Capacity optimization method and device of source load system based on linear model

Abstract

The invention provides a capacity optimization method and device of a source load system based on a linear model, the source load system comprises a plurality of power generation devices and energy storage devices, and the plurality of power generation devices comprise new energy power generation devices and traditional power generation devices. The capacity optimization method comprises the following steps: acquiring the unit capacity cost and life cycle of various power generation equipment and the unit capacity cost and life cycle of energy storage equipment; establishing a cost model based on the unit capacity cost and the life cycle of the power generation equipment and the unit capacity cost and the life cycle of the energy storage equipment; and calculating the optimal value of the capacity of the new energy power generation equipment and the optimal value of the capacity of the energy storage equipment which enable the total power generation cost obtained through the cost model to be minimized in a preset time period and meet preset constraint conditions.

Description

Technical Field

[0001] The present disclosure generally relates to the field of renewable energy power generation, and more specifically, to a capacity optimization method and device for a source-load system based on a linear model. Background Art

[0002] At present, in the scenario of renewable energy power generation, it is more common to use renewable energy power generation equipment as distributed power source to realize power supply in the whole microgrid. However, due to the characteristics of distributed power sources (for example, wind power generation equipment), the output of some distributed power sources will change with the changes in external conditions, showing intermittent and random characteristics, making it difficult for these distributed power sources to meet the power balance of the load by relying solely on their own regulation capabilities, and other power sources or energy storage devices are required to provide support and backup.

[0003] In the existing power generation models for new energy, the charging state and the discharging state need to be considered separately in the processing used when using other power sources or energy storage systems to provide support and backup. The final model involves many variables and has nonlinear characteristics. The algorithms involved are highly complex and not easy to solve and implement in engineering. Summary of the invention

[0004] An exemplary embodiment of the present disclosure provides a capacity optimization method and device for a source-load system based on a linear model, which can improve the solution efficiency of a capacity configuration optimization algorithm for a source-load system and greatly reduce the difficulty of engineering implementation.

[0005] According to one aspect of an embodiment of the present disclosure, a capacity optimization method for a source-load system based on a linear model is provided, wherein the source-load system includes a variety of power generation equipment and energy storage equipment, wherein the various power generation equipment includes new energy power generation equipment and traditional power generation equipment, and the capacity optimization method includes: obtaining the unit capacity cost and its life cycle of the various power generation equipment and the unit capacity cost and its life cycle of the energy storage equipment; establishing a cost model based on the unit capacity cost and its life cycle of the various power generation equipment and the unit capacity cost and its life cycle of the energy storage equipment; and calculating the optimal value of the capacity of the new energy power generation equipment and the optimal value of the capacity of the energy storage equipment within a preset time period so as to minimize the total power generation cost obtained by the cost model and satisfy preset constraints.

[0006] Optionally, the preset constraints may include a first constraint, a second constraint and a third constraint, wherein the first constraint includes: the sum of the first power of the new energy power generation equipment in each unit time period within the preset time period, the second power of the traditional power generation equipment in the corresponding unit time period, and the third power of the energy storage equipment in the corresponding unit time period is greater than or equal to the fourth power of the power load in the corresponding unit time period; the second constraint includes: the cumulative storage energy of the energy storage equipment in each unit time period within the preset time period is greater than or equal to zero and less than or equal to the capacity of the energy storage equipment; the third constraint includes: the capacity of the new energy power generation equipment and the capacity of the energy storage equipment are both greater than or equal to zero.

[0007] Optionally, the first power may be the product of a power generation coefficient of the new energy power generation equipment and a capacity of the new energy power generation equipment in each unit time period within the preset time period.

[0008] Optionally, the new energy power generation equipment may include at least one of wind power generation equipment and photovoltaic power generation equipment, and the first power is the sum of the product of the wind power generation coefficient and the capacity of the wind power generation equipment and the product of the solar power generation coefficient and the capacity of the photovoltaic power generation equipment or one of the two.

[0009] Optionally, the capacity optimization method may further include: obtaining wind speed data of the wind power generation equipment and wind resource data within the preset time period, wherein the wind speed data includes cut-in wind speed data and rated wind speed data, and the wind resource data includes wind speed data for each unit time period; if the wind speed data for each unit time period is greater than or equal to the corresponding cut-in wind speed data and less than or equal to the corresponding rated wind speed data, then calculating the quotient of the difference between the wind speed data for each unit time period and the corresponding cut-in wind speed data divided by the difference between the corresponding rated wind speed data and the corresponding cut-in wind speed data as the wind power generation coefficient; if the wind speed data for each unit time period is less than the corresponding cut-in wind speed data or greater than the corresponding rated wind speed data, then setting the wind power generation coefficient to zero.

[0010] Optionally, the capacity optimization method may also include: obtaining irradiance data of photovoltaic components of photovoltaic power generation equipment and light resource data within the preset time period, wherein the irradiance data includes minimum irradiance data and rated irradiance data, and the light resource data includes light irradiance data for each unit time period; if the irradiance data for each unit time period is greater than or equal to the corresponding minimum irradiance data and less than or equal to the corresponding rated irradiance data, then calculating the quotient of the difference between the irradiance data for each unit time period and the corresponding minimum irradiance data divided by the difference between the corresponding rated irradiance data and the corresponding minimum irradiance data as the light power generation coefficient; if the irradiance data for each unit time period is less than the corresponding minimum irradiance data or greater than the corresponding rated irradiance data, then setting the light power generation coefficient to zero.

[0011] Optionally, the new energy power generation equipment may include both wind power generation equipment and photovoltaic power generation equipment, and the step of establishing the cost model may include: calculating the product of the unit capacity cost of the wind power generation equipment and the capacity of the wind power generation equipment and the life cycle of the wind power generation equipment as the wind power generation cost; calculating the product of the unit capacity cost of the photovoltaic power generation equipment and the capacity of the photovoltaic power generation equipment and the life cycle of the photovoltaic power generation equipment as the solar power generation cost; calculating the product of the unit capacity cost of the energy storage equipment and the capacity of the energy storage equipment and the life cycle of the energy storage equipment as the energy storage cost; calculating the product of the unit capacity cost of the traditional power generation equipment, the preset time period and the unit time period power generation power of the traditional power generation equipment as the traditional power generation cost; calculating the sum of the wind power generation cost, the solar power generation cost, the energy storage cost and the traditional power generation cost as the cost model.

[0012] Optionally, the cumulative energy storage amount of the energy storage device in the i-th unit time period within the preset time period may be the sum of the energy storage amounts of the energy storage device from the 1st unit time period to the i-th unit time period within n unit time periods, where 1≤i≤n, and i and n are both positive integers, and the preset time period includes n unit time periods.

[0013] According to another aspect of an embodiment of the present disclosure, a capacity optimization device for a source-load system based on a linear model is provided, wherein the source-load system includes a variety of power generation equipment and energy storage equipment, wherein the various power generation equipment includes new energy power generation equipment and traditional power generation equipment, and the capacity optimization device includes: an acquisition unit, configured to: acquire the unit capacity cost and its life cycle of the various power generation equipment and the unit capacity cost and its life cycle of the energy storage equipment; a modeling optimization unit, configured to: establish a cost model based on the unit capacity cost and its life cycle of the various power generation equipment and the unit capacity cost and its life cycle of the energy storage equipment; and calculate the optimal value of the capacity of the new energy power generation equipment and the optimal value of the capacity of the energy storage equipment within a preset time period so as to minimize the total power generation cost obtained by the cost model and satisfy preset constraints.

[0014] Optionally, the preset constraints may include a first constraint, a second constraint and a third constraint, wherein the first constraint includes: the sum of the first power of the new energy power generation equipment in each unit time period within the preset time period, the second power of the traditional power generation equipment in the corresponding unit time period, and the third power of the energy storage equipment in the corresponding unit time period is greater than or equal to the fourth power of the power load in the corresponding unit time period; the second constraint includes: the cumulative storage energy of the energy storage equipment in each unit time period within the preset time period is greater than or equal to zero and less than or equal to the capacity of the energy storage equipment; the third constraint includes: the capacity of the new energy power generation equipment and the capacity of the energy storage equipment are both greater than or equal to zero.

[0015] Optionally, the first power may be the product of a power generation coefficient of the new energy power generation equipment and a capacity of the new energy power generation equipment in each unit time period within the preset time period.

[0016] Optionally, the new energy power generation equipment may include at least one of wind power generation equipment and photovoltaic power generation equipment, and the first power is the sum of the product of the wind power generation coefficient and the capacity of the wind power generation equipment and the product of the solar power generation coefficient and the capacity of the photovoltaic power generation equipment or one of the two.

[0017] Optionally, the acquisition unit can also be configured to: acquire wind speed data of the wind power generation equipment and wind resource data within the preset time period, wherein the wind speed data includes cut-in wind speed data and rated wind speed data, and the wind resource data includes wind speed data of each unit time period; and the modeling optimization unit can also be configured to: if the wind speed data of each unit time period is greater than or equal to the corresponding cut-in wind speed data and less than or equal to the corresponding rated wind speed data, then calculate the quotient of the difference between the wind speed data of each unit time period and the corresponding cut-in wind speed data divided by the difference between the corresponding rated wind speed data and the corresponding cut-in wind speed data as the wind power generation coefficient; if the wind speed data of each unit time period is less than the corresponding cut-in wind speed data or greater than the corresponding rated wind speed data, then set the wind power generation coefficient to zero.

[0018] Optionally, the acquisition unit can also be configured to: acquire the irradiance data of the photovoltaic components of the photovoltaic power generation equipment and the light resource data within the preset time period, wherein the irradiance data includes minimum irradiance data and rated irradiance data, and the light resource data includes light irradiance data for each unit time period; and the modeling optimization unit can also be configured to: if the irradiance data for each unit time period is greater than or equal to the corresponding minimum irradiance data and less than or equal to the corresponding rated irradiance data, then calculate the quotient of the difference between the irradiance data for each unit time period and the corresponding minimum irradiance data divided by the difference between the corresponding rated irradiance data and the corresponding minimum irradiance data as the light energy power generation coefficient; if the irradiance data for each unit time period is less than the corresponding minimum irradiance data or greater than the corresponding rated irradiance data, then set the light energy power generation coefficient to zero.

[0019] Optionally, the new energy power generation equipment may include both wind power generation equipment and photovoltaic power generation equipment, and the process of establishing the cost model by the modeling optimization unit may include: calculating the product of the unit capacity cost of the wind power generation equipment and the capacity of the wind power generation equipment and the life cycle of the wind power generation equipment as the wind power generation cost; calculating the product of the unit capacity cost of the photovoltaic power generation equipment and the capacity of the photovoltaic power generation equipment and the life cycle of the photovoltaic power generation equipment as the solar power generation cost; calculating the product of the unit capacity cost of the energy storage equipment and the capacity of the energy storage equipment and the life cycle of the energy storage equipment as the energy storage cost; calculating the product of the unit capacity cost of the traditional power generation equipment, the preset time period and the unit time period power generation power of the traditional power generation equipment as the traditional power generation cost; calculating the sum of the wind power generation cost, the solar power generation cost, the energy storage cost and the traditional power generation cost as the cost model.

[0020] Optionally, the cumulative energy storage amount of the energy storage device in the i-th unit time period within the preset time period may be the sum of the energy storage amounts of the energy storage device from the 1st unit time period to the i-th unit time period within n unit time periods, where 1≤i≤n, and i and n are both positive integers, and the preset time period includes n unit time periods.

[0021] According to another aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided. When instructions in the computer-readable storage medium are executed by at least one processor, the at least one processor is prompted to execute the capacity optimization method as described above.

[0022] According to another aspect of an embodiment of the present disclosure, a computer device is provided, comprising: at least one processor; and at least one memory storing computer executable instructions, wherein the computer executable instructions, when executed by the at least one processor, cause the at least one processor to execute the capacity optimization method as described above.

[0023] According to the capacity optimization method and device of the source-load system based on the linear model of the exemplary embodiment of the present disclosure, it is proposed to construct a capacity configuration optimization model of the source-load system by utilizing the linear programming method to optimize the configuration of the new energy capacity, thereby improving the solution efficiency of the capacity configuration optimization algorithm for the source-load system and greatly reducing the difficulty of engineering implementation.

[0024] In addition, through the capacity optimization method and device of the source-load system based on the linear model disclosed in the present invention, it is also possible to quickly and effectively support the economic feasibility evaluation and scheduling optimization control of grid-connected or off-grid wind, solar, storage, diesel and load multi-energy complementary source systems by processing the flexible configuration under different combinations of wind, solar, storage, diesel and load elements and the optimal configuration of the system under different scenarios.

[0025] Additional aspects and / or advantages of the present general inventive concept will be set forth in part in the following description and in part will be apparent from the description or may be learned through practice of the present general inventive concept. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The above and other objects and features of the exemplary embodiments of the present disclosure will become more apparent through the following description in conjunction with the accompanying drawings which exemplarily illustrate the embodiments, in which:

[0027] Figure 1 is a flow chart showing a capacity optimization method of a source-load system based on a linear model according to an exemplary embodiment of the present disclosure;

[0028] Figure 2 is an overall design flow chart showing an example of a capacity optimization method for a wind-solar-diesel-load storage system according to an exemplary embodiment of the present disclosure;

[0029] Figures 3 to 9 is a graph of simulation data showing an example of a capacity optimization method according to an exemplary embodiment of the present disclosure;

[0030] Fig.10 is a block diagram showing a capacity optimization device for a source-load system based on a linear model according to an exemplary embodiment of the present disclosure;

[0031] Fig.11 is a block diagram illustrating a computer device according to an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0032] Reference will now be made in detail to the embodiments of the present disclosure, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to like parts throughout. The embodiments will be described below with reference to the drawings in order to explain the present disclosure.

[0033] At present, in the existing power generation models for new energy, the charging state and the discharging state need to be considered separately in the processing used when using other power sources or energy storage systems to provide support and backup. The final model involves many variables and has nonlinear characteristics. The algorithms involved are highly complex and not easy to solve and implement in engineering.

[0034] According to the capacity optimization method and device of the source-load system based on the linear model disclosed in the present invention, a capacity configuration optimization model of the source-load system is constructed by a linear programming-based method to optimize the configuration of the new energy capacity, which can solve the problems of high complexity, inconvenience in solving and difficult engineering implementation of the currently disclosed related technical solutions.

[0035] Refer to the following Figures 1 to 11 The capacity optimization method and device of the source-load system based on the linear model according to the present disclosure are specifically described.

[0036] Figure 1 is a flow chart illustrating a capacity optimization method 100 of a source-load system based on a linear model according to an exemplary embodiment of the present disclosure.

[0037] According to an embodiment of the present disclosure, the source-load system includes a variety of power generation equipment and energy storage equipment, and the various power generation equipment includes new energy power generation equipment and traditional power generation equipment. For example, the new energy power generation equipment may include wind turbines and photovoltaic power generation equipment. For example, traditional power generation equipment may include diesel power generation equipment and gas turbines.

[0038] Reference Figure 1 In step S101, the unit capacity cost and life cycle of various power generation equipment and the unit capacity cost and life cycle of energy storage equipment are obtained.

[0039] As an example, the new energy power generation equipment includes at least one of a wind power generation equipment and a photovoltaic power generation equipment.

[0040] In step S102, a cost model is established based on the unit capacity costs and life cycles of various power generation equipment and the unit capacity costs and life cycles of energy storage equipment.

[0041] As an example, in the case where the new energy power generation equipment includes both wind power generation equipment and photovoltaic power generation equipment, step S102 may specifically include steps S1021 to S1025:

[0042] In step S1021, the quotient of the product of the unit capacity cost of the wind power generation equipment and the capacity of the wind power generation equipment and the life cycle of the wind power generation equipment is calculated as the wind energy power generation cost.

[0043] In step S1022, the quotient of the product of the unit capacity cost of the photovoltaic power generation equipment and the capacity of the photovoltaic power generation equipment and the life cycle of the photovoltaic power generation equipment is calculated as the light energy power generation cost.

[0044] In step S1023, the quotient of the product of the unit capacity cost of the energy storage device and the capacity of the energy storage device and the life cycle of the energy storage device is calculated as the energy storage cost.

[0045] In step S1024, the product of the unit capacity cost of the traditional power generation equipment, the preset time period and the power generation power per unit time period of the traditional power generation equipment is calculated as the traditional power generation cost.

[0046] In step S1025, the sum of wind power generation cost, solar power generation cost, energy storage cost and traditional power generation cost is calculated as a cost model.

[0047] Reference Figure 1 In step S103, the optimal value of the capacity of the new energy power generation equipment and the optimal value of the capacity of the energy storage equipment are calculated to minimize the total power generation cost obtained through the cost model within a preset time period and meet the preset constraints.

[0048] As an example, the preset constraint condition may include a first constraint condition, a second constraint condition, and a third constraint condition. Here, the first constraint condition includes: the sum of the first power of the new energy power generation equipment in each unit time period within the preset time period, the second power of the corresponding unit time period of the traditional power generation equipment, and the third power of the corresponding unit time period of the energy storage equipment is greater than or equal to the fourth power of the corresponding unit time period of the power load. The second constraint condition includes: the cumulative storage energy of the energy storage equipment in each unit time period within the preset time period is greater than or equal to zero and less than or equal to the capacity of the energy storage equipment. The third constraint condition includes: the capacity of the new energy power generation equipment and the capacity of the energy storage equipment are both greater than or equal to zero.

[0049] According to an embodiment of the present disclosure, for the second constraint condition, the cumulative energy storage amount of the energy storage device in the i-th unit time period within the preset time period may be the sum of the energy storage amounts of the energy storage device from the 1-th unit time period to the i-th unit time period within n unit time periods, wherein 1≤i≤n, and i and n are both positive integers, and the preset time period includes n unit time periods.

[0050] According to an embodiment of the present disclosure, the first power may be the product of the power generation coefficient of the new energy power generation equipment and the capacity of the new energy power generation equipment in each unit time period within a preset time period.

[0051] As an example, in the case where the new energy power generation equipment includes at least one of wind power generation equipment and photovoltaic power generation equipment, the first power is the sum of the product of the wind power generation coefficient and the capacity of the wind power generation equipment and the product of the solar power generation coefficient and the capacity of the photovoltaic power generation equipment or one of the two.

[0052] For example, the steps of calculating the above-mentioned wind power generation coefficient may include: 1) obtaining the wind speed data of the wind power generation equipment and the wind resource data within a preset time period, where the wind speed data includes the cut-in wind speed data and the rated wind speed data, and the wind resource data includes the wind speed data of each unit time period; 2) if the wind speed data of each unit time period is greater than or equal to the corresponding cut-in wind speed data and less than or equal to the corresponding rated wind speed data, then the quotient of the difference between the wind speed data of each unit time period and the corresponding cut-in wind speed data divided by the difference between the corresponding rated wind speed data and the corresponding cut-in wind speed data is calculated as the wind power generation coefficient; 3) if the wind speed data of each unit time period is less than the corresponding cut-in wind speed data or greater than the corresponding rated wind speed data, then the wind power generation coefficient is set to zero.

[0053] For example, the steps of calculating the above-mentioned solar power generation coefficient may include: 1) obtaining the irradiance data of the photovoltaic components of the photovoltaic power generation equipment and the light resource data within a preset time period, where the irradiance data includes the minimum irradiance data and the rated irradiance data, and the light resource data includes the light illumination data of each unit time period; 2) if the irradiance data of each unit time period is greater than or equal to the corresponding minimum irradiance data and less than or equal to the corresponding rated irradiance data, then the quotient of the difference between the irradiance data of each unit time period and the corresponding minimum irradiance data divided by the difference between the corresponding rated irradiance data and the corresponding minimum irradiance data is calculated as the solar power generation coefficient; 3) if the irradiance data of each unit time period is less than the corresponding minimum irradiance data or greater than the corresponding rated irradiance data, then the solar power generation coefficient is set to zero.

[0054] In the following, a source-load system including a wind turbine, a photovoltaic power generation equipment, and a diesel power generation equipment (hereinafter referred to as a "wind-solar-diesel-load storage system") is taken as an example to illustrate the capacity optimization method of the present disclosure.

[0055] As an example, in an off-grid wind-solar-diesel-load-storage scenario, the wind-solar-diesel-load-storage system can use wind power and photovoltaics as the main power sources, use energy storage equipment to alleviate the problem of imbalance in the timing of power generation and power consumption, and use diesel power generation equipment (for example, diesel generators) as a backup power source. Therefore, it is necessary to consider the installed capacity of wind power, the installed capacity of photovoltaics and the capacity of energy storage equipment for optimal configuration.

[0056] Here, when configuring the installed capacity of wind power, photovoltaic power and energy storage equipment, the following issues need to be considered: If the capacity of the main power source is too high, the cost of the wind-solar-diesel-load storage system will be too high; if the capacity of the main power source is too low, the fuel consumption cost of the diesel generator will be too high; if the capacity of the energy storage equipment is too high, it is likely to become an idle cost; if the capacity of the energy storage equipment is too low, frequent wind and solar power abandonment and long-term output of diesel generators will occur due to power imbalance.

[0057] Therefore, the exemplary capacity optimization method according to the present disclosure considers the following wind, solar, diesel and storage cost model, which comprehensively considers the unit kilowatt life cycle cost of wind farms, photovoltaic farms and energy storage equipment and the unit kilowatt-hour power generation fuel cost of diesel generators. Here, the unit kilowatt life cycle cost is positively correlated with the configured capacity, and the unit kilowatt-hour power generation fuel cost is positively correlated with the hourly power generation power in operation.

[0058] As an example, the expression of the wind-solar-diesel-storage cost model can be as shown in the following formula (1):

[0059]

[0060] Among them, p pv represents the life cycle cost of photovoltaic power per kilowatt, p w represents the life cycle cost of wind power per kilowatt, p d represents the diesel unit kilowatt-hour cost, p s represents the life cycle cost of energy storage per kilowatt, C pv Represents the photovoltaic installed capacity, C w represents the installed capacity of wind power, C s represents the energy storage optical machine capacity, l pv Indicates the photovoltaic operating life, l w Indicates the wind power operation life, l s represents the energy storage operating life, and n represents the hourly sequence number and is a positive integer.

[0061] From the above formula (1), it can be concluded that the wind, solar, diesel and energy storage cost model can include wind power generation cost, solar power generation cost, energy storage cost and traditional power generation cost. The following will explain these four costs in detail.

[0062] Regarding the cost of wind power generation, in order to calculate the cost of wind power generation, a linear model with wind power installed capacity as a variable is generated according to the capacity optimization method disclosed in the present invention. Specifically, the wind power generation power model can be constructed through the following steps:

[0063] Step F1, obtaining the cut-in wind speed and rated wind speed of the wind turbine generator set;

[0064] Step F2, obtaining hourly wind resource data;

[0065] Step F3, calculate the wind power generation coefficient, as shown in the following formula (2):

[0066]

[0067] Among them, n represents the hourly sequence number, α n represents the wind power generation coefficient at the nth hour, w n Indicates the wind speed data for the nth hour, w cutin represents the cut-in wind speed, and w rate Indicates rated wind speed.

[0068] Step F4, constructing a wind power generation model, as shown in the following formula (3):

[0069] P w,n =α n *C w (3)

[0070] Among them, P w,n represents the wind power generation at the nth hour, and C w Represents wind power installed capacity.

[0071] Here, the design principle of the above-mentioned wind power generation coefficient is: by introducing the cut-in wind speed and the rated wind speed, the curve of the wind speed data (the shape is similar to a part of a parabola) is approximated as a straight line for calculating the wind power generation coefficient. This design process equates the time-varying wind speed to a value with a predetermined rule. Although a certain degree of accuracy is sacrificed, this design is the simplest, most effective, time-saving and convenient way to implement the project. In addition, the above-mentioned wind power generation coefficient can be calculated by using wind speed data with a finer granularity (for example, wind speed data with a granularity of minutes / seconds from hours) to further improve the accuracy of the above-mentioned wind power generation coefficient.

[0072] Regarding the cost of solar power generation, in order to calculate the cost of solar power generation, a linear model with photovoltaic installed capacity as a variable is generated according to the capacity optimization method disclosed in the present invention. Specifically, the photovoltaic power generation model can be constructed in the following manner:

[0073] Step G1, obtaining the minimum irradiance and rated irradiance of the photovoltaic module;

[0074] Step G2, obtaining hourly light resource data;

[0075] Step G3, calculate the photovoltaic power generation coefficient, as shown in the following formula (4):

[0076]

[0077] Among them, n represents the hourly sequence number, β n represents the photovoltaic power generation coefficient at the nth hour, i n Indicates the irradiance data for the nth hour, i min Indicates the minimum irradiance, i rate Indicates rated irradiance.

[0078] Step G4, constructing a photovoltaic power generation model, as shown in the following formula (5):

[0079] P pv,n =β n *C pv (5)

[0080] Among them, P pv,n represents the photovoltaic power generation at the nth hour, and C pv Represents photovoltaic installed capacity.

[0081] Here, the design principle of the above photovoltaic power generation coefficient is: by introducing the minimum irradiance and the rated irradiance, the curve of the light data is approximated as a straight line for calculating the coefficient. Since the time-varying nature of light is much weaker than that of wind speed (because the light absorbed by the photovoltaic module is much more stable than the wind speed), the design process equates the time-varying (relatively weak) irradiation data to a value with a predetermined regularity. This design is the simplest, most effective, time-saving and convenient way to implement the project while basically ensuring accuracy.

[0082] As an example, in order to characterize the adjustable characteristics of wind power and photovoltaic power, a linear model with wind power installed capacity and photovoltaic installed capacity as variables is generated according to the capacity optimization method disclosed in the present invention. Specifically, the wind-solar-diesel-storage-load operation model can be constructed in the following manner, as shown in the following formula (6):

[0083]

[0084] Where N represents the number of hours in the planning period, αn represents the wind power generation coefficient at the nth hour, β n represents the photovoltaic power generation coefficient at the nth hour, Indicates the power generated by the diesel generator in the nth hour, P s,n represents the power of the energy storage in the nth hour (its value is positive during discharge and negative during charge), C pv Represents the photovoltaic installed capacity, C w represents the installed capacity of wind power, and L n Indicates the load power.

[0085] By establishing the above-mentioned wind, solar, diesel, storage and load operation model, the balance between the real-time power associated with the wind, solar, diesel and storage in the wind, solar, diesel, storage and load system and the real-time power of the load can be ensured, and on this basis, the above inequality is used to characterize the adjustable characteristics of wind power and photovoltaic power.

[0086] In addition, for the energy storage device, an energy storage capacity constraint model is established so that the energy storage capacity at any time is constrained by the capacity of the energy storage device, as shown in the following formula (7):

[0087]

[0088] Among them, P s,n represents the power of the energy storage in the nth hour (its value is positive during discharge and negative during charge), and N represents the number of hours in the planning period.

[0089] According to an embodiment of the present disclosure, after the above models according to equations (1) to (7) are established, a linear programming problem model can be constructed through the above models, and its objective function is: minC. Here, C represents the wind-solar-diesel-storage cost model shown in equation (1). The constraints of the linear programming problem model include the above equation (6) and the following equations (8) and (9), and the boundary conditions of the linear programming problem model are the following equations (10) to (12):

[0090]

[0091]

[0092] C w ≥0 (10)

[0093] C pv ≥0 (11)

[0094] C s ≥0 (12)

[0095] The output result of the linear programming problem model that satisfies the above objective function and the constraints and boundary conditions is the optimal capacity configuration, for example, including the optimal wind power installed capacity, the optimal photovoltaic installed capacity, and the optimal energy storage photovoltaic capacity. As an example, the linear programming problem model can be solved by a linear programming solver, and the solution result is the optimal capacity configuration.

[0096] Below, refer to Figures 2 to 9 Let’s take an example to illustrate the capacity optimization method of wind-solar-diesel-load storage system. Figure 2 is an overall design flow chart showing an example of a capacity optimization method for a wind-solar-diesel-storage-load system according to an exemplary embodiment of the present disclosure. The specific process is as follows Figure 2 As shown, no further description is given here. Figures 3 to 9 is a graph of simulation data illustrating an example of a capacity optimization method according to an exemplary embodiment of the present disclosure.

[0097] Reference Figures 2 to 9 According to an exemplary embodiment of the present disclosure, an example of a capacity optimization method of a wind-solar-diesel-load storage system may include the following processing:

[0098] 1) Set the planning period to 240 hours;

[0099] 2) Obtain wind resource data and generate wind speed sequence data (a total of 240 items of data) based on the wind resource data. Figure 3 As shown;

[0100] 3) Obtain light resource data, and generate light irradiance sequence data (a total of 240 items of data) based on the light resource data, as follows: Figure 4 As shown;

[0101] 4) According to the wind speed related setting parameters (the cut-in wind speed value is 2.5 and the rated wind speed value is 8), the wind power generation coefficient sequence is calculated using the wind power generation power model, which can be specifically as follows: Figure 5 As shown;

[0102] 5) According to the setting parameters related to light (the minimum irradiance value is 100 and the rated irradiance value is 1088), the photovoltaic power generation coefficient sequence is calculated using the photovoltaic power generation power model, which can be specifically as follows: Figure 6 As shown;

[0103] 6) Set the relevant parameters involved in the cost model, as shown in Table 1 below:

[0104] Table 1

[0105] Parameter name Parameter Value unit Wind power EPC cost per kilowatt 4000 Yuan Photovoltaic EPC cost per kilowatt 3500 Yuan Energy storage unit kilowatt EPC cost 1500 Yuan Cost per kWh of diesel power generation 1.5 Yuan Wind power life 20 Year Photovoltaic life 20 Year Energy storage life 5 Year

[0106] In Table 1, EPC represents the general contracting model (ie, Engineering Procurement Construction). For example, aspects such as loss and capital cost may be additionally considered in the above linear model to further improve the linear model.

[0107] 7) Set the load curve to a constant value of 2000kW;

[0108] 8) Based on the above parameters, the above linear model is used to solve the problem using the preset linear programming solver. The result items are shown in Table 2 below:

[0109] Table 2

[0110] Result Item Optimal value Wind power installed capacity 9.2MW Photovoltaic installed capacity 8.8MW Energy storage optical machine capacity 6.2MWh Diesel generator power generation 1.9MW

[0111] In addition, according to the optimized capacity simulation, the 240h power generation curves of wind power, photovoltaic power and diesel generator are as follows: Figure 7 As shown in Figure 2, the charge and discharge conditions of the energy storage device within 240 hours can be shown as Figure 8 As shown, the actual power load within 240 hours (for example, slightly lower than the set power load value) can be as follows Fig. 9 shown.

[0112] According to the embodiments of the present disclosure, a capacity optimization method and device for a source-load system based on a linear model is proposed to construct a capacity configuration optimization model for a source-load system by utilizing a linear programming method to optimize the configuration of new energy capacity, thereby improving the solution efficiency of the capacity configuration optimization algorithm for the source-load system and greatly reducing the difficulty of engineering implementation.

[0113] In addition, through the capacity optimization method and device of the source-load system based on the linear model disclosed in the present invention, it is also possible to quickly and effectively support the economic feasibility evaluation and scheduling optimization control of off-grid (and / or grid-connected) wind, solar, storage, diesel and load multi-energy complementary source systems by processing the flexible configuration under different combinations of wind, solar, storage, diesel and load elements and the optimal configuration of the system under different scenarios.

[0114] For example, through the capacity optimization method and device of the source-load system based on the linear model disclosed in the present invention, the capacity configuration is optimized by using the normalized power generation coefficient to characterize the linear model of wind and solar power generation power. The optimization process is highly interpretable, and the capacity optimization method and device can reduce the consumption of traditional energy such as diesel through over-allocation of new energy such as wind energy and solar energy, thereby reducing the overall cost.

[0115] Fig.10 1 is a block diagram illustrating a capacity optimization device 1000 (hereinafter referred to as “capacity optimization device 1000 ”) of a source-load system based on a linear model according to an exemplary embodiment of the present disclosure.

[0116] According to an embodiment of the present disclosure, the source-load system includes a variety of power generation equipment and energy storage equipment, and the various power generation equipment includes new energy power generation equipment and traditional power generation equipment. For example, the new energy power generation equipment may include wind turbines and photovoltaic power generation equipment. For example, traditional power generation equipment may include diesel power generation equipment and gas turbines.

[0117] Reference Fig.10 , the capacity optimization device 1000 includes an acquisition unit 1010 and a modeling optimization unit 1020.

[0118] According to an embodiment of the present disclosure, the acquisition unit 1010 is configured to: acquire the unit capacity cost and life cycle of various power generation equipment and the unit capacity cost and life cycle of energy storage equipment.

[0119] According to an embodiment of the present disclosure, the modeling optimization unit 1020 is configured to: establish a cost model based on the unit capacity cost and its life cycle of various power generation equipment and the unit capacity cost and its life cycle of energy storage equipment; calculate the optimal value of the capacity of the new energy power generation equipment and the optimal value of the capacity of the energy storage equipment that minimizes the total power generation cost obtained through the cost model within a preset time period and satisfies preset constraints.

[0120] As an example, the preset constraint condition may include a first constraint condition, a second constraint condition, and a third constraint condition. Here, the first constraint condition includes: the sum of the first power of the new energy power generation equipment in each unit time period within the preset time period, the second power of the corresponding unit time period of the traditional power generation equipment, and the third power of the corresponding unit time period of the energy storage equipment is greater than or equal to the fourth power of the corresponding unit time period of the power load. The second constraint condition includes: the cumulative storage energy of the energy storage equipment in each unit time period within the preset time period is greater than or equal to zero and less than or equal to the capacity of the energy storage equipment. The third constraint condition includes: the capacity of the new energy power generation equipment and the capacity of the energy storage equipment are both greater than or equal to zero.

[0121] As an example, the cumulative energy storage of the energy storage device in the i-th unit time period within the preset time period may be the sum of the energy storage of the energy storage device from the 1st unit time period to the i-th unit time period within n unit time periods. Here, 1≤i≤n, i and n are both positive integers, and the preset time period includes n unit time periods.

[0122] As an example, the new energy power generation equipment may include both wind power generation equipment and photovoltaic power generation equipment. In this case, the process of establishing the cost model by the modeling optimization unit 1020 may include: calculating the unit capacity cost of the wind power generation equipment and the product of the capacity of the wind power generation equipment and the life cycle of the wind power generation equipment as the wind power generation cost; calculating the unit capacity cost of the photovoltaic power generation equipment and the product of the capacity of the photovoltaic power generation equipment and the life cycle of the photovoltaic power generation equipment as the light power generation cost; calculating the unit capacity cost of the energy storage equipment and the product of the capacity of the energy storage equipment and the life cycle of the energy storage equipment as the energy storage cost; calculating the unit capacity cost of the traditional power generation equipment, the preset time period and the unit time period power generation power of the traditional power generation equipment The product of the three is used as the traditional power generation cost; calculating the sum of the wind power generation cost, the light power generation cost, the energy storage cost and the traditional power generation cost as the cost model.

[0123] For example, the first power may be the product of the power generation coefficient of the new energy power generation equipment and the capacity of the new energy power generation equipment in each unit time period within a preset time period.

[0124] As an example, the new energy power generation equipment may include at least one of wind power generation equipment and photovoltaic power generation equipment, and the first power is the sum of the product of the wind power generation coefficient and the capacity of the wind power generation equipment and the product of the solar power generation coefficient and the capacity of the photovoltaic power generation equipment or one of the two.

[0125] As an example, the acquisition unit 1010 may also be configured to: acquire wind speed data of the wind power generation equipment and wind resource data in a preset time period. Here, the wind speed data includes cut-in wind speed data and rated wind speed data, and the wind resource data includes wind speed data in each unit time period.

[0126] In the case of this example, the modeling optimization unit 1020 can also be configured as follows: if the wind speed data of each unit time period is greater than or equal to the corresponding cut-in wind speed data and less than or equal to the corresponding rated wind speed data, then the quotient of the difference between the wind speed data of each unit time period and the corresponding cut-in wind speed data divided by the difference between the corresponding rated wind speed data and the corresponding cut-in wind speed data is calculated as the wind power generation coefficient; if the wind speed data of each unit time period is less than the corresponding cut-in wind speed data or greater than the corresponding rated wind speed data, then the wind power generation coefficient is set to zero.

[0127] As an example, the acquisition unit 1010 may also be configured to: acquire irradiance data of the photovoltaic components of the photovoltaic power generation equipment and light resource data in a preset time period. Here, the irradiance data includes minimum irradiance data and rated irradiance data, and the light resource data includes light irradiance data for each unit time period.

[0128] In the case of this example, the modeling optimization unit 1020 can also be configured as follows: if the irradiance data of each unit time period is greater than or equal to the corresponding minimum irradiance data and less than or equal to the corresponding rated irradiance data, then the quotient of the difference between the irradiance data of each unit time period and the corresponding minimum irradiance data divided by the difference between the corresponding rated irradiance data and the corresponding minimum irradiance data is calculated as the solar power generation coefficient; if the irradiance data of each unit time period is less than the corresponding minimum irradiance data or greater than the corresponding rated irradiance data, then the solar power generation coefficient is set to zero.

[0129] It should be understood that the specific processing performed by the capacity optimization device of the source-load system based on the linear model according to the exemplary embodiment of the present disclosure has been referred to. Figures 1 to 9 It has been described in detail and will not be repeated here.

[0130] It should be understood that the various units in the capacity optimization device for a source-load system based on a linear model according to the exemplary embodiment of the present disclosure may be implemented as hardware components and / or software components. Those skilled in the art may implement the various units, for example, using a field programmable gate array (FPGA) or an application specific integrated circuit (ASIC), according to the processing performed by the various defined units.

[0131] An exemplary embodiment of the present disclosure provides a computer-readable storage medium, when the instructions in the computer-readable storage medium are executed by at least one processor, the at least one processor is prompted to perform the capacity optimization method as described above. The computer-readable storage medium is any data storage device that can store data read by a computer system. Examples of computer-readable storage media include: read-only memory, random access memory, read-only optical disk, magnetic tape, floppy disk, optical data storage device, and carrier wave (such as data transmission through the Internet via a wired or wireless transmission path).

[0132] Fig.11 is a block diagram illustrating a computer device 1100 according to an exemplary embodiment of the present disclosure.

[0133] The computer device 1100 according to the exemplary embodiment of the present disclosure includes: at least one processor 1110 and at least one memory 1120. The memory 1120 stores computer executable instructions, which, when executed by the at least one processor 1110, prompt the at least one processor 1110 to perform the capacity optimization method described above.

[0134] Although some exemplary embodiments of the present disclosure have been shown and described, it will be appreciated by those skilled in the art that modifications may be made to these embodiments without departing from the principles and spirit of the present disclosure, the scope of which is defined by the claims and their equivalents.

Claims

1. A capacity optimization method for a source-load system based on a linear model, characterized in that: The source-load system includes a variety of power generation equipment and energy storage equipment, the various power generation equipment includes new energy power generation equipment and traditional power generation equipment, and the capacity optimization method includes: Obtaining the unit capacity cost and life cycle of the various power generation equipment and the unit capacity cost and life cycle of the energy storage equipment; Establishing a cost model based on the unit capacity costs and life cycles of the various power generation equipment and the unit capacity costs and life cycles of the energy storage equipment; The optimal value of the capacity of the new energy power generation equipment and the optimal value of the capacity of the energy storage equipment that minimize the total power generation cost obtained through the cost model and meet the preset constraints within a preset time period are calculated.

2. The capacity optimization method according to claim 1, characterized in that: The preset constraint conditions include a first constraint condition, a second constraint condition and a third constraint condition. The first constraint condition includes: the sum of the first power of the new energy power generation equipment in each unit time period within the preset time period, the second power of the traditional power generation equipment in the corresponding unit time period, and the third power of the energy storage equipment in the corresponding unit time period is greater than or equal to the fourth power of the power load in the corresponding unit time period; The second constraint condition includes: the cumulative storage energy of the energy storage device in each unit time period within the preset time period is greater than or equal to zero and less than or equal to the capacity of the energy storage device; The third constraint condition includes: the capacity of the new energy power generation equipment and the capacity of the energy storage equipment are both greater than or equal to zero.

3. The capacity optimization method according to claim 2, characterized in that: The first power is the product of the power generation coefficient of the new energy power generation equipment and the capacity of the new energy power generation equipment in each unit time period within the preset time period.

4. The capacity optimization method according to claim 2, characterized in that: The new energy power generation equipment includes at least one of a wind power generation equipment and a photovoltaic power generation equipment, and The first power is the sum of the product of the wind power generation coefficient and the capacity of the wind power generation equipment and the product of the light power generation coefficient and the capacity of the photovoltaic power generation equipment, or one of the two.

5. The capacity optimization method according to claim 4, characterized in that: The capacity optimization method further includes: Acquire wind speed data of the wind power generation equipment and wind resource data in the preset time period, wherein the wind speed data includes cut-in wind speed data and rated wind speed data, and the wind resource data includes wind speed data in each unit time period; If the wind speed data of each unit time period is greater than or equal to the corresponding cut-in wind speed data and less than or equal to the corresponding rated wind speed data, the quotient of the difference between the wind speed data of each unit time period and the corresponding cut-in wind speed data divided by the difference between the corresponding rated wind speed data and the corresponding cut-in wind speed data is calculated as the wind power generation coefficient; If the wind speed data of each unit time period is less than the corresponding cut-in wind speed data or greater than the corresponding rated wind speed data, the wind energy generation coefficient is set to zero.

6. The capacity optimization method according to claim 4, characterized in that: The capacity optimization method further includes: Acquire irradiance data of the photovoltaic components of the photovoltaic power generation equipment and light resource data within the preset time period, wherein the irradiance data includes minimum irradiance data and rated irradiance data, and the light resource data includes light irradiance data for each unit time period; If the irradiance data of each unit time period is greater than or equal to the corresponding minimum irradiance data and less than or equal to the corresponding rated irradiance data, the quotient of the difference between the irradiance data of each unit time period and the corresponding minimum irradiance data divided by the difference between the corresponding rated irradiance data and the corresponding minimum irradiance data is calculated as the solar power generation coefficient; If the irradiance data of each unit time period is less than the corresponding minimum irradiance data or greater than the corresponding rated irradiance data, the solar power generation coefficient is set to zero.

7. The capacity optimization method according to claim 1, characterized in that: The new energy power generation equipment includes both wind power generation equipment and photovoltaic power generation equipment, and the step of establishing the cost model includes: Calculate the quotient of the unit capacity cost of the wind power generation equipment and the capacity of the wind power generation equipment and the life cycle of the wind power generation equipment as the wind power generation cost; Calculate the quotient of the unit capacity cost of the photovoltaic power generation equipment and the capacity of the photovoltaic power generation equipment and the life cycle of the photovoltaic power generation equipment as the cost of solar power generation; Calculate the quotient of the unit capacity cost of the energy storage equipment and the capacity of the energy storage equipment multiplied by the life cycle of the energy storage equipment as the energy storage cost; Calculate the product of the unit capacity cost of the traditional power generation equipment, the preset time period and the power generation power per unit time period of the traditional power generation equipment as the traditional power generation cost; The sum of the wind power generation cost, the solar power generation cost, the energy storage cost and the traditional power generation cost is calculated as the cost model.

8. The capacity optimization method according to claim 1, characterized in that: The cumulative energy storage of the energy storage device in the i-th unit time period within the preset time period is the sum of the energy storage of the energy storage device from the 1st unit time period to the i-th unit time period within the n unit time periods. Wherein, 1≤i≤n, and i and n are both positive integers, and the preset time period includes n unit time periods.

9. A capacity optimization device for a source-load system based on a linear model, characterized in that: The source-load system includes a variety of power generation equipment and energy storage equipment, the various power generation equipment includes new energy power generation equipment and traditional power generation equipment, and the capacity optimization device includes: An acquisition unit is configured to: acquire the unit capacity cost and life cycle of the plurality of power generation equipment and the unit capacity cost and life cycle of the energy storage equipment; The modeling optimization unit is configured as follows: Establishing a cost model based on the unit capacity costs and life cycles of the various power generation equipment and the unit capacity costs and life cycles of the energy storage equipment; The optimal value of the capacity of the new energy power generation equipment and the optimal value of the capacity of the energy storage equipment that minimize the total power generation cost obtained through the cost model and meet the preset constraints within a preset time period are calculated.

10. The capacity optimization device according to claim 9, characterized in that: The preset constraint conditions include a first constraint condition, a second constraint condition and a third constraint condition. The first constraint condition includes: the sum of the first power of the new energy power generation equipment in each unit time period within the preset time period, the second power of the traditional power generation equipment in the corresponding unit time period, and the third power of the energy storage equipment in the corresponding unit time period is greater than or equal to the fourth power of the power load in the corresponding unit time period; The second constraint condition includes: the cumulative storage energy of the energy storage device in each unit time period within the preset time period is greater than or equal to zero and less than or equal to the capacity of the energy storage device; The third constraint condition includes: the capacity of the new energy power generation equipment and the capacity of the energy storage equipment are both greater than or equal to zero.

11. A computer-readable storage medium, characterized in that: When the instructions in the computer-readable storage medium are executed by at least one processor, the at least one processor is prompted to perform the capacity optimization method according to any one of claims 1 to 8.

12. A computer device, characterized in that: include: at least one processor; at least one memory storing computer executable instructions, Wherein, when the computer executable instructions are executed by the at least one processor, the at least one processor is prompted to perform the capacity optimization method according to any one of claims 1 to 8.

Images