Capacity optimization configuration method for grid-connected dual energy storage system based on ε constraint method

Abstract

The present invention discloses a capacity optimization configuration method for a grid-connected dual energy storage system based on the ε constraint method. First, the components in the dual energy storage system are modeled. Then, an energy management strategy is established based on the minimum operating power of the reversible turbine under different working modes. With the lowest economic cost and the smallest carbon emissions as the optimization goals, the system model is solved using a PSO algorithm based on the ε constraint method. Finally, a fuzzy decision method is used to obtain the optimal capacity configuration scheme for the system. The method proposed in the present invention can effectively improve the energy utilization rate of the system while providing stable power supply to the electrical load in the island area, reduce economic costs, and reduce pollution to the environment.

Description

Technical Field

[0001] The present invention belongs to the technical field of power systems, and in particular relates to a capacity optimization configuration method for a grid-connected dual energy storage system based on an ε constraint method. Background Art

[0002] Currently, power supply in island regions remains a significant challenge. The proportion of photovoltaic power generation in the power system is increasing, and energy storage equipment can effectively reduce the impact of photovoltaic power generation instability on the system. Pumped hydro storage, with its large storage capacity and low economic cost, has rapidly developed in areas with large power loads. However, due to the operating characteristics of reversible turbines, it cannot supply energy for some small loads. While batteries have high economic costs and significant environmental pollution, they can effectively cover small loads. Therefore, to efficiently utilize solar energy while enhancing power supply stability in island regions, a grid-connected dual energy storage system consisting of photovoltaics, pumped hydro, and batteries can effectively achieve this goal. Reasonable system capacity configuration can effectively improve the system's energy efficiency and enhance system performance. Therefore, an effective capacity optimization configuration method is needed to address this problem. Summary of the Invention

[0003] To address the above issues, the present invention proposes a capacity optimization configuration method for a grid-connected dual energy storage system based on the ε constraint method. This method can improve the system's energy utilization efficiency, effectively reduce operating costs and carbon emissions, and obtain the optimal capacity configuration result for the system while meeting the requirements of power supply reliability and power curtailment rate.

[0004] This paper proposes a capacity optimization configuration method for a grid-connected dual energy storage system based on the ε constraint method. The specific design scheme is as follows:

[0005] (1) Modeling the dual energy storage system components;

[0006] (2) Establishing a system energy management strategy;

[0007] (3) Establish a multi-objective optimization model for the system;

[0008] (4) Using the PSO algorithm based on the ε constraint method to deal with the multi-objective optimization problem of system capacity configuration;

[0009] (5) Use mathematical fuzzy decision-making method to obtain the optimal capacity configuration of the system.

[0010] Furthermore, the dual energy storage system components in step (1) include a photovoltaic array, an upper reservoir, a reversible turbine, and a battery:

[0011] Photovoltaic array:

[0012] E pv (t) = A pvI(t)η pv (t)

[0013] η pv (t) = η ref [1-β pv T c (t)-T ref +γLogI(t)]

[0014]

[0015] Upper reservoir and reversible turbine:

[0016]

[0017]

[0018]

[0019]

[0020]

[0021] Battery:

[0022]

[0023]

[0024]

[0025] Where: E pv (t), η pv (t) is the output power and actual power generation efficiency of the photovoltaic array; A pv is the total area of ​​photovoltaic panels, I(t) is the irradiation intensity; β pv ,γ,T ref ,η ref are the solar radiation coefficient, temperature coefficient, rated temperature and rated efficiency of the photovoltaic array respectively; T c 、T noct The actual temperature and rated temperature of photovoltaic operation, T a (t) is the ambient temperature at each hour. UR(t) is the amount of water in the upper reservoir at that moment; τ is the self-discharge rate of the upper reservoir in one day; Know are the water flow rates of the reversible turbine during pumping and power generation, respectively; η p and η t are the comprehensive efficiencies of the reversible turbine in pumping mode and power generation mode, respectively; Know are the pumping power and power generation power of the reversible turbine respectively; ρ is the seawater density; g is the acceleration of gravity; h is the height difference between the upper reservoir and the lower reservoir; is the nominal water flow rate of the reversible turbine in power generation mode; E b (t) is the storage capacity of the battery, σ is the self-discharge rate of the battery in one day; and are the input and output power of the battery respectively, P dv is the net load, which is the difference between the electrical load and the photovoltaic output power.

[0026] Furthermore, the purpose of establishing the system's energy management strategy in step (2) is to maximize the system's energy efficiency:

[0027] Introducing the two minimum operating coefficients of the reversible turbine, the minimum operating power of the reversible turbine in pumping and power generation modes is obtained and maximum operating power

[0028]

[0029] Where: α and β are the minimum operating coefficients of the reversible turbine in pumping and power generation modes, respectively;

[0030] When the photovoltaic output power is greater than the electrical load, and the difference is greater than When the system's surplus electricity is used as pumping power for pumped storage, the system's surplus electricity will be used as pumping power for pumped storage. When the battery is fully charged, the excess electricity will be charged into the battery which is not fully charged. Selling electricity to the grid is an option after the battery is fully charged.

[0031] When the photovoltaic output power is less than the electrical load, pumped storage power generation will be used to supplement the insufficient electrical load first, followed by battery discharge, and finally electricity will be purchased from the grid.

[0032] The objective function of the multi-objective optimization model in step (3) includes the annual minimum economic cost and the annual minimum carbon emissions, as follows:

[0033] Minimum economic cost:

[0034]

[0035]

[0036]

[0037]

[0038]

[0039]

[0040] C def =C d P def

[0041] Minimal carbon emissions:

[0042]

[0043] Where: F1 and F2 are the annual economic cost and annual carbon emissions of the system respectively; k is the type of system components, including photovoltaic array, reversible turbine, upper reservoir, and battery; are the initial investment, replacement, and operation and maintenance costs of the kth component respectively; They are the unit initial investment cost, unit operation and maintenance cost, and unit replacement cost of the component; is the number of component replacements during the system life cycle; R is the system's capital recovery factor; i is the discount rate; L p is the total life of the system, which is 20 years; C grid The difference between the system's electricity purchase expenditure from the grid and its electricity sales revenue; C e 、C g are the electricity price of the system purchasing electricity from the grid and the electricity price of the system selling electricity to the grid; C d P is the penalty fee for power failure of system units; gridin (t), P gridout (t) The amount of electricity purchased from the grid and the amount of electricity sold by the system; P def (t) is the shortfall in electrical load; G pv , G rt , G grid , G ur , G b The carbon emission coefficients of photovoltaic power generation, reversible turbine pumping and power generation, power purchase from the grid, reservoir construction, and battery construction in the system are

[0044] Furthermore, the constraints of the multi-objective optimization model in step (3) include grid power purchase and sales constraints, energy storage charging and discharging constraints, power abandonment rate, and power supply reliability constraints, specifically:

[0045] Constraints on power purchase and sales from the power grid:

[0046]

[0047]

[0048] Energy storage charging and discharging constraints:

[0049]

[0050]

[0051] Constraints on power curtailment rate and power supply reliability:

[0052]

[0053]

[0054] Where: ω and λ represent the system's power purchase rate and power sales rate, Ψ and ξ represent the system's maximum power abandonment rate and minimum power supply reliability rate, respectively. dump (t) is the discarded electrical energy.

[0055] The ε constraint method in the PSO algorithm based on the ε constraint method described in step (4) is specifically:

[0056] The main idea is to first determine a "primary" objective function, and the rest as "secondary" objective functions, and add the secondary objective functions to the constraints. By adjusting the objective functions and constraints multiple times, the Pareto solution set of the multi-objective problem can be finally obtained.

[0057] Consider a multi-objective optimization problem with n objective functions.

[0058] min{F1,F2,…,F n}

[0059] The objective function is transformed into the following formula using the ε constraint method.

[0060]

[0061] st F2-s2<=ε2

[0062] F3-s3<=ε3

[0063] …

[0064] F n -s n <=ε n

[0065] s>0

[0066]

[0067] Where: θ is a very small constant, take 10 -5 ,s2,s3,…,s n is the slack variable, r i =(F i max -F i min) represents the value range of the i-th objective function, F i max 、F i min is the maximum and minimum value that can be obtained for the i-th objective function, N is the number of segments into which the objective function range is divided; ε2, ε3, …, ε n By continuously changing according to a certain step size and solving the model after each change, the final Pareto optimal solution set can be obtained.

[0068] Furthermore, the step (4) is based on the PSO algorithm of the ε constraint method. The ε constraint method is applied to the PSO algorithm to search for the optimal decision variables, solve the model after each transformation, and obtain a set of optimal Pareto solutions. The specific steps are as follows:

[0069] (4-1) Set the particle swarm population size, maximum number of iterations, learning factors c1, c2, inertia weight w, and initialize the particle positions, i.e., the initial capacity of each component in the system;

[0070] (4-2) Iterative data initialization, setting the initial value of the Lagrange penalty function multiplier;

[0071] (4-3) Solve the objective function separately as required to find the value range of the "secondary" objective function, divide the range into segments g, so that the objective function and the constraint conditions are constantly changing, and set the number of iterations k = 1;

[0072] (4-4) Each time the objective function and constraints are updated, the value of k increases by 1, that is, the capacity optimization problem is solved according to the new constraints and objective function;

[0073] (4-5) Determine whether k is greater than g. If k is greater than g, end the solution. If k is not greater than g, return to step (4-4).

[0074] Furthermore, the step (5) uses a mathematical fuzzy decision method to obtain the optimal capacity configuration of the system, specifically:

[0075]

[0076]

[0077] Where: i is the serial number of the objective function. There are two objective functions here, so n = 2; μ i is the fuzzified objective function value, M is the number of solution groups in the solution set, w1 and w2 are the weighted weights of the first and second objective functions, γ is the sequence number of the solution in the solution set, is the weighted value of the objective function of the γth group of solutions in the solution set.

[0078] Compared with the prior art, the advantages and positive effects of the present invention are:

[0079] (1) The present invention introduces the minimum operating coefficients α and β when modeling pumped storage, which effectively improves the energy utilization efficiency of pumped storage.

[0080] (2) Consider using both batteries and pumped storage in the system and establish a reasonable energy management strategy to effectively reduce the economic cost and carbon emissions of the system.

[0081] (3) The present invention uses the PSO algorithm based on the ε constraint method to solve the model. Compared with the solution using the multi-objective algorithm, it effectively reduces the economic cost and carbon emissions of the system under the optimal capacity configuration scheme. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 This is a structural diagram of the grid-connected dual energy storage system based on the ε constraint method of the present invention.

[0083] Figure 2 This is a flow chart of the energy management strategy in an example of the present invention.

[0084] Figure 3 This is a flow chart of the PSO algorithm based on the ε constraint method in an example of the present invention.

[0085] Figure 4 The Pareto solution set diagram of the model solved by the PSO algorithm based on the ε constraint method in the example of the present invention. DETAILED DESCRIPTION

[0086] The present invention is further explained below with reference to specific embodiments and accompanying drawings. The present invention proposes a capacity optimization configuration method for a grid-connected dual energy storage system based on the ε constraint method. The dual energy storage system structure diagram is shown in FIG. Figure 1 As shown in the figure, in order to improve the energy efficiency of the system, the energy management strategy of the system is established. The energy management strategy structure is shown in the figure Figure 2 The specific implementation steps are as follows:

[0087] (1) Modeling of dual energy storage system

[0088] The structure diagram of the dual energy storage system is as follows: Figure 1 As shown, the system components include photovoltaic arrays, upper reservoirs, reversible turbines, and batteries.

[0089] Photovoltaic array:

[0090] E pv (t) = A pv I(t)η pv (t)

[0091] η pv (t) = ηref [1-β pv T c (t)-T ref +γLogI(t)]

[0092]

[0093] Upper reservoir and reversible turbine:

[0094]

[0095]

[0096]

[0097]

[0098]

[0099] Battery:

[0100]

[0101]

[0102]

[0103] Where: E pv (t), η pv (t) is the output power and actual power generation efficiency of the photovoltaic array; A pv is the total area of ​​photovoltaic panels, I(t) is the irradiation intensity; β pv ,γ,T ref ,η ref are the solar radiation coefficient, temperature coefficient, rated temperature and rated efficiency of the photovoltaic array respectively; T c 、T noct The actual temperature and rated temperature of photovoltaic operation, T a (t) is the ambient temperature at each hour. UR(t) is the amount of water in the upper reservoir at that moment; τ is the self-discharge rate of the upper reservoir in one day; and are the water flow rates of the reversible turbine during pumping and power generation, respectively; η p and η t are the comprehensive efficiencies of the reversible turbine in pumping mode and power generation mode, respectively; and are the pumping power and power generation power of the reversible turbine respectively; ρ is the seawater density; g is the acceleration of gravity; h is the height difference between the upper reservoir and the lower reservoir; is the nominal water flow rate of the reversible turbine in power generation mode; Eb (t) is the storage capacity of the battery, σ is the self-discharge rate of the battery in one day; and are the input and output power of the battery respectively, P dv is the net load, which is the difference between the electrical load and the photovoltaic output power.

[0104] (2) Establishing a system energy management strategy

[0105] The purpose of the energy management strategy is to maximize the energy efficiency of the system. Introducing the two minimum operating coefficients of the reversible turbine, the minimum operating power of the reversible turbine in pumping and power generation mode is obtained. and maximum operating power

[0106]

[0107] Where: α and β are the minimum operating coefficients of the reversible turbine in pumping and power generation modes, respectively.

[0108] When the photovoltaic output power is greater than the electrical load, and the difference is greater than When the system's surplus electricity is used as pumping power for pumped storage, the system's surplus electricity will be used as pumping power for pumped storage. When the battery is fully charged, the excess electricity will be charged into the battery which is not full. Selling electricity to the grid is an option after the battery is fully charged.

[0109] When the photovoltaic output power is less than the electrical load, pumped storage power generation will be used to supplement the insufficient electrical load first, followed by battery discharge, and finally electricity will be purchased from the grid.

[0110] (3) Establish a multi-objective optimization model for the dual energy storage system

[0111] The objective function of the multi-objective optimization model of the dual energy storage system is to minimize economic costs and carbon emissions:

[0112] Minimum economic cost:

[0113]

[0114]

[0115]

[0116]

[0117]

[0118]

[0119] C def =C d P def

[0120] Minimal carbon emissions:

[0121]

[0122] Where: F1 and F2 are the annual economic cost and annual carbon emissions of the system respectively; k is the type of system components, including photovoltaic array, reversible turbine, upper reservoir, and battery; are the initial investment, replacement, and operation and maintenance costs of the kth component respectively; They are the unit initial investment cost, unit operation and maintenance cost, and unit replacement cost of the component; is the number of component replacements during the system life cycle; R is the system's capital recovery factor; i is the discount rate; L p is the total life of the system, which is 20 years C grid The difference between the system's electricity purchase expenditure from the grid and its electricity sales revenue; C e 、C g are the electricity price of the system purchasing electricity from the grid and the electricity price of the system selling electricity to the grid; C d P is the penalty fee for power failure of system units; gridin (t), P gridout (t) The amount of electricity purchased from the grid and the amount of electricity sold by the system; P def (t) is the missing electric load; G pv , G rt , G grid , G ur , G b They are the carbon emission coefficients of photovoltaic power generation, reversible turbine pumping and power generation, electricity purchase from the power grid, reservoir construction, and battery construction in the system.

[0123] The constraints of the multi-objective optimization model for the dual energy storage system include grid power purchase and sales constraints, energy storage charging and discharging constraints, power curtailment rate, and power supply reliability constraints. Specifically, they are:

[0124] Constraints on power purchase and sales from the power grid:

[0125]

[0126]

[0127] Energy storage charging and discharging constraints:

[0128]

[0129]

[0130] Constraints on power curtailment rate and power supply reliability:

[0131]

[0132]

[0133] Where: ω and λ represent the system's power purchase rate and power sales rate, Ψ and ξ represent the system's maximum power abandonment rate and minimum power supply reliability rate, respectively. dump (t) is the discarded electrical energy.

[0134] (4) Use the ε constraint method to convert the multi-objective optimization problem into a single-objective optimization problem, and transform the objective function into the following formula using the ε constraint method.

[0135]

[0136] st F2-s2<=ε2(k)

[0137] s>0

[0138]

[0139] Where: θ is a very small constant, take 10 -5 , s2 is the slack variable, represents the range of the "secondary" objective function, and g is the number of segments into which the objective function range is divided. ε2 is continuously changed according to a certain step size, and by solving the model after each change, the final Pareto optimal solution set can be obtained.

[0140] (5) Use the PSO algorithm based on the ε constraint method to solve the multi-objective optimization problem of the model. The flow chart of the PSO algorithm based on the ε constraint method is as follows: Figure 3 As shown, the specific steps include:

[0141] (5-1) Set the particle swarm population size, maximum number of iterations, learning factors c1, c2, inertia weight w, and initialize the particle positions, i.e., the initial capacity of each component in the system;

[0142] (5-2) Iterative data initialization, setting the initial value of the Lagrange penalty function multiplier;

[0143] (5-3) Solve the objective function separately as required to find the value range of the "secondary" objective function, divide the range into segments g, so that the objective function and the constraint conditions are constantly changing, and set the number of iterations k = 1;

[0144] (5-4) Each time the objective function and constraints are updated, the value of k increases by 1, that is, the capacity optimization problem is solved according to the new constraints and objective function;

[0145] (5-5) Determine whether k is greater than g. If k is greater than g, end the solution. If k is not greater than g, return to step (5-4).

[0146] (6) Use mathematical fuzzy decision-making method to obtain the optimal capacity configuration of the system, specifically:

[0147]

[0148]

[0149] Where: i is the serial number of the objective function. This paper has two objective functions, so n = 2; M is the number of solution groups in the solution set. is the weighted value of the objective function of each solution in the solution set, and the smallest one is the optimal solution.

[0150] To verify the effectiveness of the proposed capacity configuration method, the economic costs and carbon emissions of the following three systems are compared and analyzed.

[0151] a. System 1: A dual energy storage system consisting of pumped storage and batteries.

[0152] b. System 2: A system containing only pumped storage.

[0153] c. System 3: A system containing only batteries.

[0154] Table 1 shows the optimal capacity configurations for the three systems. As shown in Table 1, compared to systems with pumped storage alone and batteries alone, the dual-storage system with pumped storage and batteries reduces economic costs by 41.7% and 10.8%, respectively, and carbon emissions by 1.1% and 62.3%, respectively. Furthermore, the energy efficiency of the pumped storage system is 9.7% higher than that of the system with pumped storage alone. Therefore, the simultaneous use of both energy storage systems can effectively reduce system operating costs.

[0155] Table 1 Optimal configuration solutions for three systems

[0156]

[0157]

[0158] The Pareto solution set of the model is solved by using the PSO algorithm based on the ε constraint method. Figure 4 As shown in the figure, it can be seen that the PSO algorithm based on the constraint method performs better than the multi-objective algorithm in dealing with the system capacity optimization configuration problem. The accuracy and distribution of the calculation results are better, and it can effectively reduce the economic cost and carbon emissions of the system.

[0159] The above embodiments are used to explain the present invention rather than to limit the present invention. Any modifications and changes made to the present invention within the spirit of the present invention and the protection scope of the claims shall fall within the protection scope of the present invention.

Claims

1. A capacity optimization configuration method for a grid-connected dual energy storage system based on the ε constraint method, characterized in that: The steps include: (1) Modeling the components of the dual energy storage system; (2) Establishing a system energy management strategy; (3) Establish a multi-objective optimization model for the system; (4) Use the PSO algorithm based on the ε constraint method to deal with the multi-objective optimization problem of system capacity configuration; (5) Use mathematical fuzzy decision-making method to obtain the optimal capacity configuration of the system; The ε constraint method in the PSO algorithm based on the ε constraint method in step (4) is specifically: The main idea of ​​the ε constraint method is to first determine a "primary" objective function, and the rest as "secondary" objective functions. The secondary objective functions are then added to the constraints. By adjusting the constraints multiple times, the Pareto solution set of the multi-objective problem can be finally obtained. Consider having Multi-objective optimization problem of objective function, ， The objective function is transformed into the following formula through the ε constraint method: ， ， Where: is a very small constant, take 10 -5 , is the slack variable, Indicates the The range of the objective function, 、 For the The maximum and minimum values ​​that can be obtained for the objective function, The number of segments into which the objective function range is divided; By changing the model at a certain step size and solving the model after each change, the final Pareto optimal solution set can be obtained; The step (4) is based on the PSO algorithm of the ε constraint method. The ε constraint method is embedded in the PSO algorithm to search for the optimal decision variables, solve the model after each transformation, and obtain a set of optimal Pareto solutions. The specific steps are as follows: (4-1) Set the particle swarm population size, maximum number of iterations, learning factors c1, c2, inertia weight w, and initialize the particle positions, i.e., the initial capacity of each component in the system; (4-2) Iterative data initialization, setting the initial value of the Lagrange penalty function multiplier; (4-3) Solve the objective function separately according to the requirements to find the value range of the "secondary" objective function, and divide the number of segments according to the range , so that the objective function and constraints are constantly changing, and the number of iterations k=1; (4-4) Each time the objective function and constraints are updated, the value of k increases by 1, that is, the capacity optimization problem is solved according to the new constraints and objective function; (4-5) Determine whether k is greater than g. If k is greater than g, end the solution. If k is not greater than g, return to step (4-4). The step (5) uses a mathematical fuzzy decision method to obtain the optimal capacity configuration of the system, specifically: ， ， Where: is the serial number of the objective function. There are two objective functions, so ; is the objective function value after fuzzification, is the number of solution groups in the solution set, 、 is the weighted weight of the first objective function and the second objective function, is the sequence number of the solution in the solution set, For the solution set The weighted value of the objective function of the group solution.

2. The method for optimizing capacity configuration of a grid-connected dual energy storage system based on the ε constraint method according to claim 1, characterized in that: The components of the dual energy storage system in step (1) include a photovoltaic array, an upper reservoir, a reversible turbine, and a battery. Each component is modeled using the following steps: (1-1) Photovoltaic array: ， ， ， (1-2) Upper reservoir and reversible turbine: ， ， ， ， ， (1-3) Battery: ， ， ， Where: 、 is the output power and actual power generation efficiency of the photovoltaic array; is the total area of ​​photovoltaic panels laid, is the irradiation intensity; 、 、 、 They are the solar radiation coefficient, temperature coefficient, and the rated temperature and rated efficiency of the photovoltaic array; 、 The actual temperature and rated temperature of photovoltaic operation. is the hourly ambient temperature; is the amount of water in the reservoir at any moment; is the self-discharge rate of the upper reservoir in one day; and are the water flow rates of the reversible turbine during pumping and power generation, respectively; and are the comprehensive efficiencies of the reversible turbine in pumping mode and power generation mode, respectively; and are the pumping power and generating power of the reversible turbine respectively; is the density of seawater; g is the acceleration due to gravity; is the height difference between the upper reservoir and the lower reservoir; is the nominal water flow rate of the reversible turbine in power generation mode; is the storage capacity of the battery, is the self-discharge rate of the battery in one day; and are the input and output power of the battery respectively, is the net load, which is the difference between the electrical load and the photovoltaic output power.

3. The method for optimizing capacity configuration of a grid-connected dual energy storage system based on the ε constraint method according to claim 1, characterized in that: The energy management strategy established in step (2) is specifically: Introducing the two minimum operating coefficients of the reversible turbine, the minimum operating power of the reversible turbine in pumping and power generation modes is obtained 、 and maximum operating power 、 ; 0≤α≤ ≤0.55， 0≤β≤ ≤1， Where, and are the minimum operating coefficients of the reversible turbine in pumping and generating modes, respectively; When the photovoltaic output power is greater than the electrical load, and the difference is greater than When the system's surplus electricity is used as pumping power for pumped storage, the system's surplus electricity will be used as pumping power for pumped storage. When the battery is fully charged, the excess electricity will be charged into the battery which is not fully charged. Selling electricity to the grid is an option after the battery is fully charged. When the photovoltaic output power is less than the electrical load, pumped storage power generation will be used to supplement the insufficient electrical load first, followed by battery discharge, and finally electricity will be purchased from the grid.

4. The method for optimizing capacity configuration of a grid-connected dual energy storage system based on the ε constraint method according to claim 1, characterized in that: The objective function of the multi-objective optimization model in step (3) includes the annual minimum economic cost and the annual minimum carbon emissions, which are as follows: ， ， ， ， ， ， ， ， Where: 、 are the annual economic cost and annual carbon emissions of the system respectively; The types of system components include photovoltaic arrays, reversible turbines, upper reservoirs, and batteries; 、 、 Respectively Initial investment, replacement, operation and maintenance costs of each component; 、 、 They are the unit initial investment cost, unit operation and maintenance cost, and unit replacement cost of the component; is the number of times a component is replaced during the system life cycle; is the capital recovery factor of the system; is the discount rate; is the total life of the system, which is 20 years; The difference between the system's electricity purchase expenditure from the grid and its electricity sales revenue; 、 are the electricity prices at which the system purchases electricity from the grid and sells electricity to the grid; Penalty fees for power outages for system units; 、 They are the electricity purchased from the grid and the electricity sold by the system; The shortfall in electrical load; 、 、 、 、 They are the carbon emission coefficients of photovoltaic power generation, reversible turbine pumping and power generation, electricity purchase from the power grid, reservoir construction, and battery construction in the system.

5. The method for optimizing capacity configuration of a grid-connected dual energy storage system based on the ε constraint method according to claim 4, characterized in that: The constraints of the multi-objective optimization model in step (3) include grid power purchase and sales constraints, energy storage charging and discharging constraints, power abandonment rate, and power supply reliability constraints, as follows: ， ， ， ， ， ， Where: and represent the system's electricity purchase rate and electricity sales rate, and Represent the maximum power abandonment rate and minimum power supply reliability rate of the system respectively, Wasted electrical energy.

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