efficiency; P dis (t) is the discharge power; T dis is the discharge duration; t is the time step.

[0010] In an exemplary embodiment, after obtaining the predefined comprehensive objective function of the current energy storage system, the method further includes: determining a third sub-formula corresponding to the system reliability objective function, wherein the third sub-formula is: A sys is system reliability; M sys MT is the mean time between failures of the system; sys is the mean repair time of the system.

[0011] In an exemplary embodiment, setting constraint conditions corresponding to parameters to be optimized includes: determining constraint parameters corresponding to the current energy storage system, wherein the constraint parameters include at least: energy balance constraints for the energy storage system, capacity constraints for the energy storage device, and charge and discharge rate limits for the energy storage device; determining multiple sub-constraint conditions based on the constraint parameters; and summarizing the multiple sub-constraint conditions to obtain constraint conditions corresponding to the parameters to be optimized.

[0012] In an exemplary embodiment, the first solution result is analyzed, and the weight coefficient corresponding to each objective function is adjusted according to the analysis result to obtain multiple control functions that meet the tuning of the parameters to be optimized, including: determining the function value corresponding to each objective function in the first solution result; drawing a permutation diagram based on the changes between the function values, and determining the trade-off relationship data between any two objective functions according to the permutation diagram; determining the priority ranking among multiple objective functions according to the trade-off relationship data; and adjusting the weight coefficient corresponding to each objective function according to the priority ranking result to obtain multiple control functions that meet the tuning of the parameters to be optimized.

[0013] According to another aspect of an embodiment of the present application, there is also provided an adjustment device for multi-objective optimization of energy storage, including: a setting module, used to define parameters to be optimized through multiple preset objective functions and decision variables corresponding to the energy storage system, and set constraints corresponding to the parameters to be optimized, so as to obtain data to be solved including at least: multiple objective functions, decision variables, and constraints, wherein the constraints are used to coordinate the relationship between multiple objective functions and indicate the value range of the decision variables; an input module, used to input the data to be solved into a solver to obtain a first solution result; an adjustment module, used to analyze the first solution result, and adjust the weight coefficient corresponding to each objective function according to the analysis result, so as to obtain multiple control functions that meet the tuning of the parameters to be optimized.

[0014] According to another aspect of the embodiments of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned adjustment method for multi-objective optimization of energy storage when running.

[0015] According to another aspect of an embodiment of the present application, an electronic device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the adjustment method for multi-objective optimization of energy storage through the computer program.

[0016] According to another aspect of the embodiments of the present application, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the above-mentioned adjustment method for multi-objective optimization of energy storage is implemented.

[0017] Through this application, the parameters to be optimized are defined by the energy storage system corresponding to the preset multiple objective functions and decision variables, and the constraints corresponding to the parameters to be optimized are set, so as to obtain data to be solved including at least: multiple objective functions, decision variables, and constraints, wherein the constraints are used to coordinate the relationship between multiple objective functions and indicate the value range of the decision variables; the data to be solved is input into the solver to obtain a first solution result; the first solution result is analyzed, and the weight coefficient corresponding to each objective function is adjusted according to the analysis result to obtain multiple control functions that meet the tuning of the parameters to be optimized. The above technical solution solves the problems of low control efficiency and high control cost of the energy storage system. Furthermore, multiple objective functions and decision variables are defined and constrained by the energy storage system, and multiple control functions that meet the parameter tuning are optimized. The optimal balance between the regulation cost, energy efficiency and system reliability of the energy storage system is effectively achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0020] Figure 1 It is a hardware structure block diagram of a computer terminal of an adjustment method for energy storage multi-objective optimization according to an embodiment of the present application;

[0021] Figure 2 is a flow chart of an adjustment method for multi-objective optimization of energy storage according to an embodiment of the present application;

[0022] Figure 3 is a flow chart of a multi-objective optimization control method for energy storage based on a mixed integer linear programming algorithm according to an embodiment of the present application;

[0023] Figure 4 It is a structural block diagram of an adjustment device for multi-objective optimization of energy storage according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] The method embodiments provided in the embodiments of the present application can be executed in a computer terminal, a mobile terminal or a similar computing device. Taking running on a computer terminal as an example, Figure 11 is a hardware structure block diagram of a computer terminal for adjusting a method for multi-objective optimization of energy storage according to an embodiment of the present application. Figure 1 As shown, the computer terminal may include one or N ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor (Central Processing Unit, CPU) or a programmable logic device (Field Programmable Gate Array, FPGA)) and a memory 104 for storing data, wherein the above-mentioned computer terminal may also include a transmission device 106 and an input and output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0027] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the adjustment method of the monitoring network point in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or N magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0028] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0029] In this embodiment, a method for adjusting energy storage multi-objective optimization is provided. Figure 2 is a flow chart of an adjustment method for multi-objective optimization of energy storage according to an embodiment of the present application, such as Figure 2 As shown, the process includes the following steps S202-S206:

[0030] Step S202: defining parameters to be optimized by using a plurality of preset objective functions and decision variables corresponding to the energy storage system, and setting constraints corresponding to the parameters to be optimized, to obtain data to be solved including at least: the plurality of objective functions, the decision variables, and the constraints, wherein the constraints are used to coordinate the relationship between the plurality of objective functions and indicate the value range of the decision variables;

[0031] Step S204: inputting the data to be solved into the solver to obtain a first solution result;

[0032] Optionally, the solver mentioned above refers to the CPLEX solver (IBM ILOG CPLEX Optimization Studio, referred to as CPLEX). CPLEX is a highly optimized solver that can quickly find the optimal solution or approximate optimal solution to a problem, even for large-scale problems. It provides a powerful verification and validation mechanism to ensure that the solution found is feasible and satisfies all constraints. CPLEX can find high-quality solutions, which means that the solution is usually close to or reaches the global optimal solution. It uses advanced branch and bound, cutting plane method and other algorithms to speed up the solution process.

[0033] Step S206: Analyze the first solution result, and adjust the weight coefficient corresponding to each objective function according to the analysis result to obtain multiple control functions that meet the tuning of the parameters to be optimized.

[0034] It is understandable that energy storage systems usually need to find a balance between multiple goals, such as minimizing costs, maximizing energy efficiency, and system reliability. By adjusting the weights, the best compromise can be found between these goals. Depending on the specific situation, different weights can be set to reflect the importance of each goal and achieve priority sorting. By weighing the relationship between different goals, decision makers can better understand the advantages and disadvantages of various decision-making plans and make more informed choices. The adjustment of weights can enable decision makers to examine the problem from multiple angles, thereby obtaining a more comprehensive perspective and evaluating the trade-offs between different goals.

[0035] In the above steps, the parameters to be optimized are defined by the energy storage system corresponding to the preset multiple objective functions and decision variables, and the constraints corresponding to the parameters to be optimized are set to obtain the data to be solved including at least: the multiple objective functions, the decision variables, and the constraints, wherein the constraints are used to coordinate the relationship between the multiple objective functions and indicate the value range of the decision variables; the data to be solved is input into the solver to obtain the first solution result; the first solution result is analyzed, and the weight coefficient corresponding to each objective function is adjusted according to the analysis result to obtain multiple control functions that meet the tuning of the parameters to be optimized. The above technical solution solves the problems of low control efficiency and high control cost of the energy storage system. Furthermore, multiple objective functions and decision variables are defined and constrained by the energy storage system, and multiple control functions that meet the parameter tuning are optimized. The optimal balance between the regulation cost, energy efficiency and system reliability of the energy storage system is effectively achieved.

[0036] In an exemplary embodiment, the parameters to be optimized are defined by a plurality of preset objective functions and decision variables corresponding to the energy storage system, including: obtaining a predefined comprehensive objective function of the current energy storage system, wherein the comprehensive objective function is determined by the following formula: F(x)=w1·C total +w2·μ eff +w3·A sys ; F(x) is the comprehensive objective function, w1 is the weight for minimizing the adjustment cost; C total is the objective function for minimizing the adjustment cost; w2 is the weight for maximizing energy efficiency; μ eff is the objective function of maximizing energy efficiency; w3 is the weight of system reliability; A sys is the system reliability objective function; obtains the predefined target decision of the current energy storage system, wherein the target decision at least includes: charging and discharging power, and equipment status, wherein the charging and discharging power is the charging and discharging power of the energy storage system at each time step, and the equipment status is the status of the energy storage equipment at each time step; defines the parameters to be optimized based on the comprehensive objective function and the target decision.

[0037] Optionally, the device state is used to represent the state of the energy storage device at each time step, specifically including charging, discharging, and standby.

[0038] In an exemplary embodiment, after obtaining the predefined comprehensive objective function of the current energy storage system, the method further includes: determining a first subformula corresponding to the regulation cost minimization objective function, wherein the first subformula is: C total is the total adjustment cost; t is the index of the time step; T is the total time step in the time period; P t is the charge and discharge power; p tis the electricity price in the electricity market; μ is the charging and discharging efficiency of the energy storage device; c w is the loss cost of the equipment caused by each unit of charge and discharge; c fix is the fixed cost; c o Additional cost for each charge and discharge operation; N cy is the number of charge and discharge cycles.

[0039] Optionally, minimizing the regulation cost represents the regulation cost during the charging and discharging process, including energy storage charging and discharging cost, equipment loss cost, and other operating costs. By minimizing the regulation cost, it is possible to ensure that the operation strategy of the energy storage system reduces additional regulation costs or opportunity costs as much as possible while meeting other constraints. By optimizing the charging and discharging strategy of the energy storage system, it can respond more flexibly under different electricity price periods and grid demands, thereby better adapting to market changes. The optimized energy storage system can utilize its storage capacity more efficiently and reduce energy losses.

[0040] In an exemplary embodiment, after obtaining the predefined comprehensive objective function of the current energy storage system, the method further includes: determining a second sub-formula corresponding to the energy efficiency maximization objective function, wherein the second sub-formula is: μ eff is the energy efficiency, P cha (t) is the charging power; μ cha is the charging efficiency; T cha is the charging duration; μ dis is the discharge efficiency; P dis (t) is the discharge power; T dis is the discharge duration; t is the time step.

[0041] Optionally, maximizing energy efficiency means maximizing the energy conversion efficiency of the energy storage system; improving energy efficiency means reducing unnecessary energy loss, which directly reduces operating costs. Higher energy efficiency enables the energy storage system to store and release more energy in the same charge and discharge cycle, thereby increasing the opportunity to participate in market transactions. Optimized energy efficiency can ensure that the energy storage system operates in the best condition, reduce energy waste, and improve the overall performance of the system.

[0042] In an exemplary embodiment, after obtaining the predefined comprehensive objective function of the current energy storage system, the method further includes: determining a third sub-formula corresponding to the system reliability objective function, wherein the third sub-formula is: A sys is system reliability; M sys MT is the mean time between failures of the system; sys is the mean repair time of the system.

[0043] Optionally, system reliability is used to ensure that the system can operate stably under various circumstances; energy storage systems with high system reliability can better meet users' electricity needs, especially during peak load periods. By ensuring that the energy storage system can respond promptly when needed, the power supply quality can be improved, and voltage fluctuations and power outages can be reduced. By optimizing the operation strategy of the energy storage system, equipment failures caused by overload or improper operation can be reduced. In an emergency, a high-reliability energy storage system can respond quickly, provide the necessary backup power, and speed up the restoration of normal power supply. The energy storage system can act as a "buffer" for the power grid, providing emergency power support when a power grid fails, thereby enhancing the resilience of the entire power system.

[0044] In an exemplary embodiment, setting constraint conditions corresponding to parameters to be optimized includes: determining constraint parameters corresponding to the current energy storage system, wherein the constraint parameters include at least: energy balance constraints for the energy storage system, capacity constraints for the energy storage device, and charge and discharge rate limits for the energy storage device; determining multiple sub-constraint conditions based on the constraint parameters; and summarizing the multiple sub-constraint conditions to obtain constraint conditions corresponding to the parameters to be optimized.

[0045] Optionally, the energy balance constraint of the energy storage system in the above embodiment is used to ensure that the energy input and output of the system are balanced at each time step; the device capacity constraint of the energy storage device is used to limit the charge and discharge capacity of the energy storage device; the charge and discharge rate constraint of the energy storage device is used to limit the charge and discharge rate of the energy storage device. Among them, the energy balance, device capacity constraint and charge and discharge rate constraint are specifically:

[0046] Energy balance, equipment capacity constraints: E min <<E Soc，t1 <<E max ;

[0047] Among them, E min Indicates the minimum residual energy state; E Soc，t1 represents the residual energy state at time t1; E max Indicates the maximum remaining energy state; charge and discharge rate limit: P cha，t2 <<P max，cha P dis，t2 <<P max，dis ; Among them, P cha，t2 represents the charging power at time t2; P dis，t2 represents the discharge power at time t2; P max，cha Indicates the maximum charging power; P max，dis Indicates the maximum discharge power.

[0048] In summary, in the above embodiments, the energy balance constraint ensures the balance between supply and demand of energy within the system, prevents overcharging or over-discharging, and helps maintain the stable state of the system. Through effective management of energy flow, energy loss can be minimized and the overall efficiency of the system can be improved. The equipment capacity constraint limits the maximum charging and discharging power and storage capacity of the energy storage device to prevent the device from being damaged due to overload. By avoiding extreme charging and discharging conditions, equipment wear can be reduced and the service life of the equipment can be extended. Considering energy balance and equipment capacity constraints at the same time, the economic benefits of the system can be maximized while ensuring stable operation of the system. Limiting the charge and discharge rate can reduce the chemical reaction rate inside the battery, thereby reducing stress and heat accumulation inside the battery and reducing the aging rate of the battery. By avoiding extreme charge and discharge rates, the number of battery cycles can be reduced and the service life of the battery can be extended. Appropriate charge and discharge rates can prevent the battery from overheating, overcharging or over-discharging, thereby ensuring the safe operation of the energy storage system. By limiting the charge and discharge rate, the risk of failure caused by excessive charging and discharging of the battery can be reduced.

[0049] In an exemplary embodiment, the first solution result is analyzed, and the weight coefficient corresponding to each objective function is adjusted according to the analysis result to obtain multiple control functions that meet the tuning of the parameters to be optimized, including: determining the function value corresponding to each objective function in the first solution result; drawing a permutation diagram based on the changes between the function values, and determining the trade-off relationship data between any two objective functions according to the permutation diagram; determining the priority ranking among multiple objective functions according to the trade-off relationship data; and adjusting the weight coefficient corresponding to each objective function according to the priority ranking result to obtain multiple control functions that meet the tuning of the parameters to be optimized.

[0050] Optionally, the arrangement diagram in the above embodiment refers to a Pareto front diagram. The Pareto front refers to a solution set in which no solution is superior to another solution in all objectives in an optimization problem of one or more objective functions. Specifically, if a solution is superior to another solution in a certain objective but not inferior to the solution in other objectives, then the solution is considered to be superior in the Pareto sense. All such solutions constitute the Pareto front.

[0051] Obviously, the embodiments described above are only some embodiments of the present application, not all embodiments. In order to better understand the method, the above process is described below in conjunction with the embodiments, but it is not intended to limit the technical solutions of the embodiments of the present application, specifically:

[0052] The optional embodiment of the present application provides a multi-objective optimization control method for energy storage based on a mixed integer linear programming algorithm to solve the problems of low control efficiency and high control cost of the energy storage system. The method constructs an objective function, including three optimization objectives: minimizing adjustment cost, maximizing energy efficiency, and system reliability, and sets different weights to reflect the priority of the objectives; then, the charging and discharging power and equipment status of the energy storage system are defined as decision variables, and constraints such as energy balance, equipment capacity limit, and charging and discharging rate limit are set; then, the CPLEX solver is used to convert the objective function, decision variables, and constraints into a mixed integer linear programming algorithm (Mixed-Integer Linear Programming, referred to as: MILP) model to solve the multi-objective optimization problem; finally, the solution results are analyzed to evaluate the trade-off relationship between different objectives. Through the MILP algorithm, combined with the multi-objective requirements of the operation of the energy storage system, the optimal balance between cost, efficiency, and reliability is achieved, providing strong technical support for the stable operation and economic dispatch of the power system.

[0053] As an optional implementation, Figure 3 : is a flow chart of a multi-objective optimization control method for energy storage based on a mixed integer linear programming algorithm according to an embodiment of the present application, and the specific steps are as follows:

[0054] Step 1: Define the objective function (equivalent to the comprehensive objective function of this application) and decision variables to clarify the target to be optimized; the objective function expression is as follows: F(x) = w1·C total +w2·μ eff +w3·A sys ; Where F(x) is the comprehensive objective function, w1 is the weight for minimizing the adjustment cost; C total is the objective function for minimizing the adjustment cost; w2 is the weight for maximizing energy efficiency; μ eff is the objective function of maximizing energy efficiency; w3 is the weight of system reliability; A sys is the system reliability objective function.

[0055] The objective function to be optimized is as follows:

[0056] (1) Adjustment cost minimization objective function:

[0057] In the formula, C total is the total adjustment cost; t is the index of the time step; T is the total time step in the time period; P t is the charge and discharge power; p t is the electricity price in the electricity market; μ is the charging and discharging efficiency of the energy storage device; c w is the loss cost of the equipment caused by each unit of charge and discharge; c fixis the fixed cost; c o Additional cost for each charge and discharge operation; N cy is the number of charge and discharge cycles.

[0058] (2) Energy efficiency maximization objective function:

[0059] In the formula, μ eff is the energy efficiency, P cha (t) is the charging power; μ cha is the charging efficiency; T cha is the charging duration; μ dis is the discharge efficiency; P dis (t) is the discharge power; T dis is the discharge duration; t is the time step.

[0060] (3) Objective function of system reliability: In the formula, A sys is system reliability; M sys MT is the mean time between failures of the system; sys is the mean repair time of the system.

[0061] Step 2: Set constraints to coordinate the relationship between objective functions and specify the value range of decision variables, where the constraints specifically include energy balance constraints, equipment capacity constraints, and charge and discharge rate limits.

[0062] Step 3: Through the CPLEX solver of mixed integer linear programming MILP, the defined objective function, decision variables and constraints are input into the solver to find the optimal solution and realize the multi-objective optimization control of the energy storage system. The specific steps to find the optimal solution through the CPLEX solver are:

[0063] Step 3.1: Define decision variables, objective function, and constraints using CPLEX modeling language.

[0064] Step 3.2: Input the model file into the CPLEX solver. Adjust the solver parameters as needed, including the time limit n and the accuracy requirement s.

[0065] Step 3.3: Start the CPLEX solver to solve the optimization problem. Then, check the results returned by CPLEX, including the optimal solution, solution quality, solution time and status. Finally, verify the optimal solution to ensure that it meets all constraints.

[0066] Step 4: Analyze the solution results, evaluate the trade-offs between different objectives, and optimize by adjusting the weight of the objective function. The steps for optimizing by adjusting the weight of the objective function are as follows:

[0067] Step 4.1: Compare the values ​​of different objective functions, including minimization of regulation cost, energy efficiency, and system reliability, to determine which objectives are optimized. Draw a Pareto front diagram to visually display the trade-offs between different objectives.

[0068] Step 4.2: Analyze the interdependencies and impacts between different objectives. Adjust the weight coefficients in the objective function based on the analysis results to reflect the new priorities between different objectives. Redefine the objective function based on the new weight coefficients and rerun the solver with the updated objective function.

[0069] Step 4.3: Continue to adjust the weight coefficients according to the analysis results until a satisfactory solution is found.

[0070] It should be noted that CPLEX is an efficient solver, but when using it, parameters need to be adjusted according to the specific problem, such as time limit, accuracy requirements, etc. Incorrect configuration may result in long solution time or failure to find the optimal solution. At the same time, it is also necessary to ensure that the objective function, decision variables, and constraints are correctly converted into the MILP model to avoid errors in the modeling process, such as incorrect variable type definition, improper function linearity, etc. Throughout the process, it is important to ensure the accuracy of the model and the practicality of the solution results, while taking into account the complexity and dynamics of the operation of the energy storage system so that it can adapt to various situations in actual operation.

[0071] In summary, the optional embodiments of the present application define the main objectives of the optimization problem by defining the objective function, including minimizing the adjustment cost, maximizing the energy efficiency, and system reliability, ensuring that all decisions are moving towards a common goal, which helps to find the solution that best meets the target requirements; specifying adjustable parameters, including charging and discharging power, equipment status, and clarifying which factors can be changed and which factors cannot be changed, providing a clear direction for solving the problem; by setting constraints, ensuring the practical feasibility of the solution; by defining the objective function, decision variables, and constraints, a structured mathematical model can be constructed to describe and solve complex optimization problems. This method can not only help find the optimal or suboptimal solution, but also provide decision support to ensure that the solution is both effective and practical. In addition, the CPLEX solver can quickly and accurately find the optimal solution or approximate optimal solution. CPLEX supports complex model construction and can handle various types of constraints and objective functions, including mixed integer linear programming problems. This means that problems can be defined very flexibly and can accurately represent complex details in practical applications. By analyzing the solution results, deep insights into system behavior can be obtained, such as the trade-off relationship between different objectives, which helps decision makers better understand the operating mechanism of the system. According to the solution results, the weights or other parameters in the objective function can be adjusted to further optimize the solution.

[0072] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of each embodiment of the present application.

[0073] In this embodiment, an adjustment device for multi-objective optimization of energy storage is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0074] Figure 4 : is a structural block diagram of an adjustment device for multi-objective optimization of energy storage according to an embodiment of the present application, the device comprising:

[0075] A setting module 42 is used to define parameters to be optimized through a plurality of preset objective functions and decision variables corresponding to the energy storage system, and to set constraint conditions corresponding to the parameters to be optimized, so as to obtain data to be solved including at least: the plurality of objective functions, the decision variables, and the constraint conditions, wherein the constraint conditions are used to coordinate the relationship between the plurality of objective functions and indicate the value range of the decision variables;

[0076] An input module 44, used for inputting the data to be solved into the solver to obtain a first solution result;

[0077] The adjustment module 46 is used to analyze the first solution result and adjust the weight coefficient corresponding to each objective function according to the analysis result to obtain multiple control functions that meet the tuning of the parameters to be optimized.

[0078] The above device defines the parameters to be optimized by the energy storage system corresponding to the preset multiple objective functions and decision variables, and sets the constraints corresponding to the parameters to be optimized, so as to obtain the data to be solved including at least: the multiple objective functions, the decision variables, and the constraints, wherein the constraints are used to coordinate the relationship between the multiple objective functions and indicate the value range of the decision variables; the data to be solved is input into the solver to obtain the first solution result; the first solution result is analyzed, and the weight coefficient corresponding to each objective function is adjusted according to the analysis result to obtain multiple control functions that meet the tuning of the parameters to be optimized. The above technical solution solves the problems of low control efficiency and high control cost of the energy storage system. Furthermore, multiple objective functions and decision variables are defined and constrained by the energy storage system, and multiple control functions that meet the parameter tuning are optimized. The optimal balance between the regulation cost, energy efficiency and system reliability of the energy storage system is effectively achieved.

[0079] In an exemplary embodiment, the setting module is further used to define the parameters to be optimized by using a plurality of preset objective functions and decision variables corresponding to the energy storage system, including: obtaining a predefined comprehensive objective function of the current energy storage system, wherein the comprehensive objective function is determined by the following formula: F(x)=w1·C total +w2·μ eff +w3·A sys ; F(x) is the comprehensive objective function, w1 is the weight for minimizing the adjustment cost; C total is the objective function for minimizing the adjustment cost; w2 is the weight for maximizing energy efficiency; μ eff is the objective function of maximizing energy efficiency; w3 is the weight of system reliability; A sysis the system reliability objective function; obtains the predefined target decision of the current energy storage system, wherein the target decision at least includes: charging and discharging power, and equipment status, wherein the charging and discharging power is the charging and discharging power of the energy storage system at each time step, and the equipment status is the status of the energy storage equipment at each time step; defines the parameters to be optimized based on the comprehensive objective function and the target decision.

[0080] In an exemplary embodiment, the setting module further includes: a first determination unit, configured to determine a first subformula corresponding to the adjustment cost minimization objective function after obtaining a predefined comprehensive objective function of the current energy storage system, wherein the first subformula is: C total is the total adjustment cost; t is the index of the time step; T is the total time step in the time period; P t is the charge and discharge power; p t is the electricity price in the electricity market; μ is the charging and discharging efficiency of the energy storage device; c w is the loss cost of the equipment caused by each unit of charge and discharge; c fix is the fixed cost; c o Additional cost for each charge and discharge operation; N cy is the number of charge and discharge cycles.

[0081] In an exemplary embodiment, the setting module further includes: a second determination unit, configured to determine a second sub-formula corresponding to the energy efficiency maximization objective function after obtaining a predefined comprehensive objective function of the current energy storage system, wherein the second sub-formula is: μ eff is the energy efficiency, P cha (t) is the charging power; μ cha is the charging efficiency; T cha is the charging duration; μ dis is the discharge efficiency; P dis (t) is the discharge power; T dis is the discharge duration; t is the time step.

[0082] In an exemplary embodiment, the setting module further includes: a third determination module, which is used to determine a third sub-formula corresponding to the system reliability objective function after obtaining the predefined comprehensive objective function of the current energy storage system, wherein the third sub-formula is: A sys is system reliability; M sys MT is the mean time between failures of the system; sys is the mean repair time of the system.

[0083] In an exemplary embodiment, the above-mentioned setting module is also used to determine the constraint parameters corresponding to the current energy storage system, wherein the constraint parameters include at least: energy balance constraints for the energy storage system, capacity constraints for the energy storage device, and charge and discharge rate limits for the energy storage device; determine multiple sub-constraint conditions based on the constraint parameters; and summarize the multiple sub-constraint conditions to obtain the constraint conditions corresponding to the parameters to be optimized.

[0084] In an exemplary embodiment, the above-mentioned adjustment module is also used to determine the function value corresponding to each objective function in the first solution result; draw a permutation diagram based on the changes between the function values, and determine the trade-off relationship data between any two objective functions according to the permutation diagram; determine the priority ranking among multiple objective functions according to the trade-off relationship data; adjust the weight coefficient corresponding to each objective function according to the priority ranking result to obtain multiple control functions that meet the tuning of the parameters to be optimized.

[0085] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0086] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps:

[0087] S1. Define the parameters to be optimized by using the preset multiple objective functions and decision variables corresponding to the energy storage system, and set the constraint conditions corresponding to the parameters to be optimized, so as to obtain the data to be solved including at least: the multiple objective functions, the decision variables, and the constraint conditions, wherein the constraint conditions are used to coordinate the relationship between the multiple objective functions and indicate the value range of the decision variables;

[0088] S2, inputting the data to be solved into the solver to obtain a first solution result;

[0089] S3. Analyze the first solution result, and adjust the weight coefficient corresponding to each objective function according to the analysis result to obtain multiple control functions that meet the tuning of the parameters to be optimized.

[0090] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0091] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0092] An embodiment of the present application further provides a computer program product, including a computer program, and when the computer program is executed by a processor, the steps in any one of the above method embodiments are performed.

[0093] An embodiment of the present application further provides another computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0094] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0095] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0096] S1. Define the parameters to be optimized by using the preset multiple objective functions and decision variables corresponding to the energy storage system, and set the constraint conditions corresponding to the parameters to be optimized, so as to obtain the data to be solved including at least: the multiple objective functions, the decision variables, and the constraint conditions, wherein the constraint conditions are used to coordinate the relationship between the multiple objective functions and indicate the value range of the decision variables;

[0097] S2, inputting the data to be solved into the solver to obtain a first solution result;

[0098] S3. Analyze the first solution result, and adjust the weight coefficient corresponding to each objective function according to the analysis result to obtain multiple control functions that meet the tuning of the parameters to be optimized.

[0099] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0100] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0101] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of N computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that here, or they can be made into individual integrated circuit modules, or N of the modules or steps can be made into a single integrated circuit module for implementation. In this way, the present application is not limited to any specific combination of hardware and software.

[0102] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for adjusting energy storage multi-objective optimization, characterized in that: include: By defining the parameters to be optimized according to the preset multiple objective functions and decision variables of the energy storage system, and setting the constraint conditions corresponding to the parameters to be optimized, the data to be solved including at least the multiple objective functions, the decision variables and the constraint conditions are obtained, wherein the constraint conditions are used to coordinate the relationship between the multiple objective functions and indicate the value range of the decision variables; Inputting the data to be solved into the solver to obtain a first solution result; The first solution result is analyzed, and the weight coefficient corresponding to each objective function is adjusted according to the analysis result to obtain multiple control functions that meet the tuning of the parameters to be optimized.

2. The energy storage multi-objective optimization adjustment method according to claim 1, characterized in that: The parameters to be optimized are defined by the energy storage system corresponding to multiple preset objective functions and decision variables, including: Obtain the predefined comprehensive objective function of the current energy storage system, wherein the comprehensive objective function is determined by the following formula: F(x) = w1·C total +w2·μ eff +w3·A sys ; F(x) is the comprehensive objective function, w1 is the weight for minimizing the adjustment cost; C total is the objective function for minimizing the adjustment cost; w2 is the weight for maximizing energy efficiency; μ eff is the objective function of maximizing energy efficiency; w3 is the weight of system reliability; A sys is the system reliability objective function; Obtaining a predefined target decision of the current energy storage system, wherein the target decision includes at least: charging and discharging power and device status, wherein the charging and discharging power is the charging and discharging power of the energy storage system at each time step, and the device status is the status of the energy storage device at each time step; Parameters to be optimized are defined based on the comprehensive objective function and the target decision.

3. The energy storage multi-objective optimization adjustment method according to claim 2 is characterized in that: After obtaining the predefined comprehensive objective function of the current energy storage system, the method further includes: Determine a first subformula corresponding to the adjustment cost minimization objective function, wherein the first subformula is: C total is the total adjustment cost; t is the index of the time step; T is the total time step in the time period; P t is the charge and discharge power; p t is the electricity price in the electricity market; μ is the charging and discharging efficiency of the energy storage device; c w is the loss cost of the equipment caused by each unit of charge and discharge; c fix is the fixed cost; c o Additional cost for each charge and discharge operation; N cy is the number of charge and discharge cycles.

4. The energy storage multi-objective optimization adjustment method according to claim 2 is characterized in that: After obtaining the predefined comprehensive objective function of the current energy storage system, the method further includes: Determine a second subformula corresponding to the energy efficiency maximization objective function, wherein the second subformula is: μ eff is the energy efficiency, P cha (t) is the charging power; μ cha is the charging efficiency; T cha is the charging duration; μ dis is the discharge efficiency; P dis (t) is the discharge power; T dis is the discharge duration; t is the time step.

5. The energy storage multi-objective optimization adjustment method according to claim 2 is characterized in that: After obtaining the predefined comprehensive objective function of the current energy storage system, the method further includes: Determine a third subformula corresponding to the system reliability objective function, wherein the third subformula is: A sys is system reliability; M sys MT is the mean time between failures of the system; sys is the mean repair time of the system.

6. The energy storage multi-objective optimization adjustment method according to claim 1, characterized in that: Setting the constraint conditions corresponding to the parameters to be optimized includes: Determine constraint parameters corresponding to the current energy storage system, wherein the constraint parameters include at least: energy balance constraint for the energy storage system, capacity constraint for the energy storage device, and charge and discharge rate limit for the energy storage device; Determine a plurality of sub-constraint conditions according to the constraint parameters; A plurality of sub-constraints are summarized to obtain constraints corresponding to the parameters to be optimized.

7. The energy storage multi-objective optimization adjustment method according to claim 1, characterized in that: Analyze the first solution result, and adjust the weight coefficient corresponding to each objective function according to the analysis result to obtain multiple control functions that meet the tuning of the parameters to be optimized, including: Determine a function value corresponding to each objective function in the first solution result; Draw a permutation diagram based on the changes between function values, and determine the trade-off relationship data between any two objective functions according to the permutation diagram; Determining the priority ranking among the multiple objective functions according to the trade-off relationship data; The weight coefficient corresponding to each objective function is adjusted according to the priority sorting result to obtain multiple control functions that meet the tuning of the parameters to be optimized.

8. An adjustment device for multi-objective optimization of energy storage, characterized in that: include: A setting module, used to define parameters to be optimized through a plurality of preset objective functions and decision variables corresponding to the energy storage system, and to set constraint conditions corresponding to the parameters to be optimized, so as to obtain data to be solved including at least: the plurality of objective functions, the decision variables, and the constraint conditions, wherein the constraint conditions are used to coordinate the relationship between the plurality of objective functions and indicate the value range of the decision variables; An input module, used for inputting the data to be solved into the solver to obtain a first solution result; The adjustment module is used to analyze the first solution result and adjust the weight coefficient corresponding to each objective function according to the analysis result to obtain multiple control functions that meet the tuning of the parameters to be optimized.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method according to any one of claims 1 to 6 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 6 through the computer program.

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