A method and device for optimizing the configuration of wind, solar, thermal and storage capacity for inter-provincial and inter-regional ultra-high voltage transmission channels

Abstract

The present invention provides a method and device for optimizing the configuration of wind, solar, thermal and storage capacity supporting cross-provincial and cross-regional ultra-high voltage transmission channels. The method includes: setting a simulated power transmission curve for the transmission channel throughout the year; generating multiple sets of supporting power source combinations by enumeration method; establishing an inner optimization model; the inner optimization model takes the lowest annual new energy curtailment power as the inner objective function; judging whether the optimal solution of the inner optimization model meets the set target constraints; establishing an outer optimization model; the outer optimization model takes the maximum net profit as the outer objective function. The present invention adopts a two-layer planning optimization algorithm, the outer layer takes the maximum net profit as the goal, and the inner layer takes the lowest annual new energy curtailment power as the goal, and takes into account constraints such as the annual new energy electricity proportion constraint and the new energy consumption and utilization rate constraint, so as to quickly optimize the wind, solar, thermal and storage capacity configuration that meets the needs of transmission channel construction, that is, meets the system power supply reliability requirements and also meets the power supply economy requirements.

Description

Technical Field

[0001] The present invention belongs to the technical field of wind, solar, thermal and storage capacity configuration optimization, and specifically provides a method and device for optimizing the configuration of wind, solar, thermal and storage capacity for inter-provincial and inter-regional ultra-high voltage transmission channels. Background Art

[0002] The supporting wind, solar, thermal and storage capacity configuration of the transmission channel refers to the capacity configuration of supporting wind energy, solar energy, thermal power and energy storage equipment when constructing the transmission channel.

[0003] The configuration of wind, solar, thermal and storage capacity directly affects the power supply reliability and economy of the power system. Effectively optimizing the configuration of wind, solar, thermal and storage capacity can not only improve the reliability of the power system, but also provide a stable energy guarantee for local economic development and promote the sustainable development of the regional economy.

[0004] In the existing technology, when optimizing the configuration of wind, solar, thermal and storage capacity, usually only economic indicators are considered, that is, the optimization goal is to minimize the on-grid electricity price after adding the AC and DC transmission costs. Due to the small number of factors considered and the lack of comprehensive consideration of the impact on power supply reliability, the optimized wind, solar, thermal and storage capacity configuration can no longer meet the needs of transmission channel construction. Summary of the Invention

[0005] In response to the defects of the existing technology, the present invention provides a method and device for optimizing the configuration of wind, solar, thermal and storage capacity for cross-provincial and cross-regional ultra-high voltage transmission channels, which can effectively solve the above problems.

[0006] The technical solution adopted in the present invention is as follows:

[0007] The present invention provides a method for optimizing the configuration of wind, solar, thermal and storage capacity supporting inter-provincial and inter-regional ultra-high voltage transmission channels, comprising:

[0008] According to the output characteristics of renewable energy at the sending end of the transmission channel and the load characteristics of the receiving end, the transmission power P of the transmission channel at each moment t throughout the year is set. TL (t), forming a simulated power transmission curve of the transmission channel throughout the year;

[0009] Generate multiple sets of supporting power source combinations; wherein each set of supporting power source combinations sets the installed capacity scale of wind power source, photovoltaic power source, thermal power source and energy storage power source;

[0010] Traversing each of the supporting power source combinations, and inputting the established inner optimization model into each of the traversed supporting power source combinations; solving the inner optimization model to obtain an optimal solution that satisfies the inner objective function and inner constraints; wherein the inner constraints include constraints on the simulated power transmission curve of the transmission channel throughout the year;

[0011] Determine whether the optimal solution meets the set target constraints, where the target constraints include the annual renewable energy power ratio constraint and the renewable energy consumption utilization rate constraint; if not, it means that the currently traversed supporting power supply combination does not meet the conditions, and continue to traverse the next group of supporting power supply combinations; if satisfied, add the supporting power supply combination and the optimal solution obtained by solving the inner optimization model to the optimal solution element set, and continue to traverse the next group of supporting power supply combinations;

[0012] After the traversal of the supporting power source combination is completed, the established outer optimization model is input; wherein, the outer optimization model takes the maximum net benefit as the outer objective function, traverses each element in the optimal solution element set finally obtained, and obtains the element with the maximum net benefit, that is, the supporting power source combination with the maximum net benefit, as well as the combination of thermal power start-up units, the real-time output of thermal power sources and energy storage power sources, and the wind power and photovoltaic power abandonment power.

[0013] Preferably, the transmission power P of the transmission channel at each moment t throughout the year is set according to the output characteristics of the new energy at the transmission end and the power load characteristics at the receiving end. TL (t), the simulated power transmission curve of the transmission channel throughout the year is formed as follows:

[0014] Combining the output characteristics of renewable energy at the sending end and the load characteristics of electricity at the receiving end, the simulated transmission curve of the transmission channel throughout the year is obtained; the transmission power P at each moment t in the transmission curve is TL (t), meeting the power transmission requirements at each moment t throughout the year and meeting the power transmission target of the transmission channel throughout the year, that is, satisfying the following expression:

[0015]

[0016] Where: P N TL is the rated transmission power of the transmission channel, and H is the equivalent annual utilization hours of the transmission channel.

[0017] Preferably, the generating of multiple sets of matching power supply combinations is specifically as follows:

[0018] Pre-set the optimized range of wind power installed capacity, photovoltaic power installed capacity, thermal power installed capacity and energy storage power installed capacity;

[0019] In the optimized range of wind power source installed capacity, according to the set wind power source installed capacity increment, multiple incremental wind power source installed capacity scales are generated to form a wind power source installed capacity scale set;

[0020] In the optimized range of photovoltaic power installed capacity, according to the set photovoltaic power installed capacity increment, multiple incremental photovoltaic power installed capacity scales are generated to form a photovoltaic power installed capacity scale set;

[0021] Within the optimization range of thermal power source installed capacity, according to the set thermal power source installed capacity increment, multiple incremental thermal power source installed capacity scales are generated to form a thermal power source installed capacity scale set;

[0022] Within the energy storage power supply installed capacity optimization range, according to the set energy storage power supply installed capacity increment, multiple incremental energy storage power supply installed capacity scales are generated to form an energy storage power supply installed capacity scale set;

[0023] Based on the rated transmission power P of the transmission channel N TL And the constraints of the equivalent annual utilization hours H of the transmission channel, traverse the wind power installed capacity set, photovoltaic power installed capacity set, thermal power installed capacity set and energy storage power installed capacity set, thus forming multiple groups of supporting power combinations.

[0024] Preferably, the inner optimization model takes the lowest annual new energy curtailment power as the inner objective function, wherein the annual new energy curtailment power is the sum of the curtailment power of wind power and photovoltaic power throughout the year. Based on the traversed supporting power supply combination, the combination of thermal power start-up units, thermal power and energy storage power real-time output are used as optimization variables, the real-time output of wind power is set as the theoretical output of wind power minus the wind power curtailment power, and the real-time output of photovoltaic power is set as the theoretical output of photovoltaic power minus the photovoltaic curtailment power. Then, the inner constraint conditions are set, including the transmission power constraint of the transmission curve, the upper and lower limit constraints of the thermal power output, and the upper and lower limit constraints of the energy storage power output, to establish the inner optimization model.

[0025] Preferably, the inner objective function f1 of the inner optimization model is expressed as:

[0026]

[0027] in: They represent the abandoned power of wind power source at time t and the abandoned power of photovoltaic power source at time t respectively;

[0028] The inner constraints include:

[0029] Inner constraint condition 1: Equality constraint of the power transmission curve, i.e. expression (3):

[0030]

[0031] in:

[0032] P TL(t) is the power transmission power of the power transmission curve at time t;

[0033] They represent the real-time output of wind power, photovoltaic power, thermal power and energy storage power at time t respectively;

[0034] P i W (t), They represent the real-time output of the i-th wind power station in the wind power source, the j-th photovoltaic power station in the photovoltaic power source, and the c-th thermal power unit in the thermal power source at time t respectively;

[0035] and They represent the energy storage discharge power and energy storage charging power of the bth energy storage unit in the energy storage power supply at time t respectively;

[0036] W, PV, T, and ESS represent the number of wind power stations in wind power sources, the number of photovoltaic power stations in photovoltaic power sources, the number of thermal power units in operation in thermal power sources, and the number of energy storage units in energy storage power sources, respectively.

[0037] in:

[0038]

[0039] in: and They represent the rated installed capacity of the i-th wind power station in the wind power source and the rated installed capacity of the j-th photovoltaic power station in the photovoltaic power source;

[0040] and They represent the theoretical output simultaneity rate of the i-th wind power station in the wind power source at time t, and the theoretical output simultaneity rate of the j-th photovoltaic power station in the photovoltaic power source at time t;

[0041] Inner constraint 2: Real-time output of thermal power source at time t Not higher than the rated installed capacity of thermal power sources, and not lower than the minimum technical output of thermal power sources;

[0042] Inner constraint three: real-time output of energy storage power supply at time t It is between the theoretical maximum output and the theoretical minimum output of the energy storage power supply.

[0043] Preferably, the target constraints include:

[0044] Target constraint condition 1: The proportion of renewable energy electricity in the transmission channel must satisfy expression (6):

[0045]

[0046] Where: μ represents the minimum proportion of renewable energy electricity in the transmission channel;

[0047] Target constraint condition 2: New energy consumption and utilization rate constraint, that is, satisfying expression (7):

[0048]

[0049] Where: η represents the set minimum value of new energy consumption and utilization rate.

[0050] Preferably, the expression of the outer optimization model is:

[0051] f2=max F in -minF inv (8)

[0052] Among them: f2 is the outer objective function; F inv 、F in They are the annualized investment cost and annualized operating income respectively.

[0053] Preferably, the annualized investment cost F inv , refers to the total investment converted to the average annual investment at a discount rate; among them, the total investment of various types of power sources such as wind, solar, thermal and storage is proportional to the installed capacity, and the transmission cost is a fixed investment; the annualized investment cost F inv The expression of is as follows:

[0054] F inv =C inv,W +C inv,PV +C inv,T +C inv,ESS +C inv,L (9)

[0055] in:

[0056] C inv,W 、C inv,PV 、C inv,T 、C inv,ESS 、C inv,L Represent the annualized investment costs of wind power, photovoltaic power, thermal power, energy storage power and transmission lines, respectively, and are determined by the total investment C inv,total , where I is the discount rate and T is the investment payback period:

[0057]

[0058] Preferably, the annualized operating income F in According to the transmission power P of the transmission channel at each moment t TL (t) is multiplied by the real-time electricity price C(t) for settlement, and the expression is as follows:

[0059]

[0060] Among them: the real-time electricity price C(t) takes three modes, namely traditional fixed electricity price, segmented electricity price and spot market time-of-use settlement electricity price.

[0061] The present invention also provides a device for optimizing the configuration of wind, solar, thermal and storage capacity for supporting inter-provincial and inter-regional ultra-high voltage transmission channels, comprising:

[0062] The module for forming the transmission curve of the transmission channel throughout the year is used to set the transmission power P of the transmission channel at each moment t throughout the year according to the output characteristics of renewable energy at the sending end of the transmission channel and the power load characteristics at the receiving end. TL (t), forming a simulated power transmission curve of the transmission channel throughout the year;

[0063] A supporting power source combination generation module is used to generate multiple groups of supporting power source combinations; wherein each group of the supporting power source combination sets the installed capacity scale of wind power source, photovoltaic power source, thermal power source and energy storage power source;

[0064] A supporting power combination processing module is used to traverse each of the supporting power combinations in the supporting power combinations, and input the established inner layer optimization model for the traversed supporting power combinations;

[0065] An inner layer optimization model establishment module, used for establishing the inner layer optimization model;

[0066] An inner optimization model solving module, configured to solve the inner optimization model to obtain an optimal solution that satisfies the inner objective function and inner constraints; wherein the inner constraints include constraints on the simulated power transmission curve of the transmission channel throughout the year;

[0067] An optimal solution judgment module is used to judge whether the optimal solution meets the set target constraints, wherein the target constraints include the annual renewable energy power proportion constraint and the renewable energy consumption utilization rate constraint; if not, it means that the currently traversed supporting power supply combination does not meet the conditions, and the next group of supporting power supply combinations is traversed; if it meets the conditions, the supporting power supply combination and the optimal solution obtained by solving the inner optimization model are added to the optimal solution element set, and the next group of supporting power supply combinations is traversed;

[0068] An outer layer optimization model establishment module, used for establishing the outer layer optimization model;

[0069] The outer optimization model solving module is used to input the established outer optimization model after the traversal of the supporting power source combination is completed; wherein, the outer optimization model takes the maximum net profit as the outer objective function, traverses each element in the optimal solution element set finally obtained, and obtains the element with the maximum net profit, that is, obtains the supporting power source combination with the maximum net profit, as well as the combination of thermal power start-up units, the real-time output of thermal power source and energy storage power source, and the wind power and photovoltaic power abandonment power.

[0070] Preferably, the transmission channel annual simulated power transmission curve forming module is specifically used to:

[0071] Combining the output characteristics of renewable energy at the sending end and the load characteristics of electricity at the receiving end, the simulated transmission curve of the transmission channel throughout the year is obtained; the transmission power P at each moment t in the transmission curve is TL (t), meeting the power transmission requirements at each moment t throughout the year and meeting the power transmission target of the transmission channel throughout the year, that is, satisfying the following expression:

[0072]

[0073] Where: P N TL is the rated transmission power of the transmission channel, and H is the equivalent annual utilization hours of the transmission channel.

[0074] Preferably, the supporting power supply combination generation module includes:

[0075] The optimization range setting submodule is used to pre-set the optimization range of wind power installed capacity, photovoltaic power installed capacity, thermal power installed capacity and energy storage power installed capacity;

[0076] A wind power source installed capacity scale set generation submodule is used to generate multiple incremental wind power source installed capacity scales according to a set wind power source installed capacity scale increment within the wind power source installed capacity scale optimization range to form a wind power source installed capacity scale set;

[0077] The photovoltaic power source installed capacity scale set generation submodule is used to generate multiple incremental photovoltaic power source installed capacity scales according to the set photovoltaic power source installed capacity scale increment within the photovoltaic power source installed capacity scale optimization range to form a photovoltaic power source installed capacity scale set;

[0078] A thermal power source installed capacity scale set generation submodule is used to generate multiple incremental thermal power source installed capacity scales according to a set thermal power source installed capacity scale increment within the thermal power source installed capacity scale optimization range to form a thermal power source installed capacity scale set;

[0079] The energy storage power supply installed capacity set generation submodule is used to generate multiple incremental energy storage power supply installed capacity scales according to the set energy storage power supply installed capacity scale increment within the energy storage power supply installed capacity optimization range to form an energy storage power supply installed capacity set;

[0080] Multiple groups of matching power supply combination generation submodules are used to generate power based on the rated transmission power P of the transmission channel. N TL And the constraints of the equivalent annual utilization hours H of the transmission channel, traverse the wind power installed capacity set, photovoltaic power installed capacity set, thermal power installed capacity set and energy storage power installed capacity set, thus forming multiple groups of supporting power combinations.

[0081] Preferably, the inner optimization model takes the lowest annual new energy curtailment power as the inner objective function, wherein the annual new energy curtailment power is the sum of the curtailment power of wind power and photovoltaic power throughout the year. Based on the traversed supporting power supply combination, the combination of thermal power start-up units, thermal power and energy storage power real-time output are used as optimization variables, the real-time output of wind power is set as the theoretical output of wind power minus the wind power curtailment power, and the real-time output of photovoltaic power is set as the theoretical output of photovoltaic power minus the photovoltaic curtailment power. Then, the inner constraint conditions are set, including the transmission power constraint of the transmission curve, the upper and lower limit constraints of the thermal power output, and the upper and lower limit constraints of the energy storage power output, to establish the inner optimization model.

[0082] Preferably, the inner objective function f1 of the inner optimization model is expressed as:

[0083]

[0084] in: They represent the abandoned power of wind power source at time t and the abandoned power of photovoltaic power source at time t respectively;

[0085] The inner constraints include:

[0086] Inner constraint condition 1: Equality constraint of the power transmission curve, i.e. expression (3):

[0087]

[0088] in:

[0089] P TL (t) is the power transmission power of the power transmission curve at time t;

[0090] They represent the real-time output of wind power, photovoltaic power, thermal power and energy storage power at time t respectively;

[0091] P i W (t), They represent the real-time output of the i-th wind power station in the wind power source, the j-th photovoltaic power station in the photovoltaic power source, and the c-th thermal power unit in the thermal power source at time t respectively;

[0092] and They represent the energy storage discharge power and energy storage charging power of the bth energy storage unit in the energy storage power supply at time t respectively;

[0093] W, PV, T, and ESS represent the number of wind power stations in wind power sources, the number of photovoltaic power stations in photovoltaic power sources, the number of thermal power units in operation in thermal power sources, and the number of energy storage units in energy storage power sources, respectively.

[0094] in:

[0095]

[0096] in: and They represent the rated installed capacity of the i-th wind power station in the wind power source and the rated installed capacity of the j-th photovoltaic power station in the photovoltaic power source;

[0097] and They represent the theoretical output simultaneity rate of the i-th wind power station in the wind power source at time t, and the theoretical output simultaneity rate of the j-th photovoltaic power station in the photovoltaic power source at time t;

[0098] Inner constraint 2: Real-time output of thermal power source at time t Not higher than the rated installed capacity of thermal power sources, and not lower than the minimum technical output of thermal power sources;

[0099] Inner constraint three: real-time output of energy storage power supply at time t It is between the theoretical maximum output and the theoretical minimum output of the energy storage power supply.

[0100] Preferably, the target constraints include:

[0101] Target constraint condition 1: The proportion of renewable energy electricity in the transmission channel must satisfy expression (6):

[0102]

[0103] Where: μ represents the minimum proportion of renewable energy electricity in the transmission channel;

[0104] Target constraint condition 2: New energy consumption and utilization rate constraint, that is, satisfying expression (7):

[0105]

[0106] Where: η represents the set minimum value of new energy consumption and utilization rate.

[0107] Preferably, the expression of the outer optimization model is:

[0108] f2=max F in -minF inv (8)

[0109] Among them: f2 is the outer objective function; F inv 、F in They are the annualized investment cost and annualized operating income respectively.

[0110] Preferably, the annualized investment cost F inv , refers to the total investment converted to the average annual investment at a discount rate; among them, the total investment of various types of power sources such as wind, solar, thermal and storage is proportional to the installed capacity, and the transmission cost is a fixed investment; the annualized investment cost F inv The expression of is as follows:

[0111] F inv =C inv,W +C inv,PV +C inv,T +C inv,ESS +C inv,L (9)

[0112] in:

[0113] C inv,W 、C inv,PV 、C inv,T 、C inv,ESS 、C inv,L Represent the annualized investment costs of wind power, photovoltaic power, thermal power, energy storage power and transmission lines, respectively, and are determined by the total investment C inv,total , where I is the discount rate and T is the investment payback period:

[0114]

[0115] Preferably, the annualized operating income F in According to the transmission power P of the transmission channel at each moment t TL (t) is multiplied by the real-time electricity price C(t) for settlement, and the expression is as follows:

[0116]

[0117] Among them: the real-time electricity price C(t) takes three modes, namely traditional fixed electricity price, segmented electricity price and spot market time-of-use settlement electricity price.

[0118] The method for optimizing the configuration of wind, solar, thermal and storage capacity for inter-provincial and inter-regional UHV transmission channels provided by the present invention has the following advantages:

[0119] The present invention provides a method for optimizing the configuration of wind, solar, thermal and storage capacity supporting cross-provincial and cross-regional ultra-high voltage transmission channels. The method adopts a two-layer planning optimization algorithm. The outer layer uses the installed capacity scale of various power sources as variables and the maximum net profit as the goal. The inner layer uses the combination of thermal power units, the real-time output of thermal power sources and energy storage power sources as optimization variables and the lowest annual new energy curtailment power as the goal. It also takes into account constraints such as the proportion of new energy electricity throughout the year and the utilization rate of new energy consumption, so as to quickly optimize the wind, solar, thermal and storage capacity configuration that meets the needs of transmission channel construction, that is, meets the system power supply reliability requirements and also meets the power supply economy requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0120] Figure 1 A flow chart of a method for optimizing wind, solar, thermal and storage capacity configuration for inter-provincial and inter-regional UHV transmission channels provided by the present invention;

[0121] Figure 2 This is a schematic diagram of a transmission curve generated for a transmission channel throughout the year;

[0122] Figure 3 Result plot of a typical week of optimization for the inner optimization model. DETAILED DESCRIPTION

[0123] In order to make the technical problems, technical solutions and beneficial effects solved by the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0124] In order to solve the related problems existing in the existing technology, the present invention provides a method for optimizing the configuration of wind, solar, thermal and storage capacity supporting cross-provincial and cross-regional ultra-high voltage transmission channels. The method adopts a two-layer planning optimization algorithm. The outer layer takes the installed capacity scale of various power sources as variables and maximizes the net profit as the goal. The inner layer takes the combination of thermal power units, the real-time output of thermal power sources and energy storage power sources as optimization variables, and minimizes the power of renewable energy abandoned throughout the year as the goal. It also takes into account constraints such as the proportion of renewable energy electricity throughout the year and the utilization rate of new energy consumption, so as to quickly optimize the wind, solar, thermal and storage capacity configuration that meets the needs of transmission channel construction, that is, meets the system power supply reliability requirements and also meets the power supply economy requirements.

[0125] See Figure 1 The present invention provides a method for optimizing the configuration of wind, solar, thermal and storage capacity for inter-provincial and inter-regional ultra-high voltage transmission channels, comprising the following steps:

[0126] Step S1: According to the output characteristics of renewable energy at the transmission end and the load characteristics of the receiving end, the transmission power P of the transmission channel is set at each time t throughout the year. TL(t), forming a simulated power transmission curve of the transmission channel throughout the year;

[0127] Step S1 is specifically as follows:

[0128] Combining the output characteristics of renewable energy at the sending end and the load characteristics of electricity at the receiving end, the simulated transmission curve of the transmission channel throughout the year is obtained; the transmission power P at each moment t in the transmission curve is TL (t), meeting the power transmission requirements at each moment t throughout the year, distinguishing the power transmission curve requirements in different months and different time periods, and meeting the power transmission target of the transmission channel throughout the year, that is, satisfying the following expression:

[0129]

[0130] Where: P N TL is the rated transmission power of the transmission channel, and H is the equivalent annual utilization hours of the transmission channel, which is generally set to no less than 4500 hours.

[0131] like Figure 2 As shown, this is a schematic diagram of a transmission channel's simulated power transmission curve generated throughout the year.

[0132] Step S2: Generate multiple sets of supporting power source combinations by enumeration method; each set of supporting power source combinations sets the installed capacity scale of wind power source, installed capacity scale of photovoltaic power source, installed capacity scale of thermal power source and installed capacity scale of energy storage power source;

[0133] Step S2 is specifically as follows:

[0134] Pre-set the optimized range of wind power installed capacity, photovoltaic power installed capacity, thermal power installed capacity and energy storage power installed capacity;

[0135] In the optimized range of wind power source installed capacity, according to the set wind power source installed capacity increment, multiple incremental wind power source installed capacity scales are generated to form a wind power source installed capacity scale set;

[0136] In the optimized range of photovoltaic power installed capacity, according to the set photovoltaic power installed capacity increment, multiple incremental photovoltaic power installed capacity scales are generated to form a photovoltaic power installed capacity scale set;

[0137] Within the optimization range of thermal power source installed capacity, according to the set thermal power source installed capacity increment, multiple incremental thermal power source installed capacity scales are generated to form a thermal power source installed capacity scale set;

[0138] Within the energy storage power supply installed capacity optimization range, according to the set energy storage power supply installed capacity increment, multiple incremental energy storage power supply installed capacity scales are generated to form an energy storage power supply installed capacity scale set;

[0139] Based on the rated transmission power P of the transmission channelN TL And the constraints of the equivalent annual utilization hours H of the transmission channel, traverse the wind power installed capacity set, photovoltaic power installed capacity set, thermal power installed capacity set and energy storage power installed capacity set, thus forming multiple groups of supporting power combinations.

[0140] For example:

[0141] The optimized range of wind power installed capacity is between 3 million and 20 million kilowatts, with 500,000 kilowatts as a level, starting from 300, forming a photovoltaic power installed capacity set of: {300, 350, 400, 450, ..., 2000};

[0142] The optimized range of photovoltaic power generation installed capacity is between 3 million and 20 million kilowatts, with 500,000 kilowatts as a level, starting from 300, forming a photovoltaic power generation installed capacity set of: {300, 350, 400, 450, ..., 2000};

[0143] The optimization range of thermal power generation installed capacity is between 2 million and 6 million kilowatts, with 1 million kilowatt as one level, starting from 200, forming a thermal power generation installed capacity set: {200, 300, 400, ..., 600};

[0144] Energy storage power supply can be divided into 10-40% of the new energy installed capacity, with 5% as a level, to form multiple energy storage power supply installed capacity scales, and then form an energy storage power supply installed capacity scale collection;

[0145] For a typical case, the transmission channel has a rated transmission power of 8 million kilowatts and an equivalent annual utilization of 5,159 hours. The selected supporting power source combination is: 4 million kilowatts of wind power, 8.5 million kilowatts of photovoltaic power, six 660,000-kilowatt thermal power units, and 3.125 million kilowatts of energy storage (4 hours).

[0146] By traversing the sets of various power installed capacity, all matching power combinations that meet the rated transmission power of the transmission channel and the equivalent annual utilization hours of the transmission channel are obtained.

[0147] Step S3, traversing each supporting power supply combination in the supporting power supply combination, and executing step S4 for the traversed supporting power supply combination;

[0148] Step S4, establishing an inner optimization model; the inner optimization model takes the lowest annual new energy curtailment power as the inner objective function, wherein the annual new energy curtailment power is the sum of the curtailment power of wind power and photovoltaic power throughout the year, based on the traversed supporting power combination, taking the combination of thermal power startup units, thermal power and energy storage power real-time output (discharge is positive, storage is negative) as optimization variables, setting the real-time output of wind power as the theoretical output of wind power minus the curtailment power of wind power, setting the real-time output of photovoltaic power as the theoretical output of photovoltaic power minus the curtailment power of photovoltaic power, and then setting inner constraint conditions, including the transmission power constraint of the transmission curve, the upper and lower limit constraints of the thermal power output, and the upper and lower limit constraints of the energy storage power output, to establish the inner optimization model;

[0149] By solving the inner optimization model, an optimal solution that satisfies the inner objective function and inner constraints is obtained, that is, the optimal combination of thermal power units, the real-time output of thermal power and energy storage power, and the wind power and photovoltaic power curtailment power is obtained;

[0150] As a specific implementation method, the expression of the inner layer objective function f1 of the inner layer optimization model is:

[0151]

[0152] in: They represent the abandoned power of wind power source at time t and the abandoned power of photovoltaic power source at time t respectively;

[0153] The inner constraints include:

[0154] Inner constraint condition 1: Equality constraint of the power transmission curve, i.e. expression (3):

[0155]

[0156] in:

[0157] P TL (t) is the power transmission power of the power transmission curve at time t;

[0158] They represent the real-time output of wind power, photovoltaic power, thermal power and energy storage power at time t respectively;

[0159] P i W (t), They represent the real-time output of the i-th wind power station in the wind power source, the j-th photovoltaic power station in the photovoltaic power source, and the c-th thermal power unit in the thermal power source at time t respectively;

[0160] and They represent the energy storage discharge power and energy storage charging power of the bth energy storage unit in the energy storage power supply at time t respectively;

[0161] W, PV, T, and ESS represent the number of wind power stations in wind power sources, the number of photovoltaic power stations in photovoltaic power sources, the number of thermal power units in operation in thermal power sources, and the number of energy storage units in energy storage power sources, respectively.

[0162] in:

[0163]

[0164] in: and They represent the rated installed capacity of the i-th wind power station in the wind power source and the rated installed capacity of the j-th photovoltaic power station in the photovoltaic power source;

[0165] and They represent the theoretical output simultaneity rate of the i-th wind power station in the wind power source at time t, and the theoretical output simultaneity rate of the j-th photovoltaic power station in the photovoltaic power source at time t;

[0166] Inner constraint 2: Real-time output of thermal power source at time t Not higher than the rated installed capacity of thermal power sources, and not lower than the minimum technical output of thermal power sources;

[0167] Inner constraint three: real-time output of energy storage power supply at time t It is between the theoretical maximum output and the theoretical minimum output of the energy storage power supply.

[0168] based on Figure 2 The simulated transmission curve of the transmission channel throughout the year and the example of the installed capacity of each power source given in step S2 are given. Through the inner optimization model of the present invention, an optimal combination of thermal power startup units, real-time output of thermal power sources and energy storage sources, and wind power and photovoltaic power curtailment are obtained as follows: Figure 3 shown. Figure 3 Results from a typical week of optimization for the inner optimization model.

[0169] Step S5: Determine whether the optimal solution obtained in step S4 meets the set target constraints, which include the annual renewable energy power ratio constraint and the renewable energy consumption utilization rate constraint. If not, it means that the currently traversed supporting power supply combination does not meet the conditions. Return to step S3 and continue to traverse the next group of supporting power supply combinations. If they do meet the conditions, add the supporting power supply combination and the optimal solution obtained by solving the inner optimization model to the optimal solution element set. Then return to step S3 and continue to traverse the next group of supporting power supply combinations. After the traversal of the supporting power supply combinations is completed, execute step S6.

[0170] As a specific implementation method, the target constraints set include:

[0171] Target constraint condition 1: The proportion of renewable energy electricity in the transmission channel must satisfy expression (6):

[0172]

[0173] Where: μ represents the minimum proportion of renewable energy electricity in the transmission channel, which is generally set to no less than 50%;

[0174] Target constraint condition 2: New energy consumption and utilization rate constraint, that is, satisfying expression (7):

[0175]

[0176] Where: η represents the set minimum value of the new energy consumption and utilization rate, which is generally set at no less than 90%.

[0177] Step S6, establishing an outer optimization model; the outer optimization model takes the maximum net benefit as the outer objective function, traverses each element in the optimal solution element set finally obtained, and obtains the element with the maximum net benefit, that is, obtains the supporting power supply combination with the maximum net benefit, as well as the combination of thermal power start-up units, the real-time output of thermal power sources and energy storage power sources, and the wind power and photovoltaic power abandonment power.

[0178] As a specific implementation method, the expression of the outer optimization model is:

[0179] f2=max F in -minF inv (8)

[0180] Among them: f2 is the outer objective function; F inv 、F in They are the annualized investment cost and annualized operating income respectively.

[0181] The total investment of wind, solar, thermal and storage power sources is proportional to the installed capacity. The transmission cost is a fixed investment. The annualized investment cost F inv The expression of is as follows:

[0182] F inv =C inv,W +C inv,PV +C inv,T +C inv,ESS +C inv,L (9)

[0183] in:

[0184] C inv,W 、Cinv,PV 、C inv,T 、C inv,ESS 、C inv,L Represent the annualized investment costs of wind power, photovoltaic power, thermal power, energy storage power and transmission lines, respectively, and are determined by the total investment C inv,total , where I is the discount rate and T is the investment payback period:

[0185]

[0186] Annual operating income F in According to the transmission power P of the transmission channel at each moment t TL (t) is multiplied by the real-time electricity price C(t) for settlement, and the expression is as follows:

[0187]

[0188] Among them: the real-time electricity price C(t) takes three modes, namely traditional fixed electricity price, segmented electricity price and spot market time-of-use settlement electricity price.

[0189] The present invention provides a method for optimizing the configuration of wind, solar, thermal and storage capacity supporting cross-provincial and cross-regional ultra-high voltage transmission channels. A two-layer planning optimization algorithm is used to optimize the configuration of power supply capacity supporting cross-provincial and cross-regional transmission channels. The outer layer uses the installed capacity scale of various power sources as variables and the maximum net profit as the goal. The inner layer uses the combination of thermal power start-up units, the real-time output of thermal power sources and energy storage power sources as optimization variables and the lowest annual new energy curtailment power as the goal. It also takes into account constraints such as the proportion of new energy electricity throughout the year and the utilization rate of new energy consumption, so as to quickly optimize the wind, solar, thermal and storage capacity configuration that meets the needs of transmission channel construction, that is, meets the system power supply reliability requirements and also meets the power supply economy requirements.

[0190] The present invention also provides a device for optimizing the configuration of wind, solar, thermal and storage capacity for supporting inter-provincial and inter-regional ultra-high voltage transmission channels, comprising:

[0191] The module for forming the transmission curve of the transmission channel throughout the year is used to set the transmission power P of the transmission channel at each moment t throughout the year according to the output characteristics of renewable energy at the sending end of the transmission channel and the power load characteristics at the receiving end. TL (t), forming a simulated power transmission curve of the transmission channel throughout the year;

[0192] The transmission channel annual simulated power transmission curve forming module is specifically used to:

[0193] Combining the output characteristics of renewable energy at the sending end and the load characteristics of electricity at the receiving end, the simulated transmission curve of the transmission channel throughout the year is obtained; the transmission power P at each moment t in the transmission curve is TL (t), meeting the power transmission requirements at each moment t throughout the year and meeting the power transmission target of the transmission channel throughout the year, that is, satisfying the following expression:

[0194]

[0195] Where: P N TL is the rated transmission power of the transmission channel, and H is the equivalent annual utilization hours of the transmission channel.

[0196] A supporting power source combination generation module is used to generate multiple groups of supporting power source combinations; wherein each group of the supporting power source combination sets the installed capacity scale of wind power source, photovoltaic power source, thermal power source and energy storage power source;

[0197] The supporting power supply combination generation module includes:

[0198] The optimization range setting submodule is used to pre-set the optimization range of wind power installed capacity, photovoltaic power installed capacity, thermal power installed capacity and energy storage power installed capacity;

[0199] A wind power source installed capacity scale set generation submodule is used to generate multiple incremental wind power source installed capacity scales according to a set wind power source installed capacity scale increment within the wind power source installed capacity scale optimization range to form a wind power source installed capacity scale set;

[0200] The photovoltaic power source installed capacity scale set generation submodule is used to generate multiple incremental photovoltaic power source installed capacity scales according to the set photovoltaic power source installed capacity scale increment within the photovoltaic power source installed capacity scale optimization range to form a photovoltaic power source installed capacity scale set;

[0201] A thermal power source installed capacity scale set generation submodule is used to generate multiple incremental thermal power source installed capacity scales according to a set thermal power source installed capacity scale increment within the thermal power source installed capacity scale optimization range to form a thermal power source installed capacity scale set;

[0202] The energy storage power supply installed capacity set generation submodule is used to generate multiple incremental energy storage power supply installed capacity scales according to the set energy storage power supply installed capacity scale increment within the energy storage power supply installed capacity optimization range to form an energy storage power supply installed capacity set;

[0203] Multiple groups of matching power supply combination generation submodules are used to generate power based on the rated transmission power P of the transmission channel. N TL And the constraints of the equivalent annual utilization hours H of the transmission channel, traverse the wind power installed capacity set, photovoltaic power installed capacity set, thermal power installed capacity set and energy storage power installed capacity set, thus forming multiple groups of supporting power combinations.

[0204] A supporting power combination processing module is used to traverse each of the supporting power combinations in the supporting power combinations, and input the established inner layer optimization model for the traversed supporting power combinations;

[0205] An inner layer optimization model establishment module, used for establishing the inner layer optimization model;

[0206] The inner optimization model takes the lowest annual new energy curtailment power as the inner objective function, wherein the annual new energy curtailment power is the sum of the curtailment power of wind power and photovoltaic power throughout the year. Based on the traversed supporting power combination, the combination of thermal power start-up units, thermal power and energy storage power real-time output are used as optimization variables, the real-time output of wind power is set as the theoretical output of wind power minus the curtailment power of wind power, and the real-time output of photovoltaic power is set as the theoretical output of photovoltaic power minus the curtailment power of photovoltaic power. Then, the inner constraint conditions are set, including the transmission power constraint of the transmission curve, the upper and lower limit constraints of the thermal power output, and the upper and lower limit constraints of the energy storage power output, to establish the inner optimization model.

[0207] The expression of the inner layer objective function f1 of the inner layer optimization model is:

[0208]

[0209] in: They represent the abandoned power of wind power source at time t and the abandoned power of photovoltaic power source at time t respectively;

[0210] The inner constraints include:

[0211] Inner constraint condition 1: Equality constraint of the power transmission curve, i.e. expression (3):

[0212]

[0213] in:

[0214] P TL (t) is the power transmission power of the power transmission curve at time t;

[0215] They represent the real-time output of wind power, photovoltaic power, thermal power and energy storage power at time t respectively;

[0216] P i W (t), They represent the real-time output of the i-th wind power station in the wind power source, the j-th photovoltaic power station in the photovoltaic power source, and the c-th thermal power unit in the thermal power source at time t respectively;

[0217] and They represent the energy storage discharge power and energy storage charging power of the bth energy storage unit in the energy storage power supply at time t respectively;

[0218] W, PV, T, and ESS represent the number of wind power stations in wind power sources, the number of photovoltaic power stations in photovoltaic power sources, the number of thermal power units in operation in thermal power sources, and the number of energy storage units in energy storage power sources, respectively.

[0219] in:

[0220]

[0221] in: and They represent the rated installed capacity of the i-th wind power station in the wind power source and the rated installed capacity of the j-th photovoltaic power station in the photovoltaic power source;

[0222] and They represent the theoretical output simultaneity rate of the i-th wind power station in the wind power source at time t, and the theoretical output simultaneity rate of the j-th photovoltaic power station in the photovoltaic power source at time t;

[0223] Inner constraint 2: Real-time output of thermal power source at time t Not higher than the rated installed capacity of thermal power sources, and not lower than the minimum technical output of thermal power sources;

[0224] Inner constraint three: real-time output of energy storage power supply at time t It is between the theoretical maximum output and the theoretical minimum output of the energy storage power supply.

[0225] An inner optimization model solving module, configured to solve the inner optimization model to obtain an optimal solution that satisfies the inner objective function and inner constraints; wherein the inner constraints include constraints on the simulated power transmission curve of the transmission channel throughout the year;

[0226] An optimal solution judgment module is used to judge whether the optimal solution meets the set target constraints, wherein the target constraints include the annual renewable energy power proportion constraint and the renewable energy consumption utilization rate constraint; if not, it means that the currently traversed supporting power supply combination does not meet the conditions, and the next group of supporting power supply combinations is traversed; if it meets the conditions, the supporting power supply combination and the optimal solution obtained by solving the inner optimization model are added to the optimal solution element set, and the next group of supporting power supply combinations is traversed;

[0227] Wherein: the target constraint conditions include:

[0228] Target constraint condition 1: The proportion of renewable energy electricity in the transmission channel must satisfy expression (6):

[0229]

[0230] Where: μ represents the minimum proportion of renewable energy electricity in the transmission channel;

[0231] Target constraint condition 2: New energy consumption and utilization rate constraint, that is, satisfying expression (7):

[0232]

[0233] Where: η represents the set minimum value of new energy consumption and utilization rate.

[0234] An outer layer optimization model establishment module, used for establishing the outer layer optimization model;

[0235] The expression of the outer optimization model is:

[0236] f2=maxF in -minF inv (8)

[0237] Among them: f2 is the outer objective function; F inv 、F in They are the annualized investment cost and annualized operating income respectively.

[0238] Annualized investment cost F inv , refers to the total investment converted to the average annual investment at a discount rate; among them, the total investment of various types of power sources such as wind, solar, thermal and storage is proportional to the installed capacity, and the transmission cost is a fixed investment; the annualized investment cost F inv The expression of is as follows:

[0239] F inv =C inv,W +C inv,PV +C inv,T +C inv,ESS +C inv,L (9)

[0240] Where: C inv,W 、C inv,PV 、C inv,T 、C inv,ESS 、C inv,L Represent the annualized investment costs of wind power, photovoltaic power, thermal power, energy storage power and transmission lines, respectively, and are determined by the total investment C inv,total , where I is the discount rate and T is the investment payback period:

[0241]

[0242] Annual operating income F in According to the transmission power P of the transmission channel at each moment t TL(t) is multiplied by the real-time electricity price C(t) for settlement, and the expression is as follows:

[0243]

[0244] Among them: the real-time electricity price C(t) takes three modes, namely traditional fixed electricity price, segmented electricity price and spot market time-of-use settlement electricity price.

[0245] The outer optimization model solving module is used to input the established outer optimization model after the traversal of the supporting power source combination is completed; wherein, the outer optimization model takes the maximum net profit as the outer objective function, traverses each element in the optimal solution element set finally obtained, and obtains the element with the maximum net profit, that is, obtains the supporting power source combination with the maximum net profit, as well as the combination of thermal power start-up units, the real-time output of thermal power source and energy storage power source, and the wind power and photovoltaic power abandonment power.

[0246] This paper proposes, for the first time, a two-tiered optimization method for optimizing the configuration of supporting power supply capacity for UHV transmission channels. The outer layer optimizes the transmission channel's annual simulated transmission curve and the real-time electricity price of operating revenue, enabling a wider range of optimization solutions. The inner layer fully considers constraints such as the transmission channel's transmission power, annual transmission volume, annual renewable energy power share, and renewable energy consumption and utilization rate, resulting in optimization results that are closer to actual operation. Overall, the proposed method better reflects the different electricity values ​​corresponding to different annual simulated transmission curves and can adapt to economic evaluations under different revenue models, including fixed electricity prices, segmented electricity prices, and spot market time-of-use settlement electricity prices.

[0247] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for optimizing the configuration of wind, solar, thermal and storage capacity for inter-provincial and inter-regional ultra-high voltage transmission channels, characterized in that: include: According to the output characteristics of renewable energy at the sending end of the transmission channel and the load characteristics of the receiving end, the transmission power P of the transmission channel at each moment t throughout the year is set. TL (t), forming a simulated power transmission curve of the transmission channel throughout the year; According to the characteristics of the new energy output at the sending end of the transmission channel and the power load characteristics at the receiving end, the transmission power P of the transmission channel is set at each time t throughout the year. TL (t), the simulated power transmission curve of the transmission channel throughout the year is formed as follows: Combining the output characteristics of renewable energy at the sending end and the load characteristics of electricity at the receiving end, the simulated transmission curve of the transmission channel throughout the year is obtained; the transmission power P at each moment t in the transmission curve is TL (t), meeting the power transmission requirements at each moment t throughout the year and meeting the power transmission target of the transmission channel throughout the year, that is, satisfying the following expression: Where: P N TL is the rated transmission power of the transmission channel, H is the equivalent annual utilization hours of the transmission channel; Generate multiple sets of matching power supply combinations; specifically: based on the rated transmission power P of the transmission channel N TL and the constraints of the equivalent annual utilization hours H of the transmission channel, traverse the wind power installed capacity set, photovoltaic power installed capacity set, thermal power installed capacity set and energy storage power installed capacity set, thereby forming multiple sets of supporting power combinations; Traversing each of the supporting power source combinations in the supporting power source combinations, and inputting the established inner optimization model into the traversed supporting power source combinations; by solving the inner optimization model, obtaining an optimal solution that satisfies the inner objective function and inner constraints; wherein the inner constraints include an equality constraint on the power transmission curve; the inner optimization model uses the lowest annual renewable energy curtailment power as the inner objective function, and uses the combination of thermal power units, thermal power sources, and energy storage power sources in real time as optimization variables; Determine whether the optimal solution meets the set target constraints, where the target constraints include the annual renewable energy power ratio constraint and the renewable energy consumption utilization rate constraint; if not, it means that the currently traversed supporting power supply combination does not meet the conditions, and continue to traverse the next group of supporting power supply combinations; if satisfied, add the supporting power supply combination and the optimal solution obtained by solving the inner optimization model to the optimal solution element set, and continue to traverse the next group of supporting power supply combinations; After the traversal of the supporting power source combination is completed, the established outer optimization model is input; wherein, the outer optimization model takes the maximum net benefit as the outer objective function, traverses each element in the optimal solution element set finally obtained, and obtains the element with the maximum net benefit, that is, the supporting power source combination with the maximum net benefit, as well as the combination of thermal power start-up units, the real-time output of thermal power sources and energy storage power sources, and the wind power and photovoltaic power abandonment power.

2. The method for optimizing the configuration of wind, solar, thermal and storage capacity for inter-provincial and inter-regional UHV transmission channels according to claim 1 is characterized in that: The specific steps of generating multiple sets of matching power supply combinations are as follows: Pre-set the optimized range of wind power installed capacity, photovoltaic power installed capacity, thermal power installed capacity and energy storage power installed capacity; In the optimized range of wind power source installed capacity, according to the set wind power source installed capacity increment, multiple incremental wind power source installed capacity scales are generated to form a wind power source installed capacity scale set; In the optimized range of photovoltaic power installed capacity, according to the set photovoltaic power installed capacity increment, multiple incremental photovoltaic power installed capacity scales are generated to form a photovoltaic power installed capacity scale set; Within the optimization range of thermal power source installed capacity, according to the set thermal power source installed capacity increment, multiple incremental thermal power source installed capacity scales are generated to form a thermal power source installed capacity scale set; Within the energy storage power supply installed capacity optimization range, according to the set energy storage power supply installed capacity increment, multiple incremental energy storage power supply installed capacity scales are generated to form an energy storage power supply installed capacity scale set; Based on the rated transmission power P of the transmission channel N TL And the constraints of the equivalent annual utilization hours H of the transmission channel, traverse the wind power installed capacity set, photovoltaic power installed capacity set, thermal power installed capacity set and energy storage power installed capacity set, thus forming multiple groups of supporting power combinations.

3. The method for optimizing the configuration of wind, solar, thermal and storage capacity for supporting inter-provincial and inter-regional UHV transmission channels according to claim 1 is characterized in that: The inner optimization model takes the lowest annual new energy curtailment power as the inner objective function, wherein the annual new energy curtailment power is the sum of the curtailment power of wind power and photovoltaic power throughout the year. Based on the traversed supporting power supply combination, the combination of thermal power start-up units, thermal power and energy storage power real-time output are used as optimization variables, the real-time output of wind power is set as the theoretical output of wind power minus the curtailment power of wind power, and the real-time output of photovoltaic power is set as the theoretical output of photovoltaic power minus the curtailment power of photovoltaic power. Then, the inner constraint conditions are set, including the transmission power constraint of the transmission curve, the upper and lower limit constraints of the thermal power output, and the upper and lower limit constraints of the energy storage power output, to establish the inner optimization model.

4. The method for optimizing the configuration of wind, solar, thermal and storage capacity for inter-provincial and inter-regional UHV transmission channels according to claim 3 is characterized in that: The expression of the inner layer objective function f1 of the inner layer optimization model is: in: They represent the abandoned power of wind power source at time t and the abandoned power of photovoltaic power source at time t respectively; The inner constraints include: Inner constraint condition 1: Equality constraint of the power transmission curve, i.e. expression (3): in: P TL (t) is the power transmission power of the power transmission curve at time t; They represent the real-time output of wind power, photovoltaic power, thermal power and energy storage power at time t respectively; P i W (t), They represent the real-time output of the i-th wind power station in the wind power source, the j-th photovoltaic power station in the photovoltaic power source, and the c-th thermal power unit in the thermal power source at time t; and They represent the energy storage discharge power and energy storage charging power of the bth energy storage unit in the energy storage power supply at time t respectively; W, PV, T, and ESS represent the number of wind power stations in wind power sources, the number of photovoltaic power stations in photovoltaic power sources, the number of thermal power units in operation in thermal power sources, and the number of energy storage units in energy storage power sources, respectively. in: in: and They represent the rated installed capacity of the i-th wind power station in the wind power source and the rated installed capacity of the j-th photovoltaic power station in the photovoltaic power source; and They represent the theoretical output simultaneity rate of the i-th wind power station in the wind power source at time t, and the theoretical output simultaneity rate of the j-th photovoltaic power station in the photovoltaic power source at time t; Inner constraint 2: Real-time output of thermal power source at time t Not higher than the rated installed capacity of thermal power sources, and not lower than the minimum technical output of thermal power sources; Inner constraint three: real-time output of energy storage power supply at time t It is between the theoretical maximum output and the theoretical minimum output of the energy storage power supply.

5. The method for optimizing the configuration of wind, solar, thermal and storage capacity for supporting inter-provincial and inter-regional UHV transmission channels according to claim 4 is characterized in that: The target constraints include: Target constraint condition 1: The proportion of renewable energy electricity in the transmission channel must satisfy expression (6): Where: μ represents the minimum proportion of renewable energy electricity in the transmission channel; Target constraint condition 2: New energy consumption and utilization rate constraint, that is, satisfying expression (7): Where: η represents the set minimum value of new energy consumption and utilization rate.

6. The method for optimizing the configuration of wind, solar, thermal and storage capacity for inter-provincial and inter-regional UHV transmission channels according to claim 1 is characterized in that: The expression of the outer optimization model is: <h2 style=";text-align:left;direction:ltr">f2 = maxF<h2 style=";text-align:left;direction:ltr"> in <h2 style=";text-align:left;direction:ltr"> -minF<h2 style=";text-align:left;direction:ltr"> inv <h2 style=";text-align:left;direction:ltr"> (8) Among them: f2 is the outer objective function; F inv 、F in They are the annualized investment cost and annualized operating income respectively.

7. The method for optimizing the configuration of wind, solar, thermal and storage capacity for inter-provincial and inter-regional UHV transmission channels according to claim 6 is characterized in that: Annualized investment cost F inv , refers to the total investment converted to the average annual investment at a discount rate; among them, the total investment of various types of power sources such as wind, solar, thermal and storage is proportional to the installed capacity, and the transmission cost is a fixed investment; the annualized investment cost F inv The expression of is as follows: F inv =C inv,W +C inv,PV +C inv,T +C inv,ESS +C inv,L (9) in: C inv,W 、C inv,PV 、C inv,T 、C inv,ESS 、C inv,L Represent the annualized investment costs of wind power, photovoltaic power, thermal power, energy storage power and transmission lines, respectively, and are determined by the total investment C inv,total, Where I is the discount rate and T is the investment payback period:

8. The method for optimizing the configuration of wind, solar, thermal and storage capacity for inter-provincial and inter-regional UHV transmission channels according to claim 6 is characterized in that: Annual operating income F in According to the transmission power P of the transmission channel at each moment t TL (t) is multiplied by the real-time electricity price C(t) for settlement, and the expression is as follows: Among them: the real-time electricity price C(t) takes three modes, namely traditional fixed electricity price, segmented electricity price and spot market time-of-use settlement electricity price.

9. A device for optimizing the configuration of wind, solar, thermal and storage capacity for inter-provincial and inter-regional ultra-high voltage transmission channels, characterized in that: The device for optimizing the capacity configuration of wind, solar, thermal and storage devices supporting an inter-provincial and inter-regional UHV transmission channel implements the method for optimizing the capacity configuration of wind, solar, thermal and storage devices supporting an inter-provincial and inter-regional UHV transmission channel according to any one of claims 1 to 8, comprising: The module for forming the transmission curve of the transmission channel throughout the year is used to set the transmission power P of the transmission channel at each moment t throughout the year according to the output characteristics of renewable energy at the sending end of the transmission channel and the power load characteristics at the receiving end. TL (t), forming a simulated power transmission curve of the transmission channel throughout the year; A supporting power source combination generation module is used to generate multiple groups of supporting power source combinations; wherein each group of the supporting power source combination sets the installed capacity scale of wind power source, photovoltaic power source, thermal power source and energy storage power source; A supporting power combination processing module is used to traverse each of the supporting power combinations in the supporting power combinations, and input the established inner layer optimization model for the traversed supporting power combinations; An inner layer optimization model establishment module, used for establishing the inner layer optimization model; An inner optimization model solving module, configured to solve the inner optimization model to obtain an optimal solution that satisfies the inner objective function and inner constraints; wherein the inner constraints include constraints on the simulated power transmission curve of the transmission channel throughout the year; An optimal solution judgment module is used to judge whether the optimal solution meets the set target constraints, wherein the target constraints include the annual renewable energy power proportion constraint and the renewable energy consumption utilization rate constraint; if not, it means that the currently traversed supporting power supply combination does not meet the conditions, and the next group of supporting power supply combinations is traversed; if it meets the conditions, the supporting power supply combination and the optimal solution obtained by solving the inner optimization model are added to the optimal solution element set, and the next group of supporting power supply combinations is traversed; An outer layer optimization model establishment module, used for establishing the outer layer optimization model; The outer optimization model solving module is used to input the established outer optimization model after the traversal of the supporting power source combination is completed; wherein, the outer optimization model takes the maximum net profit as the outer objective function, traverses each element in the optimal solution element set finally obtained, and obtains the element with the maximum net profit, that is, obtains the supporting power source combination with the maximum net profit, as well as the combination of thermal power start-up units, the real-time output of thermal power source and energy storage power source, and the wind power and photovoltaic power abandonment power.

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