CN116799870A - A planning method and device for transmission network under new energy access - Google Patents
A planning method and device for transmission network under new energy access Download PDFInfo
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- CN116799870A CN116799870A CN202310693852.2A CN202310693852A CN116799870A CN 116799870 A CN116799870 A CN 116799870A CN 202310693852 A CN202310693852 A CN 202310693852A CN 116799870 A CN116799870 A CN 116799870A
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for AC mains or AC distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/22—The renewable source being solar energy
- H02J2300/24—The renewable source being solar energy of photovoltaic origin
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind energy
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Abstract
The application provides a planning method and a planning device for a power transmission network under new energy access, which can be used for planning the power transmission network under new energy access so as to minimize the total cost of the power transmission network. The method comprises the following steps: determining power parameters according to a power transmission network to be planned, wherein the power parameters comprise wind power installed capacity and photovoltaic installed capacity of each node in a first power area of the power transmission network; establishing a first model for planning a power transmission network based on electric power parameters, wherein the first model comprises a first objective function, a double-carbon objective sub-model, a full life cycle carbon emission evaluation sub-model and a new energy permeability sub-model, the first objective function is used for optimizing the first model by minimizing the total cost of the power transmission network, and the first objective function takes the double-carbon objective sub-model and the new energy permeability sub-model as constraint conditions; optimizing the power parameters by adopting an optimization algorithm to optimize a first objective function so as to obtain an optimized first model; and planning the power transmission network according to the first model.
Description
Technical Field
The application relates to the technical field of power transmission networks, in particular to a planning method and a planning device for a power transmission network under new energy access.
Background
With energy crisis and global climate change issues, new energy (including wind and light) generation is becoming popular. Since new energy power generation has randomness and intermittence (randomness and intermittence are collectively called uncertainty), a power transmission network under new energy access has uncertainty.
Conventional power grid planning methods are based on deterministic energy (e.g., coal) determination and are no longer applicable to power grids under new energy access.
Therefore, a planning method for a power transmission network under new energy access is needed.
Disclosure of Invention
The application provides a planning method and a planning device for a power transmission network under new energy access, which can be used for planning the power transmission network under new energy access so as to minimize the total cost of the power transmission network.
In a first aspect, a method for planning a power transmission network under new energy access is provided, including:
determining power parameters according to a power transmission network to be planned, wherein the power parameters comprise wind power installed capacity and photovoltaic installed capacity of each node in a first power area of the power transmission network;
establishing a first model for planning a power transmission network based on electric power parameters, wherein the first model comprises a first objective function, a double-carbon objective sub-model, a full life cycle carbon emission evaluation sub-model and a new energy permeability sub-model, the first objective function is used for optimizing the first model by minimizing the total cost of the power transmission network, and the first objective function takes the double-carbon objective sub-model and the new energy permeability sub-model as constraint conditions;
Optimizing the power parameters by adopting an optimization algorithm to optimize a first objective function so as to obtain an optimized first model;
and planning the power transmission network according to the first model.
In one possible design, the new energy permeability submodel is implemented by the following formula:
wherein ,for wind power installation capacity, < >>For photoelectric installed capacity, < >>For wind power permeability>Is the photoelectric permeability beta NE For the consumption duty ratio of renewable energy sources, alpha fire For the standard coal coefficient of folding E water For generating electric power, E sun For generating photovoltaic power, E wind For generating electric power, E X Beta, the total energy consumption Abs The responsibility weight is absorbed for renewable energy sources, < - > and->Is the electricity consumption of the whole society.
In one possible design, the first objective function is as follows:
wherein X is a first power region, T is a planning period corresponding to a power transmission network, F cost F for the corresponding total cost of the power transmission network inv For investment costs, the investment costs include hydroelectric investment cost, thermal power investment cost, wind power investment cost and photoelectric investment cost, F ope F for operation cost inv As shown in the following formula:
wherein ,for the construction cost of the generator, < > for>For the line construction cost>Is the energy storage cost.
In one possible design, the power parameter further includes an energy storage installed capacity, the first model further includes a power balance constraint sub-model, and the first objective function further uses the power balance constraint sub-model as a constraint condition, and the power balance constraint sub-model is implemented by the following formula:
wherein ,PD For the load demand of the first power region, P gen The power generated by the generator g corresponding to the first power region,for abandoned power, +.>For the stored power generation power>Power storage for energy storage, wherein-> and />Are determined according to the capacity of the energy storage machine.
In one possible design, the two-carbon target submodel is realized by the following formula:
wherein ,Pfire For thermal power output, T is any time of the planning period T, 2030 represents year, 2060 represents year, and beta fire The carbon dioxide emission for thermal power reaches the ratio coefficient of carbon neutralization through the carbon saving and emission reduction technology and the carbon sink capacity of the ecological system.
In one possible design, the first model further includes a power generation constraint sub-model, and the first objective function is further implemented using the power generation constraint sub-model as a constraint condition, where the power generation constraint sub-model is implemented by the following formula:
wherein ,investment cost per unit installed capacity for generator g, < >>The generator installation capacity is the sum of wind power installation capacity, photoelectric installation capacity, water power installation capacity and thermal power installation capacity>For the upper limit value of the generator installation capacity, +.>Is the lower limit value of the installed capacity of the generator.
In one possible design, the first model further includes a second objective function by which the full life cycle carbon emission assessment submodel is optimized, the second objective function being as shown in the following formula:
wherein ,FIES For carbon emission operation cost, F device,t F for the running cost of the equipment carbon,t For carbon emission cost, F market,t The cost for market purchase;
the full life cycle carbon emission assessment submodel is realized by the following formula:
F carbon,t ＝E all ；
E all ＝E wind +E sun +E CHP +E GT +E EB +E P2G +E ES +E Market ；
wherein ,Eall E is the carbon emission of the comprehensive energy system wind Is a fanActual carbon emission, E sun For the actual carbon emission of the photovoltaic, E CHP For carbon emission of cogeneration units E GT For carbon emissions of gas turbines, E EB For carbon emission of electric boiler units E P2G Is the carbon emission of the P2G unit, E ES For carbon emission of energy storage devices, E Market Is the carbon emission of the market purchase.
In one possible design, the power parameters further include loads of nodes in the first power region, planning the power grid according to a first model, comprising:
and determining the connection relation among the nodes of the power transmission network according to the load of each node based on a graph theory algorithm.
In one possible design, determining a connection relationship between nodes of a power transmission network according to loads of the nodes based on a graph theory algorithm includes:
acquiring an initial grid pattern corresponding to a power transmission network;
inputting the load, wind power installation capacity, photoelectric installation capacity, water power installation capacity, thermal power installation capacity and energy storage installation capacity of each node into the grid map;
Determining a corresponding matrix according to the grid pattern;
updating a matrix based on a network maximum flow method;
acquiring an updated grid pattern according to the updated matrix;
adjusting the output of a thermal power unit, a pumping storage unit and a new energy unit for each node in the updated grid pattern;
carrying out tide calculation to obtain a tide result based on the load of each node and the output of the corresponding thermal power generating unit, pumping storage unit and new energy unit;
and determining whether the power flow result is converged, if so, outputting an updated grid pattern to determine the connection relation among all nodes of the power transmission network, and if not, updating the current grid pattern until the power flow result is converged.
In a second aspect, a planning apparatus for a power transmission network under new energy access is provided, including:
the power parameter determining module is used for determining power parameters according to a power transmission network to be planned, wherein the power parameters comprise wind power installed capacity and photovoltaic installed capacity of each node in a first power area of the power transmission network;
the first model building module is used for building a first model for planning a power transmission network based on electric power parameters, the first model comprises a first objective function, a double-carbon objective sub-model, a full life cycle carbon emission evaluation sub-model and a new energy permeability sub-model, the first objective function is used for optimizing the first model by minimizing the total cost of the power transmission network, and the first objective function takes the double-carbon objective sub-model and the new energy permeability sub-model as constraint conditions;
The first model optimization module optimizes the power parameters by adopting an optimization algorithm so as to optimize a first objective function and obtain an optimized first model;
and the power transmission network planning module is used for planning a power transmission network according to the first model.
In an embodiment of the present application, the power parameters determined according to the power transmission network to be planned include: wind installed capacity and photovoltaic installed capacity of each node in a first power region of the grid. Because wind power installed capacity is used for quantifying new energy source-wind power, and photovoltaic installed capacity is used for quantifying new energy source-photovoltaic, the first model established for planning the power transmission network based on the electric power parameters can be suitable for connecting new energy sources such as wind power, photovoltaic and the like into the power transmission network.
Further, the first model comprises a double-carbon target sub-model, a full life cycle carbon emission evaluation sub-model and a new energy permeability sub-model, so that the proportion of new energy to be connected into the power transmission network can be ensured under the condition that the requirement of a carbon emission target is met, and the requirement of high proportion of new energy to be connected into the power transmission network is met, and the power transmission network under the new energy connection can be planned through the first model.
In addition, the first objective function is used to optimize the first model by minimizing the total cost of the power grid, enabling the total cost of the power grid under new energy access planned according to the first model to be minimized.
Drawings
In order to more clearly illustrate the technical solution of the present application, the drawings that are needed in the embodiments will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
FIG. 1 is a schematic diagram of a planning method for a power transmission network under new energy access according to an exemplary embodiment of the present application;
FIG. 2 is a schematic flow chart of an example of a life cycle assessment based carbon emission and second objective function optimization model according to an exemplary embodiment of the present application;
FIG. 3 is a schematic diagram of an exemplary first model operation flow provided in an exemplary embodiment of the present application;
FIG. 4 is an illustration of an example initial grid pattern provided in accordance with an exemplary embodiment of the present application;
FIG. 5 is a diagram of an example of an optimized net rack according to an exemplary embodiment of the present application;
FIG. 6 is a graph of energy output for each day of maximum annual new energy output provided in accordance with an exemplary embodiment of the present application;
FIG. 7 is an example t provided by an exemplary embodiment of the present application 1 Annual carbon trade cost map;
fig. 8 is a schematic diagram of a planning apparatus for a power transmission network under new energy access according to an exemplary embodiment of the present application.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the application. It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless expressly stated otherwise, as understood by those skilled in the art. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or wirelessly coupled. The term "and/or" as used herein includes all or any element and all combination of one or more of the associated listed items.
In order to solve the problem that the conventional power transmission network planning method is no longer suitable for the power transmission network under the new energy access, as shown in fig. 1, the application provides a power transmission network planning method under the new energy access, which comprises the following steps:
s110, determining power parameters according to a power transmission network to be planned, wherein the power parameters comprise wind power installed capacity and photovoltaic installed capacity of each node in a first power area of the power transmission network.
Each of the power parameters may be a preset default value, and may be changed along with the optimization of the first model.
S120, based on the electric power parameters, a first model for planning the power transmission network is established, wherein the first model comprises a first objective function, a double-carbon objective sub-model, a full life cycle carbon emission evaluation sub-model and a new energy permeability sub-model, the first objective function is used for optimizing the first model by minimizing the total cost of the power transmission network, and the first objective function takes the double-carbon objective sub-model and the new energy permeability sub-model as constraint conditions.
In one possible design, the first objective function is as follows:
wherein X is a first power region, T is a transmission grid pairPlanning period of the response, F cost F for the corresponding total cost of the power transmission network inv For investment costs, the investment costs include hydroelectric investment cost, thermal power investment cost, wind power investment cost and photoelectric investment cost, F ope F for operation cost inv As shown in the following formula:
wherein ,for the construction cost of the generator, < > for>For the line construction cost>Is the energy storage cost.
In the above example, the total cost corresponding to the power transmission network is the minimum as the first objective function, so that the hydropower investment cost, the thermal power investment cost, the wind power investment cost and the photoelectric investment cost can be optimized.
In one possible design, the power parameter further includes an energy storage installed capacity, the first model further includes a power balance constraint sub-model, and the first objective function further uses the power balance constraint sub-model as a constraint condition, and the power balance constraint sub-model is implemented by the following formula:
wherein ,PD For the load demand of the first power region, P gen The power generated by the generator g corresponding to the first power region,for abandoned power, +.>For the stored power generation power>Power storage for energy storage, wherein-> and />All are determined according to the capacity of the energy storage machine, and the specific mode is set according to actual requirements, so that the application is not limited.
In the above example, the power balance constraint sub-model is used as a constraint condition, so that the first objective function can keep the power balance of the power transmission network in the process of optimization.
In one possible design, the first model further includes a power generation constraint sub-model, and the first objective function is further implemented using the power generation constraint sub-model as a constraint condition, where the power generation constraint sub-model is implemented by the following formula:
wherein ,investment cost per unit installed capacity for generator g, < >>For loading capacity of generator, the generator is installedThe machine capacity is the sum of wind power installation capacity, photoelectric installation capacity, water power installation capacity and thermal power installation capacity, and is ∈>For the upper limit value of the generator installation capacity, +.>Is the lower limit value of the installed capacity of the generator.
By way of example only, and in an illustrative,
in the above example, the investment cost of the generator g is calculated from the installed capacity of each power generation technology newly invested in the planning period T in the first power region XAnd the investment cost is not limited, and the installed capacity of different power generation technologies is limited by upper and lower limits, so that the requirements of capacity saturation, policy limitation, safety reasons, technical popularization, new energy development and the like can be met.
In one possible design, the electrical power parameters further include a hydropower installed capacity and a thermal power installed capacity, P, of each node in the first power region of the power transmission grid water In the first power region X, the following formula constraint is satisfied within the planning time T:
0≤P water (X,T)≤0.2P D ；
Thermal power loading capacity P fire In the first power region X, the following formula constraint is satisfied within the planning time T:
0≤P fire (X,T)≤3.334P D 。
in the above example, based on the electric power parameters, the first model for planning the power transmission network is set up to be suitable for new energy sources such as wind power, photoelectricity and the like to be connected to the power transmission network, and the energy sources of the connection points of the power transmission network include hydroelectric power and thermal power, so that the adaptability of the first model to the power transmission network is improved.
In one possible design, the two-carbon target submodel is realized by the following formula:
by way of the above example, carbon dioxide emissions can be made to peak before 2030, and in the case where an electric power system can be approximately understood as a power plant that consumes conventional fossil energy as represented by a thermal power plant, carbon dioxide emissions from the power plant can peak before 2030.
In one possible design, the two-carbon target submodel is also implemented by the following formula:
wherein ,Pfire For thermal power output, T is any time of the planning period T, 2030 represents year, 2060 represents year, and beta fire The carbon dioxide emission for thermal power reaches the ratio coefficient of carbon neutralization through the carbon saving and emission reduction technology and the carbon sink capacity of the ecological system.
By way of example, carbon neutralization can be achieved before 2060 years of striving for, and in the case of a power station which can be approximately represented as a thermal power plant consuming traditional fossil energy, carbon dioxide emitted by the power station reaches carbon neutralization before 2060 through carbon saving and emission reduction and ecosystem carbon sink capacity.
In one possible design, the new energy permeability submodel is implemented by the following formula:
wherein ,for wind power installation capacity, < >>For photoelectric installed capacity, < >>For wind power permeability>Is the photoelectric permeability beta NE Is a non-fossil energy consumption ratio, alpha fire For the standard coal coefficient of folding E water For generating electric power, E sun For generating photovoltaic power, E wind For generating electric power, E X Beta, the total energy consumption Abs The responsibility weight is absorbed for renewable energy sources, < - > and->Is the electricity consumption of the whole society.
In the above example, the wind power installation capacity and the photovoltaic installation capacity can meet a certain permeability through the renewable energy consumption duty ratio constraint model, and meet the high-proportion access requirement of new energy in the input network.
Because wind power and photoelectricity have intermittence and volatility, the power system can not safely and stably run when the wind power and photoelectricity are connected to a power transmission network, the problem of wind power or light power loss can occur in a risk period, and the upper limit constraint on wind power loss or light power loss is required in a first power area, in one feasible design, a first model further comprises a wind power loss model and a light power loss model, and the light power loss model is shown in the following formula:
wherein ,to reject the optical power, P sun For photovoltaic output, < > >Is the light rejection rate.
The wind curtailment model is shown in the following formula:
wherein ,to discard wind power, P wind For wind power output->Is the wind abandoning rate.
The application provides an energy storage constraint model, which is characterized in that the energy storage investment cost is calculated by the energy storage installed capacity of a new investment in a planning period T in a first power region X, and the traditional energy storage is constrained by the geographic position because the energy storage investment cost is unconstrained, and the energy storage constraint model is realized by the following formula assuming that the newly added energy storage is configured by a new energy installation according to a certain proportion:
wherein ,γES The energy storage coefficient is configured for the new energy installation,for the total cost of energy storage investment in the first power domain X planning period T +.>Investment cost is the energy storage unit.
In one possible design, the full life cycle carbon emission assessment submodel is optimized by a second objective function, which is shown in the following formula:
wherein ,FIES For carbon emission operation cost, F device,t F for the running cost of the equipment carbon,t For carbon emission cost, F market,t And purchasing cost for the market. Min represents an arithmetic function for minimum value.
Illustratively F device,t The method is realized by the following formula:
F device,t ＝F re,t +F cv,t +F es,t ；
F re,t ＝k sun P sun,t +k wind P wind,t ；
F cv,t ＝k CHP P CHP,t +k GT P GT,t +k EB P EB,t +k P2G P P2G,t ；
F es,t ＝k es P es,t ；
F re,t f, for the operation cost of the renewable energy source equipment in the t period cv,t For the operation cost of the energy conversion equipment in the period of t, F es,t And the operation cost of the energy storage equipment in the t period is set. k (k) sun For the unit operation cost of the photoelectric unit, P sun,t For operating power, k in the time t of the photoelectric unit wind For the unit operation cost of the wind turbine generator system, P wind,t And the power is the running power of the wind turbine generator in the time t.
k CHP For cogeneration (combined heat and power, C)HP) unit operation cost, P CHP,t For the operating power, k, of the CHP unit in t time GT For the unit operation cost of the gas unit, P GT,t For the operating power, k of the gas unit in t time EB For the unit operation cost of the electric boiler unit, P EB,t For the operating power, k of the electric boiler unit in t time P2G For the unit operation cost of a renewable energy power generation (P2G) unit, P P2G,t And the power is the running power of the P2G unit in t time.
k es Is the unit energy storage cost, P es,t For storing the operating power in the period t.
Illustratively F market,t The method is realized by the following steps:
F market,t ＝∫(k power P power,t +k gas P gas,t +k hot P hot,t )dt；
wherein ,kpower Per unit power cost, P power,t For the power consumption at time t, k gas Is the unit gas purchasing cost, P gas,t For the air consumption at time t, k hot Is the unit heat purchase cost, P hot,t Heat is used for time t.
Illustratively, the full life cycle carbon emission assessment submodel is implemented by the following formula:
F carbon,t ＝E all ；
E all ＝E wind +E sun +E CHP +E GT +E EB +E P2G +E ES +E Market ；
wherein ,Eall Carbon emission, E, of the comprehensive energy system obtained by the full life cycle assessment method wind E is the actual carbon emission of the fan sun For the actual carbon emission of the photovoltaic, E CHP For carbon emission of cogeneration units E GT For carbon emissions of gas turbines, E EB For carbon emission of electric boiler units E P2G Is the carbon emission of the P2G unit, E ES For carbon emission of energy storage devices, E Market Is the carbon emission of the market purchase.
Illustratively, new energy lifecycle assessment (Life Cycle Assessment, LCA) carbon emission Quantum model E wind and Esun The method is realized by the following formulas:
wherein ,εwind The carbon emission coefficient epsilon is the actual electric carbon emission coefficient epsilon of the fan sun The photovoltaic practical electric carbon emission coefficient can be set according to the requirement.
Illustratively, the energy conversion device LCA carbon emission quantum model E CHP 、E GT 、E EB and EP2G The method is realized by the following formulas:
wherein ,εCHP The actual electric carbon emission coefficient epsilon of the CHP unit m Actual electricity carbon emission coefficient epsilon for coal-fired power generation GT Electric carbon emission coefficient epsilon for gas turbine g Electric carbon emission coefficient epsilon for natural gas energy source EB Electric carbon emission coefficient epsilon for electric boiler unit e Carbon emission coefficient epsilon for electric energy actual degree P2G The actual electric carbon emission coefficient of the P2G unit.
Illustratively, the energy storage device LCA carbon emission quantum model E es As shown in the following formula:
wherein ,εes For the actual degree electric number, P, of the energy storage device es,t Is the output of the energy storage device per unit time t.
Illustratively, the integrated energy market-purchased LCA carbon emission quantum model E Market As shown in the following formula:
wherein ,EMarket Is the carbon emission of the market purchase energy,For the time period of the market energy purchase t h Electric carbon emission coefficient for the realism of a heat storage device, < >>For the time period t of the commercial energy g Electric carbon emission coefficient for the actual degree of heat energy, < >>Is commercially available natural gas power for a period of t.
Illustratively, the minimum carbon emissions operating cost minF is calculated by the following equation IES Is set up:
wherein [ MinF ] IES ] max Operating cost minF for minimum carbon emissions IES Maximum value of [ minF ] IES ] min Operating cost minF for minimum carbon emissions IES Is a minimum of (2).
In one possible design, the water installation capacity and the thermal installation capacity are determined values, which can be determined according to actual needs, and the application is not limited to this.
And S130, optimizing the power parameters by adopting an optimization algorithm to optimize the first objective function so as to obtain an optimized first model.
The optimization algorithm may be Genetic Algorithm (GA), particle Swarm Optimization (PSO), non-dominant ordered genetic algorithm (NSGA), etc., which is not limited in this regard.
In one possible design, the second objective function is optimized using a gradient method based on the power parameters in S110 and the first model in S120, as shown in fig. 2, including the steps of:
step a1: the program starts to execute.
Step a2: and calculating the upper limit and the lower limit of the installed capacity of each power generation technology.
Wherein, wind power installation capacityUpper limit of pass->Determining; photoelectric installed capacityUpper limit of pass->And (5) determining.
The values of the water installation capacity and the thermal power installation capacity are generally determined values, and the upper limits of the water installation capacity and the thermal power installation capacity can be set according to requirements.
The lower limit of the wind power installation capacity, the photoelectric installation capacity, the water power installation capacity and the thermal power installation capacity can be controlled by the generator installation capacityLower limit value of>And (5) determining.
Step a3: if the upper limit of the installed capacity of each power generation technology is smaller than the lower limit, the step a4 is performed.
Step a4: it is determined whether constraints of the two-carbon target submodel are satisfied. If yes, step a5 is performed, and if not, steps a1 to a4 are performed.
Step a5: and determining whether the new energy permeability submodel is satisfied. If yes, step a6 is performed, and if not, steps a1 to a5 are performed.
Step a6: the comprehensive energy system based on the full life cycle assessment scheme optimally operates, and comprises the following steps: the method comprises the steps of operating a new energy LCA carbon emission sub-model, operating an energy conversion equipment LCA carbon emission sub-model, operating an energy storage equipment LCA carbon emission sub-model and operating a comprehensive energy market purchased LCA carbon emission sub-model.
Step a7: determination of [ minF ] IES ] max and [minFIES ] min Whether or not to converge. If the power is converged, the step a8 is performed, if the power is not converged, the upper limit of any one or more of the power of the cogeneration unit, the power of the gas unit, the power of the electric boiler unit and the power of the renewable energy generator unit is reduced, and then the steps a1 to a7 are performed.
For example, if the flow rate is not converged, the values of the water installation capacity and the thermal installation capacity may be adjusted, and then the steps a1 to a7 may be performed.
Step a8: calculating a minimum carbon emission running cost minF according to the second objective function IES 。
Step a9: the program ends running.
In the above example, on the basis of integration of source network charge storage, the carbon emission and renewable new energy index optimization operation model of full life cycle assessment can be researched, and the minimum carbon emission operation cost can be determined.
In one possible design, the first model is divided into two layers, the first layer is a source network charge storage operation layer, and the total energy storage investment cost, the equipment operation cost and the carbon emission cost are obtained under the condition that the constraint of the double-carbon target submodel and the new energy permeability submodel are met. And the second layer performs carbon emission optimization operation based on full life cycle evaluation on the basis of the first layer, and finally obtains the respective installed capacities of wind, light, water, fire and energy storage. In combination with the power parameters and the respective sub-models in the above embodiment, as shown in fig. 3, the specific operation steps of the first model include:
Step b1: the program starts to execute.
Step b2: and calculating the upper limit and the lower limit of the installed capacity of each power generation technology.
The specific details of this step are referred to as step a2, and will not be described here again.
Step b3: if the upper limit of the installed capacity of each power generation technology is smaller than the lower limit, the step b4 is performed.
Step b4: it is determined whether constraints of the two-carbon target submodel are satisfied. If yes, step b5 is performed, and if not, steps b1 to b4 are performed.
Step b5: and determining whether the new energy permeability submodel is satisfied. If yes, step b6 is performed, and if not, steps b1 to b5 are performed.
Step b6: calculating investment costs of Generator gUpper and lower boundaries of (2).
Step b7: and the source network charge storage collaborative optimization operation is the second objective function optimization operation based on the carbon emission operation of the source network charge storage integration.
Step b8: it is determined whether the load requirements of each node of the power transmission network are met. If yes, go to step b9, if not, increase the lower limit value of the installed capacity of the generatorThen, steps b1 to b8 are performed.
Step b9: and calculating and determining the energy storage installed capacity.
Step b10: calculating total cost of energy storage investment according to capacity of energy storage installation
Step b11: computing device operating costs.
Step b12: the carbon emission costs are calculated.
Step b13: and optimizing operation of the comprehensive energy system based on the full life cycle assessment scheme. The details refer to step a6, and are not described herein.
Step b14: calculate and determine [ minF ] IES ] max and [minFIES ] min 。
Step b15: determination of [ minF ] IES ] max and [minFIES ] min Whether or not to converge. If the convergence is not reached, the step b16 is performed, and if the convergence is not reached, the installed capacity of the generator is reducedThen, step b1 to step b15 are performed.
And b16, outputting the optimal wind power installation capacity, the optimal photovoltaic installation capacity, the optimal water power installation capacity, the optimal thermal power installation capacity and the optimal energy storage installation capacity in the first model.
The above example can optimize the first model with the total cost of the first objective function being the lowest, and output the individual power parameters in the optimal first model.
And S140, planning a power transmission network according to the first model.
In one possible design, the power parameters further include loads of nodes in the first power region, planning the power grid according to a first model, comprising:
and determining the connection relation among the nodes of the power transmission network according to the load of each node based on a graph theory algorithm.
Illustratively, determining the connection relationship between the nodes of the power transmission network according to the loads of the nodes based on a graph theory algorithm includes:
Step c1: acquiring an initial grid pattern corresponding to a power transmission network;
step c2: determining the optimized wind power installed capacity, the optimized photoelectric installed capacity, the optimized hydroelectric installed capacity, the optimized thermal power installed capacity and the optimized energy storage installed capacity of a planned year t, and inputting the load of each node and each optimized installed capacity into a grid diagram;
step c3: determining a corresponding matrix according to the grid diagram, wherein the value of the matrix is used for representing the connection relation between the corresponding node and other nodes;
step c4: updating the matrix based on a network maximum flow method;
step c5: acquiring an updated grid pattern according to the updated matrix;
step c6: adjusting the output of a thermal power unit, a pumping storage unit and a new energy unit for each node in the updated grid pattern;
step c7: carrying out tide calculation to obtain a tide result based on the load of each node and the output of the corresponding thermal power generating unit, pumping storage unit and new energy unit;
step c8: and (3) determining whether the tide results are converged, if so, outputting an updated grid pattern to determine the connection relation among the nodes of the power transmission network, ending the operation, and if not, repeating the steps c 3-c 8.
Fig. 4 is an initial grid diagram according to an exemplary embodiment of the present application, and after determining the connection relationship of each node by using the graph theory algorithm based on the grid diagram of fig. 4, a new grid diagram is shown in fig. 5.
The evaluation standard of the new energy access mode based on the graph theory has three methods of accurate judgment result, high algorithm fault tolerance and quick operation, so that the evaluation standard based on the graph theory has wider adaptability under the condition that the new energy is accessed to the power transmission network. Meanwhile, aiming at the characteristic that the new energy access node is flexible and changeable, the dynamic adjacent matrix after the new energy is accessed is described by using the graph theory, so that the self-adaptability of the matrix algorithm can be enhanced. Meanwhile, the branch types can be classified according to the node relation of the complex power transmission network, an access basis is provided, the complex branch interval judgment is embodied and simplified, and the accuracy of the new light source access to the power transmission network scheme is improved.
In an embodiment of the present application, the power parameters determined according to the power transmission network to be planned include: wind installed capacity and photovoltaic installed capacity of each node in a first power region of the grid. Because wind power installed capacity is used for quantifying new energy source-wind power, and photovoltaic installed capacity is used for quantifying new energy source-photovoltaic, the first model established for planning the power transmission network based on the electric power parameters can be suitable for connecting new energy sources such as wind power, photovoltaic and the like into the power transmission network.
Further, the first model comprises a double-carbon target sub-model, a full life cycle carbon emission evaluation sub-model and a new energy permeability sub-model, so that the proportion of new energy to be connected into the power transmission network can be ensured under the condition that the requirement of a carbon emission target is met, and the requirement of high proportion of new energy to be connected into the power transmission network is met, and the power transmission network under the new energy connection can be planned through the first model.
In addition, the first objective function is used to optimize the first model by minimizing the total cost of the power grid, enabling the total cost of the power grid under new energy access planned according to the first model to be minimized.
In combination with the above method for planning a power transmission network under new energy access, the present application also provides a specific example of planning a power transmission network under new energy access, including the following steps:
step S1: t at the planning time T of the power region X 1 The annual determining thermal power installation 7209MW, hydroelectric installation 3610MW, maximum load 68310MW, electricity consumption 4000 hundred million kWh, non-fossil energy consumption ratio 31.25% and renewable energy consumption responsibility weight 40%.
Step S2: a first model is determined.
According to the power scene proposed in the step one, T of the power area X in the planning time T 1 Carrying out power planning model solving analysis year by year to obtain t 1 The output curves of each energy source in the day of maximum annual new energy source output are shown in figure 6, and t 1 Annual carbon trade costs are schematically represented in fig. 7.
As can be seen from fig. 6 and 7, t 1 The maximum output of new energy in the annual region X occurs at 13 points in the midday of spring for 15 minutes, and the maximum output of new energy in the whole province is 7480MW.
wherein ,t1 The annual zone X power detail table is as follows:
| sequence number | Project | Electric quantity analysis (one hundred million kwh) |
| 1 | Photovoltaic power generation capacity | 1203 |
| 2 | Photovoltaic real power generation | 1031 |
| 3 | Photovoltaic use hours | 1271 |
| 4 | Wind power generation capacity | 446 |
| 5 | Wind power generation capacity | 445 |
| 6 | Hours of wind power utilization | 2204 |
| 7 | New energy power rejection rate | 10.42％ |
| 8 | Hydroelectric power generation | 124 |
| 9 | Thermal power generation | 2734 |
| 10 | Total electric quantity of load | 3953 |
| 11 | Energy storage charge and discharge amount | 59 |
| 12 | Pumping, storing, charging and discharging quantity | 183 |
Step S3: and planning the power transmission network according to the first model.
And determining the connection relation among the nodes of the power transmission network according to the load of each node based on a graph theory algorithm. The specific steps are referred to in steps c1 to c8, and are not described herein.
In combination with the above method for planning a power transmission network under new energy access, the present application also provides a device for planning a power transmission network under new energy access, as shown in fig. 8, where the device includes:
the power parameter determining module is used for determining power parameters according to a power transmission network to be planned, wherein the power parameters comprise wind power installed capacity and photovoltaic installed capacity of each node in a first power area of the power transmission network;
The first model building module is used for building a first model for planning a power transmission network based on electric power parameters, the first model comprises a first objective function, a double-carbon objective sub-model, a full life cycle carbon emission evaluation sub-model and a new energy permeability sub-model, the first objective function is used for optimizing the first model by minimizing the total cost of the power transmission network, and the first objective function takes the double-carbon objective sub-model and the new energy permeability sub-model as constraint conditions;
the first model optimization module optimizes the power parameters by adopting an optimization algorithm so as to optimize a first objective function and obtain an optimized first model;
and the power transmission network planning module is used for planning a power transmission network according to the first model.
In one possible design, the new energy permeability submodel is implemented by the following formula:
wherein ,for wind power installation capacity, < >>Is light ofCapacity of electric installation->For wind power permeability>Is the photoelectric permeability beta NE For the consumption duty ratio of renewable energy sources, alpha fire For the standard coal coefficient of folding E water For generating electric power, E sun For generating photovoltaic power, E wind For generating electric power, E X Beta, the total energy consumption Abs The responsibility weight is absorbed for renewable energy sources, < - > and->Is the electricity consumption of the whole society.
In one possible design, the first objective function is as follows:
wherein X is a first power region, T is a planning period corresponding to a power transmission network, F cost F for the corresponding total cost of the power transmission network inv For investment costs, the investment costs include hydroelectric investment cost, thermal power investment cost, wind power investment cost and photoelectric investment cost, F ope F for operation cost inv As shown in the following formula:
wherein ,for the construction cost of the generator, < > for>For the line construction cost>Is the energy storage cost.
In one possible design, the power parameter further includes an energy storage installed capacity, the first model further includes a power balance constraint sub-model, and the first objective function is further implemented using the power balance constraint sub-model as a constraint condition, where the power balance constraint sub-model is implemented by the following formula:
wherein ,PD For the load demand of the first power region, P gen The power generated by the generator g corresponding to the first power region,for abandoned power, +.>For the stored power generation power>Power storage for energy storage, wherein-> and />Are determined according to the capacity of the energy storage machine.
In one possible design, the two-carbon target submodel is realized by the following formula:
wherein ,Pfire For thermal power output, T is any time of the planning period T, 2030 represents year, 2060 represents year, and beta fire The carbon dioxide emission for thermal power reaches the ratio coefficient of carbon neutralization through the carbon saving and emission reduction technology and the carbon sink capacity of the ecological system.
In one possible design, the first model further includes a power generation constraint sub-model, and the first objective function is further implemented using the power generation constraint sub-model as a constraint condition, where the power generation constraint sub-model is implemented by the following formula:
wherein ,investment cost per unit installed capacity for generator g, < >>The generator installation capacity is the sum of wind power installation capacity, photoelectric installation capacity, water power installation capacity and thermal power installation capacity>For the upper limit value of the generator installation capacity, +.>Is the lower limit value of the installed capacity of the generator.
In one possible design, the first model further includes a second objective function by which the full life cycle carbon emission assessment submodel is optimized, the second objective function being as shown in the following formula:
wherein ,FIES For carbon emission operation cost, F device,t F for the running cost of the equipment carbon,t For carbon emission cost, F market,t The cost for market purchase;
the full life cycle carbon emission assessment submodel is realized by the following formula:
F carbon,t ＝E all ；
E all ＝E wind +E sun +E CHP +E gt +E eb +E P2G +E es +E Market ；
wherein ,Eall E is the carbon emission of the comprehensive energy system wind E is the actual carbon emission of the fan sun For the actual carbon emission of the photovoltaic, E CHP For carbon emission of cogeneration units E GT For carbon emissions of gas turbines, E EB For carbon emission of electric boiler units E P2G Is the carbon emission of the P2G unit, E ES For carbon emission of energy storage devices, E Market Is the carbon emission of the market purchase.
In one possible design, the power parameters further include loads of nodes in the first power region, and the grid planning module implements planning the grid according to the first model by:
and determining the connection relation among the nodes of the power transmission network according to the load of each node based on a graph theory algorithm.
In one possible design, the grid planning module determines the connection relationship between nodes of the grid from the loads of the nodes based on a graph theory algorithm by:
acquiring an initial grid pattern corresponding to a power transmission network;
inputting the load, wind power installation capacity, photoelectric installation capacity, water power installation capacity, thermal power installation capacity and energy storage installation capacity of each node into the grid map;
determining a corresponding matrix according to the grid pattern;
updating a matrix based on a network maximum flow method;
acquiring an updated grid pattern according to the updated matrix;
adjusting the output of a thermal power unit, a pumping storage unit and a new energy unit for each node in the updated grid pattern;
Carrying out tide calculation to obtain a tide result based on the load of each node and the output of the corresponding thermal power generating unit, pumping storage unit and new energy unit;
and determining whether the power flow result is converged, if so, outputting an updated grid pattern to determine the connection relation among all nodes of the power transmission network, and if not, updating the current grid pattern until the power flow result is converged.
Other implementation manners and effects of the device refer to descriptions in the embodiments of the grid planning method under the access of new energy, and are not repeated here.
The basic principles of the present application have been described above in connection with specific embodiments, however, it should be noted that the advantages, benefits, effects, etc. mentioned in the present application are merely examples and not intended to be limiting, and these advantages, benefits, effects, etc. are not to be considered as essential to the various embodiments of the present application. Furthermore, the specific details disclosed herein are for purposes of illustration and understanding only, and are not intended to be limiting, as the application is not necessarily limited to practice with the above described specific details.
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited in order and may be performed in other orders, unless explicitly stated herein. Moreover, at least some of the steps in the flowcharts of the figures may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order of their execution not necessarily being sequential, but may be performed in turn or alternately with other steps or at least a portion of the other steps or stages.
The block diagrams of the devices, apparatuses, devices, systems referred to in the present application are only illustrative examples and are not intended to require or imply that the connections, arrangements, configurations must be made in the manner shown in the block diagrams. As will be appreciated by one of skill in the art, the devices, apparatuses, devices, systems may be connected, arranged, configured in any manner. Words such as "including," "comprising," "having," and the like are words of openness and mean "including but not limited to," and are used interchangeably therewith. The terms "or" and "as used herein refer to and are used interchangeably with the term" and/or "unless the context clearly indicates otherwise. The term "such as" as used herein refers to, and is used interchangeably with, the phrase "such as, but not limited to.
It is also noted that in the apparatus, devices and methods of the present application, the components or steps may be disassembled and/or assembled. Such decomposition and/or recombination should be considered as equivalent aspects of the present application.
The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the application. Thus, the present application is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing description has been presented for purposes of illustration and description. Furthermore, this description is not intended to limit embodiments of the application to the form disclosed herein. Although a number of example aspects and embodiments have been discussed above, a person of ordinary skill in the art will recognize certain variations, modifications, alterations, additions, and subcombinations thereof.
Claims (10)
1. The planning method of the power transmission network under the new energy access is characterized by comprising the following steps of:
determining power parameters according to a power transmission network to be planned, wherein the power parameters comprise wind power installed capacity and photovoltaic installed capacity of each node in a first power area of the power transmission network;
establishing a first model for planning the power transmission network based on the electric power parameters, wherein the first model comprises a first objective function, a two-carbon objective sub-model, a full life cycle carbon emission evaluation sub-model and a new energy permeability sub-model, the first objective function is used for optimizing the first model by minimizing the total cost of the power transmission network, and the first objective function takes the two-carbon objective sub-model and the new energy permeability sub-model as constraint conditions;
optimizing the power parameters by adopting an optimization algorithm to optimize the first objective function so as to obtain the optimized first model;
And planning the power transmission network according to the first model.
2. The method of claim 1, wherein the new energy permeability submodel is implemented by the following formula:
wherein ,for said wind power installation capacity,/->For said optoelectric installation capacity,/->For wind power permeability>Is the photoelectric permeability beta NE For the consumption duty ratio of renewable energy sources, alpha fire For the standard coal coefficient of folding E water For generating electric power, E sun For generating photovoltaic power, E wind For generating electric power, E X Beta, the total energy consumption Abs The responsibility weight is absorbed for renewable energy sources, < - > and->Is the electricity consumption of the whole society.
3. A method according to claim 1 or 2, characterized in that the first objective function is as shown in the following formula:
wherein X is the first power region, T is the planning period corresponding to the power transmission network, F cost For the total cost of the power transmission network, F inv For the investment cost, the investment cost includes hydroelectric investment cost, thermal power investment cost, wind power investment cost and photoelectric investment cost, the F ope For the operation cost, the F inv As shown in the following formula:
wherein ,for the construction cost of the generator, < > for>For the line construction cost>Is the energy storage cost.
4. A method according to claim 3, wherein the power parameters further comprise energy storage installed capacity, the first model further comprises a power balance constraint sub-model, the first objective function is further constrained by the power balance constraint sub-model, the power balance constraint sub-model is implemented by the following formula:
wherein ,PD For the load demand of the first power region, P gen For the power generated by the generator g corresponding to the first power region,for abandoned power, +.>For the stored power generation power>Power storage for energy storage, wherein-> and />Are determined according to the stored energy loading capacity.
5. The method of claim 4, wherein the two-carbon target submodel is implemented by the following formula:
wherein ,Pfire For thermal power output, T is any time of the planning period T, 2030 represents year, 2060 represents year, and beta fire The carbon dioxide emission for thermal power reaches the ratio coefficient of carbon neutralization through the carbon saving and emission reduction technology and the carbon sink capacity of the ecological system.
6. The method of claim 4, wherein the first model further comprises a power generation constraint sub-model, the first objective function further being constrained by the power generation constraint sub-model, the power generation constraint sub-model being implemented by:
wherein ,investment cost for unit installed capacity of said generator g, < > j >>The generator installation capacity is the sum of wind power installation capacity, photoelectric installation capacity, water power installation capacity and thermal power installation capacity >For the upper limit value of the generator installation capacity, < > for>Is the lower limit value of the installed capacity of the generator.
7. The method according to claim 1 or 2, wherein the first model further comprises a second objective function, the full life cycle carbon emission assessment sub-model being optimized by the second objective function, the second objective function being as shown in the following formula:
wherein ,FIES For carbon emission operation cost, F device,t F for the running cost of the equipment carbon,t For carbon emission cost, F market,t The cost for market purchase;
the full life cycle carbon emission assessment submodel is realized by the following formula:
F carbon,t ＝E all ；
E all ＝E wind +E sun +E CHP +E GT +E EB +E P2G +E ES +E Market ；
wherein ,Eall E is the carbon emission of the comprehensive energy system wind E is the actual carbon emission of the fan sun For the actual carbon emission of the photovoltaic, E CHP For carbon emission of cogeneration units E GT For carbon emissions of gas turbines, E EB For carbon emission of electric boiler units E P2G Is the carbon emission of the P2G unit, E ES For carbon emission of energy storage devices, E Market Is the carbon emission of the market purchase.
8. The method of claim 4, wherein the power parameters further comprise loads of nodes in the first power region, the planning the power grid according to the first model comprising:
And determining the connection relation among the nodes of the power transmission network according to the loads of the nodes based on a graph theory algorithm.
9. The method of claim 8, wherein the graph-theory-based algorithm determining the connection relationship between the nodes of the power transmission network according to the loads of the nodes comprises:
acquiring an initial grid pattern corresponding to the power transmission network;
inputting the load of each node, the wind power installation capacity, the photovoltaic installation capacity, the hydropower installation capacity, the thermal power installation capacity and the energy storage installation capacity into the grid map;
determining a corresponding matrix according to the grid pattern;
updating the matrix based on a network maximum flow method;
acquiring the updated grid pattern according to the updated matrix;
adjusting the output of a thermal power unit, a pumping storage unit and a new energy unit for each node in the updated grid pattern;
carrying out tide calculation to obtain a tide result based on the load of each node and the output of the corresponding thermal power generating unit, pumping storage unit and new energy unit;
and determining whether the power flow result is converged, if so, outputting the updated grid pattern to determine the connection relation among all nodes of the power transmission network, and if not, updating the current grid pattern until the power flow result is converged.
10. A planning apparatus for a power transmission network under new energy access, comprising:
the power parameter determining module is used for determining power parameters according to a power transmission network to be planned, wherein the power parameters comprise wind power installed capacity and photovoltaic installed capacity of each node in a first power area of the power transmission network;
a first model building module for building a first model for planning the power transmission network based on the power parameters, wherein the first model comprises a first objective function, a two-carbon objective sub-model, a full life cycle carbon emission evaluation sub-model and a new energy permeability sub-model, the first objective function is used for optimizing the first model by minimizing the total cost of the power transmission network, and the first objective function takes the two-carbon objective sub-model and the new energy permeability sub-model as constraint conditions;
the first model optimization module optimizes the electric power parameters by adopting an optimization algorithm so as to optimize the first objective function, thereby obtaining the optimized first model;
and the power transmission network planning module is used for planning the power transmission network according to the first model.
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| CN118747693B (en) * | 2024-07-15 | 2025-10-21 | 南方电网能源发展研究院有限责任公司 | Method, device and equipment for determining planning scheme of inter-provincial power transmission project |
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