Power transmission curve determination method, device, equipment and storage medium

Abstract

The present application relates to the technical field of power system planning, and discloses a power transmission curve determination method, device, equipment and storage medium, the method comprising: constructing an operation objective function of a renewable energy base, the objective function taking maximum income as the target, the objective function comprising electricity sales income, coal-fired power generation cost, local grid interaction cost, wind farm operation and maintenance cost, photovoltaic power plant operation and maintenance cost and energy storage operation and maintenance cost; constructing an operation constraint condition of the renewable energy base, the operation constraint condition comprising a carbon emission constraint; obtaining a power transmission curve based on the operation objective function and the operation constraint condition. The present application realizes low-carbon economic operation of the renewable energy base.

Description

Technical Field

[0001] This invention relates to the field of power system planning technology, specifically to a method, apparatus, equipment, and storage medium for determining power transmission curves. Background Technology

[0002] The power transmission curve of a renewable energy (i.e., new energy) base describes the changing pattern of electricity transmitted from the renewable energy base (such as wind power and photovoltaics) to the grid at different time periods. The determination of this curve involves multiple factors, including the installed capacity of renewable energy, output characteristics, rated DC power, and DC utilization hours, while also needing to comprehensively consider the balance between power supply security and renewable energy consumption.

[0003] In related technologies, the method for determining this power transmission curve includes:

[0004] Full-time simulation analysis: Based on the installed capacity and output characteristics of renewable energy, full-time (such as one year or one month) simulation analysis is conducted to understand the changing patterns of renewable energy power generation in different time periods.

[0005] A mathematical optimization model is established: taking into account both power supply security and renewable energy consumption factors, and based on data such as renewable energy installed capacity, output characteristics, DC rated power, and DC utilization hours, a mathematical optimization model is established with the objective of minimizing power curtailment and power shortage. By solving this model, the optimal power transmission curve can be obtained.

[0006] Multiple Scheme Comparison: When developing a power transmission curve, multiple schemes may need to be considered and compared. By comparing the economic, technical feasibility, and reliability of different schemes, the optimal power transmission curve scheme is selected.

[0007] However, the power transmission curves developed in related technologies cannot meet the requirements of economic efficiency and environmental protection. Summary of the Invention

[0008] In view of this, the present invention provides a method, apparatus, device and storage medium for determining power transmission curves, in order to solve the problem that power transmission curves of renewable energy bases are not economical and environmentally friendly.

[0009] In a first aspect, the present invention provides a method for determining a power transmission curve, the method comprising:

[0010] The objective function for constructing the renewable energy base is to maximize revenue. The objective function includes electricity sales revenue, coal-fired power generation costs, local grid interaction costs, wind farm operation and maintenance costs, photovoltaic power plant operation and maintenance costs, and energy storage operation and maintenance costs.

[0011] Operational constraints for building renewable energy bases include carbon emission constraints;

[0012] The power transmission curve is obtained based on the objective function and constraints.

[0013] In one alternative implementation, the cost of coal-fired power generation includes: carbon emission costs, fuel costs, pollutant treatment costs, and unit start-up and shutdown costs.

[0014] In one alternative implementation, local grid interaction costs include electricity purchase costs and electricity sales costs.

[0015] In one alternative implementation, the operational constraints include network power flow constraints, electricity sales constraints, coal-fired unit operation constraints, wind power generation constraints, photovoltaic power generation constraints, energy storage operation constraints, local grid interaction constraints, and system planning constraints, with the system planning constraints including carbon emission constraints.

[0016] In one alternative implementation, network power flow constraints include maximum and minimum bus voltage constraints, line capacity constraints, and / or bus power constraints.

[0017] In one alternative implementation, the operational constraints for constructing a renewable energy base include:

[0018] Obtain historical wind speed data from renewable energy bases to estimate wind farm output data for different periods throughout the year; construct wind power generation constraints based on the wind farm output data for different periods throughout the year; and / or,

[0019] Historical solar irradiance and temperature data of renewable energy bases are obtained to estimate the power output data of photovoltaic power plants at various times throughout the year; based on the power output data of photovoltaic power plants at various times throughout the year, photovoltaic power generation constraints are constructed.

[0020] In one alternative implementation, system planning constraints also include renewable energy utilization constraints and DC channel cost recovery constraints.

[0021] In a second aspect, the present invention provides a power transmission curve optimization device, the device comprising:

[0022] The objective function construction module is used to construct the operational objective function of the renewable energy base. The objective function aims to maximize revenue and includes electricity sales revenue, coal-fired power generation cost, local grid interaction cost, wind farm operation and maintenance cost, photovoltaic power plant operation and maintenance cost, and energy storage operation and maintenance cost.

[0023] The constraint construction module is used to construct the operating constraints for renewable energy bases, including carbon emission constraints.

[0024] The acquisition module is used to obtain the power transmission curve based on the objective function and constraints.

[0025] Thirdly, the present invention provides a computer device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the power supply curve optimization method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0026] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the power supply curve optimization method of the first aspect or any corresponding embodiment thereof.

[0027] Fifthly, the present invention provides a computer program product, including computer instructions for causing a computer to execute the power supply curve optimization method of the first aspect or any corresponding embodiment described above.

[0028] The present invention provides a method, apparatus, equipment, and storage medium for determining power transmission curves. The method establishes an objective function with the goal of maximizing the revenue of the renewable energy base, and establishes constraints including carbon emission limits. Then, based on the established objective function and constraints, the power transmission curve of the base is determined, thereby achieving low-carbon economic operation of the renewable energy base.

[0029] The power transmission curve determination method, apparatus, equipment, and storage medium provided in this invention construct an objective function for optimizing the power transmission curve under the low-carbon economic operation of renewable energy bases, based on the hourly output of renewable energy. It takes into account the carbon emission costs of coal-fired units and external supporting power, and constructs optimization constraints for the power transmission curve under the low-carbon economic operation of renewable energy bases. It considers the real-time carbon emission constraints of DC channels and the overall project revenue constraints, and generates an optimized power transmission curve for renewable energy bases, which can be used as a reference when signing long-term contracts in actual power transactions. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the specific embodiments or related technologies of the present invention, the drawings used in the description of the specific embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0031] Figure 1 This is a flowchart illustrating the power supply curve determination method according to an embodiment of the present invention;

[0032] Figure 2 This is a structural block diagram of a power transmission curve determination device according to an embodiment of the present invention;

[0033] Figure 3 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0035] Renewable energy bases refer to energy production, storage, and transmission bases built using clean and renewable energy technologies, including solar and wind power, and potentially hydropower and biomass energy. The output of these energy sources, especially wind and solar power, is highly random and volatile. Therefore, these bases are typically equipped with a certain number of coal-fired power units (also known as coal-fired power plants) and energy storage power stations to facilitate peak shaving for renewable energy and channel regulation, while also providing peak load support for the receiving end during the evening peak hours, thereby improving the renewable energy absorption rate and ensuring that the electricity transmitted through the transmission channels meets the power supply needs of the receiving end load as much as possible. However, with the annual tightening of free carbon allowances in the carbon market and the annual increase in carbon allowance prices, the carbon emission costs of renewable energy bases will significantly rise. Therefore, it is necessary to optimize the DC power transmission curve of renewable energy base transmission channels from the perspective of low-carbon economic operation.

[0036] This invention provides a method for optimizing the power transmission curve of a renewable energy base for low-carbon economic operation.

[0037] According to an embodiment of the present invention, a method for determining a power supply curve is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of executable computer instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0038] This embodiment provides a method for determining a power transmission curve, which can be used in power transmission control equipment of renewable energy (i.e., new energy) bases. Figure 1 This is a flowchart of a power transmission curve determination method according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps:

[0039] Step S101: Construct the operational objective function of the renewable energy base. The objective function aims to maximize revenue and includes electricity sales revenue, coal-fired power generation costs, local grid interaction costs, wind farm operation and maintenance costs, photovoltaic power plant operation and maintenance costs, and energy storage operation and maintenance costs.

[0040] The local power grid interaction cost includes the cost of purchasing electricity and the cost of selling electricity. The cost of purchasing electricity refers to the cost for renewable energy bases to buy electricity from the local power grid, while the cost of selling electricity refers to the revenue generated from selling electricity to the local power grid. When renewable energy output is insufficient, renewable energy bases can purchase electricity from the local power grid, that is, use external power to support power transmission requirements. When there is a surplus of renewable energy, they can sell electricity to the local power grid.

[0041] Step S102: Construct the operational constraints for the renewable energy base, including carbon emission constraints. Carbon emission constraints are restrictions on carbon emissions.

[0042] Step S103: Based on the objective function and constraints, obtain the power transmission curve, which can be obtained by solving the problem.

[0043] This embodiment provides a method for determining the power transmission curve. An objective function is established with the goal of maximizing the benefits of the renewable energy base, and constraints including carbon emission constraints are established. Then, the power transmission curve of the base is determined based on the established objective function and constraints, thereby realizing the low-carbon economic operation of the renewable energy base.

[0044] This embodiment provides a method for determining a power transmission curve, which can be used in power transmission control equipment of a renewable energy base. The method includes the following steps:

[0045] Step S201: Construct the operational objective function of the renewable energy base. The objective function aims to maximize revenue and includes electricity sales revenue, coal-fired power generation costs, local grid interaction costs, wind farm operation and maintenance costs, photovoltaic power plant operation and maintenance costs, and energy storage operation and maintenance costs.

[0046] Specifically, the objective function is: max f = RC coal -C grid -C WT -C PV -C bat Where R is the revenue from electricity sales, and C coal For the cost of coal-fired power generation, C grid For local power grid interaction costs, C WT For wind farm operation and maintenance costs, C PV For the operation and maintenance costs of photovoltaic power plants, C bat This is for energy storage operation and maintenance costs.

[0047] Electricity sales revenue R represents the revenue from selling electricity through DC channels at renewable energy bases, and its calculation formula is as follows: Where P t dc Let β be the DC transmission power during time period t. telec,dc Let t be the intraday price for time period t.

[0048] In some optional implementations, the cost of coal-fired power generation C coal Including: carbon emission costs C coal_emission Fuel cost C coal_fuel Pollutant treatment costs C coal_en and unit start-up and shutdown costs C coal_start .

[0049] Specifically, carbon emission cost C coal_emission The carbon emission cost of coal-fired unit i in time period t is calculated based on carbon emission prices, the power generation capacity of the coal-fired unit, and the carbon emission cost per unit of power generation (actual carbon emission per unit of power generation minus the baseline carbon emission). The calculation formula is: Let β be the power generation (i.e., active power) of coal-fired unit i during time period t. emission,coal For carbon emission prices, γ real,coal γ is the actual carbon emission factor per unit of power generation. base,coal This serves as a baseline for carbon emissions per unit of electricity generated. Carbon emissions within the baseline are exempt from fees, while emissions exceeding the baseline are subject to fees.

[0050] Fuel cost C coal_fuel The fuel cost of coal-fired unit i during time period t is determined based on fuel prices, power generation per unit time (which can be time period t, specifically 1 hour), and the coal consumption coefficient per unit time. The calculation formula is: Where β fuel,coal Where is the fuel price, and a, b, and d are the coal consumption coefficients. Let t be the power generation of coal-fired unit i during time period t.

[0051] Pollutant treatment cost C coal_en The cost of treating various air pollutants per unit of power generation is determined based on the comprehensive treatment price of each type of air pollutant per unit of power generation, the power generation of the coal-fired unit per unit time (which can be a time period t, specifically 1 hour), and the cost of treating pollutant i for the coal-fired unit during time period t. The calculation formula is: β en,coal The price for comprehensive treatment of various air pollutants, Let t be the power generation of coal-fired unit i during time period t.

[0052] Unit start-up and shutdown cost C coal_start The calculation formula is: β on,coal The start-up and shutdown costs of coal-fired power units (also known as coal-fired power plants or thermal power units). Let be the start / stop status of coal-fired unit i during time period t, which is a Boolean variable.

[0053] Coal-fired power generation cost C coal The calculation formula is: Among them, Ω coal This represents a collection of coal-fired power units, where T represents the number of hours per year.

[0054] In some implementations, the local power grid interaction cost C grid Including electricity purchase cost C grid_buy and electricity sales cost C grid _sell Electricity purchase cost C grid_buy The calculation formula is: in, Let P be the electricity purchase price (per unit power) from the local power grid during time period t. t grid For time period t, the electricity purchased by the renewable energy base from the local grid; the electricity sales cost C. grid_sell The calculation formula is: in, Let C be the electricity price sold to the local power grid during time period t. The local power grid interaction cost C is also included. grid The calculation formula is:

[0055] In addition, the operation and maintenance cost C of the wind farm WT The following formula can be used for calculation: in, Let β be the active power of wind farm i. WT Ω represents the operating cost coefficient for wind power. WT For wind farms.

[0056] Photovoltaic power plant operation and maintenance cost C PV The following formula can be used for calculation: in, Let β be the active power of the photovoltaic electric field (also known as a photovoltaic power station) i. PV Ω represents the operating cost coefficient for photovoltaics. PV It is a collection of photovoltaic electric fields.

[0057] Energy storage operation and maintenance cost C bat The following formula can be used for calculation: in, For the discharge power and charging power of energy storage power station i, β bat Ω represents the operating cost coefficient for energy storage. bat This is a collection of energy storage power stations. Energy storage power stations can use electrochemical energy storage.

[0058] Step S202: Construct the operating constraints for the renewable energy base, including carbon emission constraints.

[0059] In some optional implementations, the operational constraints include network power flow constraints, electricity sales constraints, coal-fired power unit (also known as thermal power unit) operation constraints, wind power generation constraints, photovoltaic power generation constraints, energy storage operation constraints, local grid interaction constraints, and system planning constraints, including carbon emission constraints.

[0060] Specifically, network power flow constraints include maximum and minimum bus voltage constraints, line capacity constraints, and / or bus power constraints. Buses include those connecting lines, wind farms, photovoltaic power plants, energy storage stations, DC transmission lines, and coal-fired power plants.

[0061] Bus voltage U i,t The maximum and minimum values ​​are U max and U min , that is U min ≤U i,t ≤U max .

[0062] Line capacity For branch current, Among them, y i0 For the line's ground admittance, y ij For the admittance of the line, Let be the voltage at bus i and the voltage at bus j during time period t. The line capacity S. ij,t The constraint is that it is less than or equal to the rated capacity.

[0063] The active and reactive power injected into bus i during time period t is P i,t and Q i,t , The voltage at bus i is U i,t ∠δ i,t δ i,t The phase angle of bus voltage i is U, and the phase angle of bus voltage j is U. j,t ∠δ j,t δ j,t The phase angle of the voltage at bus j is given by G, and the line conductance and susceptance between buses i and j are given by G. ij and B ij δ ij,t =δ i,t -δ j,t .

[0064] Electricity sales constraints include constraints on the power transmission curve imposed by the demand side, and may also include constraints on grid transmission requirements. Specifically, electricity sales constraints may include the following:

[0065] 1. Peak load requirements during special periods: Specifically, the peak load during a special period can be greater than or equal to the peak load during the daily peak hours. This can be expressed by the formula: in This represents the peak load during the daily peak load period, Ω peak This is a collection of daily peak load periods. This represents the DC power during time period i on day j of month k. Peak demand during specific time periods represents the constraint imposed on the power supply curve by the demand side.

[0066] 2. Throughout the 12 months of the year, the active power curves of the DC transmission lines of renewable energy bases remain consistent. This is a constraint imposed on the power transmission curve by the grid's transmission requirements. This can be expressed as a formula: in This represents the DC power during time period i on day j of month k.

[0067] 3. The number of power steps per day shall not exceed the preset limit, that is, the sum of the number of power steps before and after each day shall not exceed the limit St. max This value, which is also a constraint imposed on the power transmission curve by the power grid transmission requirements, is expressed by the formula:

[0068]

[0069] Among them, Da j This is a set of daily output values, totaling 365 days and 365 sets, St. i This is a Boolean variable, where M is a relatively large number that can be set to the maximum power.

[0070] The specific operating constraints of coal-fired power units include the following:

[0071] 1. Output and gradeability constraints.

[0072]

[0073] in, To minimize the adjustment coefficient, This represents the maximum active power output of coal-fired unit i. Let be the start / stop state of coal-fired unit i during time period t, and let be a Boolean variable. Let be the power generation capacity of coal-fired unit i during time period t. The ramp rate is the range between the minimum output power and the rated value. Let be the reactive power of coal-fired unit i during time period t. This refers to the rated reactive power of coal-fired unit i over a given period.

[0074] 2. Start-up and shutdown constraints.

[0075]

[0076] Among them, T on T off Let i be the minimum daily operating time and minimum daily downtime of the coal-fired power unit.

[0077] The formula for the constraints of wind power generation is expressed as: in, The actual power output of wind farm i (after optimized scheduling) during time period t. Let t represent the available power output of wind farm i during time period t. Let be the reactive power generated by wind farm i during time period t. This represents the rated reactive power of wind farm i over a given time period.

[0078] The formula for photovoltaic power generation constraints is expressed as: in, Let t be the actual photovoltaic output of photovoltaic power plant i (after optimized scheduling) during time period t. Let t be the available photovoltaic output of photovoltaic electric field i during time period t. Let t be the reactive power generated by photovoltaic power generation in photovoltaic electric field i during time period t. The rated reactive power of photovoltaic power generation of photovoltaic electric field i within a certain time period.

[0079] The formula for energy storage operation constraints is expressed as follows:

[0080]

[0081] in, Let i be the output of energy storage power station i during time period t. This represents the rated output of energy storage power station i. The discharge power of energy storage station i during time period t. Let t be the charging power of energy storage station i during time period t. For Boolean variables, Let i be the capacity of energy storage station i during time period t. Let i be the initial capacity of the energy storage power station (which can be 0). For the rated capacity of energy storage power station i, To improve the charging efficiency of energy storage power stations. For the discharge efficiency of the energy storage power station, Let i be the reactive power of energy storage station i during time period t. This is the rated reactive power of energy storage power station i.

[0082] Local grid interaction constraints, i.e., power constraints of local grid interconnection lines, are expressed by the following formula:

[0083]

[0084] Among them, P tgrid For time period t, the amount of electricity purchased by the renewable energy base from the local power grid. Rated active power, Reactive power This is the rated reactive power.

[0085] The formula for the carbon emission constraint (for the whole year) in the system planning constraints is expressed as follows:

[0086]

[0087] Where CE represents total carbon emissions, including carbon emissions from electricity purchased from the local power grid, γ real,grid This represents the average carbon emission coefficient of the external power grid.

[0088] System planning constraints also include renewable energy utilization rate (annual) constraints and DC channel cost recovery constraints.

[0089] The formula for the renewable energy utilization rate constraint is expressed as:

[0090]

[0091] in, Let be the actual available power output of wind farm i (after optimized scheduling) during time period t. Let θ be the actual output value of photovoltaic power plant i (after optimized scheduling) during time period t, and θ be the renewable energy utilization rate.

[0092] The formula for the DC channel cost recovery constraint (also known as the reasonable return constraint for renewable energy bases) is expressed as follows:

[0093]

[0094] ff invest ≥π(f invest -f+R)

[0095] in, The DC power is the power during time period i on day j of month k. This refers to the rated power output of the DC channel over a given time period. invest π represents the annual equivalent investment cost of the renewable energy base (excluding equipment decommissioning revenue), π represents the expected rate of return on investment, and R represents the revenue from electricity sales.

[0096] In some optional implementations, step S202, namely the operational constraints for constructing a renewable energy base, includes:

[0097] Step S2021: Obtain historical wind speed data of the renewable energy base and estimate the power output data of the wind farm at various times throughout the year; the time period here can be an hour.

[0098] Step S2022: Construct wind power generation constraints based on the output data of the wind farm at various times throughout the year.

[0099] Specifically, the wind farm can be evenly divided into multiple areas, with a wind measuring device installed in each area to measure wind speed data for various time periods throughout the year. If wind speed data for multiple years is available, the average value is calculated as the historical wind speed data for that area. Then, using a wind power generation engineering output model from relevant technologies, the wind power output data for each area at various time periods can be calculated. Finally, the wind power output data for each area at various time periods are summed to obtain the wind power output data for the wind farm at each time period.

[0100] Alternatively, one can obtain the historical wind speed data of a wind farm directly without dividing the wind farm into regions, and then use the wind power generation engineering output model to calculate the wind power output data of the wind farm at various times throughout the year.

[0101] In some alternative implementations, step S202, namely the operational constraints for constructing a renewable energy base, includes:

[0102] Step S2023: Obtain historical sunshine data and historical temperature data of the renewable energy base, and estimate the output data of the photovoltaic power plant at various times throughout the year; the time period here can be an hour.

[0103] Step S2024: Construct photovoltaic power generation constraints based on the output data of the photovoltaic power plant at various times throughout the year.

[0104] Specifically, the photovoltaic (PV) power field can be evenly divided into multiple regions, with a device for measuring sunlight and temperature installed in each region. The sunlight and temperature data for each time period throughout the year can be measured. If multi-year data is available, the average value is calculated as the historical sunlight and temperature data for that region. Then, using a PV power generation engineering output model from relevant technologies, the PV power generation output data for each region at each time period can be calculated. Finally, the PV power generation output data for each region at each time period are summed to obtain the PV power generation output data for the entire PV power field at each time period.

[0105] Alternatively, one can obtain the historical sunshine and temperature data of a photovoltaic power plant directly without dividing the photovoltaic power plant into regions, and then use the photovoltaic power generation engineering output model to calculate the photovoltaic power generation output data of the photovoltaic power plant at various times of the year.

[0106] Step S203: Based on the objective function and constraints, obtain the power transmission curve.

[0107] This embodiment provides a method for optimizing the power transmission curve of a renewable energy base for low-carbon economic operation. It focuses on considering the potential impact of factors such as carbon emission policies, user demand, and reasonable base revenue on the economic operation of the renewable energy base, making the optimization results more adaptable to the carbon emission requirements of the power industry and closer to the actual medium- and long-term power contract signing scenario, thus providing a reference for the signing of actual medium- and long-term power contracts.

[0108] This embodiment also provides a power delivery curve optimization device, which is used to implement the above embodiments and preferred embodiments, and will not be repeated as already described. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0109] This embodiment provides a power transmission curve optimization device, such as... Figure 2 As shown, it includes:

[0110] The objective function construction module 201 is used to construct the operational objective function of the renewable energy base. The objective function aims to maximize revenue and includes electricity sales revenue, coal-fired power generation cost, local grid interaction cost, wind farm operation and maintenance cost, photovoltaic power plant operation and maintenance cost, and energy storage operation and maintenance cost.

[0111] The constraint construction module 202 is used to construct the operating constraints of the renewable energy base, including carbon emission constraints.

[0112] The acquisition module 203 is used to obtain the power transmission curve based on the objective function and the constraints.

[0113] In some alternative implementations, the cost of coal-fired power generation includes: carbon emission costs, fuel costs, pollution treatment costs, and unit start-up and shutdown costs.

[0114] In some alternative implementations, local grid interaction costs include electricity purchase costs and electricity sales costs.

[0115] In some alternative implementations, operational constraints include network power flow constraints, electricity sales constraints, coal-fired power unit operation constraints, wind power generation constraints, photovoltaic power generation constraints, energy storage operation constraints, local grid interaction constraints, and system planning constraints, including carbon emission constraints.

[0116] In some alternative implementations, network power flow constraints include maximum and minimum bus voltage constraints, line capacity constraints, and / or bus power constraints.

[0117] In some alternative implementations, the constraint construction module 202 includes:

[0118] A wind power constraint construction unit is used to acquire historical wind speed data of renewable energy bases, estimate the power output data of wind farms at various times throughout the year; construct wind power constraints based on the power output data of wind farms at various times throughout the year; and / or,

[0119] The photovoltaic power generation constraint construction unit is used to acquire historical sunshine and temperature data of renewable energy bases, estimate the output data of photovoltaic power plants at various times throughout the year, and construct photovoltaic power generation constraints based on the output data of photovoltaic power plants at various times throughout the year.

[0120] In some alternative implementations, system planning constraints also include renewable energy utilization constraints and DC channel cost recovery constraints.

[0121] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0122] In this embodiment, the power supply curve determination device is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0123] This invention also provides a computer device having the above-described features. Figure 2 The device shown is for determining the power transmission curve.

[0124] Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 3 As shown, this computer device can be used as a power transmission control device for a renewable energy base, including: one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other using different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 3 Take a processor 10 as an example.

[0125] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0126] The memory 20 stores instructions executable by at least one processor 10 to cause at least one processor 10 to perform the method shown in the above embodiments.

[0127] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0128] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0129] The computer device also includes an input device 30 and an output device 40. The processor 10, memory 20, input device 30, and output device 40 can be connected via a bus or other means. Figure 3 Taking the example of a connection between China and Israel via a bus.

[0130] Input device 30 can receive input numerical or character information, and generate key signal inputs related to user settings and function control of the computer device, such as a touchscreen, keypad, mouse, trackpad, touchpad, joystick, one or more mouse buttons, trackball, joystick, etc. Output device 40 may include display devices, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors). The aforementioned display devices include, but are not limited to, liquid crystal displays, light-emitting diodes, displays, and plasma displays. In some alternative embodiments, the display device may be a touchscreen.

[0131] The computer device also includes a communication interface for communicating with other devices or communication networks.

[0132] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0133] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0134] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for determining a power transmission curve, characterized in that, The method includes: The objective function for constructing the operation of a renewable energy base is to maximize revenue. The objective function includes revenue from electricity sales, coal-fired power generation costs, local grid interaction costs, wind farm operation and maintenance costs, photovoltaic power plant operation and maintenance costs, and energy storage operation and maintenance costs. The operating constraints of the renewable energy base shall be established, including carbon emission constraints. Based on the objective function and the constraints, the power transmission curve is obtained; The operational constraints include network power flow constraints, electricity sales constraints, coal-fired power unit operation constraints, wind power generation constraints, photovoltaic power generation constraints, energy storage operation constraints, local power grid interaction constraints, and system planning constraints, including the carbon emission constraints. The electricity sales constraints include peak load during special periods being greater than or equal to peak load during daily peak periods, active power curves of DC channels of renewable energy bases being consistent throughout the year, and the sum of the number of power step jumps before and after each day not exceeding a limit.

2. The method according to claim 1, characterized in that, The costs of coal-fired power generation include: carbon emission costs, fuel costs, pollutant treatment costs, and unit start-up and shutdown costs.

3. The method according to claim 1, characterized in that, The local power grid interaction cost includes the cost of purchasing electricity and the cost of selling electricity.

4. The method according to claim 1, characterized in that, The network power flow constraints include maximum and minimum bus voltage constraints, line capacity constraints, and / or bus power constraints.

5. The method according to claim 1, characterized in that, The operational constraints for constructing the renewable energy base include: Obtain historical wind speed data from the renewable energy base to estimate the power output data of the wind farm at various times throughout the year; construct wind power generation constraints based on the power output data of the wind farm at various times throughout the year; and / or, Historical sunshine and temperature data of the renewable energy base are obtained to estimate the output data of the photovoltaic power plant at various times throughout the year; based on the output data of the photovoltaic power plant at various times throughout the year, photovoltaic power generation constraints are constructed.

6. The method according to claim 1, characterized in that, The system planning constraints also include renewable energy utilization constraints and DC channel cost recovery constraints.

7. A power transmission curve optimization device, characterized in that, The device includes: The objective function construction module is used to construct the operational objective function of the renewable energy base. The objective function aims to maximize revenue and includes electricity sales revenue, coal-fired power generation cost, local grid interaction cost, wind farm operation and maintenance cost, photovoltaic power plant operation and maintenance cost, and energy storage operation and maintenance cost. A constraint construction module is used to construct the operational constraints of the renewable energy base, including carbon emission constraints. The acquisition module is used to obtain the power transmission curve based on the objective function and the constraints. The operational constraints include network power flow constraints, electricity sales constraints, coal-fired power unit operation constraints, wind power generation constraints, photovoltaic power generation constraints, energy storage operation constraints, local power grid interaction constraints, and system planning constraints, including the carbon emission constraints. The electricity sales constraints include peak load during special periods being greater than or equal to peak load during daily peak periods, active power curves of DC channels of renewable energy bases being consistent throughout the year, and the sum of the number of power step jumps before and after each day not exceeding a limit.

8. A computer device, characterized in that, include: A memory and a processor are communicatively connected, the memory stores computer instructions, and the processor executes the power supply curve determination method according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the power supply curve determination method according to any one of claims 1 to 6.

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