Method for optimizing capacity of grid-connected wind-solar heat storage multi-energy complementary system based on two-stage stochastic programming

Abstract

The invention provides a grid-connected wind and light heat storage multi-energy complementary system capacity optimization method based on two-stage stochastic programming, and relates to the field of energy storage system optimization design. The method comprises the following steps: collecting geographic information, component technical economic parameters and historical meteorological data of a planning area of the multi-energy complementary system, and constructing an initial scene taking a day as a unit; the capacity optimization problem is divided into two stages: the optimization problem in the first stage is component capacity optimization of the multi-energy complementary system, and the objective function is the investment cost of the minimized system; the second-stage optimization problem is that the operation strategy optimization decision variable is the component output of the multi-energy complementary system, and the target function is the annual average operation cost of the minimum system; combining the first-stage objective function and the second-stage objective function to obtain a capacity optimization model; after a typical scene is extracted from the initial scene, the capacity optimization model is solved through benders decomposition, and the optimal component capacity of the multi-energy complementary system is obtained.

Description

Technical Field

[0001] The present invention relates to the field of energy storage system optimization design, and in particular to a capacity optimization method for a grid-connected wind-solar-thermal storage multi-energy complementary system based on two-stage stochastic programming. Background Art

[0002] In order to achieve the "dual carbon goals", the installed capacity of renewable energy power generation systems such as wind power, photovoltaics, and solar thermal power continues to rise. While renewable energy power generation systems reduce carbon emissions, the randomness and volatility of their output also pose challenges to the safe and stable operation of the power system. Energy storage is an effective way to smooth out the output fluctuations of renewable energy units and improve the utilization rate of renewable energy. Various forms of power sources and energy storage often have complementary characteristics. Therefore, multi-energy complementary power generation systems composed of multiple power sources and multiple energy storage have attracted much attention.

[0003] Capacity optimization is an important issue in the planning period of a multi-energy complementary system and a key technology to give full play to the advantages of a multi-energy complementary system. At present, for the capacity design of a grid-connected multi-energy complementary system, only the distribution characteristics of wind and light resources and the initial investment cost are often considered, while the operation strategy and operation cost after the system construction is completed are rarely considered. In the capacity design of a grid-connected multi-energy complementary system, how to balance the initial investment cost of the system and the later operation cost, how to effectively combine the early capacity design with the later operation strategy, and how to effectively use the distribution characteristic information of wind and light random variables are issues that cannot be ignored in order to improve the scientificity and rationality of capacity optimization design. Summary of the invention

[0004] Purpose of the invention: To propose a capacity optimization method for a grid-connected wind-solar-thermal storage multi-energy complementary system based on two-stage stochastic programming to solve the above-mentioned problems existing in the prior art.

[0005] In order to achieve the above-mentioned purpose of the invention, the present invention adopts the following technical solutions.

[0006] A capacity optimization method for a grid-connected wind-solar-thermal storage multi-energy complementary system based on two-stage stochastic programming comprises the following steps:

[0007] For the target multi-energy complementary system, the geographical information of the planning area, technical and economic parameters of the components, and historical meteorological data were collected to construct a total of 365 initial scenarios in units of days.

[0008] The capacity optimization problem of the multi-energy complementary system is divided into two stages according to time: the first stage optimization problem is the component capacity optimization of the target multi-energy complementary system, and the objective function is to minimize the investment cost of the system; the second stage optimization problem is the operation strategy optimization decision variable, which is the component output of the multi-energy complementary system, and the objective function is to minimize the average annual operation cost of the system;

[0009] Combining the objective function of the first stage and the objective function of the second stage, the total optimization objective function is obtained and used as the capacity optimization model;

[0010] After extracting typical scenarios from the initial scenarios using the rapid predecessor elimination method, the capacity optimization model is solved through Benders decomposition to obtain the optimal component capacity of the wind-solar-thermal storage multi-energy complementary system that takes into account the balance between the initial system investment and the later operation.

[0011] In a further embodiment, the geographic information includes longitude, latitude, and altitude data of a planning area corresponding to the target multi-energy complementary system;

[0012] The technical and economic parameters of the components include the service life, energy efficiency coefficient and price of each component in the target multi-energy complementary system;

[0013] The historical meteorological data include wind speed, radiation intensity and ambient temperature in the planning area; the time span of the historical meteorological data is not less than 1 year, and the sampling period is not more than 1 hour.

[0014] In a further embodiment, the first stage objective function and the second stage objective function are combined to obtain the overall optimization objective function, which is expressed as follows:

[0015]

[0016] In the formula, Σλ k C k represents the construction cost of the grid-connected wind-solar-thermal storage multi-energy complementary system, which is the objective function of the first-stage optimization problem, k is the component index, C is the component capacity, k∈{WT,BT,SF,TES,PB,EH}, WT is the wind turbine, BT is the battery, SF is the trough solar field, TES is the thermal storage system, PB is the power module, EH is the electric heater, λ is the average annual construction cost per unit capacity of the component; E(·) represents the expected function, β represents the grid electricity price, and Δt represents the time interval for system operation optimization; They represent the system's electricity purchase cost and electricity sales revenue in the tth time period of the sth scenario respectively; S represents the scenario set, and t∈{1,2,3,...,24} is the time index.

[0017] In a further embodiment, the average annual cost per unit capacity of component k is k The calculation formula is as follows:

[0018]

[0019] In the formula, i r represents the real interest rate, N P represents the life span of the multi-energy complementary system, lcc k(1) represents the full cost cycle cost of component k per unit capacity;

[0020] The life cycle cost of component k per unit capacity LCC k The calculation formula of (1) is as follows:

[0021]

[0022] In the formula, γ k , π k represents the initial installation cost and replacement cost of unit capacity component k; θ k Nc represents the maintenance cost of unit capacity component k per year; k represents the life of component k; Nr k Indicates the number of times the fan is replaced during the life cycle of the multi-energy complementary system.

[0023] In a further embodiment, the constraints of the first stage optimization problem are as follows:

[0024]

[0025] In the formula, represents the optimal upper limit of the capacity of component k.

[0026] In a further embodiment, the operating constraints that need to be satisfied by the second stage problem include: power balance constraints, wind turbine constraints, trough solar field constraints, battery constraints, electric heater constraints, thermal storage system constraints, power module constraints, and power purchase and sale constraints;

[0027] The power balance constraint is as follows:

[0028]

[0029] In the formula, They represent the power provided by wind power, battery, power module and power grid to users and the power demanded by users in the tth time period of the kth scenario respectively; They respectively represent the heating power provided by the system to the user and the user's heat demand power in the tth time period in the kth scenario.

[0030] In a further embodiment, for the fan, the external power supply is less than or equal to the maximum power that can be generated under the current working conditions as follows:

[0031]

[0032] In the formula, They represent the power supplied by the wind turbine to the user, battery, electric heater and grid in the tth time period of the kth scenario, respectively. represents the maximum power generation of the wind turbine per unit capacity in the kth scenario and the tth time period; C WT represents the capacity of the wind turbine, which is the decision variable of the first stage problem;

[0033] For a trough solar field, the external heating power is less than or equal to the maximum thermal power that can be generated under the current working conditions as follows:

[0034]

[0035] In the formula, They represent the heating power of the trough solar field to the heat storage system and the power module in the kth scene and the tth time period respectively; C represents the maximum thermal power generated by the trough solar field of unit capacity in the kth scene and the tth time period; SF Represents the capacity of the trough solar field and is the decision variable for the first stage problem.

[0036] In a further embodiment, the expression of the battery constraint is as follows:

[0037]

[0038] In the formula, represents the battery charging power in the kth scenario and the tth time period, They represent the charging power of the wind power, power module and power grid to the battery in the tth time period of the kth scenario respectively; represents the battery discharge power in the kth scenario and the tth time period, They represent the power supplied by the battery to the electric user and the electric heater in the tth time period of the kth scenario; PE BT,C,max PE BT,D,max Respectively represent the maximum charge and discharge power allowed by the battery; Respectively represent the charging and discharging status of the battery in the tth time period of the kth scene, which are 0-1 variables; Indicates the battery storage capacity; η C,BT , η D,BT Respectively represent the charging and discharging efficiency of the battery; DoD BT Indicates the maximum discharge depth of the battery; Indicates the energy storage of the battery at the last moment of its operating cycle. Indicates the energy storage of the battery at the first moment of the operating cycle, C BT Represents the battery capacity, which is the first-stage decision variable.

[0039] In a further embodiment, the power module constraint expression is as follows:

[0040]

[0041] In the formula, They represent the power generation power of the power module, the heat storage system, and the heating power of the trough solar energy for the power module in the tth time period of the kth scenario respectively; They represent the power provided by the power module to the power user, battery and grid in the tth time period of the kth scenario respectively; PB Indicates the ratio of the minimum output power to the rated power when the power module is running; C PB Represents the capacity of the power module, which is the first-stage decision variable. It is the status mark of the power module start and stop, which is a 0-1 variable. Indicates that the power module is started. Indicates that the power module is shut down.

[0042] In a further embodiment, the wind power and power modules in the multi-energy complementary system can sell electricity to the grid through the grid converter, and the expression is as follows:

[0043]

[0044] The multi-energy complementary system can purchase electricity through the grid converter to provide electric power for the electric heater, battery and electricity users. The expression is as follows:

[0045]

[0046] Where η cov Represents the grid converter efficiency.

[0047] Beneficial effects: The capacity optimization method of the grid-connected wind, solar, heat storage multi-energy complementary system based on two-stage random programming of the present invention comprehensively considers the construction cost and operation cost of the grid-connected wind, solar, heat storage multi-energy complementary system, and divides the problem into two stages according to the different investment and operation decision sequences. The objective function of the first stage is to minimize the investment cost of the components of the multi-energy complementary system, and the decision variable is the component capacity; the objective function of the second stage is to minimize the operating cost of the multi-energy complementary system, that is: the multi-energy complementary system subtracts the electricity sales income from the power grid through the electricity purchase expenditure, and the decision variable is the component output. The random characteristics of wind speed and light are reflected by the scenario method, typical scenarios are screened out by the fast previous generation elimination method, and the model is solved by the Benders decomposition method. The present invention comprehensively considers the construction cost and operation cost of the grid-connected wind, solar, heat storage multi-energy complementary system, takes into account the balance between the initial investment and the later operation of the system, and makes full use of the distribution characteristics of the random factors of wind and light. Using this method, the capacity optimization problem of the grid-connected wind, solar, heat storage multi-energy complementary system can be effectively solved, and the scientificity and economy of the capacity planning of the multi-energy complementary system can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is the logic diagram of the capacity optimization method for the grid-connected wind-solar-thermal storage multi-energy complementary system based on two-stage stochastic programming.

[0049] Figure 2 This is the energy flow structure diagram of the grid-connected wind-solar-thermal storage multi-energy complementary system of the present invention. DETAILED DESCRIPTION

[0050] In the following description, a large number of specific details are provided to provide a more thorough understanding of the present invention. However, it is apparent to those skilled in the art that the present invention can be implemented without one or more of these details. In other examples, in order to avoid confusion with the present invention, some technical features known in the art are not described.

[0051] The present invention proposes a capacity optimization method for a grid-connected wind-solar-heat-storage multi-energy complementary system based on two-stage random programming, which is applied to the grid-connected wind-solar-heat-storage multi-energy complementary system; the grid-connected wind-solar-heat-storage multi-energy complementary system includes wind power, battery energy storage, trough solar field, heat storage system, power module, electric heater, electric load and heat load. The energy flow structure diagram of the wind-solar-heat-storage multi-energy complementary system is shown in the figure. Figure 2 shown.

[0052] The overall logical structure of the method is as follows Figure 1 As shown, the following steps are included:

[0053] Taking into account the construction cost and operation cost of the grid-connected wind-solar-thermal storage multi-energy complementary system, the problem is divided into two stages according to the different investment and operation decision-making sequences, including the first stage optimization problem and the second stage optimization problem.

[0054] The objective function of the capacity optimization problem can be divided into two stages according to time, as shown in formula (1):

[0055]

[0056] In the formula, Σλ k C k represents the construction cost of the grid-connected wind-solar-thermal storage multi-energy complementary system, which is the objective function of the first-stage optimization problem. k is the component index, C represents the component capacity, k∈{WT,BT,SF,TES,PB,EH}, WT represents wind turbine, BT represents battery, SF represents trough solar field, TES represents thermal storage system, PB represents power module, EH represents electric heater, λ represents the average annual cost per unit capacity of the component; E(·) represents the expected function, β represents the grid electricity price, Δt represents the time interval for system operation optimization, which is selected as 1 hour in this patent. They represent the system's electricity purchase cost and electricity sales revenue in the tth time period of the sth scenario respectively; S represents the scenario set, and t∈{1,2,3,...,24} is the time index.

[0057] Furthermore, the average annual cost per unit capacity of component k is k As shown in formula (2).

[0058]

[0059] In the formula, i r represents the real interest rate, N P represents the life span of the multi-energy complementary system, lcc k (1) represents the full-cost cycle cost of component k per unit capacity.

[0060] Furthermore, the full cost cycle cost of component k per unit capacity is shown in formula (3).

[0061]

[0062] In the formula, γ k , π k represents the initial installation cost and replacement cost of unit capacity component k, θ k Nc represents the annual maintenance cost of unit capacity component k, k represents the lifetime of component k, Nr k Indicates the number of times the fan is replaced during the life cycle of the multi-energy complementary system.

[0063] Furthermore, the constraint conditions of the first-stage optimization problem are shown in formula (4).

[0064]

[0065] In the formula, represents the optimal upper limit of the capacity of component k.

[0066] Furthermore, the second stage problem is operation optimization, and the operation constraints that need to be met include: power balance constraints, wind turbine constraints, trough solar field constraints, battery constraints, electric heater constraints, thermal storage system constraints, power module constraints and power purchase and sales constraints.

[0067] Furthermore, the power balance constraint includes electrical power balance and thermal power balance, as shown in equations (5)-(7):

[0068]

[0069] Formula (5) is the power balance constraint, where: They represent the power provided by wind power, battery, power module and power grid to users and the power demanded by users in the tth time period of the kth scenario respectively; They respectively represent the heating power provided by the system to the user and the user's heat demand power in the tth time period in the kth scenario.

[0070] Furthermore, for the fan, the external power supply is less than or equal to the maximum power that can be generated under the current working conditions, as shown in formula (8):

[0071]

[0072] In the formula, They represent the power supplied by the wind turbine to the user, battery, electric heater and grid in the tth time period of the kth scenario, respectively. represents the maximum power generation of the wind turbine per unit capacity in the kth scenario and the tth time period; C WT Represents the capacity of the wind turbine and is the decision variable for the first stage problem.

[0073] Furthermore, for the trough solar field, the external heating power is less than or equal to the maximum thermal power that can be generated under the current working conditions, as shown in formula (9):

[0074]

[0075] In the formula, They represent the heating power of the trough solar field to the heat storage system and the power module in the kth scene and the tth time period respectively; C represents the maximum thermal power generated by the trough solar field of unit capacity in the kth scene and the tth time period; SF Represents the capacity of the trough solar field and is the decision variable for the first stage problem.

[0076] Furthermore, the battery constraints are shown in equations (10)-(18):

[0077]

[0078] Formula (10) indicates that the battery can be charged by wind power, power module and power grid. represents the battery charging power in the kth scenario and the tth time period, They represent the charging power of wind power, power module and power grid to the battery in the kth scenario and the tth time period respectively; Formula (11) indicates that the battery can provide electric power to the electric user and the electric heater by discharging, represents the battery discharge power in the kth scenario and the tth time period, They represent the power supplied by the battery to the electric user and the electric heater in the tth time period of the kth scenario respectively; Equations (12) and (13) are the battery operation limit constraints, PE BT,C,max PE BT,D,max Respectively represent the maximum charge and discharge power allowed by the battery; They represent the charging and discharging states of the battery in the tth time period of the kth scenario, which are 0-1 variables. Formula (14) ensures that the battery cannot be charged and discharged at the same time. Formulas (15)-(17) are the constraints on the battery energy storage capacity. Indicates the battery storage capacity; η C,BT , η D,BT Respectively represent the charging and discharging efficiency of the battery; DoD BT Indicates the maximum discharge depth of the battery; Indicates the energy storage of the battery at the last moment of its operating cycle. Indicates the energy storage of the battery at the first moment of the operating cycle, C BT Represents the battery capacity, which is the first-stage decision variable.

[0079] Furthermore, the electric heater constraints are shown in equations (19) and (20):

[0080]

[0081] In the formula, represents the thermal power input from the electric heater to the heat storage system in the kth scene and the tth time period; η EH represents the efficiency of the electric heater, It represents the electric power provided by the power grid to the electric heater in the tth time period of the kth scenario.

[0082] Furthermore, the heat storage system constraints are shown in equations (21)-(28):

[0083]

[0084] Formula (21) indicates that the energy storage system can store energy through electric heaters and trough solar fields. represents the energy storage power of the energy storage system in the kth scenario and the tth time period; Formula (22) indicates that the energy storage system can provide thermal power for the power module and the thermal user, represents the energy storage system release power in the kth scenario and the tth time period, represents the thermal power input to the power module by the thermal storage system in the kth scenario and the tth time period; ToS represents the additional thermal power required when the power module is started, is a 0-1 state variable. , indicating that the power module starts at time t; Equations (23) and (24) are the heat storage and release constraints of the heat storage system, PT TES,C,max , PTTES,D,max represents the maximum charging and discharging power of the heat storage system; Equations (25)-(27) are the heat storage capacity constraints of the heat storage system, represents the heat storage amount of the heat storage system in the kth scene and the tth time period, represents the heat storage capacity of the heat storage system in the kth scenario and the t-1th time period, η C,TES , η D,TES Respectively represent the charging and discharging efficiency of the heat storage system; DoD TES Indicates the maximum heat release depth of the heat storage system; represents the storage energy of the heat storage system at the last moment of the operation cycle, Represents the storage energy of the heat storage system at the first moment of the operation cycle; C TES Represents the capacity of the heat storage system and is the first-stage decision variable.

[0085] Furthermore, the power module constraints are shown in equations (29)-(33):

[0086]

[0087] In the formula, They represent the power generation power of the power module, the heat storage system, and the heating power of the trough solar energy for the power module in the kth scenario and the tth time period respectively; Formula (30) indicates that the power module can provide power to the power user, the battery, and the power grid. They represent the power provided by the power module to the power user, battery and grid in the tth time period of the kth scenario respectively; Formula (31) is the power module operation restriction constraint, σ PB Indicates the ratio of the minimum output power to the rated power when the power module is running; C PB Represents the capacity of the power module, which is the first-stage decision variable. It is the status mark of the power module start and stop, which is a 0-1 variable. Indicates that the power module is started. Indicates that the power module is shut down; Formula (32) is used to identify the startup action of the power module, and Formula (33) ensures that the running and stopping states of the power module cannot appear at the same time.

[0088] Furthermore, in the multi-energy complementary system, wind power and power modules can sell electricity to the grid through the grid converter, as shown in formula (34):

[0089]

[0090] Where η cov Represents the grid converter efficiency.

[0091] Furthermore, the multi-energy complementary system can purchase electricity through the grid converter to provide electric power for the electric heater, battery and electricity users, as shown in equations (36) and (37):

[0092]

[0093] Furthermore, the rapid predecessor elimination method is used to obtain typical scenarios, and the Benders decomposition method is used to solve the model to obtain the optimal component capacity of the grid-connected wind-solar-thermal storage multi-energy complementary system that comprehensively considers the investment cost and operating cost.

[0094] As described above, although the present invention has been shown and described with reference to specific preferred embodiments, it should not be construed as limiting the present invention itself. Various changes may be made to it in form and detail without departing from the spirit and scope of the present invention as defined in the appended claims.

Claims

1. A capacity optimization method for a grid-connected wind-solar-thermal-storage multi-energy complementary system based on two-stage stochastic programming, characterized in that: The steps include: For the target multi-energy complementary system, the geographical information of the planning area, technical and economic parameters of the components, and historical meteorological data are collected to construct an initial scenario in days. The capacity optimization problem of the multi-energy complementary system is divided into two stages according to time: the first stage optimization problem is the component capacity optimization of the target multi-energy complementary system, and the objective function is to minimize the investment cost of the system; The optimization problem in the second stage is the optimization of the operation strategy. The decision variable is the component output of the multi-energy complementary system, and the objective function is to minimize the average annual operation cost of the system. Combining the objective function of the first stage and the objective function of the second stage, the total optimization objective function is obtained and used as the capacity optimization model; After extracting typical scenarios from the initial scenarios using the rapid predecessor elimination method, the capacity optimization model is solved through Benders decomposition to obtain the optimal component capacity of the wind-solar-thermal storage multi-energy complementary system that takes into account the balance between the initial investment and the later operation of the system.

2. The capacity optimization method of the grid-connected wind-solar-thermal-storage multi-energy complementary system according to claim 1 is characterized in that: The geographic information includes the longitude, latitude, and altitude data of the planning area corresponding to the target multi-energy complementary system; The technical and economic parameters of the components include the service life, energy efficiency coefficient and price of each component in the target multi-energy complementary system; The historical meteorological data include wind speed, radiation intensity and ambient temperature in the planning area; the time span of the historical meteorological data is not less than 1 year, and the sampling period is not more than 1 hour.

3. The capacity optimization method of the grid-connected wind-solar-thermal-storage multi-energy complementary system according to claim 1 is characterized in that: Combining the first-stage objective function and the second-stage objective function, the total optimization objective function is obtained, and the expression is as follows: In the formula, ∑λ k C k represents the construction cost of the grid-connected wind-solar-thermal storage multi-energy complementary system, which is the objective function of the first-stage optimization problem, k is the component index, C is the component capacity, k∈{WT,BT,SF,TES,PB,EH}, WT is the wind turbine, BT is the battery, SF is the trough solar field, TES is the thermal storage system, PB is the power module, EH is the electric heater, λ is the average annual construction cost per unit capacity of the component; E(·) represents the expected function, β represents the grid electricity price, and Δt represents the time interval for system operation optimization; They represent the system's electricity purchase cost and electricity sales revenue in the tth time period of the sth scenario respectively; S represents the scenario set, and t∈{1,2,3,...,24} is the time index.

4. The capacity optimization method of the grid-connected wind-solar-thermal-storage multi-energy complementary system according to claim 3 is characterized in that: The average annual cost per unit capacity of component k is λ k The calculation formula is as follows: In the formula, i r represents the real interest rate, N P represents the life span of the multi-energy complementary system, lcc k (1) represents the full cost cycle cost of component k per unit capacity; The life cycle cost of component k per unit capacity LCC k The calculation formula of (1) is as follows: In the formula, γ k , π k represents the initial installation cost and replacement cost of unit capacity component k; θ k Nc represents the maintenance cost of unit capacity component k per year; k represents the life of component k; Nr k Indicates the number of times the fan is replaced during the life cycle of the multi-energy complementary system.

5. The capacity optimization method of the grid-connected wind-solar-thermal-storage multi-energy complementary system according to claim 1 is characterized in that: The constraints of the first stage optimization problem are as follows: In the formula, represents the optimal upper limit of the capacity of component k.

6. The capacity optimization method of the grid-connected wind-solar-thermal-storage multi-energy complementary system according to claim 1 is characterized in that: The operating constraints that need to be met in the second stage include: power balance constraints, wind turbine constraints, trough solar field constraints, battery constraints, electric heater constraints, thermal storage system constraints, power module constraints, and power purchase and sales constraints; The power balance constraint is as follows: In the formula, They represent the power provided by wind power, battery, power module and power grid to users and the power demanded by users in the tth time period of the kth scenario respectively; They respectively represent the heating power provided by the system to the user and the user's heat demand power in the tth time period in the kth scenario.

7. The capacity optimization method of the grid-connected wind-solar-thermal-storage multi-energy complementary system according to claim 6 is characterized in that: For the fan, the external power supply is less than or equal to the maximum power that can be generated under the current working conditions as follows: In the formula, They represent the power supplied by the wind turbine to the user, battery, electric heater and grid in the tth time period of the kth scenario, respectively. represents the maximum power generation of the wind turbine per unit capacity in the kth scenario and the tth time period; C WT represents the capacity of the wind turbine, which is the decision variable of the first stage problem; For a trough solar field, the external heating power is less than or equal to the maximum thermal power that can be generated under the current working conditions as follows: In the formula, They represent the heating power of the trough solar field to the heat storage system and the power module in the kth scene and the tth time period respectively; C represents the maximum thermal power generated by the trough solar field of unit capacity in the kth scene and the tth time period; SF Represents the capacity of the trough solar field and is the decision variable for the first stage problem.

8. The capacity optimization method of the grid-connected wind-solar-thermal-storage multi-energy complementary system according to claim 6 is characterized in that: The expression of the battery constraint is as follows: In the formula, represents the battery charging power in the kth scenario and the tth time period, They represent the charging power of the wind power, power module and power grid to the battery in the tth time period of the kth scenario respectively; represents the battery discharge power in the kth scenario and the tth time period, They represent the power supplied by the battery to the electric user and the electric heater in the tth time period of the kth scenario; PE BT,C,max PE BT,D,max Respectively represent the maximum charge and discharge power allowed by the battery; Respectively represent the charging and discharging status of the battery in the tth time period of the kth scene, which are 0-1 variables; Indicates the battery storage capacity; η C,BT , η D,BT Respectively represent the charging and discharging efficiency of the battery; DoD BT Indicates the maximum discharge depth of the battery; Indicates the energy storage of the battery at the last moment of its operating cycle. Indicates the energy storage of the battery at the first moment of the operating cycle, C BT Represents the battery capacity, which is the first-stage decision variable.

9. The capacity optimization method of the grid-connected wind-solar-thermal-storage multi-energy complementary system according to claim 6 is characterized in that: The power module constraint expression is as follows: In the formula, They represent the power generation power of the power module, the heat storage system, and the heating power of the trough solar energy for the power module in the tth time period of the kth scenario respectively; They represent the power provided by the power module to the power user, battery and grid in the tth time period of the kth scenario respectively; PB Indicates the ratio of the minimum output power to the rated power when the power module is running; C PB Represents the capacity of the power module, which is the first-stage decision variable. It is the status mark of the power module start and stop, which is a 0-1 variable. Indicates that the power module is started. Indicates that the power module is shut down.

10. The capacity optimization method of the grid-connected wind-solar-thermal-storage multi-energy complementary system according to claim 6 is characterized in that: In the multi-energy complementary system, wind power and power modules can sell electricity to the grid through the grid converter, and the expression is as follows: The multi-energy complementary system can purchase electricity through the grid converter to provide electric power for the electric heater, battery and electricity users. The expression is as follows: Where η cov Represents the grid converter efficiency.

Images