Ecological power system low-carbon transformation planning method considering meteorology

Abstract

The invention discloses an ecological power system low-carbon transformation planning method considering meteorology, and belongs to the field of energy low-carbon transformation planning, and the method comprises the steps: introducing meteorological uncertain factors, and comprehensively considering the effects of ecological carbon sink and renewable energy in low-carbon transformation planning, deep coupling of an ecological system and an electric power energy system is realized through uncertainty of various meteorological factors, and an energy low-carbon transformation planning framework of an ecological-electric power coupling system is constructed; on the basis of the planning framework, taking the electrification rate of the system in a planning period as a low-carbon transformation path constraint, and establishing an energy low-carbon transformation planning model of the ecological-electric power coupling system; performing conversion and linearization processing on the planning model through a cone duality theory and a plurality of linearization methods to obtain a two-stage robust planning model; an IEEE-33 node power distribution network system is adopted for simulation, and results are compared through typical scenes. According to the invention, the total cost of planning and operation is effectively reduced, and the low-carbon benefit of ecological carbon sink is improved.

Description

Technical Field

[0001] The present invention relates to the field of energy low-carbon transformation planning, and in particular to a low-carbon transformation planning method for an ecological power system taking meteorology into consideration. Background Art

[0002] Low-carbon industry upgrading is the core means to achieve long-term climate change mitigation and sustainable industrial development. Among them, the carbon emissions of the power industry rank the highest among all industries in the energy system, and ecological forestry plays an important role in carbon sequestration, climate change mitigation and achieving carbon neutrality. Therefore, it is urgent to explore the development path of the ecological-power industry under the conditions of carbon peak and carbon neutrality. The coordinated planning that combines the power system, ecological environment and climate impact is of great significance to the green and low-carbon transformation.

[0003] As the pace of decarbonization in the energy sector accelerates, the transformation and development of the power system needs to consider a variety of factors: deploying a large amount of intermittent renewable energy, installing distributed energy storage equipment and gas-fired generator equipment, etc.; at the same time, in the process of global low-carbon transformation, afforestation to increase ecological forest carbon sinks is considered to be a cost-effective and ready-made option for mitigating climate change. The carbon sequestration capacity of ecological carbon sinks also plays an important role in the low-carbon transformation process.

[0004] There is growing evidence that the important role of wind and solar energy in low-carbon transition pathways is complicated by climate change. In terms of electricity supply, weather-dependent wind and solar power generation is directly controlled by changes in meteorological inputs, mainly temperature, wind speed, and solar irradiance. At the same time, the carbon sequestration capacity of ecological forest carbon sinks is also directly affected by the above-mentioned meteorological uncertainties. However, there is currently a lack of research on low-carbon transformation planning for energy and power systems that comprehensively considers meteorological uncertainty fluctuations, the carbon sequestration role of ecological forest carbon sinks, the clean transformation of fossil energy, and the deployment of renewable energy. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a low-carbon transformation planning method for an ecological power system that takes meteorology into consideration. By rationally planning the capacity of wind turbines, distributed photovoltaics, and energy storage equipment and the area of ​​ecological forest carbon sinks, the electrification degree of fossil energy in the planned area can be improved, the total cost of planning and operation can be effectively reduced, and the low-carbon benefits of ecological carbon sinks can be enhanced.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] A low-carbon transformation planning method for an ecological power system considering meteorology includes the following steps:

[0008] Introduce meteorological uncertainties, comprehensively consider the role of ecological carbon sinks and renewable energy in low-carbon transformation planning, achieve deep coupling between the ecosystem and the power energy system through the uncertainty of multiple meteorological factors, and build an energy low-carbon transformation planning framework for the ecological-power coupling system;

[0009] Based on the planning framework, the energy low-carbon transformation planning model of the ecological-electricity coupling system is established with the system electrification rate within the planning period as the low-carbon transformation path constraint;

[0010] The two-stage robust planning model is obtained by transforming and linearizing the planning model through cone duality theory and multiple linearization methods.

[0011] The IEEE-33 node distribution network system is used for simulation, and the results are compared through typical scenarios to verify the effectiveness of the energy low-carbon transformation planning model of the ecological-power coupling system.

[0012] A further improvement of the technical solution of the present invention is that the energy low-carbon transformation planning framework of the ecological-electricity coupling system is divided into three layers: the first layer is the low-carbon transformation planning layer of the energy ecosystem, the second layer is the meteorological uncertainty layer, and the third layer is the coupling system operation layer.

[0013] The further improvement of the technical solution of the present invention is that: on the basis of the planning framework, the electrification rate of the system within the planning period is used as the low-carbon transformation path constraint, and an energy low-carbon transformation planning model of the ecological-electricity coupling system is established, which specifically includes the following contents:

[0014] Step 1: Establish an energy low-carbon transformation planning model for an ecological-electricity coupling system with investment, operation and maintenance costs, low-carbon benefits, and economic benefits as goals, taking into account meteorological uncertainty, the expansion of ecological forest carbon sinks, low-carbon transformation of fossil energy, and system operation constraints, and use the Gurobi solver to solve the planning scheme;

[0015] Step 2, determine the initial amount of ecological carbon sinks, photovoltaic power generation equipment, wind power generation equipment, and energy storage equipment in the ecological power system and the maximum planned amount within the planning period;

[0016] Step 3: Determine the various transformation paths for fossil energy, as well as the carbon emission intensity and electricity price of the upper-level main grid power supply and carbon trading price data;

[0017] Step 4: Determine the penetration rate and utilization rate index requirements of new energy in the ecological power system, as well as the carbon emission intensity and power supply price of the upper main grid and carbon trading price data;

[0018] Step 5: Input the historical data set of various meteorological factors in the planning area, perform statistical processing on the data set, and determine the mean and fluctuation range of each meteorological factor;

[0019] Step 6: Based on the fluctuation range of various meteorological factors, the constraint model of the impact of meteorological uncertainties on ecological carbon sinks and renewable energy output is derived, and the meteorological uncertainty set and its uncertainty parameters are given;

[0020] Step 7: Process the energy low-carbon transformation planning model of the ecological-electricity coupling system to obtain the main problem and sub-problems of robust planning and give the solution algorithm process.

[0021] The further improvement of the technical solution of the present invention is to construct an ecological-electricity coupling system planning model in the low-carbon transformation planning layer of the energy ecosystem. The specific process is as follows: This layer aims to minimize the cost of low-carbon transformation planning, and plans the electrification transformation path of traditional fossil energy such as coal, oil, and natural gas, the forest carbon sink area of ​​the ecosystem, and the wind, light, and storage renewable energy in the power system:

[0022] 1) Planning layer objective function

[0023] The electrification transformation costs of the three types of fossil energy and the carbon emission costs of fossil energy are shown in the following equations (6)-(7):

[0024]

[0025]

[0026] In the formula, is the electrification transformation cost of a certain type of fossil energy per unit power, where class includes coal, oil, and natural gas; Life class is the life of the equipment required for the electrification transformation of a certain type of fossil energy; T is the total number of hours of the planning cycle; is the electrification transformation power of a certain type of fossil energy load at hour t;

[0027] 2) Planning level constraints

[0028] The planning layer plans the transformation path of fossil energy, uses the electrification rate to constrain the transformation path, and constrains the range of fossil energy transformation at each node;

[0029] In addition, during the planning process, constraints are placed on the construction locations and capacities of photovoltaic, wind power, and energy storage equipment in the power grid;

[0030] At the same time, the land area occupied by the grid expansion and ecological carbon sink planning in the planning area and the total land area available for planning in the area, the ecological carbon sink area AE i,t∈year The planning cycle is also one year; iis the total land area available for planning near node i; during the planning period of electrical equipment and carbon sinks, the overall available equipment capacity and carbon sink area of ​​the system remain at the value at the end of the previous planning period, and the rotation period of forest carbon sinks is not considered in the planning process.

[0031] The further improvement of the technical solution of the present invention is to construct a power grid operation and carbon sink sequestration model in the meteorological uncertainty layer. The specific process is as follows: This layer optimizes the operating state of the ecological-power system with the goal of optimizing the power grid operation cost and the ecological carbon sink sequestration benefit:

[0032] 1) Operational layer objective function

[0033] In order to facilitate the description of the objective function, the carbon sequestration benefit of the ecological carbon sink is converted into a negative cost form, as shown in the following formula (16):

[0034]

[0035] In the above formula (16), NEP represents the net primary productivity of the ecosystem. Formula (16) is specifically divided into two steps: first, the total amount of carbon sequestration in the ecosystem is calculated in detail according to the NEP of the ecosystem per unit area; further, the carbon reduction benefits of the carbon sequestration amount per carbon sink area are calculated according to the carbon trading price in the carbon trading market;

[0036] 2) Distribution network operation constraints

[0037] Each node in the distribution network needs to meet the active power balance constraint and reactive power balance constraint; among them, the purchased active power only exists at the power supply node of the distribution network, and each node has its own reactive power compensation device, which allows the reactive power to be generated locally to supply the load;

[0038] In addition, the energy storage battery needs to meet the constraints of charge and discharge power and energy storage capacity; the relationship between the voltage, current, active and reactive power at each node in the distribution network can be accurately relaxed using the second-order cone form;

[0039] When the power grid is running, the active power, reactive power, voltage and current variables at the nodes and branches need to meet the upper and lower limit constraints for safe operation;

[0040] For renewable energy output from photovoltaic and wind power generation, the grid operation needs to meet the constraints of new energy penetration and utilization rate;

[0041] To ensure the stability of system operation, the penetration rate in each period shall not exceed the maximum penetration rate limit, and the load demand response and the electric energy stored in the energy storage equipment shall be regarded as the consumed new energy electric energy; the new energy utilization rate of the system shall be constrained every 24 hours to ensure the full utilization of the new energy output electric energy;

[0042] 3) Renewable energy model

[0043] The output of photovoltaic equipment is affected by solar irradiance, temperature and wind speed; therefore, the following model is used to describe the output of photovoltaic equipment;

[0044] The output power of photovoltaic equipment presents a complex functional relationship with various meteorological factors, which needs to be simplified to achieve decoupling between meteorological uncertain variables; the output of wind power generation equipment is determined by the blade shape and wind speed of the wind turbine; the blade shape is evaluated by the Weber shape coefficient to obtain the optimal wind speed value V of the wind turbine. opt ;

[0045] The actual output power of the wind turbine during operation is in the form of a piecewise power function, that is, the wind turbine must output electrical energy within a suitable wind speed range. The power function term of the wind turbine output needs to be linearized during the solution process.

[0046] 4) Ecological carbon sink and carbon fixation model

[0047] The canopy two-leaf model was improved and linearized, and the improved two-leaf model was used to accurately calculate the carbon fixation capacity of ecological forest carbon sink photosynthesis;

[0048] Further, the solar radiation of the shade leaves and the sun leaves is obtained

[0049] Forest carbon sink photosynthesis is affected by leaf stomatal conductance. By temperature T t The light quantum density of photosynthesis of yin-yang leaves Decide;

[0050] In addition to the above external factors, the leaf photosynthesis rate of forest carbon sinks is also limited by the light reaction and dark reaction processes inside the leaves; among them, the reaction rates of the two stages mainly depend on the photoelectron transfer rate and CO2 carboxylation rate;

[0051] According to all the above model formulas, the calculation of forest ecological carbon accumulation respiration RE and net ecosystem productivity NEP is shown in the following formula (42):

[0052]

[0053] In summary, the carbon sequestration capacity of carbon sinks in the ecological-electricity coupling system can be accurately described and calculated through the above model. The more refined and near-real-time carbon sequestration capacity of carbon sink resources obtained by this model will greatly improve the economic benefits and low-carbon benefits of the system's transformation planning.

[0054] The further improvement of the technical solution of the present invention is that: a meteorological uncertainty model of renewable energy and ecological carbon sink is constructed in the coupled system operation layer, and the specific process is as follows: this layer aims to find the severe scenarios for the planning and operation of the coupled system, introduces three types of meteorological uncertainties, namely, light, ambient temperature, and wind speed, and uses box-type uncertainty sets and uncertain parameters to constrain and process the uncertainty of meteorological factors;

[0055] For the photovoltaic model, the terms of light, temperature and wind speed in the formula are replaced by the sum of the mean and variance, and the original is simplified to obtain the average photovoltaic power P for light and temperature factors. t PVavr , the photovoltaic power variance P with respect to illumination t PVDSS and the photovoltaic power variance P with respect to temperature t PVDST ;

[0056] For the wind turbine model, according to the Weber shape coefficient, there is a piecewise linear relationship between the wind turbine output power and the square of the wind speed; therefore, the wind speed is replaced by the sum of the mean value and the variance, and the original formula is rearranged to obtain the mean and variance of the wind turbine output with respect to the wind speed;

[0057] For the ecological carbon sink model, the same simplification method as the photovoltaic model is used to obtain the average and variance of the carbon fixation rate related to light and temperature factors; in order to facilitate the subsequent solution of the uncertainty term of meteorological factors, the complex root part Rf is ignored. t c～ , Rf t j～ The volatility of , which is considered as a constant;

[0058] At the same time, the final form of the meteorological uncertainty model for photovoltaics, wind turbines, and ecological carbon sinks is obtained.

[0059] A further improvement of the technical solution of the present invention is that the final form of the energy low-carbon transformation planning model of the ecological-electricity coupling system is shown in the following formula (55):

[0060]

[0061] The further improvement of the technical solution of the present invention is that the planning model is converted and linearized by cone duality theory and multiple linearization methods to obtain a two-stage robust planning model, which specifically includes the following contents:

[0062] The two-leaf carbon fixation model of ecological carbon sink and the meteorological uncertainty of wind and photovoltaic power generation are linearized through cone duality theory, second-order cone relaxation method, Big-M method and exponential linearization method.

[0063] During the solution process, the planning layer of the energy low-carbon transformation planning model of the ecological-power coupling system plans the installation location and ecological carbon sink area of ​​various types of equipment invested in the system, and conducts operation tests under deterministic meteorological scenarios; in the dual sub-problem of the operation layer, the worst meteorological uncertainty scenario is found based on the planning quantity of the upper layer, and passed to the planning layer; it is iterated repeatedly until the problem converges.

[0064] The further improvement of the technical solution of the present invention is that the two-leaf carbon fixation model of ecological carbon sink and the meteorological uncertainty of wind power and photovoltaic power generation are linearized by cone duality theory, second-order cone relaxation method, Big-M method and exponential linearization method, which specifically includes the following contents:

[0065] The energy low-carbon transformation planning model of the ecological-electricity coupling system is written in standard form as shown in equation (56):

[0066]

[0067] The standard form of the original problem in equation (56) above is transformed into the main problem at the planning level and the sub-problems at the operation level;

[0068] In the subproblem, dual variables λ and ξ,φ are introduced for linear constraints and SOCP constraints respectively. According to the duality theorem and Cauchy-Schwarz theorem, LP and SOCP constraints are processed respectively to obtain the dual subproblem;

[0069] The linearization processing method is as follows:

[0070] There are nonlinear terms in the objective function of the dual subproblem. Since the uncertain variables are binary variables, the Big-M method is used to linearize them, and uλ→π is set.

[0071] After linearization, the final form of the dual subproblem is obtained, as shown in equation (61):

[0072]

[0073] In summary, the original problem model is equivalently transformed into the upper-level main problem (57) and the lower-level sub-problem (61), and then an algorithm is designed to solve the model;

[0074] The energy low-carbon transformation planning model of the ecological-electricity coupling system is a two-level robust optimization problem, which is solved by the column and constraint generation algorithm. The main problem of the energy low-carbon transformation planning model of the ecological-electricity coupling system obtains the energy transformation planning solution of the ecological-electricity coupling system and the lower bound of the objective function of the original problem; it is substituted into the dual sub-problem to find the severe scenario of the meteorological uncertainty set and the upper bound of the objective function of the original problem; the column and constraint generation algorithm iterates the main and sub-problems, and after the upper and lower bounds converge, the optimal robust solution for the planning and operation of the original problem can be obtained.

[0075] Due to the adoption of the above technical solution, the technical progress achieved by the present invention is:

[0076] 1. The present invention realizes the deep coupling of the ecosystem and the electric energy system through the uncertainty of various meteorological factors, and constructs an energy low-carbon transformation planning framework for the ecological-electricity coupling system; taking the electrification rate of the system's traditional fossil energy within the planning period as the low-carbon transformation path constraint, an energy low-carbon transformation planning model for the ecological-electricity coupling system is established, which is conducive to the coupled analysis of meteorological uncertainties between the ecosystem and the electric energy system.

[0077] 2. The energy low-carbon transformation planning model of the ecological-electricity coupling system constructed in the present invention can improve the electrification degree of fossil energy in the planning area by rationally planning the capacity of wind turbines, distributed photovoltaics, energy storage equipment and the area of ​​ecological forest carbon sinks, effectively reducing the total cost of planning and operation and improving the low-carbon benefits of ecological carbon sinks.

[0078] 3. The present invention clarifies the advantages of the post-accelerated energy electrification transformation route in terms of economic benefits and low-carbon benefits; at the same time, the constructed energy low-carbon transformation planning model of the ecological-electricity coupling system plays a guiding role in building an integrated energy system with deep ecological-electricity coupling for the carbon neutrality goal. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art are briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work.

[0080] Figure 1 Schematic diagram of the energy low-carbon transformation planning framework of the ecological-electricity coupling system in an embodiment of the present invention;

[0081] Figure 2 It is a flow chart of the algorithm for solving the energy low-carbon transformation planning model of the ecological-electricity coupling system in an embodiment of the present invention. DETAILED DESCRIPTION

[0082] It should be noted that the terms "including" and "having" and any variations thereof in the specification and claims of the present invention and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or apparatus.

[0083] The present invention is further described in detail below with reference to the accompanying drawings and embodiments:

[0084] like Figure 1 , 2 As shown in the figure, a low-carbon transformation planning method for ecological power systems considering meteorology is introduced. Three types of uncertain factors, namely solar irradiance, temperature and wind speed, are introduced. The role of ecological carbon sinks and renewable energy is comprehensively considered in the low-carbon transformation planning. The deep coupling of the ecosystem and the power energy system is achieved through the uncertainty of various meteorological factors, and an energy low-carbon transformation planning framework for the ecological-power coupling system is constructed. On the basis of the planning framework, the electrification rate of the system within the planning period is used as the low-carbon transformation path constraint to establish an energy low-carbon transformation planning model for the ecological-power coupling system. The planning model is converted and linearized through cone duality theory and multiple linearization methods to obtain a two-stage robust planning model. The IEEE-33 node distribution network system is used for simulation, and the results are compared through typical scenarios to verify the effectiveness of the energy low-carbon transformation planning model for the ecological-power coupling system. Specifically, it includes the following contents:

[0085] Step 1: Establish an energy low-carbon transformation planning model for an ecological-electricity coupling system with investment, operation and maintenance costs, low-carbon benefits, and economic benefits as goals, taking into account meteorological uncertainty, the expansion of ecological forest carbon sinks, low-carbon transformation of fossil energy, and system operation constraints, and use the Gurobi solver to solve the planning scheme;

[0086] Step 2, determine the initial amount of ecological carbon sinks, photovoltaic power generation equipment, wind power generation equipment, and energy storage equipment in the ecological power system and the maximum planned amount within the planning period;

[0087] Step 3: Determine the various transformation paths for fossil energy, as well as data such as the carbon emission intensity and electricity price of the upper main grid power supply, and carbon trading prices;

[0088] Step 4: Determine the penetration rate and utilization rate of new energy in the ecological power system, as well as the carbon emission intensity and power supply price of the upper main grid, carbon trading price and other data;

[0089] Step 5: Input the historical data set of various meteorological factors in the planning area, perform statistical processing on the data set, and determine the mean and fluctuation range of each meteorological factor;

[0090] Step 6: Based on the fluctuation range of various meteorological factors mentioned above, the constraint model of the impact of meteorological uncertainties on ecological carbon sinks and renewable energy output is derived, and the meteorological uncertainty set and its uncertainty parameters are given;

[0091] Step 7, process the planning model to obtain the main problem and sub-problems of robust planning and give the solution algorithm flow;

[0092] Step 8, linearize the two-leaf carbon fixation model of ecological carbon sink and the meteorological uncertainty of wind power and photovoltaic power generation through cone duality theory, second-order cone relaxation method, Big-M method and exponential linearization method;

[0093] Step 9. During the solution process, the planning layer of the model plans the installation locations and ecological carbon sink areas of various types of equipment (such as photovoltaic systems, energy storage, and wind turbines) invested in the system, and conducts operation tests under deterministic meteorological scenarios. In the dual subproblem of the operation layer, the worst meteorological uncertainty scenario is found based on the planning quantity of the upper layer, and passed to the planning layer. Iterate repeatedly until the problem converges.

[0094] Step 10: Based on the energy low-carbon transformation planning model of the ecological-electricity coupling system established above, the IEEE33 node system is used for testing to verify the effectiveness of the proposed model.

[0095] Specific:

[0096] 1. Establishment of the energy low-carbon transformation planning model for the ecological-electricity coupling system (hereinafter referred to as the planning model), specifically including:

[0097] The energy low-carbon transformation planning framework of the constructed ecological-electricity coupling system is divided into three layers: the first layer is the low-carbon transformation planning layer of the energy ecosystem, the second layer is the meteorological uncertainty layer, and the third layer is the coupling system operation layer. The planning model fully considers the coupling effect of fossil energy electrification transformation, renewable energy and ecological carbon sink. The establishment, transformation process and solution algorithm of the planning model specifically include the following contents:

[0098] 1.1 Constructing an ecological-electricity coupling system planning model at the low-carbon transformation planning layer of the energy ecosystem

[0099] This layer aims to minimize the cost of low-carbon transformation planning, and mainly plans the electrification transformation paths of traditional fossil energy such as coal, oil, and natural gas, the forest carbon sink area of ​​ecosystems, and renewable energy such as wind, light, and storage in the power system.

[0100] 1) Planning layer objective function

[0101] The planning target is determined by the grid planning cost Ecological carbon sink planning cost Cost of transition from fossil fuels to electrification and the carbon emission costs of fossil energy The composition is shown in the following formula (1):

[0102]

[0103] The equipment planned and constructed for the power grid includes photovoltaic, wind power, and energy storage equipment. The planning costs are shown in the following equations (2)-(3):

[0104]

[0105]

[0106] The ecosystem plans the forest carbon sink, and its planning cost is composed of the seedling cost C em , planting cost C ed , land preparation cost C ez , raising cost C ef The composition is shown in the following formula (4)-(5):

[0107]

[0108]

[0109] Among them, AE i represents the ecological carbon sink area near the i-node in the power grid; M pr , D pr , Z pr , F pr The seedling cost, planting cost, land preparation cost and cultivation cost of ecological carbon sink per unit area respectively.

[0110] The coupled system considers three types of fossil energy: coal, oil, and natural gas, and electrifies these three types of traditional fossil energy. Therefore, the electrification transformation costs of the three types of energy and the carbon emission costs of fossil energy are shown in the following equations (6)-(7):

[0111]

[0112]

[0113] In the formula, is the electrification transformation cost of a certain type of fossil energy per unit power (where class includes coal, oil, and natural gas); Life class is the life of the equipment required for the electrification transformation of a certain type of fossil energy; T is the total number of hours of the planning cycle; It is the electrification transformation power of a certain type of fossil energy load at the tth hour.

[0114] 2) Planning level constraints

[0115] The planning layer plans the transformation path of fossil energy, uses the electrification rate to constrain the transformation path, and constrains the range of fossil energy transformation at each node, as shown in the following equations (8)-(10):

[0116]

[0117]

[0118]

[0119] In the above formula, ETR start , ETR end ,ETR year The system energy electrification rate indicators at the beginning and end of the planning period and in a certain year in the planning period. year represents any year in the planning period. start , year end They are the starting year and ending year of the planning cycle respectively. is the power load value of node i at time t. Δ Trans It is an auxiliary variable to ensure the changing trend of the power load curve during the electrification transformation of fossil energy. is the total consumption of a certain type of fossil energy in year year. class Indicates the calorific value of a certain type of fossil energy.

[0120] In addition, during the planning process, constraints are imposed on the construction locations and capacities of photovoltaic, wind power, and energy storage equipment in the power grid, as shown in the following formula (11):

[0121]

[0122] In the above formula, It is the planned capacity of photovoltaic at each node and each time period, and the construction period of photovoltaic equipment is 0.5 years. It is the planned capacity of wind turbine equipment at each node and each time period, and the construction period of wind turbine equipment is 1 year. The planned capacity of energy storage equipment at each node and each time period, and the construction period of energy storage equipment is 1 year. At the same time, the expansion of the power grid in the planning area (A PV , A W , A sto are the land area occupied by photovoltaic, wind turbine and energy storage equipment respectively, the land area occupied by ecological carbon sink planning and the total land area available for planning in the region. The specific constraints are shown in formula (12):

[0123]

[0124] From the above formulas (11)-(12), we can see that the ecological carbon sink area AE i,t∈year The planning period is also one year. iis the total land area available for planning near node i. During the planning period of electrical equipment and carbon sink, the overall available equipment capacity and carbon sink area of ​​the system remain at the value at the end of the previous planning period, and the rotation period of forest carbon sink is not considered in the planning process.

[0125] 1.2 Constructing a grid operation and carbon sink model in the meteorological uncertainty layer

[0126] This layer optimizes the operating status of the ecological-power system with the goal of optimizing the grid operation cost and the ecological carbon sink carbon sequestration benefits.

[0127] 1) Operational layer objective function

[0128] Total operating cost target C oper The cost of grid operation Ecological carbon sink carbon sequestration benefits The composition is shown in the following formula (13):

[0129]

[0130] The grid operation cost includes the cost of purchasing electricity from the distribution network C buy , the carbon emission cost of electricity load C ess and load demand response costs (load costs that can be reduced C cut , translatable load C move )3 items, the specific power grid cost function is shown in the following equations (14)-(15):

[0131]

[0132]

[0133] In the above formula, P t buy is the power purchased by the distribution network at time t, Tr elect is the main grid electricity price, Tr CO2 represents the carbon trading price, Indicates the carbon emission intensity of the upper main power grid. Load demand response only considers the curtailable load With translatable load Types, among which the load cost factor k can be reduced Lcut and the movable load cost factor k Lmove The demand response behavior is regulated by using 50% and 25% of the electricity purchase price. To facilitate the description of the objective function, the carbon sequestration benefit of the ecological carbon sink is converted into a negative cost form, as shown in the following formula (16):

[0134]

[0135] In the above formula (16), NEP represents the net primary productivity of the ecosystem. Formula (16) can be divided into two steps: first, the total amount of carbon sequestration in the ecosystem is calculated in detail based on the NEP of the ecosystem per unit area; further, the carbon reduction benefits of the carbon sequestration amount per carbon sink area are calculated based on the carbon trading price in the carbon trading market.

[0136] 2) Distribution network operation constraints

[0137] Each node in the distribution network needs to meet the active power balance constraint and the reactive power balance constraint. Among them, the purchased active power only exists at the power supply node of the distribution network, and each node has its own reactive power compensation device, which allows the reactive power to be generated locally to supply the load. At this time, the power balance constraint is shown in the following equations (17)-(18):

[0138]

[0139]

[0140] In the above formula, P ij,t , Q ij,t represents the active and reactive power transmitted on branch ij at time t, I ij,t Represents the square of the current value on branch ij; R ij , X ij are the resistance and reactance values ​​on branch ij respectively, is the reactive power output value of node j; are the active and reactive load values ​​on the node. is the output value of the photovoltaic equipment and wind turbine on node j at time t, is the discharge power and charging power of the energy storage device at node j at time t.

[0141] In addition, the energy storage battery needs to meet the constraints of charge and discharge power and energy storage capacity, as shown in the following formula (19):

[0142]

[0143] In the above formula, is the energy storage capacity of the energy storage device on node j at time t, η sto is the charging and discharging efficiency of the energy storage device. The relationship between the voltage, current, active and reactive power at each node in the distribution network can be accurately relaxed using a second-order cone form, that is, the constraints of equations (20)-(21) need to be satisfied.

[0144] U i,t -U j,t =2(P ij,t ·R ij +Q ij,t ·Xij )-I ij,t Z ij (20)

[0145] U i,t +I ij,t ≥||2P ij,t ,2Q ij,t ,U i,t -I ij,t ||2 (21)

[0146] In the above formula, U i,t is the square value of the node voltage at node i at time t, Z ij is the impedance value of branch ij. When the power grid is running, the active power, reactive power, voltage and current variables on the nodes and branches need to meet the upper and lower limit constraints for safe operation, as shown in the following formula (22):

[0147]

[0148] For renewable energy output of photovoltaic and wind power generation, the grid operation needs to meet the constraints of new energy penetration and utilization rate, as shown in the following equations (23)-(24):

[0149]

[0150]

[0151] In the above formula, STR and LYR are the system's renewable energy penetration rate and utilization rate, respectively. To ensure the stability of system operation, the penetration rate in each period shall not exceed the maximum penetration rate limit, and the load demand response and the energy stored in the energy storage equipment shall be regarded as the renewable energy power consumed; the system's renewable energy utilization rate is constrained every 24 hours to ensure full utilization of renewable energy output power.

[0152] 3) Renewable energy model

[0153] The output of photovoltaic equipment is affected by solar irradiance, temperature and wind speed. Therefore, the following model is used to describe the output of photovoltaic equipment, as shown in equations (25)-(26):

[0154]

[0155] u L =5.7+3.8Vt (26)

[0156] In the above formula, G S is the photovoltaic standard incident radiation (1kW / m 2 ), T ref is the battery operating temperature under reference conditions (25°C), T NOCTis the rated operating temperature (46°C), η ref is the photovoltaic reference efficiency (16%), ατ, β ref is the photovoltaic absorption transmittance coefficient and temperature efficiency (0.8, -0.41% / K), u LNOCT is the nominal heat transfer coefficient (9.5), u L is the actual heat transfer coefficient. As can be seen from the above formula (25), the output power of photovoltaic equipment presents a complex functional relationship with various meteorological factors, which needs to be simplified to achieve decoupling between meteorological uncertain variables. The output of wind power generation equipment is determined by the blade shape and wind speed of the wind turbine. Among them, the blade shape is evaluated by the Weber shape coefficient to obtain the optimal wind speed value V of the wind turbine. opt , as shown in the following formula (27):

[0157]

[0158] In the above formula (27), are the mean and variance of wind speed, respectively, and Γ is the gamma function. The Weber shape coefficient k wb , c wb Determined by the wind energy reserves in the planned area, this paper calculates the wind speed statistical data in the area; the actual output power of the wind turbine during operation is shown in the following formula (28):

[0159]

[0160] In formula (28), V C ,V F is the wind speed at which the fan is cut in and cut off (2.0, 15m / s), V R is the rated wind speed of the fan. The fan output is in the form of a piecewise power function, that is, the fan must output electrical energy within the appropriate wind speed range. The power function term of the fan output needs to be linearized during the solution process.

[0161] 4) Ecological carbon sink and carbon fixation model

[0162] The canopy two-leaf model is improved and linearized, and the improved two-leaf model is used to accurately calculate the carbon fixation capacity of ecological forest carbon sink photosynthesis. The model is based on the solar altitude angle θ of the Julian day. t The leaf area index LAI of the forest canopy is obtained as shown in the following formula (29):

[0163]

[0164] In the above formula, Lat is the latitude of the planning area, Ω is the blade aggregation index, and S0 is the solar constant (1376W / m 2 ), is the solar altitude angle at noon, θavr represents the zenith angle, α leaf The average distribution angle of the blades (60°), is the leaf area index of the sun leaf and shade leaf. The canopy scattering threshold is calculated by the received light intensity. This threshold can be used to obtain the direct sunlight radiation at the top of the canopy Diffuse solar radiation under the canopy Further, the solar radiation of the shade leaves and the sun leaves is obtained As shown in the following formulas (30)-(32):

[0165]

[0166]

[0167]

[0168] Forest carbon sink photosynthesis is affected by leaf stomatal conductance. By temperature T t The light quantum density of photosynthesis of yin-yang leaves The specific model is shown in the following equations (33)-(34):

[0169]

[0170]

[0171] In the above formula, f Tg is the temperature effect function of stomatal conductance, f t PPFDshade , f t PPFDsun are the light quantum density influence functions of stomatal conductance of yin and yang leaves, g max is the maximum conductance of the leaf pores, PPFD coef is the coefficient between leaf stomatal conductance and photosynthetic photon flux density, PAR and PARER are photosynthetically active radiation and photosynthetically active radiation energy ratio, respectively, T range ,T opt The optimal temperature for leaf stomatal conductance is the range of leaf stomatal control temperature. In addition to the above external factors, the leaf photosynthesis rate of forest carbon sinks is also limited by the light reaction and dark reaction processes inside the leaves. Among them, the reaction rates of the two stages mainly depend on the photoelectron transfer rate and the CO2 carboxylation rate. The specific model is shown in the following equations (35)-(37):

[0172]

[0173]

[0174]

[0175] In the above formula, R gas is the gas volume constant (8.1343m3·Pa / mol); is the enzymatic reaction constant of carbon dioxide and oxygen, K t ek Enzyme reaction rate function, τ t is the compensation point of dark breathing, V t m is the carboxylation rate in period t, V m25 is the maximum carboxylation rate at 25°C, f t Vm is the function of carboxylation rate under the influence of temperature, Q 10 is the temperature sensitivity coefficient, T b Base temperature for photosynthesis, is the amount of CO2 in dark respiration, is the maximum light saturation rate during period t, is the light saturation rate of yin-yang leaves during period t.

[0176] In summary, the total primary productivity GPP of the two-leaf model of forest carbon sink canopy leaf photosynthesis rate is shown in the following equations (38)-(39):

[0177]

[0178]

[0179] In the above formula, is the light reaction rate limit and carboxylation rate limit of photosynthesis in yin-yang leaves, The concentration of CO2 and O2 in the atmosphere. Forest carbon sinks not only fix carbon through photosynthesis, but also require respiration for the growth of plants themselves. Growth respiration Maintaining breathing Therefore, the models of growth respiration and maintenance respiration in various parts of the plant body are shown in the following equations (40)-(41):

[0180]

[0181]

[0182] In the above formula, r g is the overall growth respiration coefficient, r aroot ,r aother , is the carbon distribution coefficient of root and other parts of the growth respiration, is the maintenance respiration of leaves, branches and roots, M leaf ,M stem ,M root is the biomass of leaves, branches and roots, r mleaf ,r mstem ,r mrootf ,r mrootc is the maintenance respiration coefficient of leaves, branches, coarse roots and fine roots, α rootc ,α rootf is the biomass ratio of coarse roots and fine roots. According to all the above model formulas, the calculation of forest ecological carbon accumulation includes respiration RE and net ecosystem productivity NEP, as shown in the following formula (42):

[0183]

[0184] In summary, the carbon sequestration capacity of carbon sinks in the ecological-electricity coupling system can be accurately described and calculated through the above model. The more refined and near-real-time carbon sequestration capacity of carbon sink resources obtained by this model will greatly improve the economic benefits and low-carbon benefits of the system's transformation planning.

[0185] 1.3 Constructing meteorological uncertainty models for renewable energy and ecological carbon sinks at the coupled system operation level

[0186] This layer aims to find the severe scenarios for the planning and operation of the coupled system. It introduces three types of meteorological uncertainties: light, ambient temperature, and wind speed. Box-type uncertainty sets and uncertain parameters are used to constrain and process the uncertainties of meteorological factors.

[0187] For the photovoltaic model, the light, temperature and wind speed terms in the formula are replaced with the sum of the mean and the variance, and the original is simplified to obtain the photovoltaic average power P for light and temperature factors. t PVavr , the photovoltaic power variance P with respect to illumination t PVDSS and the photovoltaic power variance P with respect to temperature t PVDST , as shown in the following equations (43)-(44):

[0188]

[0189]

[0190] For the wind turbine model, according to the Weber shape coefficient, there is a piecewise linear relationship between the wind turbine output power and the square of the wind speed. Therefore, the wind speed is replaced by the sum of the mean and the variance, and the original formula is rearranged to obtain the mean and variance of the wind turbine output with respect to the wind speed, as shown in the following equations (45)-(46):

[0191]

[0192]

[0193] In the derivation of equation (46), the variance of wind speed data is used to calculate the mean of wind power output: Primary fluctuation and the second-order fluctuation The above expression contains linear and quadratic terms about wind speed variance. Previous literature often ignores the impact of quadratic terms on the model. However, the present invention does not ignore the impact of the quadratic term of wind speed variance on wind turbine output power fluctuations, which enables the planning model to more accurately describe the meteorological volatility of wind power output.

[0194] For the ecological carbon sink model, the same simplification method as the photovoltaic model is used to obtain the average and variance of the carbon fixation rate for light and temperature factors. The difference is that in order to facilitate the solution of the uncertainty term of the subsequent meteorological factors, the complex root part Rf is ignored. t c～ , Rf t j～ The volatility of is considered as a constant, and the simplified ecological carbon sink model is shown in the following equations (47)-(50):

[0195]

[0196]

[0197]

[0198]

[0199] In the above model, the binary meteorological uncertain variables of light, temperature, and wind speed during period t are substituted Get the light intensity S during period t t , temperature T t , wind speed V t Uncertain variables; at the same time, the final form of the meteorological uncertainty model for photovoltaics, wind turbines, and ecological carbon sinks is obtained, as shown in the following equations (51)-(54):

[0200]

[0201]

[0202]

[0203]

[0204] In the above formula (54), is the average amount of total photosynthesis of yin-yang leaves limited by light and Rubisco, is the uncertainty of photosynthesis fluctuation of yin-yang leaves limited by light and Rubisco, are the photosynthesis variables of yin-yang leaves subject to light limitation and Rubisco limitation.

[0205] Combining the contents of 1.1-1.3, the final form of the energy low-carbon transformation planning model for the ecological-electricity coupling system is given as shown in the following equation (55):

[0206]

[0207] 2. Transformation and solution of planning model

[0208] The planning model in equation (55) in 1.3 can be written in standard form as shown in equation (56):

[0209]

[0210] 1) Cone dual transformation of subproblems

[0211] The standard form of the original problem in equation (56) above is transformed into the main problem at the planning level and the sub-problems at the operation level, as shown in equations (57)-(58) below:

[0212]

[0213]

[0214] In subproblem (57), dual variables λ and ξ,φ are introduced for linear constraints and SOCP constraints respectively. According to the duality theorem and Cauchy-Schwarz theorem, LP and SOCP constraints are processed respectively, and the dual subproblem is obtained as shown in equation (59):

[0215]

[0216] 2) Linearization processing method

[0217] It can be seen from formula (59) that there are nonlinear terms in the objective function of the dual subproblem. Since the uncertain variables are binary variables, the Big-M method is used to linearize them. Let uλ→π, then the following formula (60) is obtained:

[0218]

[0219] After linearization, the final form of the dual subproblem is obtained, as shown in equation (61):

[0220]

[0221] In summary, the original problem model is equivalently transformed into the upper-level main problem (57) and the lower-level sub-problem (61), and then an algorithm is designed to solve the model.

[0222] 3) Problem Solving Algorithm

[0223] The planning model is a two-level robust optimization problem, which can be solved using the column and constraint generation algorithm (C&CG). The solution process is shown in Figure 2 .

[0224] The main problem of the planning model obtains the energy transformation planning solution of the ecological-electricity coupling system and the lower bound of the objective function of the original problem; it is substituted into the dual sub-problem to find the severe scenario of the meteorological uncertainty set and the upper bound of the objective function of the original problem; the C&CG algorithm iterates the main and sub-problems, and after the upper and lower bounds converge, the optimal robust solution for the planning and operation of the original problem can be obtained.

[0225] 3. Cost-benefit analysis of the example system

[0226] The five-year planning simulation is carried out using the example of the ecological-electric coupling system powered by the IEEE-33 node distribution network. The planning period is 20 typical days selected according to the season, totaling 480 hours. The Gurobi solver is used to solve the example, and the feasibility and effectiveness of the low-carbon transformation planning model proposed in this invention are verified by analyzing the results.

[0227] The simulation sets up 4 scenarios:

[0228] 1) Post-acceleration electrification transformation under the meteorological deterministic scenario (uncertainty parameters are set to 0);

[0229] 2) Post-accelerated electrification transformation scenario under the meteorological robust scenario;

[0230] 3) Uniform electrification transformation scenario under meteorological robust scenario;

[0231] 4) Pre-accelerated electrification transformation scenario under meteorological robust scenario (the uncertain parameter is taken as 30).

[0232] The overall economic cost and low-carbon benefit results of the ecological-electricity energy coupling system under different scenarios are shown in Table 1 below:

[0233] Table 1 System planning operation costs and low-carbon benefits

[0234]

[0235] From the cost-effectiveness and total system cost data of the four scenarios in Table 1 above, it can be seen that the post-acceleration electrification transformation route has the lowest total cost and the highest low-carbon benefit. Under the same influence of meteorological factors and uncertainty, by comparing scenarios 2, 3, and 4, it can be seen that the post-acceleration route in scenario 2 can reduce the total cost by about 23.24%-46.72% compared with the uniform speed and pre-acceleration routes.

[0236] Comparing scenarios 1 and 2, it can be seen that under the same transition path, in terms of ecological planning costs, the investment cost of scenario 2 is about 4.67% higher than that of the deterministic scenario 1; because scenario 2 takes into account the worst fluctuations in meteorological factors, the low-carbon benefits of ecological forest carbon sinks in this scenario are nearly 2,200 yuan lower than those of ecological forest carbon sinks in scenario 1. In terms of electrification transformation costs, the planning cost of electrification transformation in scenario 2 is about 8.85% lower than that in scenario 1. This is because the output power of renewable energy PV and WT equipment in scenario 2 is affected by the uncertainty of meteorological factors during the transformation planning process, so the planned capacity of such equipment is lower than that in scenario 1, so the cost of this item in scenario 2 is lower. In terms of system operation costs, due to the influence of meteorological factors, the system operation cost of scenario 2 is about 77,200 yuan higher than that of scenario 1.

[0237] Comparing scenarios 2, 3, and 4, from the perspective of different energy electrification transformation paths, it can be seen that the pre-acceleration transformation path of scenario 4 has a higher electrification rate target in the early stage of planning, and the system needs to make more transformation investment and equipment construction investment. Due to the high cost of energy electrification transformation technology, the electrification transformation cost of scenario 4 is the highest, which is about 32.82% higher than that of scenario 2 of the post-acceleration transformation path and about 11.60% higher than that of scenario 3 of the uniform transformation path. At the same time, scenario 4 has a large amount of fossil energy transformed into power load in the early stage of planning, so the power system operation cost of this scenario is also about 31.50%-15.00% higher than that of scenarios 2 and 3. The total available land area in the planning area is limited during the transformation process, and the planned area of ​​ecological forest carbon sinks is reduced due to the increase in the construction area of ​​electrical equipment in scenarios 2, 3, and 4. At this time, the carbon sink planning cost and low-carbon benefits also decreased. The ecological planning cost of scenario 2 was about 8.97%-15.47% higher than that of scenario 3 and scenario 4, and the low-carbon benefits of ecological carbon sinks in scenario 2 were about 7.83%-13.44% higher than those in scenario 3 and scenario 4.

[0238] From the above two aspects of analysis, it can be seen that, considering the influence of meteorological uncertainties, the post-acceleration energy electrification transformation route of scenario 2 has more advantages in terms of total cost, various costs and low-carbon benefits.

[0239] In summary, the present invention introduces three types of uncertain factors, namely solar irradiance, temperature and wind speed, comprehensively considers the role of ecological carbon sinks and renewable energy in low-carbon transformation planning, realizes the deep coupling of ecosystems and power energy systems through the uncertainty of various meteorological factors, and constructs an energy low-carbon transformation planning framework for ecological-power coupling systems. On the basis of the above framework, the electrification rate of the system within the planning period is used as the low-carbon transformation path constraint, and an energy low-carbon transformation planning model for the ecological-power coupling system is established; further, the low-carbon transformation model is converted and linearized by cone duality theory and multiple linearization methods to obtain a two-stage robust planning model. Finally, the IEEE-33 node distribution network system is used for simulation, and the simulation results are compared by setting 4 typical scenarios; the simulation results show that the model can improve the electrification degree of fossil energy in the planning area by reasonably planning the capacity of wind turbines, distributed photovoltaics, energy storage equipment and the area of ​​ecological forest carbon sinks, effectively reduce the total cost of planning and operation, and improve the low-carbon benefits of ecological carbon sinks. At the same time, the simulation results of the present invention clarify the advantages of the post-accelerated energy electrification transformation route in terms of economic benefits and low-carbon benefits, and verify the effectiveness of the low-carbon transformation planning model.

[0240] The method of the present invention verifies the effectiveness of the scheme by building a reasonable experimental scenario.

[0241] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A low-carbon transformation planning method for ecological power system considering meteorology, characterized in that: Includes the following: Introduce meteorological uncertainties, comprehensively consider the role of ecological carbon sinks and renewable energy in low-carbon transformation planning, achieve deep coupling between the ecosystem and the power energy system through the uncertainty of multiple meteorological factors, and build an energy low-carbon transformation planning framework for the ecological-power coupling system; Based on the planning framework, the energy low-carbon transformation planning model of the ecological-electricity coupling system is established with the system electrification rate within the planning period as the low-carbon transformation path constraint; The two-stage robust planning model is obtained by transforming and linearizing the planning model through cone duality theory and multiple linearization methods. The IEEE-33 node distribution network system is used for simulation, and the results are compared through typical scenarios to verify the effectiveness of the energy low-carbon transformation planning model of the ecological-power coupling system.

2. According to the method of low-carbon transformation planning of ecological power system considering meteorology in claim 1, it is characterized in that: The energy low-carbon transformation planning framework of the ecological-electricity coupling system is divided into three layers: the first layer is the low-carbon transformation planning layer of the energy ecosystem, the second layer is the meteorological uncertainty layer, and the third layer is the coupling system operation layer.

3. A method for low-carbon transformation planning of an ecological power system considering meteorology according to claim 2, characterized in that: Based on the planning framework, the electrification rate of the system within the planning period is used as the low-carbon transformation path constraint to establish an energy low-carbon transformation planning model for the ecological-electricity coupling system, which specifically includes the following contents: Step 1: Establish an energy low-carbon transformation planning model for an ecological-electricity coupling system with investment, operation and maintenance costs, low-carbon benefits, and economic benefits as goals, taking into account meteorological uncertainty, the expansion of ecological forest carbon sinks, low-carbon transformation of fossil energy, and system operation constraints, and use the Gurobi solver to solve the planning scheme; Step 2, determine the initial amount of ecological carbon sinks, photovoltaic power generation equipment, wind power generation equipment, and energy storage equipment in the ecological power system and the maximum planned amount within the planning period; Step 3: Determine the various transformation paths for fossil energy, as well as the carbon emission intensity and electricity price of the upper-level main grid power supply and carbon trading price data; Step 4: Determine the penetration rate and utilization rate index requirements of new energy in the ecological power system, as well as the carbon emission intensity and power supply price of the upper main grid and carbon trading price data; Step 5: Input the historical data set of various meteorological factors in the planning area, perform statistical processing on the data set, and determine the mean and fluctuation range of each meteorological factor; Step 6: Based on the fluctuation range of various meteorological factors, the constraint model of the impact of meteorological uncertainties on ecological carbon sinks and renewable energy output is derived, and the meteorological uncertainty set and its uncertainty parameters are given; Step 7: Process the energy low-carbon transformation planning model of the ecological-electricity coupling system to obtain the main problem and sub-problems of robust planning and give the solution algorithm process.

4. A method for low-carbon transformation planning of an ecological power system considering meteorology according to claim 3, characterized in that: The ecological-electricity coupling system planning model is constructed at the low-carbon transformation planning layer of the energy ecosystem. The specific process is as follows: This layer aims to minimize the cost of low-carbon transformation planning, and plans the electrification transformation path of traditional fossil energy such as coal, oil, and natural gas, the forest carbon sink area of ​​the ecosystem, and the wind, light, and storage renewable energy in the power system: 1) Planning layer objective function The planning target is determined by the grid planning cost Ecological carbon sink planning cost Cost of transition from fossil fuels to electrification and the carbon emission costs of fossil energy The composition is shown in the following formula (1): The equipment planned and constructed for the power grid includes photovoltaic, wind power, and energy storage equipment. The planning costs are shown in the following equations (2)-(3): The ecosystem plans the forest carbon sink, and the planning cost is composed of the seedling cost C em , planting cost C ed , land preparation cost C ez , raising cost C ef The composition is shown in the following formula (4)-(5): Among them, AE i represents the ecological carbon sink area near the i-node in the power grid; M pr , D pr , Z pr , F pr They represent the seedling cost, planting cost, land preparation cost, and tending cost of ecological carbon sink per unit area respectively; The coupling system considers three types of fossil energy: coal, oil, and natural gas, and electrifies these three types of traditional fossil energy. Therefore, the electrification transformation costs of the three types of fossil energy and the carbon emission costs of fossil energy are shown in the following equations (6)-(7): In the formula, is the electrification transformation cost of a certain type of fossil energy per unit power, where class includes coal, oil, and natural gas; Life class is the life of the equipment required for the electrification transformation of a certain type of fossil energy; T is the total number of hours of the planning cycle; is the electrification transformation power of a certain type of fossil energy load at hour t; 2) Planning level constraints The planning layer plans the transformation path of fossil energy, uses the electrification rate to constrain the transformation path, and constrains the range of fossil energy transformation at each node, as shown in the following equations (8)-(10): In the above formula, ETR start , ETR end , ETR year They are the system energy electrification rate indicators at the beginning and end of the planning period and a certain year in the planning period; year represents any year in the planning period; year start , year end They are the starting year and ending year of the planning cycle respectively; is the power load value of node i at time t; Δ Trans It is an auxiliary variable to ensure the changing trend of the power load curve during the electrification transformation of fossil energy; is the total consumption of a certain type of fossil energy in year; class Indicates the calorific value of a certain type of fossil energy; In addition, during the planning process, constraints are imposed on the construction locations and capacities of photovoltaic, wind power, and energy storage equipment in the power grid, as shown in the following formula (11): In the above formula, It is the planned capacity of photovoltaic power at each node and each time period, and the construction period of photovoltaic equipment is 0.5 years; The planned capacity of wind turbine equipment at each node and each time period, and the construction period of wind turbine equipment is 1 year; is the planned capacity of the energy storage equipment at each node and each time period, and the construction period of the energy storage equipment is 1 year; at the same time, the land area occupied by the grid expansion and ecological carbon sink planning in the planning area and the total land area available for planning in the area are specifically constrained as shown in formula (12): In the formula, A PV , A W , A sto The floor space for photovoltaic, wind turbine and energy storage equipment respectively; From the above formulas (11)-(12), we can see that the ecological carbon sink area AE i,t∈year The planning cycle is also one year; i is the total land area available for planning near node i; during the planning period of electrical equipment and carbon sinks, the overall available equipment capacity and carbon sink area of ​​the system remain at the value at the end of the previous planning period, and the rotation period of forest carbon sinks is not considered in the planning process.

5. The method for low-carbon transformation planning of an ecological power system considering meteorology according to claim 3 is characterized in that: The grid operation and carbon sink model is constructed in the meteorological uncertainty layer. The specific process is as follows: This layer aims to optimize the operation cost of the power grid and the carbon sequestration benefits of ecological carbon sinks, and optimizes the operation status of the ecological-power system: 1) Operational layer objective function Total operating cost target C oper The cost of grid operation Ecological carbon sink carbon sequestration benefits The composition is shown in the following formula (13): The grid operation cost includes the cost of purchasing electricity from the distribution network C buy , the carbon emission cost of electricity load C ess and load demand response costs, where the load demand response costs include the cost of load reduction C cut and the translatable load C move , a total of 3 items, the specific power grid cost function is shown in the following equations (14)-(15): In the above formula, is the power purchased by the distribution network at time t, Tr elect is the main grid electricity price, Tr CO2 represents the carbon trading price, CI CO2 Indicates the carbon emission intensity of the upper main power grid; Load demand response only considers curtailable loads With translatable load Types, among which the load cost factor k can be reduced Lcut and the movable load cost factor k Lmove The demand response behavior is regulated by using 50% and 25% of the electricity purchase price. To facilitate the description of the objective function, the carbon sequestration benefit of the ecological carbon sink is converted into a negative cost form, as shown in the following formula (16): In the above formula (16), NEP represents the net primary productivity of the ecosystem. Formula (16) is specifically divided into two steps: first, the total amount of carbon sequestration in the ecosystem is calculated in detail according to the NEP of the ecosystem per unit area; further, the carbon reduction benefits of the carbon sequestration amount per carbon sink area are calculated according to the carbon trading price in the carbon trading market; 2) Distribution network operation constraints Each node in the distribution network needs to meet the active power balance constraint and the reactive power balance constraint. Among them, the purchased active power only exists at the power supply node of the distribution network, and each node has its own reactive power compensation device, which allows the reactive power to be generated locally to supply the load. At this time, the power balance constraints are shown in the following equations (17)-(18): In the above formula, P ij,t , Q ij,t represents the active and reactive power transmitted on branch ij at time t, I ij,t Represents the square of the current value on branch ij; R ij , X ij are the resistance and reactance values ​​on branch ij respectively, is the reactive power output value of node j; is the active and reactive load values ​​on the node; is the output value of the photovoltaic equipment and wind turbine on node j at time t, is the discharge power and charging power of the energy storage device at node j at time t; In addition, the energy storage battery needs to meet the constraints of charge and discharge power and energy storage capacity, as shown in the following formula (19): In the above formula, is the energy storage capacity of the energy storage device on node j at time t, η sto is the charging and discharging efficiency of the energy storage device; the relationship between the voltage, current, active and reactive power at each node in the distribution network can be accurately relaxed using a second-order cone form, that is, the constraints of equations (20)-(21) need to be satisfied; U i,t -U j,t =2(P ij,t ·R ij +Q ij,t ·X ij )-I ij,t Z ij (20) U i,t +I ij,t ≥||2P ij,t ,2Q ij,t ,U i,t -I ij,t ||2 (21) In the above formula, U i,t is the square value of the node voltage at node i at time t, Z ij is the impedance value of branch ij; when the power grid is running, the active power, reactive power, voltage and current variables on the nodes and branches need to meet the upper and lower limit constraints for safe operation, as shown in the following formula (22): For renewable energy output of photovoltaic and wind power generation, the grid operation needs to meet the constraints of new energy penetration and utilization rate, as shown in the following equations (23)-(24): In the above formula, STR and LYR are the system's renewable energy penetration rate and utilization rate, respectively. To ensure the stability of system operation, the penetration rate in each period shall not exceed the maximum penetration rate limit, and the load demand response and the electric energy stored in the energy storage equipment shall be regarded as the absorbed renewable energy. The system's renewable energy utilization rate is constrained every 24 hours to ensure full utilization of renewable energy output. 3) Renewable energy model The output of photovoltaic equipment is affected by solar irradiance, temperature and wind speed; therefore, the following model is used to describe the output of photovoltaic equipment, as shown in equations (25)-(26): you L =5.7+3.8V t (26) In the above formula, G S is the photovoltaic standard incident radiation (1kW / m 2 ), T ref is the battery operating temperature under reference conditions (25°C), T NOCT is the rated operating temperature (46°C), η ref is the photovoltaic reference efficiency (16%), ατ, β ref is the photovoltaic absorption transmittance coefficient and temperature efficiency (0.8, -0.41% / K), u LNOCT is the nominal heat transfer coefficient (9.5), u L is the actual heat transfer coefficient; from the above formula (25), it can be seen that the output power of photovoltaic equipment presents a complex functional relationship with various meteorological factors, which needs to be simplified to achieve the decoupling between meteorological uncertain variables; the output of wind power generation equipment is determined by the blade shape and wind speed of the wind turbine; among them, the blade shape is evaluated by the Weber shape coefficient to obtain the optimal wind speed value V of the wind turbine opt , as shown in the following formula (27): In the above formula (27), are the mean and variance of wind speed, Γ is the gamma function; the Weber shape coefficient k wb , c wb It is determined by the wind energy reserves in the planning area and is calculated by the statistical data of wind speed in the area; the actual output power of the wind turbine during operation is shown in the following formula (28): In formula (28), V C ,V F is the wind speed at which the fan is cut in and cut off (2.0, 15m / s), V R is the rated wind speed of the fan; the fan output is in the form of a piecewise power function, that is, the fan must output electrical energy within a suitable wind speed range. The power function term of the fan output needs to be linearized during the solution process; 4) Ecological carbon sink and carbon fixation model The canopy two-leaf model is improved and linearized, and the improved two-leaf model is used to accurately calculate the carbon fixation capacity of ecological forest carbon sink photosynthesis. The model is based on the solar altitude angle θ of the Julian day. t The leaf area index LAI of the forest canopy is obtained as shown in the following formula (29): In the above formula, Lat is the latitude of the planning area, Ω is the blade aggregation index, and S0 is the solar constant (1376W / m 2 ), is the solar altitude angle at noon, θ avr represents the zenith angle, α leaf The average distribution angle of the blades (60°), is the leaf area index of sun leaves and shade leaves; the canopy scattering threshold is calculated by the received light intensity This threshold can be used to obtain the direct sunlight radiation at the top of the canopy Diffuse solar radiation under the canopy Further, the solar radiation of the shade leaves and the sun leaves is obtained As shown in the following formulas (30)-(32): Forest carbon sink photosynthesis is affected by leaf stomatal conductance. By temperature T t The light quantum density of photosynthesis of yin-yang leaves The specific model is shown in the following equations (33)-(34): In the above formula, f Tg is the temperature influence function of stomatal conductance, are the light quantum density influence functions of stomatal conductance of yin and yang leaves, g max is the maximum conductance of the leaf pores, PPFD coef is the coefficient between leaf stomatal conductance and photosynthetic photon flux density, PAR and PARER are photosynthetically active radiation and photosynthetically active radiation energy ratio, respectively, T range ,T opt is the temperature range of leaf stomatal control and the optimal temperature of leaf stomatal conductance; in addition to the above external factors, the leaf photosynthesis rate of forest carbon sink is also limited by the light reaction and dark reaction processes inside the leaves; among them, the reaction rates of the two stages mainly depend on the photoelectron transfer rate and CO2 carboxylation rate. The specific model is shown in the following equations (35)-(37): In the above formula, R gas is the gas volume constant (8.1343m3·Pa / mol); is the enzymatic reaction constant of carbon dioxide and oxygen, Enzyme reaction rate function, τ t As compensation point for dark breathing, is the carboxylation rate in period t, V m25 is the maximum carboxylation rate at 25°C, is the function of carboxylation rate under the influence of temperature, Q 10 is the temperature sensitivity coefficient, T b Base temperature for photosynthesis, is the amount of CO2 in dark respiration, is the maximum light saturation rate during period t, is the light saturation rate of the yin-yang leaves during period t; In summary, the total primary productivity GPP of the two-leaf model of forest carbon sink canopy leaf photosynthesis rate is shown in the following equations (38)-(39): In the above formula, is the light reaction rate limit and carboxylation rate limit of photosynthesis in yin-yang leaves, Ca, The concentration of CO2 and O2 in the atmosphere; forest carbon sinks not only fix carbon through photosynthesis, but also require respiration for the growth of plants themselves Growth respiration Maintaining breathing Therefore, the models of growth respiration and maintenance respiration in various parts of the plant body are shown in the following equations (40)-(41): In the above formula, r g is the overall growth respiration coefficient, r aroot ,r aother , is the carbon distribution coefficient of root and other parts of the growth respiration, is the maintenance respiration of leaves, branches and roots, M leaf ,M stem ,M root is the biomass of leaves, branches and roots, r mleaf ,r mstem ,r mrootf ,r mrootc is the maintenance respiration coefficient of leaves, branches, coarse roots and fine roots, α rootc ,α rootf is the biomass ratio of coarse roots and fine roots; According to all the above model formulas, the calculation of forest ecological carbon accumulation includes respiration RE and net ecosystem productivity NEP, as shown in the following formula (42): In summary, the carbon sequestration capacity of carbon sinks in the ecological-electricity coupling system can be accurately described and calculated through the above model. The more refined and near-real-time carbon sequestration capacity of carbon sink resources obtained by this model will greatly improve the economic benefits and low-carbon benefits of the system's transformation planning.

6. A method for low-carbon transformation planning of ecological power system considering meteorology according to claim 3, characterized in that: The meteorological uncertainty model of renewable energy and ecological carbon sink is constructed at the coupled system operation layer. The specific process is as follows: This layer aims to find the worst scenarios for the planning and operation of the coupled system, introduces three types of meteorological uncertainties: light, ambient temperature, and wind speed, and uses box-type uncertainty sets and uncertain parameters to constrain and process the uncertainties of meteorological factors; For the photovoltaic model, the light, temperature and wind speed terms in the formula are replaced by the sum of the mean and the variance, and the original is simplified to obtain the average photovoltaic power for light and temperature factors. Photovoltaic power variance with respect to illumination and the PV power variance with respect to temperature As shown in the following formulas (43)-(44): For the wind turbine model, according to the Weber shape coefficient, there is a piecewise linear relationship between the wind turbine output power and the square of the wind speed. Therefore, the wind speed is replaced by the sum of the mean value and the variance, and the original formula is rearranged to obtain the mean and variance of the wind turbine output with respect to the wind speed, as shown in the following equations (45)-(46): In the derivation of equation (46), the variance of wind speed data is used to calculate the mean of wind power output: Primary fluctuation and the second-order fluctuation For the ecological carbon sink model, the same simplification method as the photovoltaic model is used to obtain the average and variance of the carbon fixation rate for light and temperature factors; in order to facilitate the subsequent solution of the uncertainty term of meteorological factors, the complex root part is ignored. The volatility of is considered as a constant, and the simplified ecological carbon sink model is shown in the following equations (47)-(50): In the above model, the binary meteorological uncertain variables of light, temperature, and wind speed during period t are substituted Get the light intensity S during period t t , temperature T t , wind speed V t Uncertain variables; at the same time, the final form of the meteorological uncertainty model for photovoltaics, wind turbines, and ecological carbon sinks is obtained, as shown in the following equations (51)-(54): In the above formula (54), is the average amount of total photosynthesis of yin-yang leaves limited by light and Rubisco, is the uncertainty of photosynthesis fluctuation of yin-yang leaves limited by light and Rubisco, are the photosynthesis variables of yin-yang leaves subject to light limitation and Rubisco limitation.

7. A method for low-carbon transformation planning of ecological power system considering meteorology according to claim 3, characterized in that: The final form of the energy low-carbon transformation planning model for the ecological-electricity coupling system is shown in equation (55):

8. The method for low-carbon transformation planning of an ecological power system considering meteorology according to claim 1 is characterized in that: The two-stage robust planning model is obtained by transforming and linearizing the planning model through cone duality theory and multiple linearization methods, which specifically includes the following contents: The two-leaf carbon fixation model of ecological carbon sink and the meteorological uncertainty of wind and photovoltaic power generation are linearized through cone duality theory, second-order cone relaxation method, Big-M method and exponential linearization method. During the solution process, the planning layer of the energy low-carbon transformation planning model of the ecological-power coupling system plans the installation location and ecological carbon sink area of ​​various types of equipment invested in the system, and conducts operation tests under deterministic meteorological scenarios; in the dual sub-problem of the operation layer, the worst meteorological uncertainty scenario is found based on the planning quantity of the upper layer, and passed to the planning layer; it is iterated repeatedly until the problem converges.

9. The method for low-carbon transformation planning of an ecological power system considering meteorology according to claim 8 is characterized in that: The two-leaf carbon fixation model of ecological carbon sink and meteorological uncertainty of wind power and photovoltaic power generation are linearized by cone duality theory, second-order cone relaxation method, Big-M method and exponential linearization method, which specifically includes the following contents: The energy low-carbon transformation planning model of the ecological-electricity coupling system is written in standard form as shown in equation (56): The standard form of the original problem in equation (56) above is transformed into the main problem at the planning level and the sub-problems at the operation level, as shown in equations (57)-(58) below: In subproblem (57), dual variables λ and ξ,φ are introduced for linear constraints and SOCP constraints respectively. According to the duality theorem and Cauchy-Schwarz theorem, LP and SOCP constraints are processed respectively, and the dual subproblem is obtained as shown in equation (59): The linearization processing method is as follows: It can be seen from formula (59) that there are nonlinear terms in the objective function of the dual subproblem. Since the uncertain variables are binary variables, the Big-M method is used to linearize them. Let uλ→π, then the following formula (60) is obtained: After linearization, the final form of the dual subproblem is obtained, as shown in equation (61): In summary, the original problem model is equivalently transformed into the upper-level main problem (57) and the lower-level sub-problem (61), and then an algorithm is designed to solve the model; The energy low-carbon transformation planning model of the ecological-electricity coupling system is a two-level robust optimization problem, which is solved by using the column and constraint generation algorithm. The main problem of the energy low-carbon transformation planning model of the ecological-electricity coupling system obtains the energy transformation planning solution of the ecological-electricity coupling system and the lower bound of the objective function of the original problem; Substitute it into the dual sub-problem to find the severe scenario of the meteorological uncertainty set and the upper bound of the objective function of the original problem; the column and constraint generation algorithm iterates the main and sub-problems, and the optimal robust solution for the planning and operation of the original problem can be obtained after the upper and lower bounds converge.

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