Energy storage battery scheduling optimization method, system, computer equipment and medium

Abstract

The present invention belongs to the field of electric power automation and discloses a method, system, computer equipment and medium for optimizing energy storage battery scheduling plans, including: obtaining an optimal charge and discharge power reference value under an ideal state of the energy storage battery; obtaining an initial scheduling plan for the energy storage battery based on the optimal charge and discharge power reference value and a preset power reduction function; performing an SOC verification on the initial scheduling plan; when the SOC verification passes, using the current initial scheduling plan as the iteration starting point, calling a preset optimization algorithm for iterative optimization to obtain a scheduling plan for the energy storage battery; when the SOC verification fails, using the current initial scheduling plan to update the optimal charge and discharge power reference value, and repeating the above steps. This provides a preferred iteration starting point for the preset optimization algorithm, preventing the optimization algorithm from falling into the dilemma of a local optimal solution during the process of optimizing the energy storage battery scheduling plan, improving its iterative optimization efficiency, and thus achieving fast and efficient optimization of the energy storage battery scheduling plan.

Description

Technical Field

[0001] The present invention belongs to the field of electric power automation and relates to a method, system, computer equipment and medium for optimizing energy storage battery scheduling plan. Background Art

[0002] With the development of the new energy industry, the proportion of installed capacity of new energy generators is increasing. However, the randomness, volatility, and peak-shaving characteristics of renewable energy output present significant challenges to power system scheduling, widening the peak-to-valley difference in load that conventional generators must bear. Energy storage batteries, due to their dual charging and discharging properties, can adjust to the peaks and troughs of grid load, thereby shaving peaks and filling valleys. This has become an effective solution to the problem of renewable energy integration, and the key lies in the development of energy storage battery scheduling plans.

[0003] The day-ahead scheduling curve of energy storage batteries is generally obtained by solving a constrained optimization problem. The constraints that energy storage batteries need to meet include charging and discharging power constraints, charge constraints, and charging and discharging space constraints. If an intelligent optimization algorithm is directly used to solve the optimal scheduling curve of energy storage batteries under the constraints, due to the coupling relationship between the quantities to be optimized, the selection of the previous quantity to be optimized will affect the value range of the subsequent quantities to be optimized. Therefore, the scheduling curve can only be generated from the front to the back, which will limit the performance of the intelligent optimization algorithm, resulting in low solution efficiency, and thus making it difficult to quickly and effectively formulate energy storage battery scheduling plans. Summary of the Invention

[0004] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and provide a method, system, computer device and medium for optimizing energy storage battery scheduling plan.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] In a first aspect of the present invention, a method for optimizing an energy storage battery scheduling plan comprises:

[0007] Obtain the optimal charge and discharge power reference value of the energy storage battery under ideal conditions;

[0008] Obtaining an initial scheduling plan for the energy storage battery based on the optimal charge and discharge power reference value and a preset power reduction function;

[0009] Performing SOC verification on the initial scheduling plan;

[0010] When the SOC verification passes, the current initial scheduling plan is used as the iteration starting point, and the preset optimization algorithm is called for iterative optimization to obtain the scheduling plan of the energy storage battery;

[0011] When the SOC verification fails, the optimal charge and discharge power reference value is updated using the current initial scheduling plan, and the above steps are repeated.

[0012] Optionally, obtaining an optimal charge and discharge power reference value of the energy storage battery under an ideal state includes:

[0013] The average initial net load of the power system is obtained by the following formula:

[0014]

[0015] Among them, P net_avg is the average initial net load of the power system, P load,t is the predicted value of the equivalent load of the power system at time t, P new,t is the sum of the predicted values ​​of renewable energy generation in the power system at time t, P the,t is the planned discharge power of thermal power units in the power system at time t, and T is the total number of dispatching times;

[0016] The optimal charge and discharge power reference value of the energy storage battery under ideal conditions is obtained by the following formula:

[0017] P bat_ref,t =P load,t -P new,t -P the,t -P net_avg

[0018] Among them, P bat_ref,t It is the optimal charge and discharge power reference value at the tth moment under the ideal state of the energy storage battery.

[0019] Optionally, obtaining an initial scheduling plan for the energy storage battery according to the optimal charge and discharge power reference value and a preset power reduction function includes:

[0020] The initial scheduling plan of the energy storage battery is obtained by the following formula P = {P bat,t}:

[0021]

[0022] Among them, P bat,t is the charge and discharge power of the energy storage battery at the tth moment, P bat_max is the maximum discharge power of the energy storage battery, -P bat_max is the maximum charging power of the energy storage battery, SOC t is the charge of the energy storage battery at the tth moment, f1(SOC t ) is the discharge power reduction function, f2(SOC t ) is the charging power reduction function;

[0023]

[0024] Among them, SOC mid =(SOC min +SOC max ) / 2, a=2 / (SOC max -SOC min ), SOC max is the maximum charge of the energy storage battery, SOC min The minimum charge of the energy storage battery.

[0025] Optionally, performing SOC verification on the initial scheduling plan includes:

[0026] According to the initial scheduling plan, the SOC value of the energy storage battery at each moment in the entire scheduling cycle is obtained. When the SOC value of the energy storage battery at each moment in the entire scheduling cycle meets the charging and discharging power constraints, charge constraints, and charging and discharging space constraints of the energy storage battery, the SOC test passes; otherwise, the SOC test fails.

[0027] Optionally, before performing SOC verification on the initial scheduling plan, the method further includes:

[0028] The initial scheduling plan P = {P bat,t}Iterative calculation is performed using the following formula:

[0029]

[0030] Among them, P bat,t is the charge and discharge power of the energy storage battery at the tth moment, P bat,i is the charge and discharge power of the energy storage battery at the i-th moment, k is the number of iterations, T is the total number of scheduling times; η(·) is the charge and discharge efficiency function of the energy storage battery:

[0031]

[0032] Among them, the symbol represents the independent variable of the charge and discharge efficiency function, which is or η c is the charging efficiency of the energy storage battery, η d is the discharge efficiency of the energy storage battery;

[0033] Until the average charge and discharge value of the energy storage battery meets the set error ε:

[0034]

[0035] in, is the average charge and discharge value of the energy storage battery.

[0036] A second aspect of the present invention provides an energy storage battery scheduling optimization system, comprising:

[0037] A data acquisition module is used to obtain the optimal charge and discharge power reference value of the energy storage battery under ideal conditions;

[0038] An initial optimization module, configured to obtain an initial scheduling plan for the energy storage battery based on the optimal charge and discharge power reference value and a preset power reduction function;

[0039] An iterative optimization module, configured to perform SOC verification on the initial scheduling plan;

[0040] When the SOC verification passes, the current initial scheduling plan is used as the iteration starting point, and the preset optimization algorithm is called for iterative optimization to obtain the scheduling plan of the energy storage battery;

[0041] When the SOC verification fails, the optimal charge and discharge power reference value is updated using the current initial scheduling plan, and the initial optimization module and the iterative optimization module are triggered in sequence.

[0042] Optionally, the data acquisition module is specifically used to:

[0043] The average initial net load of the power system is obtained by the following formula:

[0044]

[0045] Among them, P net_avg is the average initial net load of the power system, P load,t is the predicted value of the equivalent load of the power system at time t, P new,t is the sum of the predicted values ​​of renewable energy generation in the power system at time t, P the,t is the planned discharge power of thermal power units in the power system at time t, and T is the total number of dispatching times;

[0046] The optimal charge and discharge power reference value of the energy storage battery under ideal conditions is obtained by the following formula:

[0047] P bat_ref,t =P load,t -P new,t -P the,t -P net_avg

[0048] Among them, P bat_ref,t It is the optimal charge and discharge power reference value at the tth moment under the ideal state of the energy storage battery.

[0049] Optionally, the initial optimization module is specifically used to:

[0050] The initial scheduling plan of the energy storage battery is obtained by the following formula P = {P bat,t}:

[0051]

[0052] Among them, P bat,t is the charge and discharge power of the energy storage battery at the tth moment, P bat_max is the maximum discharge power of the energy storage battery, -P bat_max is the maximum charging power of the energy storage battery, SOC t is the charge of the energy storage battery at the tth moment, f1(SOC t ) is the discharge power reduction function, f2(SOC t ) is the charging power reduction function;

[0053]

[0054] Among them, SOC mid =(SOC min +SOC max ) / 2, a=2 / (SOC max -SOC min ), SOC max is the maximum charge of the energy storage battery, SOC min The minimum charge of the energy storage battery.

[0055] In a third aspect of the present invention, a computer device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned energy storage battery scheduling plan optimization method when executing the computer program.

[0056] In a fourth aspect of the present invention, a computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned energy storage battery scheduling optimization method.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] The energy storage battery scheduling optimization method of the present invention uses the optimal charge and discharge power reference value of the energy storage battery under ideal conditions as a basis, reduces it using a power reduction function to obtain an initial scheduling plan, then verifies the initial scheduling plan through SOC verification, effectively preventing over-discharge and overcharge of the energy storage battery. Finally, using the initial scheduling plan that passes the SOC verification as the iterative starting point, a preset optimization algorithm is invoked for iterative optimization to obtain the energy storage battery scheduling plan. This method provides a preferred iterative starting point for the preset optimization algorithm, preventing the optimization algorithm from falling into the dilemma of a local optimal solution during the process of optimizing the energy storage battery scheduling plan and improving its iterative optimization efficiency, thereby achieving fast and effective optimization of the energy storage battery scheduling plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 This is a flow chart of a method for optimizing energy storage battery scheduling plans according to an embodiment of the present invention;

[0060] Figure 2 A power reduction function curve diagram according to an embodiment of the present invention;

[0061] Figure 3 Schematic diagram of the structure of the power system according to an embodiment of the present invention;

[0062] Figure 4 A user load forecast curve diagram of the power system according to an embodiment of the present invention;

[0063] Figure 5 A photovoltaic power generation output prediction curve diagram of a power system according to an embodiment of the present invention;

[0064] Figure 6 A net load prediction curve diagram of the power system excluding energy storage batteries according to an embodiment of the present invention;

[0065] Figure 7 is a graph showing a decrease in the objective function of the power system according to an embodiment of the present invention as the number of iterations increases;

[0066] Figure 8 This is an energy storage charge and discharge curve diagram obtained by optimizing the power system of an embodiment of the present invention using the method of the present invention;

[0067] Figure 9 A net load curve diagram obtained by optimizing the power system according to an embodiment of the present invention using the method of the present invention;

[0068] Figure 10 This is a SOC curve diagram of an energy storage battery optimized by the method of the present invention in a power system according to an embodiment of the present invention;

[0069] Figure 11 This is an energy storage charge and discharge curve diagram obtained by optimizing the power system according to an embodiment of the present invention using an existing method;

[0070] Figure 12 A net load curve diagram obtained by optimizing the power system according to an embodiment of the present invention using an existing method;

[0071] Figure 13 This is a SOC curve diagram of an energy storage battery obtained by optimizing the power system according to an embodiment of the present invention using an existing method;

[0072] Figure 14 This is a structural block diagram of the energy storage battery scheduling plan optimization system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0073] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0074] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention 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 numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes 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.

[0075] As introduced in the background technology, when facing the scheduling optimization problem of energy storage batteries, if the intelligent optimization algorithm is directly used to solve the optimal planning scheduling curve of the energy storage battery under constraints, due to the coupling relationship between the quantities to be optimized, the selection of the previous quantity to be optimized will affect the value range of the subsequent quantity to be optimized. Therefore, the scheduling curve can only be generated from front to back, which will limit the performance of the intelligent optimization algorithm, resulting in low solution efficiency, and thus making it difficult to quickly and effectively formulate the energy storage battery scheduling plan.

[0076] In order to improve the above-mentioned problems, an embodiment of the present invention provides a method for optimizing a scheduling plan for an energy storage battery, by obtaining an optimal charge and discharge power reference value under an ideal state of the energy storage battery; obtaining an initial scheduling plan for the energy storage battery according to the optimal charge and discharge power reference value and a preset power reduction function; performing SOC verification on the initial scheduling plan; when the SOC verification passes, using the current initial scheduling plan as the iteration starting point, calling a preset optimization algorithm for iterative optimization to obtain a scheduling plan for the energy storage battery; when the SOC verification fails, using the current initial scheduling plan as the iteration starting point, and repeating the above steps. This provides a preferred iteration starting point for the preset optimization algorithm, preventing the optimization algorithm from falling into the dilemma of a local optimal solution during the process of optimizing the energy storage battery scheduling plan, and improving its iterative optimization efficiency, thereby achieving fast and good optimization of the energy storage battery scheduling plan. The present invention is further described in detail below in conjunction with the accompanying drawings:

[0077] See also Figure 1In one embodiment of the present invention, a method for optimizing an energy storage battery scheduling plan is provided, which provides a better iterative starting point for the optimization algorithm, thereby improving the optimization efficiency and optimization effect of the optimization algorithm, and realizing efficient and accurate optimization of the energy storage battery scheduling plan.

[0078] Specifically, the energy storage battery scheduling optimization method includes the following steps:

[0079] S1: Obtain the optimal charge and discharge power reference value of the energy storage battery under ideal conditions.

[0080] S2: Obtaining an initial scheduling plan for the energy storage battery based on the optimal charge and discharge power reference value and a preset power reduction function;

[0081] S3: Perform SOC verification on the initial scheduling plan; when the SOC verification passes, use the current initial scheduling plan as the iteration starting point, call the preset optimization algorithm for iterative optimization, and obtain the scheduling plan of the energy storage battery; when the SOC verification fails, use the current initial scheduling plan to update the optimal charge and discharge power reference value, and repeat the above steps.

[0082] The energy storage battery scheduling optimization method of the present invention uses the optimal charge and discharge power reference value of the energy storage battery under ideal conditions as a basis, reduces it using a power reduction function to obtain an initial scheduling plan, then verifies the initial scheduling plan through SOC verification, effectively preventing over-discharge and overcharge of the energy storage battery. Finally, using the initial scheduling plan that passes the SOC verification as the iterative starting point, a preset optimization algorithm is invoked for iterative optimization to obtain the energy storage battery scheduling plan. This method provides a preferred iterative starting point for the preset optimization algorithm, preventing the optimization algorithm from falling into the dilemma of a local optimal solution during the process of optimizing the energy storage battery scheduling plan and improving its iterative optimization efficiency, thereby achieving fast and effective optimization of the energy storage battery scheduling plan.

[0083] In a possible implementation, obtaining the optimal charge and discharge power reference value of the energy storage battery under an ideal state includes obtaining the average initial net load of the power system by the following formula:

[0084]

[0085] Among them, P net_avg is the average initial net load of the power system, P load,t is the predicted value of the equivalent load of the power system at time t, P new,t is the sum of the predicted values ​​of renewable energy generation in the power system at time t, P the,t is the planned discharge power of the thermal power units in the power system at time t, and T is the total number of dispatching times.

[0086] The optimal charge and discharge power reference value of the energy storage battery under ideal conditions is obtained by the following formula:

[0087] P bat_ref,t =P load,t -P new,t -P the,t -P net_avg

[0088] Among them, P bat_ref,t It is the optimal charge and discharge power reference value at the tth moment under the ideal state of the energy storage battery.

[0089] Specifically, under ideal conditions, the capacity and power of energy storage batteries are both sufficiently large. After accounting for the battery's charge and discharge power, the power system's net load can be considered a constant value, meaning the power system can be treated as a constant-power load. This minimizes the difficulty of large-scale power system scheduling and enables the full absorption of new energy. On this basis, the difference between the power system's net load and the average initial net load is compensated by energy storage batteries to achieve stable power system operation.

[0090] However, it should be noted that due to the upper limit of the capacity or charge and discharge power of the energy storage battery during actual operation, if the charge and discharge are directly performed according to the optimal charge and discharge power reference value, the energy storage battery is likely to have problems such as exceeding the charge limit or insufficient output power. In addition, since the energy storage charge and discharge efficiency is not 100%, the charge and discharge space constraints may not be met. Specifically, the scheduling plan of the energy storage battery generally needs to ensure that the following three constraints are met:

[0091] (1) Charge and discharge power constraints. The charge and discharge power of the energy storage battery during operation is limited by the installed capacity, and the corresponding charge and discharge power constraints need to be met:

[0092] -P bat_max ≤P bat,t ≤P bat_max

[0093] Among them, P bat,t P is the charge and discharge power of the energy storage battery at the tth moment, discharge is positive and charge is negative. bat_max is the maximum discharge power of the energy storage battery, -P bat_max The maximum charging power of the energy storage battery.

[0094] (2) Charge constraint. Since the deep charge and discharge of energy storage batteries has a great impact on their service life, it is necessary to reasonably stipulate the upper and lower limits of the energy storage charge during operation. For this purpose, the charge constraint is formulated as follows:

[0095] SOC min ≤SOC t ≤SOC max

[0096] Among them, SOC min SOC is the minimum charge allowed by the energy storage battery. max SOC is the maximum charge allowed by the energy storage battery. t is the charge of the energy storage battery at moment t.

[0097] (3) Charge and discharge space constraints. To ensure that the energy storage battery can operate continuously and has the same charge and discharge space after the daily scheduling cycle, the charge and discharge space constraints must also be met:

[0098] SOC T =SOC0

[0099] Among them, SOC T is the charge of the energy storage battery at time T, and SOC0 is the initial charge value of the energy storage battery.

[0100] Therefore, it is also necessary to make appropriate adjustments to the above-mentioned optimal charge and discharge power reference value. Generally, the charging power of the energy storage battery is reduced when the charge is high, and the discharge power is reduced when the charge is low. Therefore, the optimal charge and discharge power reference value needs to be reduced.

[0101] In a possible implementation, obtaining an initial scheduling plan for the energy storage battery according to the optimal charge and discharge power reference value and a preset power reduction function includes:

[0102] The initial scheduling plan of the energy storage battery is obtained by the following formula P = {P bat,t}:

[0103]

[0104] Among them, P bat,t is the charge and discharge power of the energy storage battery at the tth moment, P bat_max is the maximum discharge power of the energy storage battery, -P bat_max is the maximum charging power of the energy storage battery, SOC t is the charge of the energy storage battery at the tth moment, f1(SOC t ) and f2(SOC t ) are all preset based on SOC t is the power reduction function of the independent variable, f1(SOC t ) is the discharge power reduction function, f2(SOC t ) is the charging power reduction function.

[0105] Among them, SOC t When formulating the day-ahead scheduling plan, it is generally obtained by iterative calculation using the following formula:

[0106]

[0107] Among them, Δt is the basic scheduling period, S bat is the rated maximum capacity of the energy storage battery, η(·) is the charge and discharge efficiency function of the energy storage battery, and the symbol · represents the independent variable of the charge and discharge efficiency function, where P bat,t :

[0108]

[0109] Among them, η c is the charging efficiency of the energy storage battery, η d is the discharge efficiency of the energy storage battery.

[0110] See also Figure 2 ,f1(SOC t ) and f2(SOC t ) is as follows:

[0111]

[0112] Among them, SOC mid =(SOC min +SOC max ) / 2, a=2 / (SOC max -SOC min ), SOC max is the maximum charge of the energy storage battery, SOC min The minimum charge of the energy storage battery.

[0113] Specifically, f1(SOC t ) can make the discharge power of the energy storage battery less than SOC mid reduction occurs when the SOC min The higher the reduction degree, the more effective it is to avoid over-discharge; and f2(SOC t ) can make the charging power of the energy storage battery greater than SOC mid reduction occurs when the SOC max The higher the degree of reduction, the more effective it is to avoid overcharging. The calculated initial scheduling plan P of the energy storage battery bat,t It can ensure that the charging and discharging power constraints and charge constraints of the energy storage battery are met.

[0114] However, after the optimal charge and discharge power reference value is reduced by the power reduction function, considering that the initial scheduling plan after the power reduction function adjustment may no longer meet the charge and discharge space constraints of the energy storage battery, the initial scheduling plan is subjected to charge and discharge mean zeroing processing to ensure that the charge of the energy storage battery remains unchanged after the daily scheduling cycle. Specifically, in one possible implementation, before performing SOC verification on the initial scheduling plan, the following is also included:

[0115] The initial scheduling plan P = {P bat,t The charge and discharge mean is normalized to zero by iterative calculation using the following formula:

[0116]

[0117] Where k is the number of iterations. When k = 0, That is P bat,t , P bat,i is the charge and discharge power of the energy storage battery at the i-th moment, η(·) is the charge and discharge efficiency function of the energy storage battery, and the symbol · represents the independent variable of the charge and discharge efficiency function, which is or

[0118] If the energy storage charge and discharge efficiency is 100%, only one iterative calculation is required; otherwise, after one iterative calculation, the positive and negative charging and discharging power of the energy storage battery at some moments will change, causing the corresponding charge and discharge efficiency function to change. The updated charge and discharge mean will not be 0. Therefore, multiple iterative calculations are required. The stopping condition of the iterative calculation is that the charge and discharge mean satisfies the set error ε:

[0119]

[0120] in, is the average charge and discharge value of the energy storage battery. After the iterative calculation stops, the initial scheduling plan obtained by the last iterative calculation is selected for subsequent processing.

[0121] In a possible implementation, performing SOC verification on the initial scheduling plan includes:

[0122] According to the initial scheduling plan, the SOC value of the energy storage battery at each moment in the entire scheduling cycle is obtained. When the SOC value of the energy storage battery at each moment in the entire scheduling cycle meets the charging and discharging power constraints, charge constraints, and charging and discharging space constraints of the energy storage battery, the SOC test passes; otherwise, the SOC test fails.

[0123] Specifically, after the initial scheduling plan is normalized to zero, in order to ensure that the energy storage battery can operate stably under the initial scheduling plan, it is necessary to verify whether the initial scheduling plan meets the requirements through SOC verification, so as to ensure that the energy storage battery will not be overcharged or over-discharged when operating according to the initial scheduling plan. When the SOC verification passes, the current initial scheduling plan is used as the iteration starting point, and the preset optimization algorithm is called for iterative optimization. The energy storage peak shaving and valley filling scheduling problem is continued to be iteratively solved to obtain the scheduling plan of the energy storage battery. When the SOC verification fails, the current initial scheduling plan is used to update the optimal charge and discharge power reference value, and the process of determining the initial scheduling plan is returned to redetermine the initial scheduling plan.

[0124] Among them, the preset optimization algorithm can adopt the currently common intelligent optimization algorithm used to solve the energy storage peak shaving and valley filling scheduling problem, such as the variable inertia weight particle swarm algorithm.

[0125] In a possible implementation, a simulation example is used to illustrate the energy storage battery scheduling optimization method of the present invention. Figure 3 Taking a power system containing photovoltaic power generation as an example, the power system includes photovoltaic power stations, conventional thermal power units, user loads and hybrid energy storage systems (i.e. energy storage batteries), and each part is connected to the power grid through an AC bus.

[0126] Assume that the basic dispatching period of the power system is Δt = 15min, see Figure 4 , shows the user load forecast curve, see Figure 5 , shows the photovoltaic power generation output prediction curve, assuming the conventional unit output is constant at 20MW, see Figure 6 , which shows the net load forecast curve without considering the energy storage battery. Setting the objective function of iterative optimization to the variance of the power system net load curve, the objective function value when ignoring the energy storage battery is 28.1475.

[0127] In the simulation test, the capacity of the energy storage battery is set to 40kMVA, the upper limit of charge and discharge power is 10MW, and the SOC max =1, SOC min =0.4, SOC0=0.7, η c =η d = 0.95, respectively using the iteration starting point obtained by the energy storage battery scheduling optimization method of the present invention and the randomly generated iteration starting point, and performing iterative optimization through the variable inertia weight particle swarm algorithm, assuming that the population size of the variable inertia weight particle swarm algorithm is 10000, the upper limit of the number of iterations is 150, the learning factor c1 = c2 = 1, and the variable inertia weight decreases from 0.9 to 0.4 with the number of iterations. Figure 7 , which shows the curve of the objective function decreasing with the number of iterations after the optimization is completed. Figures 8 to 10, which shows the optimization results obtained by the energy storage battery scheduling optimization method of the present invention. Figures 11 to 13 , which shows the optimization results obtained by iterative optimization using randomly generated iterative starting points.

[0128] observe Figure 5 It can be found that the iteration starting point obtained by the energy storage battery scheduling optimization method of the present invention is already close to the final optimization result. The objective function value drops sharply from the initial 28.1475 to 5.3000, and finally drops to 1.1168 after undergoing iterations of the intelligent optimization algorithm. After the same number of iterations, the objective function of the group using randomly generated optimization starting points only drops to 1.2768. Figures 6 to 8 as well as Figures 9 to 11 It can be found that in the optimization results of iterative optimization using randomly generated optimization starting points, the net load curve of the power system has been fluctuating up and down, while in the optimization results of iterative optimization using the optimization starting point obtained by the energy storage battery scheduling plan optimization method of the present invention, after iterative optimization, the net load curve of the net power system can be kept as a straight line as much as possible.

[0129] contrast Figure 5 From the two curves, it can be found that the optimization starting point obtained by the energy storage battery scheduling optimization method of the present invention is completely superior to the optimization starting point generated randomly in terms of performance, which greatly improves the optimization efficiency. In addition, the optimization starting point generated randomly may fall into the local optimal solution, resulting in the inability to reduce the objective function value to the global minimum.

[0130] The following are device embodiments of the present invention, which can be used to perform the method embodiments of the present invention. For details not disclosed in the device embodiments, please refer to the method embodiments of the present invention.

[0131] See also Figure 12 In another embodiment of the present invention, a system for optimizing energy storage battery scheduling plans is provided, which can be used to implement the above-mentioned method for optimizing energy storage battery scheduling plans. Specifically, the system for optimizing energy storage battery scheduling plans includes a data acquisition module, an initial optimization module, and an iterative optimization module.

[0132] Among them, the data acquisition module is used to obtain the optimal charge and discharge power reference value under the ideal state of the energy storage battery; the initial optimization module is used to obtain the initial scheduling plan of the energy storage battery based on the optimal charge and discharge power reference value and the preset power reduction function; the iterative optimization module is used to perform SOC verification on the initial scheduling plan; when the SOC verification passes, the current initial scheduling plan is used as the iteration starting point, and the preset optimization algorithm is called to perform iterative optimization to obtain the scheduling plan of the energy storage battery; when the SOC verification fails, the current initial scheduling plan is used to update the optimal charge and discharge power reference value, and the initial optimization module and the iterative optimization module are triggered in sequence.

[0133] In a possible implementation, the data acquisition module is specifically configured to:

[0134] The average initial net load of the power system is obtained by the following formula:

[0135]

[0136] Among them, P net_avg is the average initial net load of the power system, P load,t is the predicted value of the equivalent load of the power system at time t, P new,t is the sum of the predicted values ​​of renewable energy generation in the power system at time t, P the,t is the planned discharge power of the thermal power units in the power system at time t, and T is the total number of dispatching times.

[0137] The optimal charge and discharge power reference value of the energy storage battery under ideal conditions is obtained by the following formula:

[0138] P bat_ref,t =P load,t -P new,t -P the,t -P net_avg

[0139] Among them, P bat_ref,t It is the optimal charge and discharge power reference value at the tth moment under the ideal state of the energy storage battery.

[0140] In a possible implementation, the initial optimization module is specifically configured to:

[0141] The initial scheduling plan of the energy storage battery is obtained by the following formula P = {P bat,t}:

[0142]

[0143] Among them, P bat,t is the charge and discharge power of the energy storage battery at the tth moment, P bat_max is the maximum discharge power of the energy storage battery, -P bat_maxis the maximum charging power of the energy storage battery, SOC t is the charge of the energy storage battery at the tth moment, f1(SOC t ) is the discharge power reduction function, f2(SOC t ) is the charging power reduction function;

[0144]

[0145]

[0146] Among them, SOC mid =(SOC min +SOC max ) / 2, a=2 / (SOC max -SOC min ), SOC max is the maximum charge of the energy storage battery, SOC min The minimum charge of the energy storage battery.

[0147] In a possible implementation, performing SOC verification on the initial scheduling plan includes:

[0148] According to the initial scheduling plan, the SOC value of the energy storage battery at each moment in the entire scheduling cycle is obtained. When the SOC value of the energy storage battery at each moment in the entire scheduling cycle meets the charging and discharging power constraints, charge constraints, and charging and discharging space constraints of the energy storage battery, the SOC test passes; otherwise, the SOC test fails.

[0149] In a possible implementation manner, before performing SOC verification on the initial scheduling plan, the method further includes: performing SOC verification on the initial scheduling plan P = {P bat,t}Iterative calculation is performed using the following formula:

[0150]

[0151] Among them, P bat,t is the charge and discharge power of the energy storage battery at the tth moment, P bat,i is the charge and discharge power of the energy storage battery at the i-th moment, k is the number of iterations, T is the total number of scheduling times; η(·) is the charge and discharge efficiency function of the energy storage battery:

[0152]

[0153] Among them, η c is the charging efficiency of the energy storage battery, η d is the discharge efficiency of the energy storage battery.

[0154] Until the average charge and discharge value of the energy storage battery meets the set error ε:

[0155]

[0156] in, is the average charge and discharge value of the energy storage battery.

[0157] All relevant contents of each step involved in the embodiment of the aforementioned energy storage battery scheduling plan optimization method can be referred to the functional description of the functional modules corresponding to the energy storage battery scheduling plan optimization system in the embodiment of the present invention, and will not be repeated here.

[0158] The module division in the embodiments of the present invention is illustrative and represents only one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in various embodiments of the present invention may be integrated into a single processor, exist physically as separate modules, or two or more modules may be integrated into a single module. The integrated modules may be implemented in either hardware or software functional modules.

[0159] In another embodiment of the present invention, a computer device is provided, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the energy storage battery scheduling plan optimization method.

[0160] In another embodiment of the present invention, the present invention further provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space. These instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the energy storage battery scheduling optimization method in the above embodiment.

[0161] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0162] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0163] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0164] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0165] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing energy storage battery scheduling plan, characterized in that: include: Obtain the optimal charge and discharge power reference value of the energy storage battery under ideal conditions; Obtaining an initial scheduling plan for the energy storage battery based on the optimal charge and discharge power reference value and a preset power reduction function; Performing SOC verification on the initial scheduling plan; When the SOC verification passes, the current initial scheduling plan is used as the iteration starting point, and the preset optimization algorithm is called for iterative optimization to obtain the scheduling plan of the energy storage battery; If the SOC verification fails, the optimal charge and discharge power reference value is updated using the current initial scheduling plan, and the above steps are repeated; The obtaining of the optimal charge and discharge power reference value of the energy storage battery under the ideal state includes: The average initial net load of the power system is obtained by the following formula: Among them, P net_avg is the average initial net load of the power system, P load,t is the predicted value of the equivalent load of the power system at time t, P new,t is the sum of the predicted values ​​of renewable energy generation in the power system at time t, P the,t is the planned discharge power of thermal power units in the power system at time t, and T is the total number of dispatching times; The optimal charge and discharge power reference value of the energy storage battery under ideal conditions is obtained by the following formula: P bat_ref,t =P load,t -P new,t -P the,t -P net_avg Among them, P bat_ref,t is the optimal charge and discharge power reference value of the energy storage battery at time t under ideal conditions; Obtaining an initial scheduling plan for the energy storage battery according to the optimal charge and discharge power reference value and a preset power reduction function includes: The initial scheduling plan of the energy storage battery is obtained by the following formula P = {P bat,t }: Among them, P bat,t is the charge and discharge power of the energy storage battery at the tth moment, P bat_max is the maximum discharge power of the energy storage battery, -P bat_max is the maximum charging power of the energy storage battery, SOC t is the charge of the energy storage battery at the tth moment, f1(SOC t ) is the discharge power reduction function, f2(SOC t ) is the charging power reduction function; Among them, SOC mid =(SOC min +SOC max ) / 2, a=2 / (SOC max -SOC min ), SOC max is the maximum charge of the energy storage battery, SOC min is the minimum charge of the energy storage battery.

2. The energy storage battery scheduling optimization method according to claim 1, characterized in that: The performing SOC verification on the initial scheduling plan includes: According to the initial scheduling plan, the SOC value of the energy storage battery at each moment in the entire scheduling cycle is obtained. When the SOC value of the energy storage battery at each moment in the entire scheduling cycle meets the charging and discharging power constraints, charge constraints, and charging and discharging space constraints of the energy storage battery, the SOC test passes; otherwise, the SOC test fails.

3. The energy storage battery scheduling optimization method according to claim 1, characterized in that: Before performing SOC verification on the initial scheduling plan, the method further includes: The initial scheduling plan P = {P bat,t }Iterative calculation is performed using the following formula: Among them, P bat,t is the charge and discharge power of the energy storage battery at the tth moment, P bat,i is the charge and discharge power of the energy storage battery at the i-th moment, k is the number of iterations, T is the total number of scheduling times; η(·) is the charge and discharge efficiency function of the energy storage battery: Among them, the symbol represents the independent variable of the charge and discharge efficiency function, which is or η c is the charging efficiency of the energy storage battery, η d is the discharge efficiency of the energy storage battery; Until the average charge and discharge value of the energy storage battery meets the set error ε: in, is the average charge and discharge value of the energy storage battery.

4. An energy storage battery scheduling optimization system, characterized in that: include: A data acquisition module is used to obtain the optimal charge and discharge power reference value of the energy storage battery under ideal conditions; An initial optimization module, configured to obtain an initial scheduling plan for the energy storage battery based on the optimal charge and discharge power reference value and a preset power reduction function; An iterative optimization module, configured to perform SOC verification on the initial scheduling plan; When the SOC verification passes, the current initial scheduling plan is used as the iteration starting point, and the preset optimization algorithm is called for iterative optimization to obtain the scheduling plan of the energy storage battery; When the SOC verification fails, the optimal charge and discharge power reference value is updated using the current initial scheduling plan, and the initial optimization module and iterative optimization module are triggered in sequence; The data acquisition module is specifically used for: The average initial net load of the power system is obtained by the following formula: Among them, P net_avg is the average initial net load of the power system, P load,t is the predicted value of the equivalent load of the power system at time t, P new,t is the sum of the predicted values ​​of renewable energy generation in the power system at time t, P the,t is the planned discharge power of thermal power units in the power system at time t, and T is the total number of dispatching times; The optimal charge and discharge power reference value of the energy storage battery under ideal conditions is obtained by the following formula: P bat_ref,t =P load,t -P new,t -P the,t -P net_avg Among them, P bat_ref,t is the optimal charge and discharge power reference value of the energy storage battery at time t under ideal conditions; The initial optimization module is specifically used for: The initial scheduling plan of the energy storage battery is obtained by the following formula P = {P bat,t }: Among them, P bat,t is the charge and discharge power of the energy storage battery at the tth moment, P bat_max is the maximum discharge power of the energy storage battery, -P bat_max is the maximum charging power of the energy storage battery, SOC t is the charge of the energy storage battery at the tth moment, f1(SOC t ) is the discharge power reduction function, f2(SOC t ) is the charging power reduction function; Among them, SOC mid =(SOC min +SOC max ) / 2, a=2 / (SOC max -SOC min ), SOC max is the maximum charge of the energy storage battery, SOC min is the minimum charge of the energy storage battery.

5. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the energy storage battery scheduling optimization method according to any one of claims 1 to 3 are implemented.

6. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the energy storage battery scheduling optimization method according to any one of claims 1 to 3 are implemented.

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