Shared energy storage system constant volume method based on optimization algorithm

Abstract

The invention provides a shared energy storage system constant volume method based on an optimization algorithm, and the method comprises the following steps: obtaining historical data, and calculating an actual value based on a reference value of a time sequence; setting an initial condition, a limit condition, a decision variable and a constraint condition of the shared energy storage system, and constructing a shared energy storage system constant volume model of which the target function is the capacity of the shared energy storage system based on the decision variable and the constraint condition; and substituting the volume fixing model of the shared energy storage system into an actual value, and obtaining a volume fixing strategy of the shared energy storage system. By establishing a mathematical model based on an optimization algorithm, considering time sequence data of new energy output and load demand, and combining initial conditions, limit conditions, decision variables and constraint conditions of the system, a time sequence-based constant volume strategy of the shared energy storage system is obtained to cope with a scene with a large reference fluctuation range in different time periods; therefore, the economical efficiency and reliability of a power system in which the shared energy storage system participates are improved.

Description

Technical Field

[0001] The present invention relates to the field of new energy technology, and in particular to a method for determining the capacity of a shared energy storage system based on an optimization algorithm. Background Art

[0002] In the energy field, traditional energy power supply has many disadvantages. Specifically, the combustion of traditional energy such as coal and oil will produce a large amount of pollutants, causing serious damage to the environment, including air pollution, water pollution and soil pollution. At the same time, traditional energy is a non-renewable resource with limited reserves. With continuous mining and use, it faces the crisis of increasing depletion.

[0003] Compared with this, new energy has significant advantages. New energy such as solar energy and wind energy are clean and renewable energy, which produces almost no pollutants during use and is environmentally friendly. In addition, new energy resources are abundant and can effectively alleviate the resource shortage problem faced by traditional energy.

[0004] However, new energy sources also have some disadvantages, mainly in the form of volatility and intermittency. Taking solar energy as an example, its power generation is affected by factors such as weather and day and night changes, and its power generation capacity drops significantly on cloudy days or at night. Wind energy is also unstable, and changes in wind speed will cause fluctuations in the output power of wind power generation. This volatility and intermittency poses a huge challenge to the stable operation of the power system, making it difficult for new energy sources to independently meet the continuous and stable power demand.

[0005] At present, technicians are studying the form of shared energy storage to solve the above problems. Specifically, the shared energy storage method is to store excess electricity through shared energy storage facilities, and release it when needed, so as to achieve flexible allocation of electricity. At the same time, the shared energy storage method can also reduce the cost of energy storage, avoid each new energy power generation entity from building energy storage facilities separately, and improve the efficiency of resource utilization. In this way, the concept of sharing is combined with energy storage, making it a typical application in the current energy storage field. By separating the "ownership" and "right to use" of energy storage resources, the maximum use of energy storage resources is achieved. Energy storage, as a flexible resource, is an important means to balance the supply and demand of electricity and improve the stability of the power grid. It can effectively alleviate the peak load pressure of the power grid. Its management and control strategy has an important impact on the economy and reliability of the power system. Therefore, how to effectively manage the shared energy storage system and determine the shared energy storage capacity to maximize cost-effectiveness and optimize system efficiency is a key technical issue.

[0006] In the prior art, in terms of algorithms, a multi-objective particle swarm algorithm based on adaptive weights and hybrid diversity strategies has been adopted, so as to consider the cost of energy storage participating in the interaction between power grid supply and demand and the voltage fluctuation of the entire power grid, construct a dual-objective function, and obtain the optimal shared energy storage capacity and power configuration; in terms of model construction, the overall cost model of centralized shared energy storage has been established using the historical data of the comprehensive output of the power grid, so as to solve the multi-time scale shared energy storage capacity planning model. However, the above technical solutions do not yet have a method for modeling from the time dimension, and in actual applications, due to the existence of volatility, the model parameters also need to consider the changes in the time dimension, so as to adapt to the fixed capacity requirements in different scenarios.

[0007] In summary, a method for determining the capacity of a shared energy storage system needs to be studied based on timing considerations. Summary of the invention

[0008] In view of the problems existing in the prior art, the present invention provides a method for sizing a shared energy storage system based on an optimization algorithm and timing considerations.

[0009] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0010] The present invention provides a method for determining the capacity of a shared energy storage system based on an optimization algorithm, which comprises the following steps:

[0011] Obtain historical data of the new energy output input by the shared energy storage system and the load demand output, and calculate a new energy output benchmark value and a load demand benchmark value based on a time series based on the historical data; calculate the actual new energy output and the actual load demand based on the new energy output benchmark value and the load demand benchmark value;

[0012] Setting the initial conditions and limit conditions of the shared energy storage system according to the threshold design of the actual output of the new energy, the actual load demand and / or empirical data;

[0013] Setting the decision variables of the shared energy storage system according to the initial conditions, the limit conditions and the working judgment conditions of the shared energy storage system;

[0014] Setting constraints of the shared energy storage system according to equipment performance constraints and system operating conditions in the shared energy storage system;

[0015] Based on the decision variables and the constraints, a shared energy storage system capacity determination model is constructed, wherein the objective function is the capacity of the shared energy storage system;

[0016] The shared energy storage system sizing model takes into account the actual output of the new energy and the actual load demand, and obtains the sizing strategy of the shared energy storage system.

[0017] Optionally, the calculation process of the new energy output reference value and the load demand reference value includes:

[0018] A power reference value within a preset time period under standard conditions is selected from the historical data, and the power reference value is used as a benchmark value.

[0019] Optionally, the calculation process of the actual output of the new energy and the actual load demand includes:

[0020] The actual output of the new energy is obtained by multiplying the new energy output benchmark value by the new energy multiplier;

[0021] The load demand reference value is multiplied by the load multiplier to obtain the actual load demand.

[0022] Optionally, the new energy output includes photovoltaic output and hydropower output, and the new energy multiplier includes a photovoltaic multiplier and a hydropower multiplier;

[0023] The photovoltaic multiplier ranges from 1 to 2, and the hydropower multiplier ranges from 3 to 4;

[0024] The load multiplier ranges from 0.8 to 1.

[0025] Optionally, the initial conditions of the shared energy storage system include initial capacity and rated charge and discharge power;

[0026] The limit conditions of the shared energy storage system include maximum capacity, minimum capacity, and maximum charging time range.

[0027] Optionally, the limit conditions of the shared energy storage system also include the minimum output power of thermal power and the maximum output power of thermal power;

[0028] The initial capacity is equipped with the thermal power initial capacity; the ratio of the thermal power initial capacity to the initial capacity is 5%-7%.

[0029] Optionally, the decision variables include the charge and discharge state, real-time charge and discharge power, load loss power and real-time energy storage capacity of the shared energy storage system;

[0030] The decision variables also include maximum energy storage power and maximum energy storage capacity.

[0031] Optionally, the constraints include energy storage capacity constraints, charging and discharging constraints, and power balance constraints.

[0032] Optionally, the constraint condition is an initialization constraint condition based on timing;

[0033] According to the constraint condition, a calculation constraint condition based on counting loop is constructed.

[0034] Optionally, the objective function of the shared energy storage system capacity is a summation formula, and the summation terms include a weighted value of the maximum charging and discharging power, a weighted value of the maximum energy storage capacity, and a weighted value of the sum of all-time load loss powers.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] The present invention establishes a mathematical model based on an optimization algorithm, considers the time series data of renewable energy output and load demand, and obtains a time-series-based sizing strategy for a shared energy storage system in combination with system initial conditions, limit conditions, decision variables and constraints, so as to cope with scenarios with large parameter fluctuations in different time periods, thereby improving the economy and reliability of the power system in which the shared energy storage system participates. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0038] Figure 1 is a flow chart of the method of the present invention;

[0039] Figure 2 This is the reference value data chart in Example 1;

[0040] Figure 3 This is a chart of actual value data in Example 1;

[0041] Figure 4 This is the energy storage system operation strategy diagram in Example 1;

[0042] Figure 5 This is the supply and demand diagram of the energy storage system in Example 1. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0045] It is worth noting that the methods used in the present invention are all conventional methods unless otherwise specified; the raw materials and devices used are all conventional commercially available products, and their sources are not specifically limited unless otherwise specified.

[0046] It should also be noted that the "fixed capacity" in the specific embodiment of the present invention refers to a dynamic allocation scheme for system capacity configuration, which is a strategy for dynamically adjusting energy storage capacity to adapt to the changing power supply and demand situation. That is, different energy storage capacities are configured according to the needs of different entities at different times, and it is not a static concept of determining the total energy storage capacity of the system at the beginning or after the upgrade in other fields.

[0047] like Figure 1 As shown, this embodiment provides a method for determining the capacity of a shared energy storage system based on an optimization algorithm, comprising the following steps:

[0048] The historical data of the new energy output input and the load demand output of the shared energy storage system are obtained, and this is used as the new energy output benchmark value and the load demand benchmark value; optionally, the calculation process of the new energy output benchmark value and the load demand benchmark value includes: selecting power reference values ​​within a preset time period under standard conditions in the historical data, such as obtaining reference values ​​of photovoltaic output, hydropower output and load demand for 24 hours a day, and using these power reference values ​​as the benchmark values ​​of photovoltaic output, hydropower output and load demand.

[0049] The actual output of new energy and the actual load demand are calculated based on the benchmark value of the output of new energy and the benchmark value of the load demand; optionally, the calculation process of the actual output of new energy and the actual load demand includes:

[0050] The actual output of new energy is obtained by multiplying the benchmark value of new energy output by the new energy multiplier. Among them, the new energy output includes photovoltaic output and hydropower output, and the new energy multiplier includes photovoltaic multiplier and hydropower multiplier. The range of new energy multiplier and load multiplier is set to adapt to the volatility and uncertainty of new energy output, as well as the variability of load demand. It can simulate the changes in factors such as weather and seasons to ensure that the model can adapt to different situations in actual operation. For example, the photovoltaic output may reach twice the benchmark value on a clear day, while it may be only half or less of the benchmark value on a cloudy or overcast day. Therefore, the photovoltaic multiplier range is set to 1-2; hydropower output is affected by seasonal precipitation, and the difference in power generation between flood season and dry season may be very significant. Setting the hydropower multiplier range to 3-4 can adapt to such seasonal fluctuations. Specifically, this implementation calculates the actual value of photovoltaic output per hour based on the photovoltaic output benchmark value and the photovoltaic multiplier 1.5 selected based on actual conditions; and calculates the actual value of hydropower output per hour based on the hydropower output benchmark value and the hydropower multiplier 3.4 selected based on actual conditions.

[0051] The load demand is affected by factors such as temperature, industrial activities, and holidays. These factors may cause the actual load demand to deviate from the predicted value. Setting a multiplier range of 0.8-1 can provide a certain adjustment space for load forecasting. The load demand benchmark value is multiplied by the load multiplier to obtain the actual load demand, and the load multiplier range is 0.8-1. Specifically, this embodiment is calculated based on the load demand benchmark value and the load multiplier 1 that is preferably selected based on the actual situation, so the load demand benchmark value can be directly used to represent the actual value of the actual load demand per hour.

[0052] According to the threshold design of the actual output of new energy, actual load demand and / or empirical data, the initial conditions and limit conditions of the shared energy storage system are set. Further, the initial conditions of the shared energy storage system include the initial capacity and the rated charge and discharge power; wherein, the initial capacity Q0 of the shared energy storage system of this embodiment is 900kWh (the initial capacity is the sum of the capacity of the shared energy storage system that can be used for energy storage when the system starts running), and the rated charge and discharge power is 0.8kW. Furthermore, the limit conditions of the shared energy storage system include the maximum capacity, minimum capacity, and maximum charging time range of the energy storage system's state of charge; wherein, the minimum capacity and maximum capacity parameters of this embodiment are 0.1kWh and 1kWh, respectively; in order to ensure that the shared energy storage system performs reasonable charge and discharge scheduling according to changes in new energy output and load demand in different time periods to balance power supply and demand, the minimum value of the maximum charging time range is set to 2h and the maximum value is set to 5h (which can be understood as the maximum time the system allows to be continuously in a charging state, and the time limit is artificially set to 2h or 5h, or a value between 2-5). Optionally, in order to balance the power supply type and supplement the power source of the shared energy storage system, the fixed capacity is designated as the thermal power capacity in the shared energy storage system, so that the initial capacity is equipped with the thermal power initial capacity. The thermal power capacity and output power setting need to consider economy to avoid excessive investment. Therefore, the ratio of the designed initial capacity of thermal power to the initial capacity of the system is 5%-7%. In this embodiment, the thermal power capacity is preferably 50kWh. Therefore, the limit conditions also include the minimum output power of thermal power and the maximum output power of thermal power. In this embodiment, the minimum output power of thermal power is preferably 0.3kW and the maximum output power of thermal power is 1kW. As a stable base load power source, the initial capacity setting (such as 50kWh) and the output power limitation (0.3kW to 1kW) of thermal power in the shared energy storage system can provide the necessary adjustment capabilities and increase the flexibility and reliability of the system.

[0053] The decision variables of the shared energy storage system are set according to the initial conditions, limit conditions and working judgment conditions of the shared energy storage system; optionally, the decision variables include the charge and discharge state of the shared energy storage system, the real-time charge and discharge power, the load loss power and the real-time energy storage capacity. Among them, for the charge and discharge state of the shared energy storage system, use binvar to define the binary variable array DES , indicating whether the energy storage system is in the state of charging 1 or discharging 0 every hour; for the real-time charging and discharging power and load loss power of the shared energy storage system, sdpvar is used to define the continuous variable array P ES , P loss and Q ES They represent the real-time charging and discharging power, load loss power and real-time energy storage capacity of the energy storage system. Furthermore, the decision variables also include the maximum energy storage power and the maximum energy storage capacity, which are respectively recorded as the maximum energy storage power P max , maximum energy storage capacity Q max .

[0054] The constraints of the shared energy storage system are set according to the equipment performance constraints and system operating conditions in the shared energy storage system; optionally, the constraints include energy storage capacity constraints, charge and discharge constraints, and power balance constraints.

[0055] Specifically, basic constraints are added to ensure the maximum energy storage capacity Q of the energy storage system max and maximum energy storage power P max Non-negative, and the real-time energy storage capacity Q at all times ES must also be non-negative, thus, we obtain the following equations (1), (2), and (3):

[0056] Q max ≥0(1)

[0057] P max ≥0(2)

[0058] Q ES ≥0(3)

[0059] Furthermore, constraints are added to ensure the maximum energy storage capacity Q of the energy storage system max Between the maximum energy storage power P max The product of the minimum parameter of the maximum charging time and the maximum power P max The product of the maximum value parameter of the maximum charging time is obtained to obtain the following formula (4):

[0060] P max ×2≤Q max ≤P max ×5(4)

[0061] Going a step further, ensure the energy storage capacity Q ES，1 Between the minimum capacity parameter of the energy storage system and Q max The product of and the maximum capacity parameter of the energy storage system and Q max The product of , where the parameter subscript 1 represents the first hour in the time series, thereby obtaining a new constraint condition, as shown in the following formula (5):

[0062] Qmax ×0.1≤Q ES，1 ≤Q max ×1(5)

[0063] Analysis shows that if the energy storage system is not charged in the first hour, the discharge power P ES，1 Between-P max and 0, at which point the energy storage capacity is Q ES，1 Equal to the initial capacity Q0 plus the discharge power P ES，1 Divide by the rated discharge power parameter; if the energy storage system is charged in the first hour, the charging power P ES，1 Between 0 and P max Between, the energy storage capacity Q ES，1 Equal to the initial capacity Q0 plus the charging power P ES，1 Multiply by the rated charging power parameter. Therefore, the constraints are added as follows (6) and (7):

[0064] -P max ≤P ES，1 ≤0,Q ES，1 =900+P ES，1 ÷0.8(6)

[0065] 0≤P ES，1 ≤P max , Q ES，1 =900+P ES，1 ×0.8(7)

[0066] Add power balance constraints to ensure that the charging and discharging power P of the energy storage system in the first hour ES，1 , load demand Load1, photovoltaic output Solar1, hydropower output Hydro1 and load loss power P loss,1 The power balance is satisfied. At the same time, the load loss power P loss，1 must be between 0 and 0.05 times Load1, thus obtaining the following equation (8):

[0067] P ES，1 +Load1=Solar1+Hydro1+P loss，1

[0068] 0≤P loss，1 ≤0.05×Load1 (8)

[0069] Optionally, since the above constraints are basic constraints based on timing, a calculation constraint based on counting cycles is constructed according to the basic constraints.

[0070] Specifically, a for loop is used to add constraints for each hour. For example, the loop starts from i=2 and continues until i=24, where i represents the i-th hour of the day. If the energy storage system is charged at the i-th hour, then the charging power P of the energy storage system at the i-th hour is ES，i Must be between 0 and the maximum power P max The energy storage capacity Q of the energy storage system at the i-th hour is ES，i Must be equal to the energy Q of the previous hour ES，i-1 Add the discharge power P in the i-th hour ES，i Multiply by the discharge efficiency, the formula is:

[0071] 0≤P ES，i ≤P max , Q ES，i =Q ES，i-1 +P ES，i *0.8(9)

[0072] Furthermore, if the energy storage system is not charged in the i-th hour, the discharge power P of the energy storage system in the i-th hour is ES，i Must be between -P max and 0, the energy storage capacity Q of the energy storage system at the i-th hour ES，i Must be equal to the energy storage capacity Q of the previous hour ES，i-1 Add the discharge power P in the i-th hour ES，i Divided by the discharge efficiency, the formula is:

[0073]

[0074] The energy storage capacity of the energy storage system at the i-th hour is Q ES，i The minimum energy storage capacity parameter must be max The product of and the maximum energy storage capacity parameter and Q max The product of is:

[0075] Q max *0.1≤Q ES，i ≤Q max *1(11)

[0076] The charging and discharging power P of the energy storage system in the i-th hour ES，i Add Load i Must be equal to the photovoltaic output Solar i 、Hydro i and load loss power P loss，i The sum of is:

[0077] P ES，i +Load i =Solar i +Hydroi +P loss，i (12)

[0078] The load loss power P of the energy storage system at the i-th hour loss，i Must be between 0 and Load i 5%, the formula is:

[0079] 0≤P loss，i ≤0.05*Load i (13)

[0080] The constraints of equations (9) to (13) above define the operating rules of the energy storage system for the optimal sizing model, ensuring that the energy storage system complies with operating restrictions while meeting power demand. The optimal sizing strategy can be found through these constraints.

[0081] Based on the aforementioned decision variables and constraints, a shared energy storage system sizing model is constructed, whose objective function is the capacity of the shared energy storage system. Optionally, the objective function of the capacity of the shared energy storage system in this embodiment is a summation formula for the overall cost of energy storage planning, and the summation terms include the power cost of the energy storage system, the capacity cost, and the load loss cost (the weighted value of the maximum charging and discharging power, the weighted value of the maximum energy storage capacity, and the weighted value of the sum of the full-time (24h) load loss power), and the formula is:

[0082]

[0083] A separate constraint is added, that is, at the end of the day, the energy storage capacity Q ES，24 It should be equal to the initial charge Q0, that is, Q ES，24 =Q0=900.

[0084] The shared energy storage system sizing model takes into account the actual output of new energy and actual load demand, and obtains the sizing strategy of the shared energy storage system. Specifically, the maximum energy storage power P of the energy storage system in this model is max 、Maximum energy storage capacity Q max , load loss power P loss is the decision variable, the minimum overall cost of energy storage planning is taken as the objective function, and the energy storage capacity constraint, charging and discharging constraint and power balance constraint of the energy storage system are taken as constraints, as shown in the following equation group:

[0085]

[0086] The initial constraints are:

[0087]

[0088] After that, based on the initial constraints, loop constraints are added for each hour, starting from i=2 and continuing to i=24:

[0089]

[0090] The above model is solved using an optimization algorithm to obtain the optimal capacity determination method for the energy storage system, that is, the capacity configuration strategy based on time series.

[0091] Specifically, the sdpsettings function can be used and the CPLEX solver can be used to solve the problem, and the solver parameter can be set to 2. The objective function is minimized under all constraints, and the optimization result is finally obtained, where Y1 represents the maximum energy storage power P max The value (kW), Y2 represents the maximum energy storage capacity Q max value (kWh), Y3 represents the energy storage capacity Q ES The value (kWh), Y4 represents the charge and discharge power P ES Value (kW), Y5 represents the load loss power P loss The value of (kW).

[0092] Embodiment 1;

[0093] Let’s use a specific scenario to illustrate the calculation. First, select a shared energy storage system that is officially in operation in a certain place and collect its local historical data. For example, sort out and select the benchmark values ​​of photovoltaic output, hydropower output and load demand for 24 hours on a certain day. The data is as follows: Figure 2 shown.

[0094] According to the above calculation method, the actual values ​​of photovoltaic output Solar, hydropower output Hydro and load demand Load are calculated, that is, the photovoltaic multiplier is 1.5, the hydropower multiplier is 3.4, and the load multiplier is 1. The data for the actual values ​​are as follows Figure 3 shown.

[0095] Import the above data into the actual data set and bring it into the shared energy storage system sizing model. Use the optimization algorithm CPLEX solver to solve and obtain the optimal energy storage system operation strategy and supply and demand strategy for each power source in 24 hours a day, such as Figure 4 and Figure 5 shown.

[0096] Combination Figure 4 and Figure 5 It can be concluded that the shared energy storage system discharges when the photovoltaic and hydropower output is insufficient, and charges when the photovoltaic and hydropower output is excessive. Moreover, when h=5, the energy storage system charging power=363kW, and the energy storage system capacity=1815kWh, the shared energy storage system reaches the optimal configuration, which can effectively balance the supply and demand of the power system.

[0097] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, rather than to limit the scope of protection of the present invention. Simple modifications or equivalent substitutions of the technical solution of the present invention by ordinary technicians in this field do not deviate from the essence and scope of the technical solution of the present invention.

Claims

1. A method for determining the capacity of a shared energy storage system based on an optimization algorithm, characterized in that: The steps include: Obtain historical data of the new energy output input by the shared energy storage system and the load demand output, and calculate a new energy output benchmark value and a load demand benchmark value based on a time series based on the historical data; calculate the actual new energy output and the actual load demand based on the new energy output benchmark value and the load demand benchmark value; Setting the initial conditions and limit conditions of the shared energy storage system according to the threshold design of the actual output of the new energy, the actual load demand and / or empirical data; Setting the decision variables of the shared energy storage system according to the initial conditions, the limit conditions and the working judgment conditions of the shared energy storage system; Setting constraints of the shared energy storage system according to equipment performance constraints and system operating conditions in the shared energy storage system; Based on the decision variables and the constraints, a shared energy storage system capacity determination model is constructed, wherein the objective function is the capacity of the shared energy storage system; The shared energy storage system sizing model takes into account the actual output of the new energy and the actual load demand, and obtains the sizing strategy of the shared energy storage system.

2. The method for determining the capacity of a shared energy storage system based on an optimization algorithm according to claim 1, characterized in that: The calculation process of the new energy output reference value and the load demand reference value includes: A power reference value within a preset time period under standard conditions is selected from the historical data, and the power reference value is used as a benchmark value.

3. The method for determining the capacity of a shared energy storage system based on an optimization algorithm according to claim 2, characterized in that: The calculation process of the actual output of the new energy and the actual demand of the load includes: The actual output of the new energy is obtained by multiplying the new energy output benchmark value by the new energy multiplier; The load demand reference value is multiplied by the load multiplier to obtain the actual load demand.

4. The method for determining the capacity of a shared energy storage system based on an optimization algorithm according to claim 3 is characterized in that: The new energy output includes photovoltaic output and hydropower output, and the new energy multiplier includes photovoltaic multiplier and hydropower multiplier; The photovoltaic multiplier ranges from 1 to 2, and the hydropower multiplier ranges from 3 to 4; The load multiplier ranges from 0.8 to 1.

5. The method for determining the capacity of a shared energy storage system based on an optimization algorithm according to claim 1, characterized in that: The initial conditions of the shared energy storage system include initial capacity and rated charge and discharge power; The limit conditions of the shared energy storage system include maximum capacity, minimum capacity, and maximum charging time range.

6. The method for determining the capacity of a shared energy storage system based on an optimization algorithm according to claim 5, characterized in that: The limit conditions of the shared energy storage system also include the minimum output power of thermal power and the maximum output power of thermal power; The initial capacity includes thermal power initial capacity; the ratio of the thermal power initial capacity to the initial capacity is 5%-7%.

7. The method for determining the capacity of a shared energy storage system based on an optimization algorithm according to claim 1, characterized in that: The decision variables include the charge and discharge state, real-time charge and discharge power, load loss power and real-time energy storage capacity of the shared energy storage system; The decision variables also include maximum energy storage power and maximum energy storage capacity.

8. The method for determining the capacity of a shared energy storage system based on an optimization algorithm according to claim 1, characterized in that: The constraints include energy storage capacity constraints, charge and discharge constraints, and power balance constraints.

9. The method for determining the capacity of a shared energy storage system based on an optimization algorithm according to claim 8, characterized in that: The constraint condition is a basic constraint condition based on time sequence; According to the basic constraints, formula constraints based on counting loops are constructed.

10. The method for determining the capacity of a shared energy storage system based on an optimization algorithm according to claim 1, characterized in that: The objective function of the shared energy storage system capacity is a summation formula, and the summation term includes the weighted value of the maximum charging and discharging power, the weighted value of the maximum energy storage capacity, and the weighted value of the sum of the full-time load loss power.

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