A multi-energy storage system configuration optimization method, device, equipment and medium

Abstract

The present invention belongs to the technical field of energy storage optimization, and particularly relates to a method, device, equipment and medium for optimizing the configuration of a multi-energy storage system. A method for optimizing the configuration of a multi-energy storage system includes the following steps: obtaining power data of a preset area, and constructing an annual operating cost objective function according to the power data of the preset area; obtaining several configuration scheme data, and calculating the solution of the objective function corresponding to each configuration scheme data; comparing each objective function solution with a preset cost, and eliminating the configuration scheme data whose objective function solution is greater than the preset cost; calculating the operating cost savings and the wind and light abandonment rates of each configuration scheme data; and selecting the optimal configuration scheme data according to the operating cost savings and the wind and light abandonment rates of each configuration scheme data. The present invention first selects the configuration scheme that meets the cost requirements through the objective function, and then selects the optimal configuration scheme data from the two aspects of operating cost savings and wind and light abandonment rates, so as to improve the energy utilization rate and ensure the stability of the power grid.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy storage optimization, and particularly relates to a method, device, equipment and medium for optimizing the configuration of a multi-energy storage system. Background Art

[0002] In recent years, the power system has evolved towards adapting to a large-scale and high-proportion of new energy. New energy power generation represented by wind power and solar power generation will continue to maintain a rapid development trend. However, new energy sources such as wind energy and solar energy generally have problems such as randomness and volatility. How to promote the consumption of new energy in the new power system is the key to improving energy utilization efficiency.

[0003] Energy storage technology is one of the key technologies to promote the consumption of new energy, achieve the efficient utilization and coordinated development of multiple energies. Introducing energy storage into the new power system will accelerate the construction of the new energy + energy storage development model of the power system, improve the level of new energy consumption, and promote the multi-energy complementary coordinated development and the integration process of the power source, grid, load and energy storage in the new power system.

[0004] The new energy power stations mainly include wind power stations and photovoltaic power stations. When equipped with energy storage devices in the new energy power station to form a multi-energy storage system, there is a lack of standards in the existing configuration of the multi-energy storage system, and it is difficult to determine the configuration ratio, which is prone to large-scale access of new energy, resulting in problems such as randomness and intermittency, increasing the wind and light abandonment amounts in the new energy power station, reducing the energy utilization efficiency, and causing a lot of waste. Summary of the Invention

[0005] The purpose of the present invention is to provide a method, device, equipment and medium for optimizing the configuration of a multi-energy storage system, so as to solve the technical problems in the existing technology that when configuring the multi-energy storage system, the access amount is prone to be too large, resulting in an increase in the wind and light abandonment amounts and a low energy utilization efficiency.

[0006] To achieve the above purpose, the present invention is implemented by adopting the following technical solutions:

[0007] In a first aspect, a method for optimizing the configuration of a multi-energy storage system includes the following steps:

[0008] Obtain the power data of a preset area, and construct an annual operating cost objective function according to the power data of the preset area;

[0009] Obtain several configuration scheme data, and calculate the objective function solutions corresponding to each configuration scheme data;

[0010] Compare each objective function solution with a preset cost, and eliminate the configuration scheme data with an objective function solution greater than the preset cost;

[0011] Calculate the operating cost savings and the wind and light abandonment rates of each configuration scheme data;

[0012] Select the optimal configuration scheme data based on the operating cost savings and curtailment rates of wind and solar power for each configuration scheme data.

[0013] A further improvement of the present invention is that in the step of obtaining the power data of a preset area and constructing an annual operating cost objective function based on the power data of the preset area, it specifically includes:

[0014] Obtain the power data of the preset area;

[0015] Based on the power data of the preset area, establish a curtailment cost model for wind and solar power;

[0016] Based on the power data of the preset area, establish an environmental cost model;

[0017] Based on the power data of the preset area, establish an annual maintenance and operation cost model;

[0018] Establish an annual operating cost objective function based on the curtailment cost model for wind and solar power, the environmental cost model, and the annual maintenance and operation cost model.

[0019] A further improvement of the present invention is that the environmental cost model is as follows:

[0020]

[0021] In the formula, is the carbon dioxide emission cost; is the carbon emission price; is the mass of carbon dioxide generated by unit i at time t.

[0022] The mass of carbon dioxide emitted during the combustion of fossil fuels:

[0023]

[0024] In the formula, is the mass of fossil fuels consumed by unit i at time t; is the carbon dioxide conversion coefficient.

[0025] Specification

[0026] The mass of fossil fuels consumed for power generation by thermal power units:

[0027]

[0028] In the formula, ε is the coefficient for converting the fuel cost of the unit into the fuel mass.

[0029] A further improvement of the present invention is that the objective function satisfies the constraint conditions, and the constraint conditions include: fuel cost constraint, system power balance constraint, wind and solar power generation output constraint, and energy storage system constraint.

[0030] A further improvement of the present invention lies in that: the configuration scheme data includes the configuration ratio and energy storage duration of the new energy energy storage station within a preset region.

[0031] A further improvement of the present invention lies in that: in the step of calculating the operation cost savings and curtailment rate of each configuration scheme data, the operation cost savings calculation formula is:

[0032]

[0033] In the formula, S3 is the operation cost savings; T is the calculation time length; is the operation cost of the system with energy storage at time t; is the operation cost of the system without energy storage at time t.

[0034] The curtailment rate calculation formula is:

[0035]

[0036] In the formula: S4 is the curtailment rate of the system; is the theoretical new energy power generation of the system at time t; is the actual new energy power generation of the system at time t.

[0037] A further improvement of the present invention lies in that: in the step of selecting the optimal configuration scheme data according to the operation cost savings and curtailment rate of each configuration scheme data, the following steps are included:

[0038] Sort all the operation cost savings and curtailment rates respectively according to the numerical size;

[0039] Use the primary and secondary index queuing classification method to assign weights to the operation cost savings and curtailment rate respectively;

[0040] Select the configuration scheme data with the largest sum of weights of the operation cost savings and curtailment rate as the optimal configuration scheme data.

[0041] In the second aspect, a multi-energy storage system configuration optimization device includes:

[0042] Objective function construction module: used to obtain the power data of the preset region and construct the annual operation cost objective function according to the power data of the preset region;

[0043] Objective function calculation module: used to obtain a number of configuration scheme data and calculate the objective function solution corresponding to each configuration scheme data;

[0044] Configuration scheme data screening module: used to compare each objective function solution with the preset cost and eliminate the configuration scheme data whose objective function solution is greater than the preset cost;

[0045] Index calculation module: used to calculate the operating cost savings and curtailment rates of wind and light for each configuration scheme data;

[0046] Optimal configuration scheme data screening module: used to select the optimal configuration scheme data based on the operating cost savings and curtailment rates of wind and light for each configuration scheme data.

[0047] Thirdly, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned multi-energy storage system configuration optimization method is implemented.

[0048] Fourthly, a computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the above-mentioned multi-energy storage system configuration optimization method is implemented.

[0049] Compared with the prior art, the present invention at least includes the following beneficial effects:

[0050] 1. The present invention first selects the configuration scheme that meets the cost requirements through the objective function, and then selects the optimal configuration scheme data from two aspects of operating cost savings and curtailment rates of wind and light, thereby improving energy utilization efficiency and ensuring the stability of the power grid;

[0051] 2. The present invention limits the objective function through constraint conditions to ensure the stability and rationality of the system operation;

[0052] 3. The present invention screens the configuration scheme data through the curtailment rates of wind and light and operating cost savings, effectively reducing the curtailment rates of wind and light of the optimal configuration scheme data and avoiding energy waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The accompanying drawings forming a part of the present invention are used to provide a further understanding of the present invention. The schematic embodiments and descriptions thereof of the present invention are used to explain the present invention and do not constitute an improper limitation to the present invention.

[0054] In the drawings:

[0055] Figure 1 is a flowchart of a multi-energy storage system configuration optimization method of the present invention;

[0056] Figure 2 is a structural block diagram of a multi-energy storage system configuration optimization device of the present invention;

[0057] Figure 3 is a bar chart of the configuration ratio and energy storage time versus operating cost savings in Embodiment 3 of the present invention;

[0058] Figure 4 is a bar chart of the configuration ratio and energy storage time versus curtailment rates of wind and light in Embodiment 3 of the present invention. Specific implementation mode

[0059] The present invention will be described in detail below with reference to the drawings and in combination with embodiments. It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0060] The following detailed descriptions are all exemplary descriptions, aiming to provide further detailed descriptions of the present invention. Unless otherwise specified, all technical terms adopted by the present invention have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs. The terms used in the present invention are only for describing specific implementation modes and are not intended to limit the exemplary implementation modes according to the present invention.

[0061] Embodiment 1

[0062] A method for optimizing the configuration of a multi-energy storage system, as Figure 1 shown, includes the following steps:

[0063] S1. Obtain the power data of a preset area, and construct an annual operating cost objective function according to the power data of the preset area;

[0064] The multi-energy storage system in this embodiment is a power operation system integrating wind, light, fire and energy storage, and is also applicable to power systems including any type of energy storage integration, etc.

[0065] The annual operating cost objective function needs to comprehensively consider the curtailment cost of wind and light, environmental cost and annual maintenance and operation cost generated during the operation of the power operation system integrating wind, light, fire and energy storage.

[0066] Specifically, S1 specifically includes the following steps:

[0067] S11. Obtain the power data of the preset area;

[0068] S12. Establish a curtailment cost model of wind and light according to the power data of the preset area;

[0069] Specifically, the curtailment cost model of wind and light in S12 is as follows:

[0070]

[0071]

[0072] In the formula: C RE is the curtailment cost of wind and light of the system; λ RE is the benchmark grid-connected electricity price; M is the number of wind power stations in the preset area; N is the number of photovoltaic power stations in the preset area; is the theoretical power generation of wind power station m at time t; is the actual power generation of wind power station m at time t; is the theoretical power generation of photovoltaic power station n during period t; is the actual power generation of photovoltaic power station n during period t.

[0073] S13. Establish an environmental cost model based on the preset regional power data;

[0074] Specifically, the environmental cost model is as follows:

[0075]

[0076] In the formula, is the carbon dioxide emission cost; is the carbon emission price; is the mass of carbon dioxide generated by unit i during period t.

[0077] The mass of carbon dioxide emitted during the combustion of fossil fuels:

[0078]

[0079] In the formula, is the mass of fossil fuels consumed by unit i during period t; is the carbon dioxide conversion coefficient.

[0080] The mass of fossil fuels consumed for power generation by thermal power units:

[0081]

[0082] In the formula, ε is the coefficient for converting the fuel cost of the unit into the fuel mass.

[0083] S14. Establish an annual maintenance and operation cost model based on the preset regional power data;

[0084] Specifically, the annual operation and maintenance cost C OM refers to the expenses generated during the annual operation and maintenance of the new power system, which can be mainly divided into the maintenance cost C E,OM of thermal power units, the maintenance cost C P,OM of new energy, and the operation and maintenance cost C labor of energy storage, that is:

[0085] C OM = C E,OM + C P,OM + C labor ;

[0086] S15. Establish an annual operation cost objective function based on the curtailment cost model of wind and solar power, the environmental cost model, and the annual maintenance and operation cost model;

[0087] Specifically, the objective function is as follows:

[0088]

[0089] Specifically, the objective function satisfies the constraint conditions, which include: fuel cost constraint, system power balance constraint, wind and photovoltaic power output constraint, and energy storage system constraint.

[0090] Fuel cost constraint:

[0091] c i,t = f i,t (p i,t )

[0092] In the formula: f i,t (·) is the fuel cost function of unit i at time t; p i,t is the output power of unit i at time t.

[0093] System power balance constraint:

[0094]

[0095] In the formula: J is the number of energy storage systems; is the charge of energy storage system j at time t; is the discharge of energy storage system j at time t; D t is the total electrical load of the system at time t.

[0096] Wind and photovoltaic power output constraint

[0097]

[0098]

[0099] Energy storage system constraint:

[0100] The energy storage system cannot perform charging and discharging operations simultaneously in one period, and its operation constraint can be expressed as:

[0101]

[0102] In the formula: is the charging operation state of energy storage system j at time t; is the discharging operation state of energy storage system j at time t, indicates that the energy storage system enters the charging state, and vice versa is 0; indicates that the energy storage system enters the discharging state, and vice versa is 0.

[0103] The charge-discharge power constraint of the energy storage system can be expressed as:

[0104]

[0105]

[0106] Where: is the minimum charge-discharge power of energy storage system j; is the maximum charge-discharge power of energy storage system j.

[0107] S2. Obtain several configuration plan data and calculate the objective function solutions corresponding to each configuration plan data;

[0108] Specifically, the configuration plan data reflects the configuration ratio and energy storage duration of the new energy storage station within the preset region.

[0109] S3. Compare each objective function solution with the preset cost and eliminate the configuration plan data with an objective function solution greater than the preset cost;

[0110] S4. Calculate the operation cost savings and curtailment rate of wind and solar power for each configuration plan data;

[0111] Specifically, considering the economy of system operation, the energy storage system can store the excess power generated by new energy power generation and release energy during the peak load period, reducing the system operation cost. The operation cost savings are as follows:

[0112]

[0113] Where S3 is the operation cost savings; T is the calculation time length; is the system operation cost including energy storage at time t; is the system operation cost without energy storage at time t.

[0114] Specifically, considering the new energy consumption capacity of the system, the curtailment rate of wind and solar power represents the loss of on-grid electricity price caused by wind and solar curtailment operations in the new power system and is an important indicator to measure the new energy consumption capacity of the system. The formula for calculating the curtailment rate of wind and solar power of the system is:

[0115]

[0116] Where: S4 is the curtailment rate of wind and solar power of the system; is the theoretical new energy power generation of the system at time t; P t RG is the actual new energy power generation at time t.

[0117] S5. Select the optimal configuration plan data according to the operation cost savings and curtailment rate of wind and solar power of each configuration plan data.

[0118] Specifically, S5 includes the following steps:

[0119] Sort all the operation cost savings and curtailment rates of wind and solar power in ascending order of numerical values;

[0120] The primary and secondary index queuing classification method is used to assign weights to the operation cost savings and the curtailment rate of wind and photovoltaic power respectively;

[0121] The configuration scheme data with the largest sum of weights of the operation cost savings and the curtailment rate of wind and photovoltaic power is selected as the optimal configuration scheme data.

[0122] Example 2

[0123] An actual application of a multi-energy storage system configuration optimization method. Based on the multi-energy storage system configuration optimization method in Example 1, an improved IEEE 24-node system is used to verify the effectiveness of the proposed method. The calculation example includes 17 load nodes, 34 transmission lines, 1 wind power station, 1 photovoltaic power station and 2 energy storage power stations. The cost generated by the operation of the new power system is calculated by selecting a typical day.

[0124] Analyze the impact of the changes in the configuration ratio S1 and the energy storage duration S2 of the energy storage system on the system operation cost savings S3 and the curtailment rate of wind and photovoltaic power S4.

[0125] The configuration ratio of installing energy storage in the wind power station and the photovoltaic power station is 5% - 30%, and the energy storage duration is 1h - 4h. Therefore, S1 = 5%, 10%, 15%, 20%, 25% and 30%, and the energy storage duration S2 = 1h, 2h, 3h and 4h. Solve the new energy consumption calculation model considering the environmental cost to obtain the values of the operation cost savings S3 and the curtailment rate of wind and photovoltaic power S5 under different configuration ratios and energy storage durations. The calculation results are as Figure 3 and 4 shown.

[0126] According to Figure 3 and Figure 4 , when the energy storage configuration ratio S1 increases from 5% to 30% and the energy storage duration S2 increases from 1h to 4h, the system operation cost savings S3 increases rapidly from 0.59 to 7.16, and the curtailment rate of wind and photovoltaic power S4 drops rapidly from 11.96% to 1.22%. The increase in the energy storage configuration ratio S1 and the energy storage duration S2 of the energy storage system has a greater impact on the system operation cost savings rate S3 and the curtailment rate of wind and photovoltaic power S5. This is because the energy storage system with a higher configuration ratio and energy storage duration can improve the new energy consumption capacity of the power system and effectively reduce the system operation cost.

[0127] Example 3

[0128] A multi-energy storage system configuration optimization device, based on the multi-energy storage system configuration optimization method in Example 1, includes:

[0129] Objective function construction module: used to obtain the power data of the preset area and construct the annual operation cost objective function according to the power data of the preset area;

[0130] Objective function calculation module: used to obtain a number of configuration plan data and calculate the objective function solutions corresponding to each configuration plan data;

[0131] Configuration plan data screening module: used to compare each objective function solution with a preset cost and eliminate the configuration plan data with an objective function solution greater than the preset cost;

[0132] Index calculation module: used to calculate the operating cost savings and curtailment rates of wind and solar power for each configuration plan data;

[0133] Optimal configuration plan data screening module: used to select the optimal configuration plan data based on the operating cost savings and curtailment rates of wind and solar power for each configuration plan data.

[0134] Specifically, the objective function construction module includes:

[0135] Obtain the power data of the preset region;

[0136] Establish a curtailment cost model of wind and solar power based on the power data of the preset region;

[0137] The curtailment cost model of wind and solar power is as follows:

[0138]

[0139] In the formula: C RE is the curtailment cost of wind and solar power of the system; λ RE is the benchmark grid-connected electricity price; M is the number of wind power stations in the preset region; N is the number of photovoltaic power stations in the preset region; is the theoretical power generation of wind power station m at time t; is the actual power generation of wind power station m at time t; is the theoretical power generation of photovoltaic power station n at time t; is the actual power generation of photovoltaic power station n at time t.

[0140] Establish an environmental cost model based on the power data of the preset region;

[0141] The environmental cost model is as follows:

[0142]

[0143] In the formula, is the carbon dioxide emission cost; is the carbon emission price; is the mass of carbon dioxide generated by unit i at time t.

[0144] The mass of carbon dioxide emitted during the combustion of fossil fuels:

[0145]

[0146] In the formula, is the mass of fossil fuel consumed by unit i during period t; is the carbon dioxide conversion coefficient.

[0147] Mass of fossil fuel consumed for power generation by thermal power units:

[0148]

[0149] In the formula, ε is the coefficient for converting the fuel cost of the unit into fuel mass.

[0150] Based on the preset regional power data, establish an annual maintenance and operation cost model;

[0151] Annual operation and maintenance cost C OM refers to the expenses generated during the annual operation and maintenance of the new power system, which can be mainly divided into the maintenance cost C E,OM of thermal power units, the maintenance cost C P,OM of new energy, and the operation and maintenance cost C labor of energy storage, that is:

[0152] C OM = C E,OM + C P,OM + C labor ;

[0153] Based on the curtailment cost model, environmental cost model and annual maintenance and operation cost model, establish an annual operation cost objective function;

[0154] The objective function is as follows:

[0155]

[0156] Specifically, the objective function satisfies the constraint conditions, and the constraint conditions include: fuel cost constraint, system power balance constraint, wind and photovoltaic power generation output constraint, and energy storage system constraint.

[0157] Fuel cost constraint:

[0158] c i,t = f i,t (p i,t )

[0159] In the formula: f i,t (·) is the fuel cost function of unit i at time t; p i,t is the output power of unit i at time t.

[0160] System power balance constraint:

[0161]

[0162] Where: J is the number of energy storage systems; is the charging amount of energy storage system j at time t; is the discharging amount of energy storage system j at time t; D t is the total electrical load of the system at time t.

[0163] Wind power and photovoltaic power generation output constraints

[0164]

[0165]

[0166] Energy storage system constraints:

[0167] The energy storage system cannot perform charging and discharging operations simultaneously in one period, and its operation constraints can be expressed as:

[0168]

[0169] Where: is the charging operation state of energy storage system j at time t; is the discharging operation state of energy storage system j at time t, When it is, it means that the energy storage system enters the charging state, otherwise it is 0; When it is, it means that the energy storage system enters the discharging state, otherwise it is 0.

[0170] The charging and discharging power constraints of the energy storage system can be expressed as:

[0171]

[0172]

[0173] Where: is the minimum charging and discharging power of energy storage system j; is the maximum charging and discharging power of energy storage system j.

[0174] Specifically, the index calculation module includes:

[0175] The operating cost savings are as follows:

[0176]

[0177] Where, S3 is the operating cost savings; T is the calculation time length; is the operating cost of the system with energy storage at time t; is the operating cost of the system without energy storage at time t.

[0178] Considering the new energy consumption capacity of the system, the curtailment rate of wind and light represents the loss of on-grid electricity price caused by curtailment of wind and light operations in the new power system, and is an important indicator to measure the new energy consumption capacity of the system. The calculation formula for the curtailment rate of wind and light of the system is as follows:

[0179]

[0180] In the formula: S4 is the curtailment rate of wind and light of the system; is the theoretical power generation of new energy in the system at time t; P t RG is the actual power generation of new energy at time t.

[0181] Specifically, the optimal configuration plan data screening module includes:

[0182] Sort all the operation cost savings and curtailment rates of wind and light in ascending order of numerical value respectively;

[0183] Use the primary and secondary index queuing classification method to assign weights to the operation cost savings and curtailment rates of wind and light respectively;

[0184] Select the configuration plan data with the largest sum of weights of operation cost savings and curtailment rates of wind and light as the optimal configuration plan data.

[0185] Embodiment 4

[0186] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, a multi-energy storage system configuration optimization method is implemented, including the following steps:

[0187] Obtain the power data of the preset area, and construct an annual operation cost objective function according to the power data of the preset area;

[0188] Obtain a number of configuration plan data, and calculate the objective function solution corresponding to each configuration plan data;

[0189] Compare each objective function solution with the preset cost, and eliminate the configuration plan data with the objective function solution greater than the preset cost;

[0190] Calculate the operation cost savings and curtailment rates of wind and light of each configuration plan data;

[0191] Select the optimal configuration plan data according to the operation cost savings and curtailment rates of wind and light of each configuration plan data.

[0192] Embodiment 5

[0193] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, a multi-energy storage system configuration optimization method in Embodiment 1 is implemented.

[0194] As is known by common technical knowledge, the present invention can be implemented by other embodiments without departing from its spirit or essential characteristics. Therefore, the above-disclosed embodiments are illustrative in all aspects and not exclusive. All changes within the scope of the present invention or within the scope equivalent to the present invention are encompassed by the present invention.

[0195] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0196] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows 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 the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0197] These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured product including instruction means, and the instruction means implement the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0198] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0199] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent substitutions can still be made to the specific implementation manners of the present invention, and any modification or equivalent substitution that does not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A multi-energy storage system configuration optimization method, characterized in that: The following steps are involved: Obtaining power data for a preset region and constructing an annual operating cost objective function based on the power data for the preset region; Obtaining a number of configuration scheme data and calculating the objective function solution corresponding to each configuration scheme data; Compare each objective function solution with the preset cost, and eliminate configuration solution data whose objective function solution is greater than the preset cost; Calculate the operating cost savings and wind and solar curtailment rates for each configuration plan data; Select the optimal configuration plan data based on the operating cost savings and wind and solar power curtailment rates of each configuration plan data; In the step of calculating the operating cost savings and wind and solar power curtailment rate of each configuration scheme data, the operating cost savings calculation formula is: Where S3 is the operating cost saving; T is the calculation time length; is the system operating cost including energy storage during period t; is the system operating cost without energy storage during period t; The formula for calculating the wind and solar power curtailment rate is: Where: S4 is the system's wind and solar power curtailment rate; is the theoretical power generation of new energy in the system during period t; P t RG is the actual power generation of renewable energy in period t; The step of selecting the optimal configuration solution data according to the operation cost savings and wind and solar power abandonment rates of each configuration solution data includes the following steps: Sort all operating cost savings and wind and solar curtailment rates by numerical value; The primary and secondary indicator queuing classification method is used to assign weights to the operating cost savings and wind and solar power curtailment rates respectively; The configuration plan data with the largest sum of operating cost savings and wind and solar power curtailment rate weights is selected as the optimal configuration plan data.

2. A multi-energy storage system configuration optimization method according to claim 1, characterized in that: The step of obtaining the power data of the preset region and constructing the annual operating cost objective function based on the power data of the preset region specifically includes: Get power data for preset areas; Establish a cost model for curtailing wind and solar power based on preset regional power data; Establish an environmental cost model based on the preset regional electricity data; Establish an annual maintenance and operation cost model based on preset regional power data; The annual operating cost objective function is established based on the wind and solar power curtailment cost model, environmental cost model and annual maintenance and operation cost model.

3. A multi-energy storage system configuration optimization method according to claim 2, characterized in that: The environmental cost model is as follows: Where, is the cost of carbon dioxide emissions; is the price of carbon emissions; is the mass of carbon dioxide produced by unit i during period t; The mass of carbon dioxide emitted during the combustion of fossil fuels: Where, is the mass of fossil fuel consumed by unit i during period t; is the carbon dioxide conversion factor; Mass of fossil fuel consumed by thermal power generation units: Where ε is the coefficient for converting the unit’s fuel cost into fuel quality.

4. The multi-energy storage system configuration optimization method according to claim 1, characterized in that: The objective function satisfies constraints, which include fuel cost constraints, system power balance constraints, wind power and photovoltaic power generation output constraints, and energy storage system constraints.

5. The multi-energy storage system configuration optimization method according to claim 1, characterized in that: The configuration plan data includes the configuration ratio and storage duration of the new energy storage station in the preset area.

6. A multi-energy storage system configuration optimization device, characterized in that: A method for optimizing the configuration of a multi-energy storage system according to any one of claims 1 to 5, comprising: Objective function construction module: used to obtain power data of preset regions and construct annual operating cost objective function based on the power data of preset regions; Objective function calculation module: used to obtain a number of configuration scheme data and calculate the objective function solution corresponding to each configuration scheme data; Configuration scheme data screening module: used to compare each objective function solution with the preset cost and eliminate configuration scheme data with objective function solutions greater than the preset cost; Index calculation module: used to calculate the operating cost savings and wind and solar power curtailment rates of each configuration scheme data; Optimal configuration scheme data screening module: used to select the optimal configuration scheme data based on the operating cost savings and wind and solar power curtailment rates of each configuration scheme data.

7. 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, a multi-energy storage system configuration optimization method according to any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, a multi-energy storage system configuration optimization method according to any one of claims 1 to 5 is implemented.

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