Quantitative evaluation method for power transmission pricing mechanism cooperating with novel power system construction

Abstract

The invention relates to the technical field of power transmission pricing mechanism quantification, and discloses a power transmission pricing mechanism quantitative evaluation method cooperating with novel power system construction, and the method comprises the following steps: S101, determining an evaluation index according to a power transmission pricing mechanism; step S102, determining an electric quantity power transmission price and a capacity power transmission price according to a power transmission pricing mechanism; s103, constructing a power supply investment time sequence and site selection prediction model according to a genetic algorithm based on a Gaussian mutation strategy, and generating an optimal solution of an investment time sequence and a site selection node; step S104, constructing a power spot market production cost simulation model and a node load growth model according to the investment decision to calculate the total power generation and power purchase cost of the market; and S105, calculating the change value of the present investment value by combining a stochastic load flow calculation method with a present investment value calculation method. According to the invention, the power grid investment efficiency is improved, and the consumption of new energy is promoted.

Description

Technical Field

[0001] The present invention belongs to the technical field of power transmission pricing mechanism quantification, and specifically relates to a power transmission pricing mechanism quantification evaluation method coordinated with the construction of a new power system. Background Art

[0002] In order to achieve the dual carbon goals of carbon peak and carbon neutrality, my country has proposed to build a new power system with new energy as the main body. However, with the large-scale access of new energy, the safe and stable operation of the power grid faces severe challenges. How to effectively promote the consumption of new energy while ensuring the "stable supply and price" of electricity has become a key issue in the construction of my country's new power system. This puts higher requirements on the design of the power market mechanism, especially in terms of transmission pricing. As a key link in the generation, transmission and distribution of electricity, a reasonable transmission pricing mechanism can not only form a synergistic effect with the electricity wholesale market, but also effectively promote the consumption of new energy and the realization of dual carbon goals. Therefore, the design and quantitative evaluation of the transmission pricing mechanism have become an important focus of electricity price reform.

[0003] However, there is a lack of research on the evaluation of transmission and distribution price mechanisms in China, and existing research results are mostly focused on the evaluation of typical transmission pricing mechanisms under specific foreign market environments and power structures. These evaluation indicators are mostly macro and qualitative indicators, such as fairness, transparency and operability. Although a few quantitative evaluation methods are used to analyze the impact of transmission prices on the efficiency of power market competition, there is still no complete system for quantitative evaluation indicators and methods for the impact of transmission prices on the construction of new power systems.

[0004] Therefore, a quantitative evaluation method for transmission pricing mechanism is urgently needed to solve the above problems. Summary of the invention

[0005] The present invention provides a quantitative evaluation method for a transmission pricing mechanism coordinated with the construction of a new power system, thereby solving the technical problem in the related art of lacking quantitative evaluation indicators and methods for the impact of transmission prices on the construction of a new power system.

[0006] The present invention provides a quantitative evaluation method for a transmission pricing mechanism coordinated with the construction of a new power system, comprising the following steps:

[0007] Step S101, determining evaluation indicators according to the impact of the transmission pricing mechanism on the grid investment efficiency, the cost of the generator set and the new energy absorption capacity, the evaluation indicators include the future investment present value change of the whole grid transmission equipment, the total market power generation and purchase costs and the new energy absorption capacity;

[0008] Step S102, determining the electricity transmission price and capacity transmission price according to the transmission pricing mechanism;

[0009] Step S103, constructing a power source investment timing and site selection prediction model according to a genetic algorithm based on a Gaussian mutation strategy, and generating an investment decision; the investment decision is an optimal solution for the investment timing and site selection node;

[0010] Step S104, constructing a power spot market production cost simulation model and a node load growth model according to the investment decision to calculate the total market power generation and purchase costs;

[0011] Step S105, using a random power flow calculation method to obtain the power flow distribution and remaining capacity change of the power transmission system, and combining the investment present value calculation method to calculate the change in the investment present value of the power transmission equipment of the entire network after the new energy unit is put into operation.

[0012] Furthermore, according to the different impact paths of the transmission pricing mechanism, the impact of transmission pricing on the investment efficiency of the power grid is quantitatively evaluated. The evaluation includes calculating the impact of the new energy units on the remaining capacity of the transmission system after commissioning, predicting the future expansion or strengthening needs of the transmission equipment, and determining the future investment present value change of the transmission equipment of the entire network; the future investment present value change of the transmission equipment of the entire network is quantified by the following formula:

[0013]

[0014] Where ΔPV Td Indicates the change in the present value of future investment, I Td,i represents the future investment in transmission equipment, r represents the discount rate, T Td,i,new represents the future investment time of equipment after the new energy unit is put into operation, T Td,i Indicates the current investment time;

[0015] According to the impact of the transmission pricing mechanism on the electricity spot market, the total market generation and purchase costs are calculated. The total market generation and purchase costs are quantified by the following formula:

[0016]

[0017] C G,T represents the total market power generation cost, C L,T represents the total electricity purchase cost in the market, represents the output of the generator set in the tth period and the kth quotation period, c i,k represents the variable cost of the unit, l t,n Indicates the power consumption of the node, represents the spot market electricity price of the node, It indicates the electricity transmission fee paid by the unit;

[0018] According to the impact of the transmission pricing mechanism on the new energy consumption capacity, the new energy consumption capacity is calculated, and the new energy consumption capacity is quantified by the following formula:

[0019]

[0020] Where N new represents the set of renewable energy units in the electricity market, T new,i represents the number of time periods included in the operating life cycle of the i-th unit, B new Indicates the quotation segment of new energy units. It represents the winning bid power generation of the new energy unit in the kth bidding segment in the tth time period.

[0021] Furthermore, the electricity quantity transmission price is charged according to the electricity quantity actually transmitted by the generator set or the user, and is used to calculate the power generation cost and quotation of the generator set in the market in the electricity spot market production cost simulation model. The capacity transmission price is charged according to the transmission capacity reserved by the generator set or the user, and is used to generate investment decisions for the generator set in the power supply investment timing and site selection prediction model.

[0022] Furthermore, the step of constructing a power source investment timing and site selection prediction model according to a genetic algorithm based on a Gaussian mutation strategy includes:

[0023] Step S201, generating an initialization population with Q number of individuals meeting the constraint conditions, where Q is a custom parameter;

[0024] Step S202, constructing an objective function;

[0025] Step S203, calculating the fitness value of each individual in the initialized population according to the objective function;

[0026] Step S204, selecting two individuals with the largest fitness values ​​in the initialization population as parents;

[0027] Step S205, generating a random number in the range of 0 to 1, and when the random number is less than a first threshold, performing a crossover operation that satisfies the constraint condition, where the first threshold is a custom parameter;

[0028] Step S206, generating a random number in the range of 0 to 1, and when the random number is less than a second threshold, performing a Gaussian mutation operation that satisfies the constraint condition, and the second threshold is a custom parameter;

[0029] Step S207, if it is determined that the current number of iterations is greater than or equal to the maximum number of iterations, the individual with the largest fitness value in the initialized population is used as the investment decision, otherwise, it returns to step S203, where the maximum number of iterations is a custom parameter.

[0030] Furthermore, the objective function of the genetic algorithm based on the Gaussian mutation strategy is:

[0031]

[0032]

[0033] Where CI g,t represents the cash inflow of the g-th unit in year t, CO n,t represents the cash outflow of the g-th unit in year t, represents the clearing price of the electricity spot market at the node n where the unit is located in the ta period, represents the winning bid quantity of the g-th unit in the electricity spot market during the ta period, It represents the fixed cost that the unit should pay annually when selecting the site at node n. represents the variable power generation cost of the generator G at node n, represents the electricity transmission price that the generator should pay at node n, It represents the winning bid quantity of unit G in the electricity spot market during period ta. It represents the capacity transmission fee that the unit should pay at node n.

[0034] Furthermore, the objective function of the electricity spot market production cost simulation model is to minimize the total power generation fuel cost of the power system during the electricity market operation cycle, and the formula is: in It indicates the electricity transmission price for new energy units.

[0035] Furthermore, the node load growth model aims to predict the future load growth of each node based on the electricity price, where the load value of node n in the tth period of the tyth year after the base year is: represents the load value of node n in the base year period t, ρ i,n It represents the load growth rate of node n in the i-th year after the base year; the load growth rate of node n in the ty-th year after the planning base year is: ρ0 represents the growth rate benchmark value, λ represents the price elasticity of electricity demand, It represents the sum of the weighted average node electricity price of node n in the tyth year after the base year, its electricity transmission price, and the capacity price converted into electricity price according to the total electricity consumption of the node in the tyth year after the planned base year. The formula is: represents the total capacity electricity charge paid by electricity users, It represents the electricity transmission price paid by the electricity user at node n in the tth year after the planning base year.

[0036] Furthermore, the calculation step of the change in the present value of the investment in the whole network transmission equipment after the new energy unit is put into operation in step S105 specifically includes:

[0037] Step S301, calculating the semi-invariant of the new energy generator set and the load;

[0038] Step S302, applying the Gram-Charlier series expansion method to obtain its probability density and cumulative distribution function;

[0039] Step S303, calculating the maximum active power flow of each transmission line after the new energy power source is connected;

[0040] Step S304, calculating the investment present value change value in combination with the investment present value change formula.

[0041] The beneficial effects of the present invention are as follows: the present invention first determines the evaluation index according to the influence of the transmission pricing mechanism on the investment efficiency of the power grid, the cost of the generator set and the new energy absorption capacity; secondly, constructs the power investment timing and site selection prediction model according to the genetic algorithm based on the Gaussian mutation strategy to generate the optimal unit investment timing and site selection node; and then constructs the power spot market production cost simulation model and the node load growth model to calculate the total power generation and purchase cost of the market; finally, the random power flow method is combined with the investment present value formula to calculate the change value of the investment present value, thereby improving the investment efficiency of the power grid and promoting the absorption of new energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a flow chart of a quantitative evaluation method of a transmission pricing mechanism coordinated with the construction of a new power system according to the present invention;

[0043] Figure 2 It is a flow chart of constructing a power source investment timing and site selection prediction model of the present invention;

[0044] Figure 3 It is a flow chart for calculating the change in present value of investment of the present invention. DETAILED DESCRIPTION

[0045] The subject matter described herein will now be discussed with reference to example embodiments. It should be understood that the discussion of these embodiments is only to enable those skilled in the art to better understand and implement the subject matter described herein, and the functions and arrangements of the elements discussed may be changed without departing from the scope of protection of the contents of this specification. Each example may omit, replace or add various processes or components as needed. In addition, the features described relative to some examples may also be combined in other examples.

[0046] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in one or more embodiments of the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in one or more embodiments of the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0047] like Figure 1-Figure 3 As shown in FIG. 1 , the quantitative evaluation method of the transmission pricing mechanism coordinated with the construction of a new power system includes the following steps:

[0048] Step S101, determining evaluation indicators according to the impact of the transmission pricing mechanism on the grid investment efficiency, the cost of the generator set and the new energy absorption capacity, the evaluation indicators include the future investment present value change of the whole grid transmission equipment, the total market power generation and purchase costs and the new energy absorption capacity;

[0049] Step S102, determining the electricity transmission price and capacity transmission price according to the transmission pricing mechanism;

[0050] Step S103, constructing a power source investment timing and site selection prediction model according to a genetic algorithm based on a Gaussian mutation strategy, and generating an investment decision; the investment decision is an optimal solution for the investment timing and site selection node;

[0051] Step S104, constructing a power spot market production cost simulation model and a node load growth model according to the investment decision to calculate the total market power generation and purchase costs;

[0052] Step S105, using a random power flow calculation method to obtain the power flow distribution and remaining capacity change of the power transmission system, and combining the investment present value calculation method to calculate the change in the investment present value of the power transmission equipment of the entire network after the new energy unit is put into operation.

[0053] In one embodiment of the present invention, according to different impact paths of the transmission pricing mechanism, the impact of transmission pricing on the investment efficiency of the power grid is quantitatively evaluated. The evaluation includes calculating the impact of the new energy unit on the remaining capacity of the transmission system after commissioning, predicting the future expansion or strengthening demand of the transmission equipment, and determining the future investment present value change of the transmission equipment of the entire network; the future investment present value change of the transmission equipment of the entire network is quantified by the following formula:

[0054]

[0055] Where ΔPV Td Indicates the change in the present value of future investment, I Td,i represents the future investment in transmission equipment, r represents the discount rate, T Td,i,new represents the future investment time of equipment after the new energy unit is put into operation, T Td,i Indicates the current investment time;

[0056] According to the impact of the transmission pricing mechanism on the electricity spot market, the total market generation and purchase costs are calculated. The total market generation and purchase costs are quantified by the following formula:

[0057]

[0058] C G,T represents the total market power generation cost, C L,T represents the total electricity purchase cost in the market, represents the output of the generator set in the tth period and the kth quotation period, c i,k represents the variable cost of the unit, l t,n Indicates the power consumption of the node, represents the spot market electricity price of the node, It indicates the electricity transmission fee paid by the unit;

[0059] According to the impact of the transmission pricing mechanism on the new energy consumption capacity, the new energy consumption capacity is calculated, and the new energy consumption capacity is quantified by the following formula:

[0060]

[0061] Where N new represents the set of renewable energy units in the electricity market, T new,i represents the number of time periods included in the operating life cycle of the i-th unit, B new Indicates the quotation segment of new energy units. It represents the winning bid power generation of the new energy unit in the kth bidding segment in the tth time period.

[0062] In one embodiment of the present invention, the amount of new energy consumption can be directly obtained by the formula, the future investment present value change of the whole network's power transmission equipment is obtained according to the formula combined with the random power flow calculation method, and the total market power generation and power purchase costs are obtained according to the power spot market production cost simulation model and the node load growth model and formula.

[0063] In one embodiment of the present invention, the electricity quantity transmission price is charged according to the electricity quantity actually transmitted by the generator set or the user, and is used to calculate the power generation cost and quotation of the generator set in the market in the electricity spot market production cost simulation model. The capacity transmission price is charged according to the transmission capacity reserved by the generator set or the user, and is used to generate investment decisions for the generator set in the power supply investment timing and site selection prediction model.

[0064] The formula for the electricity transmission price is: in represents the electricity transmission price of the nth node, represents the transmission cost shared by the nth node, The formula for the total transmission capacity price of the nth node is: in represents the capacity transmission price of the nth node, represents the capacity cost shared by the nth node, Represents the maximum transmission capacity reserved by the nth node.

[0065] In one embodiment of the present invention, the step of constructing a power source investment timing and site selection prediction model according to a genetic algorithm based on a Gaussian mutation strategy includes:

[0066] Step S201, generating an initialization population with Q number of individuals meeting the constraint conditions, where Q is a custom parameter;

[0067] The constraints include: each unit can only be installed in a specific node set L; the commissioning sequence of the unit must be within a specific period T within the planning period;

[0068] The individuals of the initialized population are represented by a sequence, which is represented as:

[0069] X={(S1,T1),…,(S N , T N )};

[0070] Where (S1, T1) means that the first unit is installed at the S1 node and the commissioning time is T1. N , T N ) indicates that the Nth unit is installed in S N Node, commissioning time is T N , S N represents the location node of the Nth unit, T N Indicates the commissioning time of the Nth unit;

[0071] Step S202, constructing an objective function;

[0072] Step S203, calculating the fitness value of each individual in the initialized population according to the objective function;

[0073] Step S204, selecting two individuals with the largest fitness values ​​in the initialization population as parents;

[0074] Step S205, generating a random number in the range of 0 to 1, and when the random number is less than a first threshold, performing a crossover operation that satisfies the constraint condition, where the first threshold is a custom parameter;

[0075] The crossover operation means randomly selecting any location node and operation time element value of the corresponding sequence of two individuals in the parent generation to exchange;

[0076] Step S206, generating a random number in the range of 0 to 1, and when the random number is less than a second threshold, performing a Gaussian mutation operation that satisfies the constraint condition, and the second threshold is a custom parameter;

[0077] Gaussian mutation generates new gene values ​​by adding noise randomly drawn from Gaussian distribution to the genes of the current individual. Unlike conventional uniform mutation, Gaussian mutation can introduce deviations that conform to natural changes, so it is more suitable for dealing with continuous optimization problems, such as the investment sequence of a unit.

[0078] The specific operation is as follows: After the crossover operation, for each newly generated individual, use Gaussian mutation to fine-tune the investment timing. Gaussian mutation can enhance the local search capability and find a better investment time through random changes in a small range. The formula is: T i '=T i +N(0,σ 2 ), σ represents the standard deviation, which determines the range of variation and has a value range of 0 to 10;

[0079] Step S207, if it is determined that the current number of iterations is greater than or equal to the maximum number of iterations, the individual with the largest fitness value in the initialized population is used as the investment decision, otherwise, it returns to step S203, where the maximum number of iterations is a custom parameter.

[0080] In one embodiment of the present invention, the objective function of the genetic algorithm based on the Gaussian mutation strategy is:

[0081]

[0082] Where CI g,t represents the cash inflow of the g-th unit in year t, CO n,t represents the cash outflow of the g-th unit in year t, represents the clearing price of the electricity spot market at the node n where the unit is located in the ta period, represents the winning bid quantity of the g-th unit in the electricity spot market during the ta period, It represents the fixed cost that the unit should pay annually when selecting the site at node n. represents the variable power generation cost of the generator G at node n, represents the electricity transmission price that the generator should pay at node n, It represents the winning bid quantity of unit G in the electricity spot market during period ta. It represents the capacity transmission fee that the unit should pay at node n.

[0083] In one embodiment of the present invention, the objective function of the genetic algorithm based on the Gaussian mutation strategy is to maximize the net present value, which is a key indicator for evaluating the profitability of an investment project.

[0084] In one embodiment of the present invention, the objective function of the electricity spot market production cost simulation model is to minimize the total power generation fuel cost of the power system during the power market operation cycle, and the formula is: in It indicates the electricity transmission price for new energy units.

[0085] The constraints of the model include:

[0086] Node power balance constraints:

[0087] Line power balance constraints:

[0088] in Indicates load value, G s represents the group number set of access node s, L s represents the set of lines connecting node s, represents the active power flow of line l, b l is the line susceptance, λ l,s represents an indicator variable. When s is the first node of l, λ l.s is -1, otherwise 1. represents the phase angle of node s, S l Represents the set of nodes at the beginning and end of line l.

[0089] In one embodiment of the present invention, the node load growth model aims to predict the future load growth of each node based on the electricity price, where the load value of node n in the tth period of the tyth year after the base year is: represents the load value of node n in the base year period t, ρ i,n It represents the load growth rate of node n in the i-th year after the base year; the load growth rate of node n in the ty-th year after the planning base year is: ρ0 represents the growth rate benchmark value, λ represents the price elasticity of electricity demand, It represents the sum of the weighted average node electricity price of node n in the tyth year after the base year, its electricity transmission price, and the capacity price converted into electricity price according to the total electricity consumption of the node in the tyth year after the planned base year. The formula is: represents the total capacity electricity charge paid by electricity users, It represents the electricity transmission price paid by the electricity user at node n in the tth year after the planning base year.

[0090] The node load growth model affects the power flow distribution of the transmission network and the supply and demand balance of the power market by predicting the load changes at different nodes, which further affects the power generation cost and the power purchase cost. Higher load may lead to higher power generation demand, thereby increasing the power generation cost; and the power purchase price will also be affected by load changes.

[0091] In one embodiment of the present invention, the step of calculating the change in the present value of the investment in the whole network transmission equipment after the new energy unit is put into operation in step S105 specifically includes:

[0092] Step S301, calculating the semi-invariant of the new energy generator set and the load;

[0093] Semi-invariants are one of the mathematical characteristics of random variables. For new energy generators, the semi-invariants of new energy generators can be calculated using sampling techniques based on the actual power generation curve of the new energy generators, or the statistical characteristics of wind speed and light. Taking wind turbines as an example, considering that the wind speed function obeys the Weibull distribution, Q wind speed sequences can be first extracted through Monte Carlo sampling, and then the active power sequence is formed according to the output characteristics of the wind turbine, and then the origin moments of each order of the wind turbine output power are calculated. The formulas for the origin moments of each order are:

[0094] After obtaining the v-order origin moment of the wind turbine output power, the semi-invariant of the wind turbine output can be obtained.

[0095] After clarifying the semi-invariants of the renewable energy generators and the semi-invariants of the loads, the semi-invariants of the node injection power can be obtained by adding the two together, and then the semi-invariants of the system state variables and the power flow vectors of all branches can be calculated. The calculation formula includes:

[0096]

[0097] ΔZ k =(T0) k ΔW k ;

[0098] Where ΔX krepresents the kth order semi-invariant of the system state variable, ΔW k represents the kth order semi-invariant of the node injected power, ΔZ k represents the kth order semi-invariant of the power flow vectors of all branches.

[0099] Step S302, applying the Gram-Charlier series expansion method to obtain its probability density and cumulative distribution function;

[0100] After obtaining the semi-invariants of each order, the probability density and cumulative distribution function of the system state variables and the power flow vector of the branch can be obtained through series expansion;

[0101] Specifically, for any expected value μ G , the mean square error is σ G The random variable θ G , can be standardized as: Then the distribution function of τ can be expressed as the Gram-Charlier series expansion:

[0102]

[0103] in, and φ(τ) represent the probability density and cumulative probability density function of the normal distribution, respectively. and φ i (τ) respectively represent the i-th order derivative of the function, c i represents the coefficient of the i-th order Gram-Charlier series;

[0104] Step S303, calculating the maximum active power flow of each transmission line after the new energy power source is connected;

[0105] After calculating the probability density distribution of power flow in each branch of the power system after the new energy power source is put into operation, the formula for the rated transmission capacity of the transmission equipment is:

[0106]

[0107] in It indicates the maximum value of the impact of the new energy unit j on the active power flow of the transmission equipment i after it is connected;

[0108] Step S304, calculating the investment present value change value in combination with the investment present value change formula.

[0109] The above describes an embodiment of the present embodiment, but the present embodiment is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present embodiment, ordinary technicians in this field can also make many forms, all of which are within the protection of the present embodiment.

Claims

1. A quantitative evaluation method for transmission pricing mechanism coordinated with the construction of new power system, characterized by: The following steps are involved: Step S101, determining evaluation indicators according to the impact of the transmission pricing mechanism on the grid investment efficiency, the cost of the generator set and the new energy absorption capacity, the evaluation indicators include the future investment present value change of the whole grid transmission equipment, the total market power generation and purchase costs and the new energy absorption capacity; Step S102, determining the electricity transmission price and capacity transmission price according to the transmission pricing mechanism; Step S103, constructing a power source investment timing and site selection prediction model according to a genetic algorithm based on a Gaussian mutation strategy, and generating an investment decision; the investment decision is an optimal solution for the investment timing and site selection node; Step S104, constructing a power spot market production cost simulation model and a node load growth model according to the investment decision to calculate the total market power generation and purchase costs; Step S105, using a random power flow calculation method to obtain the power flow distribution and remaining capacity change of the power transmission system, and combining the investment present value calculation method to calculate the change in the investment present value of the power transmission equipment of the entire network after the new energy unit is put into operation.

2. The quantitative evaluation method for transmission pricing mechanism coordinated with the construction of new power system according to claim 1 is characterized in that: According to the different impact paths of the transmission pricing mechanism, the impact of transmission pricing on the investment efficiency of the power grid is quantitatively evaluated. The evaluation includes calculating the impact of the new energy units on the remaining capacity of the transmission system after commissioning, predicting the future expansion or strengthening needs of the transmission equipment, and determining the future investment present value change of the transmission equipment of the entire network; the future investment present value change of the transmission equipment of the entire network is quantified by the following formula: Where ΔPV Td Indicates the change in the present value of future investment, I Td,i represents the future investment in transmission equipment, r represents the discount rate, T Td,i,new represents the future investment time of equipment after the new energy unit is put into operation, T Td,i Indicates the current investment time; According to the impact of the transmission pricing mechanism on the electricity spot market, the total market generation and purchase costs are calculated. The total market generation and purchase costs are quantified by the following formula: C G,T represents the total market power generation cost, C L,T represents the total electricity purchase cost in the market, represents the output of the generator set in the tth period and the kth quotation period, c i,k represents the variable cost of the unit, l t,n Indicates the power consumption of the node, represents the spot market electricity price of the node, It indicates the electricity transmission fee paid by the unit; According to the impact of the transmission pricing mechanism on the new energy consumption capacity, the new energy consumption capacity is calculated, and the new energy consumption capacity is quantified by the following formula: Where N new represents the set of renewable energy units in the electricity market, T new,i represents the number of time periods included in the operating life cycle of the i-th unit, B new Indicates the quotation segment of new energy units. It represents the winning bid power generation of the new energy unit in the kth bidding segment in the tth time period.

3. The quantitative evaluation method for transmission pricing mechanism coordinated with the construction of new power system according to claim 1 is characterized in that: The electricity quantity transmission price is charged according to the electricity quantity actually transmitted by the generator set or the user. In the electricity spot market production cost simulation model, it is used to calculate the power generation cost and quotation of the generator set in the market. The capacity transmission price is charged according to the transmission capacity reserved by the generator set or the user. In the power source investment timing and site selection prediction model, it is used to generate investment decisions for the generator set.

4. The quantitative evaluation method for transmission pricing mechanism coordinated with the construction of new power system according to claim 1 is characterized in that: The steps of constructing a power source investment timing and site selection prediction model according to a genetic algorithm based on a Gaussian mutation strategy include: Step S201, generating an initialization population with Q number of individuals meeting the constraint conditions, where Q is a custom parameter; Step S202, constructing an objective function; Step S203, calculating the fitness value of each individual in the initialized population according to the objective function; Step S204, selecting two individuals with the largest fitness values ​​in the initialization population as parents; Step S205, generating a random number in the range of 0 to 1, and when the random number is less than a first threshold, performing a crossover operation that satisfies the constraint condition, where the first threshold is a custom parameter; Step S206, generating a random number in the range of 0 to 1, and when the random number is less than a second threshold, performing a Gaussian mutation operation that satisfies the constraint condition, and the second threshold is a custom parameter; Step S207, if it is determined that the current number of iterations is greater than or equal to the maximum number of iterations, the individual with the largest fitness value in the initialized population is used as the investment decision, otherwise, it returns to step S203, where the maximum number of iterations is a custom parameter.

5. The quantitative evaluation method for transmission pricing mechanism coordinated with the construction of new power system according to claim 4 is characterized in that: The objective function of the genetic algorithm based on Gaussian mutation strategy is: CI g,t represents the cash inflow of the g-th unit in year t, CO n,t represents the cash outflow of the g-th unit in year t, represents the clearing price of the electricity spot market at the node n where the unit is located in the ta period, represents the winning bid quantity of the g-th unit in the electricity spot market during the ta period, It represents the fixed cost that the unit should pay annually when selecting the site at node n. represents the variable power generation cost of the generator G at node n, represents the electricity transmission price that the generator should pay at node n, It represents the winning bid quantity of unit G in the electricity spot market during period ta. It represents the capacity transmission fee that the unit should pay at node n.

6. The quantitative evaluation method for transmission pricing mechanism coordinated with the construction of new power system according to claim 1 is characterized in that: The objective function of the electricity spot market production cost simulation model is to minimize the total power generation fuel cost of the power system during the electricity market operation cycle. The formula is: in It indicates the electricity transmission price for new energy units.

7. The quantitative evaluation method for transmission pricing mechanism coordinated with the construction of new power system according to claim 1 is characterized in that: The goal of the node load growth model is to predict the future load growth of each node based on the electricity price, where the load value of node n in the tth period of the tyth year after the base year is: represents the load value of node n in the base year period t, ρ i,n represents the load growth rate of node n in the i-th year after the base year; The load growth rate of node n in year ty after the planning base year is: ρ0 represents the growth rate benchmark value, λ represents the price elasticity of electricity demand, It represents the sum of the weighted average node electricity price of node n in the tyth year after the base year, its electricity transmission price, and the capacity price converted into electricity price according to the total electricity consumption of the node in the tyth year after the planned base year. The formula is: represents the total capacity electricity charge paid by electricity users, It represents the electricity transmission price paid by the electricity user at node n in the tth year after the planning base year.

8. The quantitative evaluation method for transmission pricing mechanism coordinated with the construction of new power system according to claim 1 is characterized in that: The calculation steps of the present value change of the investment in the whole network transmission equipment after the new energy unit is put into operation in step S105 specifically include: Step S301, calculating the semi-invariant of the new energy generator set and the load; Step S302, applying the Gram-Charlier series expansion method to obtain its probability density and cumulative distribution function; Step S303, calculating the maximum active power flow of each transmission line after the new energy power source is connected; Step S304, calculating the investment present value change value in combination with the investment present value change formula.

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