User side energy storage optimal configuration method and device, electronic terminal and storage medium

Abstract

The invention discloses a user-side energy storage optimal configuration method and device, an electronic terminal and a storage medium in the technical field of user-side energy storage planning. The method comprises the following steps: establishing a configuration model; an upper layer in the control model takes the minimization of the total energy consumption cost of the energy storage system as an optimization target and takes an electric power balance constraint, a maximum net load constraint and an energy storage operation constraint as constraint conditions for optimization, and a lower layer in the control model takes the minimization of the operation cost of the energy storage system in a scene with worst renewable energy output as a target; optimizing by taking a power balance constraint in the worst scene, an actual renewable energy output constraint, a maximum net load constraint in the worst scene and an energy storage scheduling constraint in the worst scene as constraint conditions; and solving the user side double-layer energy storage optimal configuration model. The method can solve the technical problem that the existing energy storage configuration method does not fully consider the demand characteristics in the worst output scene of the renewable energy sources, so that the economic benefit and reliability are insufficient under extreme conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of user-side energy storage planning, and in particular to a user-side energy storage optimization configuration method, device, electronic terminal and storage medium. Background Art

[0002] In the process of implementing the "dual carbon" goals, the application and proportion of renewable energy in the power grid continues to expand, and its random and intermittent characteristics are becoming increasingly apparent. At the same time, flexible resources on the load side are also constantly developing, with a significant imbalance between supply and demand. The primary solution to this problem is energy storage. Energy storage technology is the most reasonable and optimal solution to the temporal and spatial mismatch between renewable energy generation and load power consumption. However, the high cost and low utilization rate of energy storage have always been major factors restricting its development. Existing energy storage configuration methods often fail to fully consider the demand characteristics of renewable energy in the worst-case output scenarios, resulting in insufficient economic benefits and reliability in extreme situations.

[0003] Therefore, there is an urgent need for a user-side energy storage optimization configuration method, device, electronic terminal and storage medium to solve the above technical problems. Summary of the Invention

[0004] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a method for optimizing the configuration of user-side energy storage, which can solve the technical problem that existing energy storage configuration methods often fail to fully consider the demand characteristics of renewable energy in the worst output scenario, resulting in insufficient economic benefits and reliability in extreme situations.

[0005] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0006] In a first aspect, the present invention provides a method for optimizing configuration of user-side energy storage, comprising:

[0007] Obtaining the user's monthly typical day original electricity load and renewable energy predicted power benchmark value, and establishing a user-side two-tier energy storage optimization configuration model based on the monthly typical day original electricity load and renewable energy predicted power benchmark value;

[0008] The upper layer of the user-side two-layer energy storage optimization configuration model is controlled to minimize the total energy consumption cost of the energy storage system as the optimization goal, and is optimized with the electric power balance constraint, the maximum net load constraint and the energy storage operation constraint as the constraint conditions; the lower layer of the user-side two-layer energy storage optimization configuration model is controlled to minimize the operation cost of the energy storage system under the worst renewable energy output scenario as the goal, and is optimized with the power balance constraint under the worst scenario, the actual renewable energy output constraint, the maximum net load constraint under the worst scenario and the energy storage scheduling constraint under the worst scenario as the constraint conditions;

[0009] Solve the user-side double-layer energy storage optimization configuration model to obtain the optimal rated capacity and rated power configuration results of the user-side energy storage considering the worst scenario operation.

[0010] Furthermore, the total energy cost optimization objectives for building an energy storage system include:

[0011] ,

[0012] ,

[0013] ,

[0014] in, Indicates the operating cost of the user-side energy storage system within one year, Indicates the operating cost of the user-side energy storage system during its entire life cycle, is the basic unit electricity price, is the monthly maximum net load power of the user-side energy storage system, It is the time-of-use electricity price. It is Typical day of the month The net load of a time period, that is, the electric power purchased by the user side system from the grid side, is the number of days in a month, is a monthly series, M=12, is the time series, T=24, y is the annual time series, Y is the energy storage life, is the annual inflation rate, is the discount rate, is the energy storage operation and maintenance cost coefficient, is the total energy cost, is the energy storage installation cost per unit capacity, is the unit power coefficient, is the rated capacity of energy storage, is the rated power.

[0015] Furthermore, the operating cost optimization objectives for the energy storage system under the worst load scenario include:

[0016] ,

[0017] Where, is the operating cost under the worst load scenario, is the net load of the user-side energy storage system relative to the grid side under the worst load scenario, is the basic unit electricity price, is the monthly maximum net load power, is the time-of-use electricity price, D is the number of days in a month, is a monthly series, is a time series, T=24.

[0018] Furthermore, the electric power balance constraint, maximum net load constraint and energy storage operation constraint include:

[0019] Power balance constraints,

[0020] ,

[0021] in, It is Typical day of the month The net load of a time period, Forecasting power benchmarks for renewable energy sources, is the monthly typical daily original electricity load, The energy storage charging power, is the energy storage discharge power;

[0022] Maximum payload constraint,

[0023] ,

[0024] in, is the monthly maximum net load power;

[0025] Energy storage installation power constraints,

[0026] ;

[0027] in, is the energy storage rated power, is the maximum energy storage rated power;

[0028] Energy storage installation capacity constraints,

[0029] ,

[0030] in, is the rated capacity of energy storage, is the maximum energy storage rated capacity;

[0031] Energy storage charging power constraints,

[0032] ;

[0033] Energy storage discharge power constraints,

[0034] ;

[0035] The recursive relationship of the remaining power in the energy storage is:

[0036] ,

[0037] ,

[0038] in, For energy storage charging efficiency, is the energy storage discharge efficiency, is the remaining power in the energy storage, is the time interval, is the amount of energy stored in the initial state, at this time t=0; is the energy storage charging power at time t, is the energy storage charging power at time t=1, is the energy storage discharge power at time t, is the energy storage discharge power at time t=1;

[0039] Remaining power constraints in energy storage,

[0040] ,

[0041] ,

[0042] in, is the minimum limit of the energy storage charge state, is the maximum limit of the energy storage charge state, is the energy storage capacity at the end of the day, when t=24.

[0043] Furthermore, the power balance constraint under the worst scenario, the actual renewable energy output constraint, the maximum net load constraint under the worst scenario, and the energy storage scheduling constraint under the worst scenario include:

[0044] Power balance constraints under the worst load scenario,

[0045] ,

[0046] in, The worst load scenario Typical day of the month The net load of a time period, is the monthly typical daily original electricity load, is the energy storage charging power under the worst load scenario, is the energy storage discharge power under the worst load scenario, is the actual output power of renewable energy under the worst load scenario;

[0047] Actual renewable energy output constraints,

[0048] ,

[0049] ,

[0050] in, The base value for predicting power from renewable energy sources, is the downward deviation limit of renewable energy power, is the upward deviation limit of renewable energy power, is the robust parameter representing the uncertainty margin;

[0051] Maximum net load constraint under the worst load scenario,

[0052] ;

[0053] Energy storage charging power constraints under the worst load scenario,

[0054] ;

[0055] Energy storage discharge power constraints under the worst load scenario,

[0056] ;

[0057] The recursive relationship of the remaining power in the energy storage under the worst load scenario,

[0058] ,

[0059] ,

[0060] Constraints on the remaining power in the energy storage under the worst load scenario,

[0061] ,

[0062] ,

[0063] in, is the remaining power in the energy storage under the worst load scenario, is the amount of energy stored in the initial state, at t=0.

[0064] Furthermore, solving the user-side double-layer energy storage optimization configuration model includes:

[0065] Based on the power balance constraint under the worst load scenario, according to the duality theory, by introducing the dual variable, replace , an equivalent robust correspondence is obtained, and the lower model in the user-side two-layer energy storage optimization configuration model is converted into a mixed integer linear programming model. The expression of the converted mixed integer linear programming model includes:

[0066] ,

[0067] in, 、 and is the dual variable of the uncertain model, and the constraint of the dual variable is,

[0068] ,

[0069] ,

[0070] ,

[0071] ,

[0072] The double-level mixed integer linear programming model is transformed into a single-level mixed integer linear programming model by using KKT conditions, and the model is solved by using a solver corresponding to the mixed integer linear programming model.

[0073] In a second aspect, the present invention provides a user-side energy storage optimization configuration device, comprising:

[0074] An acquisition module is used to obtain the user's monthly typical day original electricity load and renewable energy predicted power benchmark value, and establish a user-side double-layer energy storage optimization configuration model based on the monthly typical day original electricity load and renewable energy predicted power benchmark value;

[0075] An optimization module is used to control the upper layer of the user-side two-layer energy storage optimization configuration model to minimize the total energy consumption cost of the energy storage system as the optimization goal, and to optimize with the electric power balance constraint, the maximum net load constraint and the energy storage operation constraint as the constraint conditions; and to control the lower layer of the user-side two-layer energy storage optimization configuration model to minimize the operation cost of the energy storage system under the worst renewable energy output scenario as the goal, and to optimize with the power balance constraint under the worst scenario, the actual renewable energy output constraint, the maximum net load constraint under the worst scenario and the energy storage scheduling constraint under the worst scenario as the constraint conditions;

[0076] The configuration module is used to solve the user-side double-layer energy storage optimization configuration model to obtain the optimal rated capacity and rated power configuration results of the user-side energy storage considering the worst scenario operation.

[0077] In a third aspect, the present invention provides an electronic terminal comprising a processor and a memory connected to the processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the steps of any of the above methods are performed.

[0078] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of any of the above methods when executed by a processor.

[0079] Compared with the prior art, the present invention has the following beneficial effects:

[0080] The present invention addresses the problem that the worst load scenario is not taken into account in the current energy storage configuration model. It proposes to establish a user-side two-layer energy storage optimization configuration model based on the monthly typical day original electricity load and the renewable energy predicted power benchmark value. The upper layer of the model takes minimizing the total energy cost of the energy storage system as the optimization goal, and the lower layer of the model takes minimizing the operating cost of the energy storage system under the worst renewable energy output scenario as the goal. The solution is solved under the condition of satisfying the constraints, and the optimal rated capacity and rated power configuration results of the user-side energy storage considering the worst scenario are obtained. The occurrence of distribution network power exceeding the limit, load loss, transformer overload and other situations that may occur in actual operation is avoided, and the peak power of the system is effectively reduced, thereby improving the economic benefits and reliability under extreme conditions.

[0081] Through model optimization, the total energy cost and the operating cost of the energy storage system under the worst scenario are minimized, which can effectively reduce the energy cost of the user's energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 This is a flow chart of a method for optimizing user-side energy storage configuration provided by the first embodiment of the present invention;

[0083] Figure 2 This is a curve of energy storage rated capacity and user-side system electricity expenditure in a user-side energy storage optimization configuration method provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0084] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. Unless there is a conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0085] The term "and / or" in this document simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, or B exists alone. Additionally, the character " / " in this document generally indicates an "or" relationship between the related objects.

[0086] KKT conditions (Karush-Kuhn-Tucker conditions) are a set of necessary optimization conditions for solving nonlinear programming problems with inequality constraints. KKT conditions are necessary, but not sufficient, for solving nonlinear programming problems. If a point satisfies the KKT conditions, it may be a local optimal solution, but not necessarily a global optimal solution. In practical applications, it is often necessary to combine other methods (such as gradient descent and Newton's method) to solve nonlinear programming problems.

[0087] Example 1:

[0088] Figure 1 This is a flow chart of the user-side energy storage optimization configuration method in the first embodiment of the present invention. This flow chart only shows the logical sequence of the method described in this embodiment. Under the premise of no conflict, in other possible embodiments of the present invention, different Figure 1 The steps shown or described are accomplished in the order shown.

[0089] The user-side energy storage optimization configuration method provided in this embodiment can be applied to a terminal and can be executed by a high-speed rail gearbox damage status assessment device, which can be implemented by software and / or hardware and can be integrated into a terminal, such as any smart phone, tablet computer or computer device with communication function. Figure 1 As shown, the method of this embodiment specifically includes the following steps:

[0090] Step 1: Obtain the user's monthly typical day original electricity load and renewable energy predicted power benchmark value, and establish a user-side two-tier energy storage optimization configuration model based on the monthly typical day original electricity load and renewable energy predicted power benchmark value;

[0091] Step 2: Control the upper layer of the user-side two-layer energy storage optimization configuration model to minimize the total energy consumption cost of the energy storage system as the optimization goal, and optimize it with the electric power balance constraint, the maximum net load constraint and the energy storage operation constraint as the constraint conditions; control the lower layer of the user-side two-layer energy storage optimization configuration model to minimize the energy storage system operation cost under the worst renewable energy output scenario as the goal, and optimize it with the power balance constraint under the worst scenario, the actual renewable energy output constraint, the maximum net load constraint under the worst scenario and the energy storage scheduling constraint under the worst scenario as the constraint conditions;

[0092] The total energy cost optimization objectives for building an energy storage system include:

[0093] , ,

[0094] ,

[0095] in, Indicates the operating cost of the user-side energy storage system within one year, Indicates the operating cost of the user-side energy storage system during its entire life cycle, It also includes three parts: the electricity bill based on the daily time-of-use electricity price, the demand bill based on the monthly basic electricity price, and the operation and maintenance costs of the electric energy storage. is the basic unit electricity price, is the monthly maximum net load power of the user-side energy storage system, It is the time-of-use electricity price. It is Typical day of the month The net load of a time period is the electric power purchased by the user side system from the grid side. D is the number of days in a month. is a monthly series, M=12, is the time series, T=24, y is the annual time series, Y is the energy storage life, is the annual inflation rate, is the discount rate, is the energy storage operation and maintenance cost coefficient, is the total energy cost, is the energy storage installation cost per unit capacity, is the unit power coefficient, is the rated capacity of energy storage, is the rated power;

[0096] The operating cost optimization objectives for the energy storage system under the worst load scenario include:

[0097] ,

[0098] Where, is the operating cost under the worst load scenario, is the net load of the user-side energy storage system relative to the grid side under the worst load scenario, is the basic unit electricity price, is the monthly maximum net load power, is the time-of-use electricity price, D is the number of days in a month, is a monthly series, is a time series, T=24. It should be noted that in this embodiment, in the lower layer of the user-side two-layer energy storage optimization configuration model, the scheduling scale is 1 day, and the basic electricity bill is averaged to each day to unify the time axis;

[0099] The expression of the power balance constraint is:

[0100] ,

[0101] in, It is Typical day of the month The net load of a time period, Forecasting power benchmarks for renewable energy sources, is the monthly typical daily original electricity load, The energy storage charging power, is the energy storage discharge power;

[0102] The maximum payload constraint is expressed as:

[0103] ,

[0104] in, is the monthly maximum net load power, that is, the net load of the user-side system relative to the grid side in any period of the mth month is lower than the peak net load of the user system under the worst load scenario;

[0105] The energy storage operation constraints include energy storage installation power constraints, energy storage installation capacity constraints, energy storage charging power constraints, energy storage discharge power constraints, and energy storage remaining power constraints.

[0106] The expression of energy storage installation power constraint is:

[0107] ;

[0108] in, is the energy storage rated power, is the maximum energy storage rated power;

[0109] The expression of energy storage installation capacity constraint is:

[0110] ,

[0111] in, is the rated capacity of energy storage, is the maximum energy storage rated capacity;

[0112] The expression of energy storage charging power constraint is:

[0113] ;

[0114] The expression of energy storage discharge power constraint is:

[0115] ;

[0116] It is worth noting that no binary variable representing the charging and discharging status is introduced into the energy storage charging and discharging power constraint. This is because the energy storage charging and discharging efficiency is lower than 1, and if the state of charge at the beginning and end of the scheduling cycle is the same, simultaneous charging and discharging is definitely not an economically optimal strategy.

[0117] The recursive relationship of the remaining power in the energy storage satisfies:

[0118] ,

[0119] ,

[0120] in, For energy storage charging efficiency, is the energy storage discharge efficiency, is the remaining power in the energy storage, is the time interval, is the amount of energy stored in the initial state, at this time t=0; is the energy storage charging power at time t, is the energy storage charging power at time t=1, is the energy storage discharge power at time t, is the energy storage discharge power at time t=1;

[0121] The expression for the remaining power constraint in the energy storage is:

[0122] ,

[0123] ,

[0124] in, is the minimum limit of the energy storage charge state, is the maximum limit of the energy storage charge state, is the amount of energy stored at the end of the day, when t=24;

[0125] The expression of the power balance constraint under the worst load scenario is:

[0126] ,

[0127] in, The worst load scenario Typical day of the month The net load of a time period, is the monthly typical daily original electricity load, is the energy storage charging power under the worst load scenario, is the energy storage discharge power under the worst load scenario, is the actual output power of renewable energy under the worst load scenario;

[0128] The expression of the actual renewable energy output constraint is:

[0129] ,

[0130] ,

[0131] in, The base value for predicting power from renewable energy sources, is the downward deviation limit of renewable energy power, is the upward deviation limit of renewable energy power, is the robust parameter representing the uncertainty margin;

[0132] The expression of the maximum net load constraint under the worst load scenario is:

[0133] ;

[0134] The expression of energy storage charging power constraint under the worst load scenario is:

[0135] ;

[0136] The expression of energy storage discharge power constraint under the worst load scenario is:

[0137] ;

[0138] The recursive relationship of the remaining power in the energy storage under the worst load scenario satisfies:

[0139] ,

[0140] ,

[0141] The expression for the remaining power constraint in the energy storage under the worst load scenario is:

[0142] ,

[0143] ,

[0144] in, is the remaining power in the energy storage under the worst load scenario, is the initial state of energy storage capacity, at this time t=0, where under the worst load scenario This means that the initial and final states of energy storage remain consistent in different scenarios to ensure its dispatchability.

[0145] Step 3: Solve the user-side double-layer energy storage optimization configuration model to obtain the optimal rated capacity and rated power configuration of the user-side energy storage considering the worst-case scenario;

[0146] Specifically, solving the user-side double-layer energy storage optimization configuration model includes:

[0147] Based on the power balance constraint under the worst load scenario, according to the duality theory, by introducing the dual variable, replace , an equivalent robust correspondence is obtained, and the lower model in the user-side two-layer energy storage optimization configuration model is converted into a mixed integer linear programming model. The expression of the converted mixed integer linear programming model includes:

[0148] ,

[0149] in, 、 and is the dual variable of the uncertain model, and the constraint of the dual variable is,

[0150] ,

[0151] ,

[0152] ,

[0153] ,

[0154] At this point, both the upper and lower layers of the two-layer model are mixed integer linear programming models. The KKT condition is used to transform the two-layer mixed integer linear programming model into a single-layer mixed integer linear programming model, and the solver corresponding to the mixed integer linear programming model is used to solve the model. The result obtained from the solution is the optimal rated capacity and rated power configuration of the user-side energy storage considering the worst scenario operation.

[0155] Specifically, we took a large industrial enterprise as the research object and processed the monthly historical load data to obtain the typical load for each month. The electricity price data is shown in Table 1.

[0156] During the energy storage project lifecycle, the discount rate and inflation rate are set to 10% and 2% respectively. Considering the maturity of energy storage system technology, lithium-ion batteries are selected. Their relevant parameters are shown in Table 2. The time interval for energy storage configuration and worst-case scenario scheduling is 1 hour. In the basic calculation example, the uncertainty boundary of wind turbine output is its predicted output. 10%.

[0157] Table 1 Time-of-use electricity prices for industrial and commercial users

[0158]

[0159] Table 2 Lithium-ion battery related parameters

[0160]

[0161] Assuming a wind curtailment penalty of 0.8 yuan / kWh, the total energy cost of the user-side system without energy storage is 176,389,396 yuan, consisting of 169,207,709 yuan for electricity and 718,168,708 yuan for wind curtailment. After installing energy storage, the total energy cost of the user-side system is 169,642,061 yuan, consisting of electricity and zero wind curtailment. The results show that deploying user-side lithium-ion energy storage can effectively reduce user-side electricity costs and achieve 100% absorption of renewable energy generation. For power suppliers, while deploying user-side energy storage reduces electricity sales revenue, it results in a flatter net load curve, reduced peak load and load-side volatility, and lowered dispatch requirements for generators. Furthermore, energy storage deployment objectively delays the upgrading of infrastructure such as line transmission capacity and user-side transformers.

[0162] In order to further study the impact of renewable energy uncertainty on energy storage configuration results, we set =10000kW, = 10000kWh, the uncertainty deviation of wind power ranges from 10% to 50% of its predicted power value. The optimized energy storage rated capacity and user-side system electricity expenditure curve are as follows: Figure 2 As shown in the figure, the specific rated power values ​​of lithium-ion batteries are 3142kW, 3138.7kW, 3135.4kW, 3132kW, and 3128.7kW. It can be seen that as the uncertainty of renewable energy output increases, the rated capacity of the deployed energy storage and user-side electricity expenditures are both increasing to cope with the fluctuations of wind power.

[0163] Example 2:

[0164] A second embodiment of the present invention provides a user-side energy storage optimization configuration device, including:

[0165] An acquisition module is used to obtain the user's monthly typical day original electricity load and renewable energy predicted power benchmark value, and establish a user-side double-layer energy storage optimization configuration model based on the monthly typical day original electricity load and renewable energy predicted power benchmark value;

[0166] An optimization module is used to control the upper layer of the user-side two-layer energy storage optimization configuration model to minimize the total energy consumption cost of the energy storage system as the optimization goal, and to optimize with the electric power balance constraint, the maximum net load constraint and the energy storage operation constraint as the constraint conditions; and to control the lower layer of the user-side two-layer energy storage optimization configuration model to minimize the operation cost of the energy storage system under the worst renewable energy output scenario as the goal, and to optimize with the power balance constraint under the worst scenario, the actual renewable energy output constraint, the maximum net load constraint under the worst scenario and the energy storage scheduling constraint under the worst scenario as the constraint conditions;

[0167] The configuration module is used to solve the user-side double-layer energy storage optimization configuration model to obtain the optimal rated capacity and rated power configuration results of the user-side energy storage considering the worst scenario operation.

[0168] The user-side energy storage optimization configuration device provided in the second embodiment of the present invention can execute the user-side energy storage optimization configuration method provided in the first embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0169] Example 3:

[0170] The third embodiment of the present invention further provides an electronic terminal, comprising a processor and a memory connected to the processor, wherein a computer program is stored in the memory, and the processor is configured to operate according to the instructions to execute the steps of the method described in the first embodiment.

[0171] The electronic terminal provided in the third embodiment of the present invention can execute the user-side energy storage optimization configuration method provided in the first embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0172] Example 4:

[0173] Embodiment 4 of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in embodiment 1 are implemented, and the computer program has functional modules and beneficial effects corresponding to the execution method.

[0174] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, apparatuses, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application 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.

[0175] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (apparatus), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to 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 flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0176] 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.

[0177] 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.

[0178] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for optimizing configuration of energy storage at the user side, characterized in that: include: Obtain the user's monthly typical day original power load and renewable energy predicted power benchmark value, and establish a user-side double-layer energy storage optimization configuration model based on the monthly typical day original power load and renewable energy predicted power benchmark value; The upper layer of the user-side double-layer energy storage optimization configuration model is controlled to minimize the total energy consumption cost of the energy storage system as the optimization goal, and is optimized with the electric power balance constraint, the maximum net load constraint and the energy storage operation constraint as the constraint conditions; the lower layer of the user-side double-layer energy storage optimization configuration model is controlled to minimize the operation cost of the energy storage system under the worst renewable energy output scenario as the goal, and is optimized with the power balance constraint under the worst scenario, the actual renewable energy output constraint, the maximum net load constraint under the worst scenario and the energy storage scheduling constraint under the worst scenario as the constraint conditions; Solve the user-side double-layer energy storage optimization configuration model to obtain the optimal rated capacity and rated power configuration results of the user-side energy storage considering the worst scenario operation.

2. The user-side energy storage optimization configuration method according to claim 1 is characterized in that: The total energy cost optimization objectives for building an energy storage system include: ， ， ， in, Represents the operating cost of the user-side energy storage system within one year, Represents the operating cost of the user-side energy storage system during its entire life cycle, is the basic electricity price per unit, is the monthly maximum net load power of the user-side energy storage system, It is the time-of-use electricity price. It is Typical day of the month The net load in a time period is the electric power purchased by the user side system from the grid side. is the number of days in a month, is a monthly series, M=12, is the time series, T=24, y is the annual time series, Y is the energy storage life, is the annual inflation rate, is the discount rate, is the energy storage operation and maintenance cost coefficient, is the total energy cost, is the energy storage installation cost per unit capacity, is the unit power factor, is the rated capacity of energy storage, is the rated power.

3. The user-side energy storage optimization configuration method according to claim 1, characterized in that: The operating cost optimization objectives for building the energy storage system under the worst load scenario include: ， In the formula, is the operating cost under the worst load scenario, is the net load of the user-side energy storage system relative to the grid side under the worst load scenario, is the basic electricity price per unit, is the monthly maximum net load power, is the time-of-use electricity price, D is the number of days in a month, is a monthly series, is a time series, T=24.

4. The user-side energy storage optimization configuration method according to claim 1 is characterized in that: The electric power balance constraint, maximum net load constraint and energy storage operation constraint include: Power balance constraints, ， in, It is Typical day of the month The net load of a time period, Forecasting power benchmarks for renewable energy sources, is the monthly typical daily original power load, The charging power for energy storage, is the energy storage discharge power; Maximum net load constraint, ， in, is the monthly maximum net load power; Energy storage installation power constraints, ； in, is the energy storage rated power, is the maximum energy storage rated power; Energy storage installation capacity constraints, ， in, is the rated capacity of energy storage, is the maximum energy storage rated capacity; Energy storage charging power constraints, ； Energy storage discharge power constraints, ； The recursive relationship of the remaining power in the energy storage is: ， ， in, For energy storage charging efficiency, is the energy storage discharge efficiency, is the remaining power in the energy storage, is the time interval, is the amount of energy stored in the initial state, at this time t=0; is the energy storage charging power at time t, is the energy storage charging power at time t=1, is the energy storage discharge power at time t, is the energy storage discharge power at time t=1; Remaining power in energy storage is constrained. ， ， in, is the minimum limit of energy storage charge state, is the maximum limit of the energy storage charge state, is the amount of energy stored at the end of the day, when t=24.

5. The user-side energy storage optimization configuration method according to claim 1 is characterized in that: The power balance constraint under the worst scenario, the actual renewable energy output constraint, the maximum net load constraint under the worst scenario, and the energy storage dispatch constraint under the worst scenario include: Power balance constraints under the worst load scenario, ， in, The worst load scenario Typical day of the month The net load of a time period, is the monthly typical daily original power load, is the energy storage charging power under the worst load scenario, is the energy storage discharge power under the worst load scenario, is the actual output power of renewable energy under the worst load scenario; Actual renewable energy output constraints, ， ， in, Baseline values ​​for forecasting power from renewable energy sources, is the downward deviation limit of renewable energy power, is the upward deviation limit of renewable energy power, is a robust parameter representing the uncertainty margin; Maximum net load constraint under the worst load scenario, ； Energy storage charging power constraints under the worst load scenario, ； Energy storage discharge power constraints under the worst load scenario, ； The recursive relationship of the remaining power in the energy storage under the worst load scenario, ， ， The remaining power in the energy storage under the worst load scenario is constrained. ， ， in, is the remaining power in the energy storage under the worst load scenario, is the energy storage capacity in the initial state, at which t=0.

6. The user-side energy storage optimization configuration method according to claim 5 is characterized in that: Solving the user-side double-layer energy storage optimization configuration model includes: Based on the power balance constraint under the worst load scenario, according to the duality theory, by introducing dual variables, replace , an equivalent robust corresponding relationship is obtained, and the lower model in the user-side double-layer energy storage optimization configuration model is converted into a mixed integer linear programming model. The expression of the converted mixed integer linear programming model includes: ， in, , and is the dual variable of the uncertain model, and the constraint of the dual variable is, ， ， ， ， The double-level mixed integer linear programming model is transformed into a single-level mixed integer linear programming model by using KKT conditions, and the mixed integer linear programming model is solved by using a solver corresponding to the mixed integer linear programming model.

7. A user-side energy storage optimization configuration device, characterized in that: include: An acquisition module is used to obtain the user's monthly typical day original power load and renewable energy predicted power benchmark value, and establish a user-side double-layer energy storage optimization configuration model based on the monthly typical day original power load and renewable energy predicted power benchmark value; An optimization module is used to control the upper layer in the user-side double-layer energy storage optimization configuration model to minimize the total energy consumption cost of the energy storage system as the optimization goal, and optimize with the electric power balance constraint, the maximum net load constraint and the energy storage operation constraint as the constraint conditions; and control the lower layer in the user-side double-layer energy storage optimization configuration model to minimize the operation cost of the energy storage system under the worst renewable energy output scenario as the goal, and optimize with the power balance constraint under the worst scenario, the actual renewable energy output constraint, the maximum net load constraint under the worst scenario and the energy storage scheduling constraint under the worst scenario as the constraint conditions; The configuration module is used to solve the user-side double-layer energy storage optimization configuration model to obtain the optimal rated capacity and rated power configuration results of the user-side energy storage considering the worst scenario operation.

8. An electronic terminal, characterized in that: The method comprises a processor and a memory connected to the processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 6 are executed.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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