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

Abstract

The invention relates to the technical field of power system planning, and discloses a power transmission curve determination method, device and equipment and a storage medium, and the method comprises the steps: constructing an operation objective function of a renewable energy base, and enabling the objective function to achieve the maximum revenue as a target; the objective function comprises electricity selling income, coal-fired power generation cost, local power grid interaction cost, wind power plant operation and maintenance cost, photovoltaic electric field operation and maintenance cost and energy storage operation and maintenance cost; constructing an operation constraint condition of the renewable energy base, wherein the operation constraint condition comprises a carbon emission constraint; and obtaining a power transmission curve based on the operation objective function and the operation constraint condition. According to the invention, low-carbon economic operation of the renewable energy base is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of power system planning, and in particular to a method, device, equipment and storage medium for determining a power transmission curve. Background Art

[0002] The transmission curve of renewable energy (i.e. new energy) bases is a curve that describes the changing pattern of renewable energy bases (such as wind power, photovoltaic power, etc.) transmitting electricity to the power grid in different time periods. The determination of this curve involves multiple factors, including the installed capacity, output characteristics, DC rated power, and DC utilization hours of renewable energy. At the same time, it is also necessary to comprehensively consider the balance between power supply and renewable energy consumption.

[0003] In the related art, the method for formulating the power transmission curve includes:

[0004] Full-time simulation analysis: Based on the installed capacity and output characteristics of renewable energy, a 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] Establish a mathematical optimization model: Taking into account the factors of power supply and renewable energy consumption, and based on the data of renewable energy installed capacity, output characteristics, DC rated power, DC utilization hours, etc., a mathematical optimization model with the goal of minimizing abandoned power and power shortage is established. By solving this model, the optimal power transmission curve can be obtained.

[0006] Comparison of multiple schemes: When formulating the transmission curve, it may be necessary to consider multiple schemes and conduct comparative analysis. The optimal transmission curve scheme can be selected by comparing the economic, technical feasibility, reliability and other indicators of different schemes.

[0007] However, the power transmission curve developed in the relevant technology cannot meet the requirements of economy and environmental protection. Summary of the invention

[0008] In view of this, the present invention provides a method, device, equipment and storage medium for determining a power transmission curve to solve the problem that the power transmission curve of a renewable energy base is 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] Construct the operation objective function of the renewable energy base. The objective function aims to maximize the benefits. The objective function includes electricity sales revenue, coal-fired power generation costs, local grid interaction costs, wind farm operation and maintenance costs, photovoltaic farm operation and maintenance costs, and energy storage operation and maintenance costs.

[0011] Constructing the operating constraints of the renewable energy base, including carbon emission constraints;

[0012] Based on the operation objective function and operation constraints, the power transmission curve is obtained.

[0013] In an optional embodiment, the cost of coal-fired power generation includes: carbon emission cost, fuel cost, pollutant control cost and unit start-up and shutdown cost.

[0014] In an optional implementation, the local grid interaction cost includes electricity purchase cost and electricity sales cost.

[0015] In an optional implementation, the operating constraints include network flow constraints, power sales constraints, coal-fired unit operation constraints, wind power generation constraints, photovoltaic power generation constraints, energy storage operation constraints, local power grid interaction constraints and system planning constraints, and the system planning constraints include carbon emission constraints.

[0016] In an optional embodiment, the network power flow constraints include maximum and minimum constraints on bus voltage, line capacity constraints and / or bus power constraints.

[0017] In an optional implementation, the operating constraints for constructing a renewable energy base include:

[0018] Obtain historical wind speed data of renewable energy bases and estimate output data of wind farms at different time periods in a year; construct wind power generation constraints based on the output data of wind farms at different time periods in a year; and / or,

[0019] Obtain historical sunlight data and historical temperature data of renewable energy bases, and estimate the output data of photovoltaic power fields at various time periods within a year; construct photovoltaic power generation constraints based on the output data of photovoltaic power fields at various time periods within a year.

[0020] In an optional implementation, the 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 operation objective function of the renewable energy base. The objective function aims to maximize the benefits. The objective function includes electricity sales revenue, coal-fired power generation costs, local grid interaction costs, wind farm operation and maintenance costs, photovoltaic power farm operation and maintenance costs, and energy storage operation and maintenance costs.

[0023] A constraint building module is used to build the operating constraints of the renewable energy base, and the operating constraints include carbon emission constraints;

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

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

[0026] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the power transmission curve optimization method of the first aspect or any corresponding embodiment thereof.

[0027] In a fifth aspect, the present invention provides a computer program product, comprising computer instructions, wherein the computer instructions are used to enable a computer to execute the power transmission curve optimization method of the first aspect or any corresponding embodiment thereof.

[0028] The power transmission curve determination method, device, equipment and storage medium provided in the embodiments of the present invention provide a power transmission curve determination method, which establishes an objective function with the goal of maximizing the profit of a renewable energy base, and establishes constraints including carbon emission constraints, and then determines the power transmission curve of the base based on the established objective function and constraints, thereby realizing low-carbon economic operation of the renewable energy base.

[0029] The power transmission curve determination method, device, equipment and storage medium provided in the embodiments of the present invention construct an optimization objective function for the power transmission curve under the low-carbon economic operation of the renewable energy base based on the renewable energy output with hourly granularity, take the carbon emission costs of coal-fired units and external supporting electricity into account, and construct optimization constraints for the power transmission curve under the low-carbon economic operation of the renewable energy base, consider the real-time carbon emission constraints of the DC channel and the overall benefit constraints of the project, and generate an optimization curve for power transmission from the renewable energy base as a reference when signing medium- and long-term contracts for actual power transactions. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the related technologies, the drawings required for use in the specific embodiments or the related technical descriptions will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0031] Figure 1 is a schematic flow chart of a method for determining a power transmission curve according to an embodiment of the present invention;

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

[0033] Figure 3 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0035] Renewable energy base refers to an energy production, storage and transmission base built using clean and renewable energy technologies, including solar energy, wind energy, and may also include hydropower and biomass energy. The output of these energy sources, especially wind energy and solar energy, is highly random and volatile, so the base is usually equipped with a certain number of coal-fired units (also known as coal-fired power units) and energy storage power stations to play a role in peak load regulation for renewable energy and channel regulation, while providing a certain peak effect for the evening peak at the receiving end, thereby increasing the renewable energy consumption rate and allowing the electricity transmitted by the transmission channel to meet the power supply needs of the receiving end load as much as possible. However, as the free carbon quota in the carbon market is tightened year by year and the price of carbon quotas increases year by year, the carbon emission cost of renewable energy bases will be significantly raised. Therefore, it is necessary to optimize the DC transmission curve of the transmission channel of renewable energy bases from the perspective of low-carbon economic operation.

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

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

[0038] In this embodiment, a method for determining a power transmission curve is provided, which can be used for power transmission control equipment of a renewable energy (ie, new energy) base. Figure 1 is a flow chart of a method for determining a power transmission curve according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0039] Step S101, constructing an operation objective function of a renewable energy base, wherein the objective function aims at maximizing revenue, and includes revenue from electricity sales, coal-fired power generation costs, local grid interaction costs, wind farm operation and maintenance costs, photovoltaic farm operation and maintenance costs, and energy storage operation and maintenance costs.

[0040] Among them, the local grid interaction cost includes the electricity purchase cost and the electricity sales cost. The electricity purchase cost refers to the cost of renewable energy bases purchasing electricity from the local grid, and the electricity sales cost refers to the income from selling electricity to the local grid. When the renewable energy output is insufficient, the renewable energy base can purchase electricity from the local grid, that is, use external electricity to support the power transmission requirements, and when there is surplus renewable energy, it can sell electricity to the local grid.

[0041] Step S102: constructing operating constraints of the renewable energy base, wherein the operating constraints include carbon emission constraints, which are limits on carbon emissions.

[0042] Step S103, based on the operation objective function and the operation constraint conditions, a power transmission curve is obtained. Specifically, the power transmission curve can be obtained by solving calculations.

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

[0044] In this embodiment, a method for determining a power transmission curve is provided, which can be used for a power transmission control device of a renewable energy base. The method comprises the following steps:

[0045] Step S201, constructing an operation objective function of the renewable energy base, wherein the objective function aims at maximizing the profit, and the objective function includes the profit from electricity sales, the cost of coal-fired power generation, the cost of local grid interaction, the operation and maintenance cost of wind farms, the operation and maintenance cost of photovoltaic farms, and the operation and maintenance cost of energy storage.

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

[0047] The electricity sales revenue R is the electricity sales revenue of the renewable energy base through the DC channel, and its calculation formula is: Where P t dc is the DC channel power transmission during period t, β telec,dc is the intraday price during period t.

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

[0049] Specifically, the carbon emission cost C coal_emission It is calculated based on the carbon emission price, the power generation capacity of the coal-fired unit and the carbon emissions per unit of power generation (the actual carbon emissions per unit of power generation minus the benchmark carbon emissions). The carbon emission cost of coal-fired unit i in period t is The calculation formula is: is the power generation power (i.e. active output) of coal-fired unit i in period t, β emission,coal is the carbon emission price, γ real,coal is the actual carbon emission coefficient per unit power generation, γ base,coal It is the baseline of carbon emissions per unit of power generation. Carbon emissions within the baseline do not need to be paid, but carbon emissions exceeding the baseline do need to be paid.

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

[0051] Pollutant control costs C coal_en It is determined based on the comprehensive treatment price of various air pollutants per unit power generation and the power generation per unit time (here it can be t period, specifically 1 hour). The pollutant treatment cost of coal-fired unit i in period t is The calculation formula is: β en,coal To provide comprehensive treatment prices for various air pollutants, is the power generation capacity of coal-fired unit i during period t.

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

[0053] Coal-fired power generation cost C coal The calculation formula is: Among them, Ω coal is the set of coal-fired units, and T is the annual hours.

[0054] In some embodiments, the local 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, is the electricity purchase price (per unit power) from the local power grid during period t, P t grid is the power purchased by the renewable energy base from the local power grid during period t; the power selling cost C grid_sell The calculation formula is: in, is the electricity price sold to the local power grid during period t. The local power grid interaction cost C grid The calculation formula is:

[0055] In addition, the wind farm operation and maintenance cost C WT It can be calculated according to the following formula: in, is the active power of wind farm i, β WT is the operating cost coefficient of wind power, Ω WT Assemble for wind farm.

[0056] Photovoltaic power plant operation and maintenance cost C PV It can be calculated according to the following formula: in, is the active power of photovoltaic field (also called photovoltaic power station) i, ​​β PV is the photovoltaic operation cost coefficient, Ω PV The photovoltaic field collection.

[0057] Energy storage operation and maintenance cost C bat It can be calculated according to the following formula: in, is the discharge power and charging power of energy storage station i, β bat is the operating cost coefficient of energy storage, Ω bat Energy storage power station can use electrochemical energy storage.

[0058] Step S202: constructing operation constraints of the renewable energy base, where the operation constraints include carbon emission constraints.

[0059] In some optional specific implementations, the operating constraints include network flow constraints, electricity sales constraints, coal-fired unit (also known as thermal 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, and the system planning constraints include carbon emission constraints.

[0060] Specifically, the network flow constraints include the maximum and minimum constraints of bus voltage, the capacity constraints of the line and / or the bus power constraints. The bus includes the bus connected to the line, wind farm, photovoltaic farm, energy storage power station, DC channel and coal-fired power plant.

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

[0062] Line capacity is the branch current, Among them, y i0 is the line-to-ground admittance, y ij is the line admittance, is the voltage of bus i and bus j during period t. The capacity of the line is S ij,t The constraint is less than or equal to the rated capacity

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

[0064] Among them, power sales constraints include the constraints on the power transmission curve from the demand side, and can also include the constraints on the power transmission requirements of the power grid. The power sales constraints can specifically include the following:

[0065] 1. Peak requirements during special periods: Specifically, the peak load during special periods can be greater than or equal to the peak load during the peak period of daily load. The formula is: in is the peak load during the daily peak period, Ω peak The peak load period of the day. is the DC power in the i-th period on the j-th day of the k-th month. The peak requirement in a special period is the constraint on the power transmission curve imposed by the power demand side.

[0066] 2. In the 12 months of the year, the active power curve of the DC channel of the renewable energy base is consistent in each month. This is the constraint of the power transmission curve imposed by the power grid transmission requirements. It can be expressed in the formula: in is the DC power in the i-th period on the j-th day of the k-th month.

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

[0068]

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

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

[0071] 1. Output and gradeability constraints.

[0072]

[0073] in, is the minimum value of the adjustment coefficient, is the maximum active power output of coal-fired unit i, is the start / stop status of coal-fired unit i in period t, which is a Boolean variable. is the power generation capacity of coal-fired unit i in period t, is the climbing rate, that is, the output power this time can be between the minimum output and the rated value. is the reactive power of coal-fired unit i in period t, is the rated reactive power of coal-fired unit i in a period of time.

[0074] 2. Start and stop constraints.

[0075]

[0076] Among them, T on , T off is the minimum daily operating time and minimum daily shutdown time of coal-fired unit i.

[0077] The formula for wind power generation constraints is expressed as: in, is the actual output of wind farm i (after optimized scheduling) during period t, is the available output of wind farm i in period t, is the reactive power of wind power generation in wind farm i during period t, is the wind power reactive power rating of wind farm i in a period of time.

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

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

[0080]

[0081] in, is the output of energy storage station i during period t, is the output rating of energy storage power station i, is the discharge power of energy storage station i during period t, is the charging power of energy storage station i during period t, is a Boolean variable, is the capacity of energy storage station i in period t, is the initial capacity of energy storage station i (can be 0), is the rated capacity of energy storage station i, The charging efficiency of the energy storage station i, is the discharge efficiency of energy storage station i, is the reactive power of energy storage station i during period t, is the reactive power rating of energy storage station i.

[0082] The local grid interaction constraint, that is, the local grid interconnection line power constraint, is expressed as follows:

[0083]

[0084] Among them, P tgrid is the power purchased by the renewable energy base from the local power grid during period t, is the rated active power, is the reactive power, is the rated reactive power.

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

[0086]

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

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

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

[0090]

[0091] in, is the actual output value of wind farm i (after optimized scheduling) in period t, is the actual output value of photovoltaic field i (after optimized scheduling) in period t, and θ is the utilization rate of renewable energy.

[0092] The formula for the DC channel cost recovery constraint (also known as the renewable energy base reasonable income constraint) is:

[0093]

[0094] ff invest ≥π(f invest -f+R)

[0095] in, is the DC power in the i-th period of the j-th day of the k-th month, It is the rated power of the DC channel during a period of time. invest is the annual equivalent investment cost of the renewable energy base (excluding the income from equipment decommissioning and recovery), π is the expected investment return rate, and R is the income from electricity sales.

[0096] In some optional specific implementations, step S202, i.e., constructing the operating constraints of the renewable energy base, includes:

[0097] Step S2021, obtaining historical wind speed data of the renewable energy base, and estimating the output data of the wind farm in each period of a year; the period here can be hours.

[0098] Step S2022: construct wind power generation constraints based on the output data of the wind farm in each period of a year.

[0099] Specifically, the wind farm can be evenly divided into multiple areas, and a wind measuring device is set up in each area to measure the wind speed data at each time period within a year. If the wind speed data for many years is measured, the average value is calculated as the historical wind speed data of the area. Then, the wind power generation project output model in the relevant technology can be used to calculate the wind power output data of each area at each time period, and finally the wind power output data of each area at each time period are summed to obtain the wind power output data of each time period of the wind farm.

[0100] It is also possible to directly obtain the historical wind speed data of a wind farm without dividing the wind farm into regions, and then use the wind power generation project output model to calculate the wind power output data of the wind farm in various periods of a year.

[0101] In some other optional specific implementations, step S202, i.e., constructing the operating constraints of the renewable energy base, includes:

[0102] Step S2023, obtaining historical illumination data and historical temperature data of the renewable energy base, and estimating output data of the photovoltaic power field in each period of a year; the period here may be hours.

[0103] Step S2024, constructing photovoltaic power generation constraints based on the output data of the photovoltaic power field in each period of a year.

[0104] Specifically, the photovoltaic electric field can be evenly divided into multiple areas, and a device for measuring light and temperature is set in each area to measure the light and temperature data at each time period in a year. If the light and temperature data for many years are measured, the average value is calculated as the historical light and temperature data of the area. Then, the photovoltaic power generation engineering output model in the relevant technology can be used to calculate the photovoltaic power generation output data of each area at each time period, and finally the photovoltaic power generation output data of each area at each time period are summed to obtain the photovoltaic power generation output data of each time period of the photovoltaic electric field.

[0105] It is also possible to directly obtain the historical light and temperature data of a photovoltaic field without dividing the photovoltaic field into regions, and then use the photovoltaic power generation project output model to calculate the photovoltaic power generation output data of the photovoltaic field in various periods of a year.

[0106] Step S203, obtaining a power transmission curve based on the operation objective function and the operation constraint conditions.

[0107] This embodiment provides a method for optimizing the transmission curve of low-carbon economic operation of a renewable energy base, focusing on the potential impact of factors such as carbon emission policies, user demand and reasonable base income on the economic operation of the renewable energy base, so that the optimization result can better adapt to the carbon emission requirements of the power industry, and is closer to the actual scenario of signing medium- and long-term electricity contracts, providing a reference for the signing of actual medium- and long-term electricity contracts.

[0108] In this embodiment, a power transmission curve optimization device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. 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, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

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

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

[0111] A constraint condition building module 202 is used to build operating constraint conditions of the renewable energy base, where the operating constraint conditions include carbon emission constraints;

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

[0113] In some optional embodiments, the cost of coal-fired power generation includes: carbon emission costs, fuel costs, pollutant control costs and unit start-up and shutdown costs.

[0114] In some optional embodiments, the local grid interaction cost includes electricity purchase cost and electricity sales cost.

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

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

[0117] In some optional implementations, the constraint condition building module 202 includes:

[0118] A wind power generation constraint building unit is used to obtain historical wind speed data of renewable energy bases, estimate output data of wind farms at various periods of a year; build wind power generation constraints based on the output data of wind farms at various periods of a year; and / or,

[0119] The photovoltaic power generation constraint construction unit is used to obtain the historical light data and historical temperature data of the renewable energy base, estimate the output data of the photovoltaic field in each period of a year; and construct the photovoltaic power generation constraints based on the output data of the photovoltaic field in each period of a year.

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

[0121] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

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

[0123] The embodiment of the present invention also provides a computer device having the above Figure 2 The power transmission curve determination device shown.

[0124] See also Figure 3 , Figure 3 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 3 As shown, the computer device can be used as a power transmission control device for a renewable energy base, including: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components are connected to each other using different buses for communication, and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed in the computer device, including instructions stored in or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 3 A processor 10 is taken as an example.

[0125] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0126] The memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiment.

[0127] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0128] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a 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, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Figure 3 The example of connecting through bus is taken in the following.

[0130] The input device 30 can receive input digital or character information, and generate key signal input related to the user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator bar, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display and a plasma display. In some optional embodiments, the display device can be a touch screen.

[0131] The computer device also includes a communication interface, which is used for the computer device to communicate with other devices or a communication network.

[0132] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0133] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

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

Claims

1. A method for determining a power transmission curve, characterized in that: The method comprises: Constructing an operation objective function of a renewable energy base, wherein the objective function aims at maximizing revenue, and the objective function includes revenue from electricity sales, coal-fired power generation costs, local grid interaction costs, wind farm operation and maintenance costs, photovoltaic farm operation and maintenance costs, and energy storage operation and maintenance costs; Establishing operating constraints of the renewable energy base, wherein the operating constraints include carbon emission constraints; Based on the operation objective function and the operation constraint condition, a power transmission curve is obtained.

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

3. The method according to claim 1, characterized in that: The local grid interaction cost includes electricity purchase cost and electricity sales cost.

4. The method according to claim 1, characterized in that: The operating constraints include network flow constraints, electricity sales constraints, coal-fired unit operation constraints, wind power generation constraints, photovoltaic power generation constraints, energy storage operation constraints, local power grid interaction constraints and system planning constraints, and the system planning constraints include the carbon emission constraints.

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

6. The method according to claim 1 or 4, characterized in that: The operation constraints for constructing the renewable energy base include: Obtaining historical wind speed data of the renewable energy base, estimating output data of the wind farm at various time periods within a year; constructing wind power generation constraints based on the output data of the wind farm at various time periods within a year; and / or, The historical illumination data and the historical temperature data of the renewable energy base are obtained to estimate the output data of the photovoltaic power field in each period of a year; and the photovoltaic power generation constraints are constructed according to the output data of the photovoltaic power field in each period of a year.

7. The method according to claim 4, characterized in that The system planning constraints also include renewable energy utilization constraints and DC channel cost recovery constraints.

8. A power transmission curve optimization device, characterized in that: The device comprises: An objective function construction module is used to construct an operation objective function of a renewable energy base, wherein the objective function aims at maximizing revenue, and the objective function includes revenue from electricity sales, cost of coal-fired power generation, cost of local grid interaction, operation and maintenance cost of a wind farm, operation and maintenance cost of a photovoltaic farm, and operation and maintenance cost of energy storage; A constraint condition building module, used to build operating constraint conditions of the renewable energy base, wherein the operating constraint conditions include carbon emission constraints; The acquisition module is used to obtain a power transmission curve based on the operation objective function and the operation constraint condition.

9. A computer device, characterized in that: include: 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 transmission curve determination method according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the power transmission curve determination method according to any one of claims 1 to 7.

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