Long-term and short-term energy storage optimal configuration method considering uncertainties of both sides of source and load

Abstract

The invention relates to the technical field of energy storage configuration, and particularly discloses a long and short-term energy storage optimal configuration method considering source-load double-side uncertainty, which comprises the following steps: taking electrochemical energy storage as short-term energy storage, taking hydrogen energy storage as long-term energy storage, and taking configuration power and capacity of the electrochemical energy storage and the hydrogen energy storage as optimization variables; establishing a target function with the lowest comprehensive cost; establishing a power grid operation constraint and an investment strategy constraint considering fan and photovoltaic output and user price type demand response characteristics; and optimizing the established objective function based on the power grid operation constraint and investment strategy constraint considering the uncertain source and load double sides to obtain an optimal configuration scheme of long-term and short-term energy storage. Compared with the prior art, the fluctuation and randomness of new energy can be effectively coordinated, the stability of a power system is improved, and reliable reference is provided for long-term and short-term energy storage configuration.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage configuration, and in particular to a method for optimizing the configuration of long- and short-term energy storage taking into account uncertainties on both sides of a source and a load. Background Art

[0002] In the power sector, the proportion of renewable energy is gradually increasing. At the same time, the volatility and randomness of renewable energy generation also bring challenges to traditional power systems. In order to coordinate the grid connection problems of distributed energy and reduce the negative impact of its grid connection on the stable operation of the power system, configuring an appropriate energy storage system has become a major solution.

[0003] With the continuous increase in the penetration rate of new energy, a single form of energy storage can no longer meet the needs of flexible regulation of future power systems from hourly to seasonal levels. Therefore, there is an urgent need to coordinate and optimize the configuration of long-term and short-term energy storage. As a short-term energy storage technology, electrochemical energy storage (Battery Energy Storage System, BESS) has the advantages of rapid response and efficient energy conversion. It is suitable for smoothing short-term fluctuations in new energy and providing daily system frequency regulation and peak regulation services. However, due to the limitations of capacity and charge and discharge life, electrochemical energy storage is difficult to meet the energy regulation needs of long time scales. Hydrogen energy storage (HESS) is a long-term energy storage technology with large capacity, long life and low cost. It has excellent energy storage capacity, clean and low carbon, long-term storage, low expansion cost, and the site selection is not restricted by the geographical environment. Therefore, it has attracted widespread attention from researchers. Summary of the invention

[0004] The purpose of the present invention is to overcome the defects of the above-mentioned existing energy storage configuration technology and provide a long-term and short-term energy storage optimization configuration method taking into account the uncertainty of both the source and the load.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] A method for optimizing the configuration of long- and short-term energy storage taking into account uncertainty on both sides of source and load, comprising the following steps:

[0007] Using electrochemical energy storage as short-term energy storage and hydrogen energy storage as long-term energy storage, the configuration power and capacity of electrochemical energy storage and hydrogen energy storage are used as optimization variables to establish an objective function with the lowest comprehensive cost;

[0008] Establish grid operation constraints and investment strategy constraints that take into account wind turbine and photovoltaic output and user price-based demand response characteristics;

[0009] Based on the uncertain grid operation constraints and investment strategy constraints on both the source and load sides, the established objective function is optimized to obtain the optimal configuration plan for long-term and short-term energy storage.

[0010] As a preferred technical solution, the objective function with the lowest comprehensive cost is:

[0011] minC1=C TCC +C MC +C OC +C OP

[0012] Where: C1 represents the comprehensive cost of the system; C TCC is the investment and construction cost of energy storage; C MC is the maintenance cost of energy storage; C OC is the operating cost of energy storage; C OP The overall operating cost of the system.

[0013] As a preferred technical solution, the investment and construction costs of the energy storage include:

[0014] C Tcc =C T-BESS +C T-HESS

[0015]

[0016] Where: C T-BESS and C T-HESS They are the annual investment and construction costs of electrochemical energy storage and hydrogen energy storage respectively; and N is the fixed investment and construction cost of electrochemical energy storage and hydrogen energy storage; BESS and N HESS are the configuration quantities of electrochemical energy storage and hydrogen energy storage respectively; P BESS,n and E BESS,n are the power and capacity of the nth electrochemical energy storage device respectively; P HESS,n and E HESS,n are the power and capacity of the nth hydrogen energy storage, respectively; a and b are the unit power cost of energy storage and the battery conversion cost, respectively; μ CRF is the annual capital recovery rate; y is the useful life of energy storage; r is the discount rate;

[0017] The maintenance costs of the energy storage include:

[0018] C MC =C MC-BESS +C MC-HESS

[0019]

[0020] Where: MC-BESS and ρ MC-HESS are the maintenance factors of electrochemical energy storage and HESS, respectively;

[0021] The operating costs of the energy storage include:

[0022] C OC =C OC-BESS +C OC-HESS

[0023]

[0024] Where: P ch,BESS (t) and P di,BESS (t) are the charge and discharge power of electrochemical energy storage; P ch,HESS (t) and P di,HESS (t) are the charging and discharging power of energy storage; e in (t) and e out (t) are the electricity purchase price and electricity sales price during the energy storage charging and discharging process at time t.

[0025] As a preferred technical solution, the comprehensive operating cost of the system includes:

[0026] C OP =(C fix +C Th +C Fe )ρ z

[0027]

[0028] Where: Comprehensive operating cost C OP Including fixed operation and maintenance costs of various power supplies C fix , power generation and start-up and shutdown costs of thermal power units C Th , fuel and environmental cost composition C Fe ρ z is the annual conversion factor, which aims to convert the operating costs into an annual time scale; represents the fixed operation and maintenance cost of power unit i in year t; Represents the capacity of the nth unit in the tth year; C g (P g,t ) is the power generation cost function of the thermal power unit; S g , D g and Respectively represent the start-stop cost and start-stop status of thermal power units; c fule E is the coal burning cost of thermal power units; gt is the power generation of the thermal power unit in year t; T h Represents a collection of thermal power units; Characterizes CO2 emission intensity; ω env and ω pun are the environmental value coefficient and the penalty coefficient respectively.

[0029] As a preferred technical solution, the charge and discharge model of the electrochemical energy storage is:

[0030]

[0031] Where: E BESS (t) is the residual energy of BESS at time t; P ch,BESS (t) and P di,BESS (t) are the charge and discharge power of electrochemical energy storage; Δt is the charge and discharge time of electrochemical energy storage; η ch and η di They are the charging efficiency and discharging efficiency of electrochemical energy storage respectively;

[0032] During the charge and discharge process of electrochemical energy storage, the changes in SOC are as follows:

[0033]

[0034] In the formula, E BESS,rated is the rated capacity of electrochemical energy storage.

[0035] As a preferred technical solution, the charging and discharging mode model of the hydrogen energy storage is:

[0036]

[0037] Where: Q HESS (t) is the storage capacity of hydrogen; P ch,HESS (t) and P di,HESS (t) are the charging and discharging power of hydrogen energy storage; Δt is the charging and discharging time of hydrogen energy storage; ρ el is the conversion coefficient of electricity to hydrogen; p fc The hydrogen capacity consumed for hydrogen power generation; η fc is the storage efficiency of hydrogen; η hy is the calorific value constant of hydrogen;

[0038] During the charging and discharging process of hydrogen energy storage, the change of SOC is:

[0039]

[0040] In the formula, E HESS,rated is the rated capacity of hydrogen energy storage.

[0041] As a preferred technical solution, the grid operation constraints considering wind turbine and photovoltaic output and user price-based demand response characteristics specifically include:

[0042] Power balance constraints:

[0043] P G +P W +P PV +Pdi,BESS +P di,HESS -P ch,BESS -P ch,HESS =P L

[0044] Where: P W , P PV Respectively represent wind turbine and photovoltaic output; P G Indicates the output of other generator sets; P L It is the active load of the power grid after taking into account the user price type demand response characteristics;

[0045] Thermal power unit operation constraints:

[0046] 0≤P g,t ≤G g

[0047]

[0048] Where: G g It is the installed capacity of thermal power; and is the climbing rate of the thermal power unit; O g,t is the online startup capacity of the thermal power unit in period t; and are the starting and stopping capacities of thermal power units in period t respectively; It is the minimum technical output ratio of thermal power units; and They are the minimum startup and shutdown time of thermal power units respectively.

[0049] As a preferred technical solution, the fan output model is as follows:

[0050] According to the actual wind speed, the random variable v(i) is calculated: when the wind speed is lower than the cut-in wind speed or higher than the cut-out wind speed, the fan output is 0; when the wind speed is higher than the cut-in wind speed and lower than the rated wind speed, the fan output is related to the wind speed; when the wind speed is higher than the rated wind speed but lower than the cut-out wind speed, the fan output is the rated power. The wind speed in each state is expressed as follows:

[0051]

[0052] The probability of each state occurring is as follows:

[0053]

[0054] Where v is a random variable representing wind speed; f(v) represents the probability density function of v; v ci 、v cr 、v co are cut-in wind speed, rated wind speed and cut-out wind speed; NW Indicates that the cut-in wind speed and the rated wind speed are adjusted by a step size (v cr -v ci ) / N W Divide into N W status.

[0055] As a preferred technical solution, the photovoltaic output model is as follows:

[0056] When the light intensity is between the minimum light intensity and the maximum light intensity, the photovoltaic output is not zero; when the light intensity is higher than the minimum light intensity and lower than the rated light intensity, the photovoltaic output is related to the light intensity; when the light intensity is higher than the rated light intensity and lower than the maximum light intensity, the photovoltaic output is rated power. The light intensity in each state is expressed as follows:

[0057]

[0058] The probability of each state occurring is as follows:

[0059]

[0060] Where f(r) represents the probability density function of light intensity r; r is the light intensity; r max is the maximum light intensity in a certain period of time; r(i) is the variable related to the photovoltaic output under each state; r min 、r s 、r max Respectively represent the minimum light intensity, rated light intensity and maximum light intensity; N G It means that the minimum light intensity and the rated light intensity are divided by the step length (r s -r min ) / N G Divide into N G status.

[0061] As a preferred technical solution, the active load of the power grid after taking into account the user price-type demand response characteristics is as follows:

[0062]

[0063] Where: P L,t is the load after taking into account the price-based demand response strategy during period t; P L0,t is the load before taking into account the price-based demand response strategy during period t; X kt is the gear mark of the price-based demand response strategy; η kt is the expected load response rate of the k-gear position in period t; ρ t is the electricity price after implementing the price-based demand response strategy in period t; ρ 0,tis the time-of-use electricity price before taking into account the price-based demand response strategy during period t; α kt is the price rate of the k-level in period t.

[0064] Compared with the prior art, the present invention has the following beneficial effects:

[0065] 1) This invention proposes a long-term and short-term energy storage optimization configuration scheme that takes into account the uncertainty of both the source and the load, aiming to solve the problem of energy storage optimization configuration. This scheme constructs an optimization problem with energy storage investment and operation and maintenance costs as objective functions by establishing wind and solar power generation output, user demand response, long-term and short-term energy storage, and grid operation simulation models. This model can effectively coordinate the volatility and randomness of new energy, improve the stability of the power system, and provide a reliable reference for long-term and short-term energy storage configuration.

[0066] 2) The wind turbine and photovoltaic output model established by the present invention divides the wind speed and light intensity into multiple states according to the step size, and calculates the probability of each state according to the probability density function of the wind speed and light intensity, which can fully consider the uncertainty of the wind and photovoltaic output on the source side.

[0067] 3) The present invention, by establishing a load price type demand response model, can reflect that the user's electricity consumption behavior is indirectly affected by the fluctuation of electricity prices, and obtain the active load of the power grid after taking into account the user's price type demand response characteristics, which fully considers the uncertainty on the load side caused by adjusting the user's electricity consumption behavior by adjusting the electricity price. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 The present invention is a flow chart of a method for optimizing the configuration of long- and short-term energy storage taking into account uncertainties on both the source and load sides. DETAILED DESCRIPTION

[0069] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0070] Example 1

[0071] The present invention aims at the problem of optimal configuration of energy storage and provides a method for optimal configuration of long-term and short-term energy storage taking into account the uncertainty of both sides of source and load. The core idea is to build a model for optimal configuration of long-term and short-term energy storage taking into account the uncertainty of both sides of source and load: first, a model related to wind and solar power generation output and wind speed and light intensity, a user demand response model, a long-term and short-term energy storage model, and a power grid operation simulation model are established. On this basis, an optimal configuration problem with the investment and operation and maintenance costs of energy storage as the objective function is constructed. Based on the power grid operation constraints and investment strategy constraints taking into account the uncertainty of both sides of source and load, the established objective function is optimized to obtain the optimal configuration plan of long-term and short-term energy storage. The specific steps are as follows:

[0072] Step 1: Establish a wind and solar output model

[0073] 1.1) Fan output model

[0074] Wind speed generally obeys a two-parameter Weibull distribution, and its probability density function is shown in formula (1), which is a unimodal function. According to the actual wind speed, formula (2) calculates the random variable v(i): when the wind speed is lower than the cut-in wind speed or higher than the cut-out wind speed, the wind turbine output is 0; when the wind speed is higher than the cut-in wind speed and lower than the rated wind speed, the wind turbine output is related to the wind speed; when the wind speed is higher than the rated wind speed but lower than the cut-out wind speed, the wind turbine output is the rated power. According to the probability density function of the Weibull distribution in formula (1), the probability of each state in formula (2) is calculated.

[0075]

[0076] Wherein, formula (1) represents the probability density function of v; formula (2) represents the wind speed under each state; formula (3) represents the probability of each state occurring; v is a random variable representing wind speed; k is the shape index of the Weibull distribution curve; c is the scale index; v ci 、v cr 、v co are cut-in wind speed, rated wind speed and cut-out wind speed; N W Indicates that the cut-in wind speed and the rated wind speed are adjusted by a step size (v cr -v ci ) / N W Divide into N W status.

[0077] 1.2) Photovoltaic output model

[0078] The output probability of photovoltaic panels varies with different light intensities. The random characteristics of light intensity can be described by Beta distribution, and its probability density function is shown in formula (4). When the light intensity is between the minimum light intensity and the maximum light intensity, the photovoltaic output is not zero; when the light intensity is higher than the minimum light intensity and lower than the rated light intensity, the photovoltaic output is related to the light intensity; when the light intensity is higher than the rated light intensity and lower than the maximum light intensity, the photovoltaic output is rated power, as shown in formula (5). According to the probability density function of the Beta distribution in formula (4), the probability of each state in formula (5) is calculated, as shown in formula (6).

[0079]

[0080] Wherein, formula (4) represents the probability density function of light intensity r; formula (5) represents the light intensity under each state; formula (6) represents the probability of each state occurring; r is the light intensity; r max is the maximum light intensity in a certain period of time; α and β are the two shape parameters of Beta distribution, which can be calculated from the mean and mean square error of light intensity; Γ is the Gamma function; r(i) is the variable related to the photovoltaic output under each state; r min 、r s 、r max Respectively represent the minimum light intensity, rated light intensity and maximum light intensity; N G It means that the minimum light intensity and the rated light intensity are divided by the step length (r s -r min ) / N G Divide into N G status.

[0081] Step 2: Establish a load price demand response model

[0082] The user's electricity consumption behavior is indirectly affected by the fluctuation of electricity prices. The electricity price can be adjusted to adjust the user's electricity consumption behavior and guide them to form electricity consumption behavior that meets the system scheduling requirements. Assuming that users are sensitive to electricity prices, the active load of the power grid after taking into account the user's price-type demand response characteristics is as follows:

[0083]

[0084] Where: P L,t is the load after taking into account the price-based demand response strategy during period t; P L0,t is the load before taking into account the price-based demand response strategy in period t, i.e., the benchmark load; X kt is the gear mark of the price-based demand response strategy, which is a 0-1 variable; η kt is the expected load response rate of the k-gear position in period t; ρ tis the electricity price after implementing the price-based demand response strategy in period t; ρ 0,t is the time-of-use electricity price before taking into account the price-based demand response strategy in period t, i.e., the benchmark electricity price; α kt is the price rate of the k-level in period t.

[0085] Step 3: Establish a long-term and short-term energy storage system model

[0086] The present invention selects electrochemical energy storage as short-term energy storage because of its characteristics of rapid response and efficient energy conversion; hydrogen energy storage is selected as long-term energy storage. Hydrogen energy storage is a long-term storage technology with large capacity, long life and low cost. It has the characteristics of strong energy storage capacity, clean and low carbon, long storage time, low expansion cost, and the construction location is not restricted by the geographical environment.

[0087] 3.1) BESS model

[0088] Electrochemical energy storage can be used to store and transfer energy through charging and discharging. Its model is:

[0089]

[0090] Where: E BESS (t) is the remaining energy of BESS at time t; subscripts ch and di are the charge and discharge states of BESS, respectively; P ch,BESS (t) and P di,BESS (t) are the charging and discharging power of BESS respectively; η ch and η di are the charging efficiency and discharging efficiency of BESS respectively.

[0091] During the charging and discharging process of BESS, the change of SOC is:

[0092]

[0093] In the formula, E BESS,rated is the rated capacity of electrochemical energy storage.

[0094] 3.2) Hydrogen Energy Storage (HESS) Model

[0095] The surplus electricity of HESS is converted into hydrogen through the electrolyzer to achieve energy storage; the stored hydrogen energy is converted into electricity through the fuel cell to release the energy. The charging and discharging mode of HESS is:

[0096]

[0097] Where: Q HESS (t) is the storage capacity of hydrogen; P ch,HESS (t) and P di,HESS (t) are the charging and discharging power of hydrogen energy storage; ρel is the conversion coefficient of electricity to hydrogen (the amount of electricity required to produce unit volume of hydrogen); η fc is the storage efficiency of hydrogen; η hy is the calorific value constant of hydrogen; p fc The capacity of hydrogen consumed for hydrogen power generation.

[0098] During the HESS charging and discharging process, the change of SOC is:

[0099]

[0100] In the formula, E HESS,rated is the rated capacity of the HESS.

[0101] Step 4: Establish grid operation constraints

[0102] 4.1) Power balance constraints

[0103] P G +P W +P PV +P di,BESS +P di,HESS -P ch,BESS -P ch,HESS =P L (12)

[0104] Where: P W , P PV Respectively represent wind and solar power output; P G Indicates the output of other generator sets; P L is the load after taking into account the price-based demand response strategy.

[0105] 4.2) Operation constraints of thermal power units

[0106]

[0107] Where: G g It is the installed capacity of thermal power; and is the climbing rate of the thermal power unit; O g,t is the online startup capacity of the thermal power unit in period t; and are the starting and stopping capacities of thermal power units in period t respectively; It is the minimum technical output ratio of thermal power units; and They are the minimum startup and shutdown time of thermal power units respectively.

[0108] Step 5: Establish investment strategy constraints

[0109] The maximum buildable capacity constraints of electrochemical energy storage and hydrogen energy storage systems are:

[0110]

[0111] Where: They are the maximum installable capacities for short-term and long-term energy storage respectively.

[0112] Step 6: Establish the objective function with the lowest comprehensive cost

[0113] The objective function is:

[0114] minC1=C TCC +C MC +C OC +C OP (15)

[0115] Where: C1 represents the comprehensive cost of the system; C TCC is the investment and construction cost of energy storage; C MC is the maintenance cost of energy storage; C OC is the operating cost of energy storage; C OP It is the comprehensive operating cost of the system, mainly including the operating cost and carbon emission cost of the units.

[0116] The investment and construction costs of energy storage include:

[0117]

[0118] Where: C T-BESS and C T-HESS are the annual investment and construction costs of BESS and HESS respectively; and is the fixed investment and construction cost of BESS and HESS; N BESS and N HESS are the configuration quantities of BESS and HESS respectively; P BESS,n and E BESS,n are the power and capacity configured for the nth BESS respectively; P HESS,n and E HESS,n are the power and capacity configured for the nth HESS; a and b are the unit power cost of energy storage and battery conversion cost respectively; μ CRF is the annual capital recovery rate; y is the useful life of energy storage; r is the discount rate.

[0119] The maintenance costs of energy storage include:

[0120]

[0121] Where: C MC-BESS and C MC-HESS are the maintenance costs of BESS and HESS respectively; ρ MC-BESS and ρMC-HESS They are the maintenance coefficients of BESS and HESS respectively (the proportion of maintenance cost to equipment investment cost).

[0122] The operating costs of energy storage include:

[0123]

[0124] Where: C OC-BESS and C OC-HESS are the operating costs of BESS and HESS respectively; e in (t) and e out (t) are the purchase price and sales price of electricity during the energy storage charging and discharging process at time t.

[0125] The comprehensive operating costs of the system include:

[0126]

[0127] Where: Comprehensive operating cost C OP Including fixed operation and maintenance costs of various power supplies C fix , power generation and start-up and shutdown costs of thermal power units C Th , fuel and environmental cost composition C Fe ρ z is the annual conversion factor, which aims to convert the operating costs into an annual time scale; represents the fixed operation and maintenance cost of power unit i in year t; Represents the capacity of the nth unit in the tth year; C g (P g,t ) is the power generation cost function of the thermal power unit; S g , D g and Represent the start-stop cost and start-stop status of thermal power units respectively; c fule is the coal burning cost of thermal power units; E gt is the power generation of the thermal power unit in year t; T h Represents a collection of thermal power units; Characterizes CO2 emission intensity; ω env and ω pun are the environmental value coefficient and the penalty coefficient respectively.

[0128] Example 2

[0129] As a second aspect of the present invention, the present application also provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned long-term and short-term energy storage optimization configuration method taking into account the uncertainty of both sides of the source and load. In addition to the above-mentioned processor, memory and interface, any device with data processing capability in which the device in the embodiment is located may also include other hardware according to the actual function of the device with data processing capability, which will not be described in detail.

[0130] Example 3

[0131] As a third aspect of the present invention, the present application also provides a computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implements the above-mentioned long-term and short-term energy storage optimization configuration method taking into account the uncertainty of both sides of the source and load. The computer-readable storage medium may be an internal storage unit of any device with data processing capability as described in any of the foregoing embodiments, such as a hard disk or a memory. The computer-readable storage medium may also be an external storage device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), an SD card, a flash card (Flash Card), etc. equipped on the device. Furthermore, the computer-readable storage medium may also include both an internal storage unit and an external storage device of any device with data processing capability. The computer-readable storage medium is used to store the computer program and other programs and data required by any device with data processing capability, and may also be used to temporarily store data that has been output or is to be output.

[0132] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A method for optimizing the configuration of long- and short-term energy storage taking into account the uncertainty of both the source and the load, characterized in that the steps include: Using electrochemical energy storage as short-term energy storage and hydrogen energy storage as long-term energy storage, the configuration power and capacity of electrochemical energy storage and hydrogen energy storage are used as optimization variables to establish an objective function with the lowest comprehensive cost; Establish grid operation constraints and investment strategy constraints that take into account wind turbine and photovoltaic output and user price-based demand response characteristics; Based on the uncertain grid operation constraints and investment strategy constraints on both the source and load sides, the established objective function is optimized to obtain the optimal configuration plan for long-term and short-term energy storage.

2. The method for optimizing the configuration of long- and short-term energy storage taking into account the uncertainty of both the source and the load according to claim 1, characterized in that: The objective function with the lowest comprehensive cost is: minC1=C TCC +C MC +C OC +C OP Where: C1 represents the comprehensive cost of the system; C TCC is the investment and construction cost of energy storage; C MC is the maintenance cost of energy storage; C OC is the operating cost of energy storage; C OP The overall operating cost of the system.

3. The method for optimizing the configuration of long- and short-term energy storage taking into account the uncertainty of both the source and the load according to claim 2, characterized in that: The investment and construction costs of the energy storage include: C Tcc =C T-BESS +C T-HESS Where: C T-BESS and C T-HESS They are the annual investment and construction costs of electrochemical energy storage and hydrogen energy storage respectively; and N is the fixed investment and construction cost of electrochemical energy storage and hydrogen energy storage; BESS and N HESS are the configuration quantities of electrochemical energy storage and hydrogen energy storage respectively; P BESS,n and E BESS,n are the power and capacity of the nth electrochemical energy storage device respectively; P HESS,n and E HESS,n are the power and capacity of the nth hydrogen energy storage, respectively; a and b are the unit power cost of energy storage and the battery conversion cost, respectively; μ CRF is the annual capital recovery rate; y is the useful life of energy storage; r is the discount rate; The maintenance costs of the energy storage include: C MC =C MC-BESS +C MC-HESS Where: MC-BESS and ρ MC-HESS are the maintenance factors of electrochemical energy storage and HESS, respectively; The operating costs of the energy storage include: C OC =C OC-BESS +C OC-HESS Where: P ch,BESS (t) and P di,BESS (t) are the charge and discharge power of electrochemical energy storage; P ch,HESS (t) and P di,HESS (t) are the charging and discharging power of energy storage; e in (t) and e out (t) are the electricity purchase price and electricity sales price during the energy storage charging and discharging process at time t.

4. The method for optimizing the configuration of long- and short-term energy storage taking into account the uncertainty of both the source and the load according to claim 3 is characterized in that: The charge and discharge model of the electrochemical energy storage is: Where: E BESS (t) is the residual energy of BESS at time t; P ch,BESS (t) and P di,BESS (t) are the charge and discharge power of electrochemical energy storage; Δt is the charge and discharge time of electrochemical energy storage; η ch and η di They are the charging efficiency and discharging efficiency of electrochemical energy storage respectively; During the charge and discharge process of electrochemical energy storage, the changes in SOC are as follows: In the formula, E BESS,rated is the rated capacity of electrochemical energy storage.

5. The method for optimizing the configuration of long- and short-term energy storage taking into account the uncertainty of both the source and the load according to claim 3, characterized in that: The charging and discharging mode model of the hydrogen energy storage is: Where: Q HESS (t) is the storage capacity of hydrogen; P ch,HESS (t) and P di,HESS (t) are the charging and discharging power of hydrogen energy storage; Δt is the charging and discharging time of hydrogen energy storage; ρ el is the conversion coefficient of electricity to hydrogen; p fc The hydrogen capacity consumed for hydrogen power generation; η fc is the storage efficiency of hydrogen; η hy is the calorific value constant of hydrogen; During the charging and discharging process of hydrogen energy storage, the change of SOC is: In the formula, E HESS,rated is the rated capacity of hydrogen energy storage.

6. The method for optimizing the configuration of long- and short-term energy storage taking into account the uncertainty of both the source and the load according to claim 2, characterized in that: The comprehensive operating costs of the system include: C OP =(C fix +C Th +C Fe )ρ z Where: Comprehensive operating cost C OP Including fixed operation and maintenance costs of various power supplies C fix , power generation and start-up and shutdown costs of thermal power units C Th , fuel and environmental cost composition C Fe ρ z is the annual conversion factor, which aims to convert the operating costs into an annual time scale; represents the fixed operation and maintenance cost of power unit i in year t; Represents the capacity of the nth unit in the tth year; C g (P g,t ) is the power generation cost function of the thermal power unit; S g , D g and Represent the start-stop cost and start-stop status of thermal power units respectively; c fule is the coal burning cost of thermal power units; E gt is the power generation of the thermal power unit in year t; T h Represents a collection of thermal power units; Characterizes CO2 emission intensity; ω env and ω pun are the environmental value coefficient and the penalty coefficient respectively.

7. The method for optimizing the configuration of long- and short-term energy storage taking into account the uncertainty of both the source and the load according to claim 1, characterized in that: The grid operation constraints considering wind turbine and photovoltaic output and user price-based demand response characteristics specifically include: Power balance constraints: P G +P W +P PV +P di,BESS +P di,HESS -P ch,BESS -P ch,HESS =P L Where: P W , P PV Respectively represent wind turbine and photovoltaic output; P G Indicates the output of other generator sets; P di,BESS , P ch,BESS are the charge and discharge power of electrochemical energy storage; P di,HESS , P ch,HESS are the charging and discharging power of hydrogen energy storage; P L It is the active load of the power grid after taking into account the user price type demand response characteristics; Thermal power unit operation constraints: 0≤P g,t ≤G g Where: G g It is the installed capacity of thermal power; and is the climbing rate of the thermal power unit; O g,t is the online startup capacity of thermal power units in period t; and are the starting and stopping capacities of thermal power units in period t respectively; It is the minimum technical output ratio of thermal power units; and They are the minimum startup and shutdown time of thermal power units respectively.

8. The method for optimizing the configuration of long- and short-term energy storage taking into account the uncertainty of both the source and the load according to claim 7, characterized in that: The fan output model is as follows: According to the actual wind speed, the random variable v(i) is calculated: when the wind speed is lower than the cut-in wind speed or higher than the cut-out wind speed, the fan output is 0; when the wind speed is higher than the cut-in wind speed and lower than the rated wind speed, the fan output is related to the wind speed; when the wind speed is higher than the rated wind speed but lower than the cut-out wind speed, the fan output is the rated power. The wind speed in each state is expressed as follows: The probability of each state occurring is as follows: Where v is a random variable representing wind speed; f(v) represents the probability density function of v; v ci 、v cr 、v co are cut-in wind speed, rated wind speed and cut-out wind speed; N W Indicates that the cut-in wind speed and the rated wind speed are adjusted by a step size (v cr -v ci ) / N W Divide into N W status.

9. The method for optimizing the configuration of long- and short-term energy storage taking into account the uncertainty of both the source and the load according to claim 7, characterized in that: The photovoltaic output model is as follows: When the light intensity is between the minimum light intensity and the maximum light intensity, the photovoltaic output is not zero; when the light intensity is higher than the minimum light intensity and lower than the rated light intensity, the photovoltaic output is related to the light intensity; when the light intensity is higher than the rated light intensity and lower than the maximum light intensity, the photovoltaic output is rated power. The light intensity in each state is expressed as follows: The probability of each state occurring is as follows: Where f(r) represents the probability density function of light intensity r; r is the light intensity; r max is the maximum light intensity in a certain period of time; r(i) is the variable related to the photovoltaic output under each state; r min 、r s 、r max Respectively represent the minimum light intensity, rated light intensity and maximum light intensity; N G It means that the minimum light intensity and the rated light intensity are divided by the step length (r s -r min ) / N G Divide into N G status.

10. The method for optimizing the configuration of long- and short-term energy storage taking into account the uncertainty of both the source and the load according to claim 7, characterized in that: The active load of the power grid after taking into account the user price type demand response characteristics is as follows: Where: P L,t is the load after taking into account the price-based demand response strategy during period t; P L0,t is the load before taking into account the price-based demand response strategy during period t; X kt It is the gear mark of the price-based demand response strategy; η kt is the expected load response rate of the k-speed gear in period t; ρ t is the electricity price after implementing the price-based demand response strategy in period t; ρ 0,t is the time-of-use electricity price before taking into account the price-based demand response strategy during period t; α kt is the price rate of the k-level in period t.

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