CN114444915B

CN114444915B - New energy consumption cost calculation method and device for electric power system in market environment

Info

Publication number: CN114444915B
Application number: CN202210068009.0A
Authority: CN
Inventors: 葛毅; 夏天; 李冰洁; 胡晓燕; 刘国静; 李琥; 史静; 袁晓昀
Original assignee: Tsinghua University; Economic and Technological Research Institute of State Grid Jiangsu Electric Power Co Ltd
Current assignee: Tsinghua University; Economic and Technological Research Institute of State Grid Jiangsu Electric Power Co Ltd
Priority date: 2022-01-20
Filing date: 2022-01-20
Publication date: 2024-06-28
Anticipated expiration: 2042-01-20
Also published as: CN114444915A

Abstract

The application discloses a method and a device for calculating new energy consumption cost of an electric power system in a market environment, wherein the method comprises the following steps: acquiring a first annual market clearing result and running cost of the power system by using a preset random unit combination model; acquiring a second annual market clearing result and running cost of the power system by using a preset first deterministic unit combination model; converting a new energy unit in the electric power system into a target controllable unit, and acquiring a third annual market clearing result and operation cost of the target controllable unit by using a preset second deterministic unit combination model; and comparing the annual market clearing result with the operation cost, and calculating the new energy consumption cost of the power system based on the comparison result. Therefore, the technical problem that the power system is difficult to directly quantify the consumption cost caused by the uncertainty and uncontrollability of the new energy output caused by the fact that the related technology focuses on calculating the change of the total operation cost of the power system after the new energy installation is increased is solved.

Description

New energy consumption cost calculation method and device for electric power system in market environment

Technical Field

The application relates to the technical field of operation of power systems, in particular to a method and a device for calculating new energy consumption cost of a power system in a market environment.

Background

Under the aim of carbon peak carbon neutralization, the installed capacity of new energy such as wind photovoltaic in the electric power system in China is continuously increased. Compared with the traditional thermal power generating unit, the new energy has uncertainty and uncontrollable output, so that when the daily operation mode of the electric power system is set, the uncertainty of the new energy output makes the electric power system have to reserve more regulating capacity, and a large number of possible new energy output scenes are dealt with, so that the economical efficiency of the operation mode is reduced. In addition, when the uncontrollable output of the new energy unit causes the new energy to provide electric quantity for the electric power system, the output curve is mainly determined by uncontrollable wind speed, solar irradiation and other external meteorological conditions, unlike the traditional thermal power unit, the flexible controllable output curve can be provided in a certain range.

The uncertainty and uncontrollability of the new energy output add additional running cost to the power system, i.e., new energy consumption cost. The new energy consumption cost calculation has important significance for low-carbon transformation cost estimation of the power system, and establishment of electricity price policies and allocation rules. With the continuous promotion of the construction of the electric power market in China, the running mode of the electric power system is gradually determined by the planning mode and is changed into the market clearing mode. Therefore, the method has important significance in researching new energy consumption cost calculation of the power system in the market environment.

In the related art, there have been some researches on new energy power system operation and consumption cost calculation. In the aspect of the operation of the new energy power system, the related technology mainly adopts uncertainty optimization methods such as deterministic optimization methods or random optimization, robust optimization and the like to determine the operation mode of the power system. In the aspect of calculating the new energy cost of the electric power system, the related technology generally adopts an electric power system operation simulation method, and after the new energy installation is added in the electric power system, the change of the operation cost of the electric power system is investigated, so that the electricity metering cost of the new energy is calculated.

However, the related art mainly focuses on the change of the overall operation cost of the new energy installation of the electric power system after the increase, and the system is not used for coping with the consumption cost caused by the uncertainty and uncontrollability of the new energy output. Therefore, for a given power system, there is still a need to solve the problem of how to measure the new energy uncertainty and the cost of the new energy uncontrollably.

Content of the application

The application provides a new energy consumption cost calculation method and device for an electric power system in a market environment, aiming at solving the technical problems that the related technology focuses on calculating the change of the total operation cost of the new energy installation of the electric power system after the new energy installation is increased, but the consumption cost caused by uncertainty and uncontrollable performance of the new energy output is not solved by the system, so that the consumption cost caused by uncertainty and uncontrollable performance of the new energy output is difficult to directly quantify by the electric power system.

An embodiment of a first aspect of the present application provides a method for calculating new energy consumption cost of an electric power system in a market environment, including the following steps: obtaining a first annual market clearing result and running cost of the power system by using a preset random unit combination model, wherein the random unit combination model is established by an actual scene of new energy uncertainty and uncontrollable output curve; acquiring a second annual market clearing result and operation cost of the power system by using a preset first deterministic unit combination model, wherein the first deterministic unit combination model is established by a virtual scene without uncertainty of new energy; converting a new energy unit in the electric power system into a target controllable unit, and acquiring a third annual market clearing result and running cost of the target controllable unit by using a preset second deterministic unit combination model, wherein the second deterministic unit combination model is established by a virtual scene without uncertainty of the controllable unit; and comparing the first annual market clearing result and the operation cost, the second annual market clearing result and the operation cost and the third annual market clearing result and the operation cost, and calculating new energy consumption cost of the power system based on the comparison result.

Optionally, in one embodiment of the present application, before the obtaining the first annual market clearing result and the running cost of the electric power system by using the preset random set combination model, the method further includes: based on the uncertainty of the output of the renewable energy unit, setting a plurality of renewable energy output scenes serving as actual scenes in a preset sampling mode; estimating the occurrence probability of each renewable energy output scene, and establishing the random set combination model considering multiple scenes based on the programming problem considering uncertainty.

Optionally, in one embodiment of the present application, before obtaining the second annual market clearing result and the running cost of the electric power system using the preset first deterministic aggregate model, the method further includes; obtaining a new energy output single-point prediction scene serving as a virtual scene according to the new energy output probability distribution characteristics; and converting the random unit combination model to obtain the first deterministic unit combination model based on the new energy output single-point prediction scene.

Optionally, in an embodiment of the present application, the converting the new energy unit in the electric power system into the target controllable unit, and obtaining the third annual market clearing result and the running cost of the target controllable unit by using a preset second deterministic unit combination model includes: and calculating conventional unit parameters obtained by equivalent of all renewable energy units in the power system one by one according to a preset description rule for each day in the whole year, wherein the conventional unit parameters comprise minimum output/maximum output, maximum up-regulation/down-regulation quantity in a single period, minimum start-up time and minimum stop time.

Optionally, in one embodiment of the application, the new energy consumption cost includes a consumption cost due to new energy output uncertainty, a consumption cost due to new energy output uncontrollability, and/or a total cost of the new energy consumption.

An embodiment of a second aspect of the present application provides a new energy consumption cost calculation device for an electric power system in a market environment, including: the first acquisition module is used for acquiring a first annual market clearing result and operation cost of the power system by utilizing a preset random unit combination model, wherein the random unit combination model is established by an actual scene of new energy uncertainty and uncontrollability of an output curve; the second acquisition module is used for acquiring a second annual market clearing result and operation cost of the electric power system by utilizing a preset first deterministic unit combination model, wherein the first deterministic unit combination model is established by a virtual scene without uncertainty of new energy; the third acquisition module is used for converting a new energy unit in the electric power system into a target controllable unit and acquiring a third annual market clearing result and operation cost of the target controllable unit by utilizing a preset second deterministic unit combination model, wherein the second deterministic unit combination model is established by a virtual scene without uncertainty of the controllable unit; and the calculation module is used for comparing the first annual market clearing result with the operation cost, the second annual market clearing result with the third annual market clearing result with the operation cost, and calculating the new energy consumption cost of the power system based on the comparison result.

Optionally, in one embodiment of the present application, the apparatus further includes: the scene presetting module is used for setting a plurality of renewable energy source output scenes serving as actual scenes in a preset sampling mode based on the uncertainty of the output of the renewable energy source unit; the modeling module is used for estimating the occurrence probability of each renewable energy output scene and establishing the random set combination model considering multiple scenes based on the programming problem considering uncertainty.

Optionally, in one embodiment of the present application, the apparatus further includes; the prediction module is used for obtaining a new energy output single-point prediction scene serving as a virtual scene according to the new energy output probability distribution characteristics; and the conversion module is used for converting the random unit combination model into the first deterministic unit combination model based on the new energy output single-point prediction scene.

Optionally, in an embodiment of the present application, the third obtaining module is further configured to: and calculating conventional unit parameters obtained by equivalent of all renewable energy units in the power system one by one according to a preset description rule for each day in the whole year, wherein the conventional unit parameters comprise minimum output/maximum output, maximum up-regulation/down-regulation quantity in a single period, minimum start-up time and minimum stop time.

An embodiment of a third aspect of the present application provides an electronic device, including: the system comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to realize the new energy consumption cost calculation method of the electric power system in the market environment according to the embodiment.

A fourth aspect of the present application provides a computer-readable storage medium having stored thereon a computer program for execution by a processor for implementing the method for calculating new energy consumption costs of an electric power system in a market environment according to any one of claims 1 to 5.

According to the embodiment of the application, the new energy consumption cost of the power system is firstly decomposed into the additional operation cost of the power system caused by the uncertainty and uncontrollable property of the new energy, then the market clear of the new energy power system and the measurement and calculation of the operation cost are simulated by adopting a random programming technology, the daily simulation is carried out on the annual market clear of the power system, the annual new energy consumption cost of the power system is finally obtained through case comparison, the quantitative evaluation of the new energy consumption cost is realized, the consumption cost caused by the uncertainty and uncontrollable property can be intuitively compared, and the demand of a user on the calculation of the new energy consumption cost of the power system can be more met. Therefore, the technical problem that the power system is difficult to directly quantify the consumption cost caused by the uncertainty and uncontrollability of the new energy output is solved because the related technology focuses on calculating the change of the total operation cost of the new energy installation of the power system after the new energy output is increased, but not the consumption cost caused by the uncertainty and uncontrollability of the new energy output is solved.

Additional aspects and advantages of the application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the application.

Drawings

The foregoing and/or additional aspects and advantages of the application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:

fig. 1 is a flowchart of a method for calculating new energy consumption cost of an electric power system in a market environment according to an embodiment of the present application;

Fig. 2 is a schematic diagram of a method for calculating new energy consumption cost of an electric power system in a market environment according to an embodiment of the present application;

FIG. 3 is a schematic diagram of a new energy consumption cost calculation device of an electric power system in a market environment according to an embodiment of the present application;

fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present application and should not be construed as limiting the application.

The method and the device for calculating the new energy consumption cost of the electric power system in the market environment are described below with reference to the accompanying drawings. Aiming at the problems that the related technology mentioned in the background technology center focuses on calculating the change of the total running cost of the new energy installation of the electric power system after the new energy installation is increased, but the system is not used for coping with the consumption cost caused by the uncertainty and uncontrollability of the new energy output, so that the electric power system is difficult to directly quantify the consumption cost caused by the uncertainty and uncontrollability of the new energy output. Therefore, the problems that the related technology focuses on calculating the change of the total operation cost of the new energy installation of the electric power system after the new energy installation is increased, but the system is not used for coping with the consumption cost caused by the uncertainty and uncontrollability of the new energy output, the consumption cost caused by the uncertainty and uncontrollability of the new energy output is difficult to directly quantify by the electric power system and the like are solved.

Specifically, fig. 1 is a schematic flow chart of a method for calculating new energy consumption cost of an electric power system in a market environment according to an embodiment of the present application.

As shown in fig. 1, the method for calculating new energy consumption cost of the electric power system in the market environment comprises the following steps:

In step S101, a first annual market clearing result and an operation cost of the electric power system are obtained by using a preset stochastic set combination model, wherein the stochastic set combination model is established by an actual scene of new energy uncertainty and uncontrollable output curve.

In the actual execution process, the embodiment of the application can establish a random unit combination model of the electric power system under the actual scene of uncertainty of new energy and uncontrollable output curve, and solve the random unit combination model day by day, thereby obtaining the first annual market clearing result and running cost. Specifically, the random unit combination model established by the embodiment of the application can be a mixed integer linear programming model, and can be solved by adopting a commercial solver CPLEX or Gurobi and the like. After the optimization model is solved, the embodiment of the application can obtain the annual market clearing result and the annual running cost C ₁ of the system.

The preset random set combination model will be described in detail below.

Optionally, in one embodiment of the present application, before the first annual market clearing result and the running cost of the electric power system are obtained by using the preset random set combination model, the method further includes: based on the uncertainty of the output of the renewable energy unit, setting a plurality of renewable energy output scenes serving as actual scenes in a preset sampling mode; and estimating the occurrence probability of each renewable energy output scene, and establishing a random set combination model considering multiple scenes based on a planning problem considering uncertainty.

It will be appreciated that the above-described predetermined random set combination model is described in detail herein. In the electric power market, the running mode arrangement of the unit can be solved by a unit combination model to obtain an economical optimal solution. However, due to uncertainty in new energy output, the crew combination model needs to consider the renewable energy crew output randomness. Therefore, the embodiment of the application gives uncertainty of the output of the renewable energy unit, sets a plurality of renewable energy output scenes through a sampling technology, estimates the probability of each scene, and establishes a planning problem considering the uncertainty as a random unit combination model considering multiple scenes.

Specifically, the random set combination objective function is:

Constraints of the optimization model include:

a. system power balance constraint:

b. Power transmission network constraints:

c. Conventional unit operation constraints:

d. renewable energy source output constraint:

e. system capacity reserve constraints:

The method comprises the steps of carrying out upper and lower labels, wherein s represents an s-th scene, t represents a t-th period, i represents an i-th unit, n represents an n-th bus node, and l represents a l-th transmission line. S represents a scene set, T represents a period set, I represents a unit set, N represents all node sets, omega ^Syn represents a conventional unit set, omega ^Vre represents a renewable energy unit set, and Omega _n is a unit set at a node N.

For constants in the optimization model, SU _i represents the start-up cost of the ith unit, pi _s represents the probability of occurrence of the s-th scene, voLL represents the penalty cost of the cut load unit, B _l represents the susceptance of the first line, F _l ^Max represents the upper limit of the capacity of the first line, P _i ^min/P_i ^max represents the minimum/maximum output of the ith unit,Refers to the nth row of the admittance matrix in the meaning of the direct current flow of the power system,Representing s scene, t period, maximum Xu Keqie load of nth node, D _t,n is load of period t node n,The maximum power up/down adjustment amount in a period of time of the unit i is respectively, T _i ^on/T_i ^off is the minimum start-up/stop time of the unit i,The method is characterized in that the method is s scenes, t time periods, namely the maximum available output of an ith unit belonging to renewable energy, r _Load is load reserve rate, r _RE is renewable energy reserve rate, and VoLL is unit cut load penalty coefficient.

For variables in the optimization model, θ _s,t is the s scene, the column vector composed of phase angles of all nodes of the power system in t time periods, P _s,t,i is the s scene, t time periods, the i-th machine set output,For the period t, the start-up/stop action indicating variable of the ith unit, x _t,i is the period t, whether the ith unit indicates the variable online,The system represents s scene, t period, n-th node cut load quantity, F _s,t,l is s scene, t period, power of l lines, ENS _t,s is s scene, t period, system total cut load quantity, and F _i is a functional relation of unit i power generation cost with respect to unit output.

In step S102, a second annual market clearing result and an operation cost of the electric power system are obtained by using a preset first deterministic train combination model, wherein the first deterministic train combination model is established by a virtual scene where no uncertainty exists in new energy.

As a possible implementation manner, the embodiment of the application can establish the deterministic unit combination model under the virtual scene of no uncertainty of the new energy, and solve the result and the running cost of the second annual duration every day under the virtual scene of no uncertainty of the new energy obtained by the deterministic unit combination model. For example, for a given power system, 365 days a year, a combined model of uncertainty units for new energy treatment is built, and the combined model can be a mixed integer linear programming model, and is solved by a commercial solver CPLEX or Gurobi. After the optimization model is solved, the embodiment of the application obtains the annual market clearing result and the annual running cost C ₂ of the system.

Optionally, in one embodiment of the present application, before the second annual market clearing result and the running cost of the electric power system are obtained by using the preset first deterministic aggregate model, the method further includes; obtaining a new energy output single-point prediction scene serving as a virtual scene according to the new energy output probability distribution characteristics; and based on the new energy output single-point prediction scene, converting the random unit combination model to obtain a first deterministic unit combination model.

In some specific embodiments, in order to examine the result of the power market and the annual running cost of the system in a virtual scenario where the new energy source does not have uncertain output, the random unit combination model established in the above steps may be converted into a deterministic unit combination model. According to the embodiment of the application, the new energy output single-point prediction scene can be obtained by examining the new energy output probability distribution characteristics and is used as the only considered scene, and further, the embodiment of the application sets the renewable energy reserve rate r _RE in the model to 0, so that the random unit combination model can be converted into the deterministic unit combination model.

In step S103, a new energy unit in the electric power system is converted into a target controllable unit, and a third annual market clearing result and running cost of the target controllable unit are obtained by using a preset second deterministic unit combination model, wherein the second deterministic unit combination model is established by a virtual scene where uncertainty does not exist in the controllable unit.

In the actual execution process, the embodiment of the application can establish the deterministic unit combination model under the virtual scene without uncertainty, and solve the third annual market clearing result and the running cost day by day under the virtual scene without uncertainty of new energy obtained by the deterministic unit combination model. The embodiment of the application can establish a deterministic unit combination model with new energy equivalent in a conventional controllable unit combination virtual scene for a given power system in 365 days of the whole year, wherein the model can be a mixed integer linear programming model, and is solved by adopting a commercial solver CPLEX or Gurobi and the like. After the optimization model is solved, the embodiment of the application obtains the annual market clearing result and the annual running cost C ₃ of the system.

Optionally, in an embodiment of the present application, converting a new energy unit in the electric power system into a target controllable unit, and obtaining a third annual market clearing result and an operation cost of the target controllable unit by using a preset second deterministic unit combination model, including: and for each day in the whole year, calculating conventional unit parameters obtained by equivalent of all renewable energy units in the power system one by one according to a preset description rule, wherein the conventional unit parameters comprise minimum output/maximum output, maximum up-regulation/down-regulation quantity in a single period, minimum start-up time and minimum stop time.

Specifically, for each day in the whole year, the embodiment of the application can calculate the conventional unit parameters obtained by equivalent of all renewable energy units in the system one by one according to the rule described below, and the conventional unit parameters comprise: minimum/maximum force, maximum up/down amount per time period, minimum on time and minimum off time. For any appointed date, the new energy unit with the number of i has the maximum available output of each period in the day of

The equivalent conventional unit parameter determination rules are as follows:

a. equivalent minimum/maximum force

By solving the nonlinear programming problem as follows:

The objective function is:

The constraint conditions are as follows:

Wherein sgn (·) represents a sign function, the function value is 1 when the argument is non-negative, otherwise the function value is 0; epsilon is a given punishment coefficient, and the recommended value range is 0.01-0.05; η is the ratio of the minimum equivalent capacity to the maximum equivalent capacity, and the recommended value range is 0.3-0.5; and C ⁱ is the capacity of the new energy unit i.

B. Equivalent single period maximum up/down amount

Calculated by the following formula.

Wherein k ^UP and k ^DN are respectively relative up-regulation/down-regulation coefficients of an equivalent conventional unit, and the recommended value range is 0.4-0.6.

C. Equivalent minimum on-time and minimum off-time

The equivalent minimum on-time and minimum off-time are set as the dominant force conventional unit parameters of the electric power system under study.

After the new energy unit is equivalent to a virtual conventional controllable unit, solving the following equivalent deterministic unit combination optimization problem:

Constraints of the optimization model include:

a. system power balance constraint:

b. Power transmission network constraints:

c. Conventional unit operation constraints:

d. Renewable energy equivalent conventional unit output constraint:

e. renewable energy equivalent conventional unit available electricity constraint:

f. system capacity reserve constraints:

The method comprises the steps of carrying out upper and lower marks, wherein t represents a t-th period, i represents an i-th unit, n represents an n-th bus node, and l represents a l-th transmission line. S represents a scene set, T represents a period set, I represents a unit set, N represents all node sets, omega ^Syn represents a conventional unit set, omega ^Vre represents a renewable energy unit set, and Omega _n is a unit set at a node N.

For constants in the optimization model, SU _i represents the start-up cost of the ith unit, voLL represents the cut load unit penalty cost, B _l represents the first line susceptance, F _l ^Max represents the upper limit of the first line capacity, P _i ^min/P_i ^max represents the i-th unit minimum/maximum output,Refers to the nth row of the admittance matrix in the meaning of the direct current flow of the power system,Representing s scene, t period, maximum Xu Keqie load of nth node, D _t,n is load of period t node n,The maximum power up/down adjustment amount in a period of time of the unit i is respectively, T _i ^on/T_i ^off is the minimum start-up/stop time of the unit i,The method is characterized in that the method is s scenes, t time periods, namely the maximum available output of an ith unit belonging to renewable energy, r _Load is load reserve rate, r _RE is renewable energy reserve rate, and VoLL is unit cut load penalty coefficient.Minimum/maximum output of a conventional controllable unit equivalent to the new energy unit; The maximum power up-regulating/down-regulating quantity of the conventional controllable unit i equivalent to the new energy unit in a period of time; the conventional controllable unit i equivalent to the new energy unit has the minimum startup/shutdown time.

For variables in the optimization model, θ _t is a column vector composed of phase angles of all nodes of the power system in t time period, P _t,i is output of the ith unit in t time period,For the period t, the start-up/stop action indicating variable of the ith unit, x _t,i is the period t, whether the ith unit indicates the variable online,The t period is represented, the nth node cuts load quantity, F _t,l is t period, the power of l lines, ENS _s is t period, the total system cuts load quantity, and F _i is a functional relation of the generating cost of the unit i with respect to the unit output.

In step S104, the first annual market clearing result and the operation cost, the second annual market clearing result and the operation cost, and the third annual market clearing result and the operation cost are compared, and new energy consumption cost of the electric power system is calculated based on the comparison result.

The embodiment of the application can be obtained through the steps: the first annual market clearing result and the operation cost, the second annual market clearing result and the operation cost and the third annual market clearing result and the operation cost are compared, and the consumption cost caused by the uncertainty and uncontrollable output of the new energy source is calculated based on the comparison result. It can be appreciated that in the embodiment of the application, the new energy consumption cost of the electric power system is firstly decomposed into the additional operation cost of the electric power system caused by the uncertainty and uncontrollability of the new energy, then the market clear of the new energy electric power system and the measurement and calculation of the operation cost are simulated by adopting a random planning technology, the annual market clear of the electric power system is simulated daily, the annual new energy consumption cost of the electric power system is finally obtained through case comparison, the quantitative evaluation of the new energy consumption cost is realized, the consumption cost caused by the uncertainty and uncontrollability can be intuitively compared, and the requirement of a user on the calculation of the new energy consumption cost of the electric power system can be more met

Optionally, in one embodiment of the application, the new energy consumption cost includes a consumption cost due to uncertainty in new energy output, a consumption cost due to uncontrollability in new energy output, and/or a total cost of new energy consumption.

In some specific embodiments, the cost of new energy consumption of the power system in the market environment may be factored into the cost of consumption due to new energy uncertainty and uncontrollability.

The consumption cost S ₁ of the power system, which is caused by the uncertainty of new energy output, is as follows:

S₁＝C₁-C₂。

the consumption cost S ₂ of the power system caused by uncontrollable new energy output is as follows:

S₁＝C₃-C₂。

the total cost S of new energy is consumed all the year around in the power system is as follows:

S＝C₃-C₁。

One embodiment of the present application is described in detail below in conjunction with fig. 2.

As shown in fig. 2, one embodiment of the present application includes the steps of:

step S201: and establishing a random unit combination model of the electric power system under the actual scene of considering the uncertainty of the new energy and the uncontrollability of the output curve.

In the electric power market, the running mode arrangement of the unit can be solved by a unit combination model to obtain an economical optimal solution. Because of uncertainty of new energy output, the unit combination model needs to consider output randomness of the renewable energy unit. According to the embodiment of the application, the uncertainty of the output of the renewable energy unit is given, a plurality of renewable energy output scenes are set through a sampling technology, the probability of each scene is estimated, and the planning problem considering the uncertainty is established as a random unit combination model considering multiple scenes.

Specifically, the random set combination objective function is:

Constraints of the optimization model include:

a. system power balance constraint:

b. Power transmission network constraints:

c. Conventional unit operation constraints:

d. renewable energy source output constraint:

e. system capacity reserve constraints:

Step S202: solving the random unit combination model day by day to obtain the annual market clearing result and the running cost.

For a given power system, a random unit combination model is established for 365 days all the year round, and can be a mixed integer linear programming model, and a commercial solver CPLEX or Gurobi and the like is adopted for solving. After the optimization model is solved, the embodiment of the application obtains the annual market clearing result and the annual running cost C ₁ of the system.

S203: and establishing a deterministic unit combination model under the virtual scene of no uncertainty of the new energy.

In order to examine the result of the power market and the annual running cost of the system in the virtual scenario that the new energy source does not have uncertain output, the random unit combination model established in the step S201 needs to be converted into a deterministic unit combination model. According to the embodiment of the application, the new energy output single-point prediction scene is obtained by examining the new energy output probability distribution characteristics and is taken as the only considered scene. Further, in the embodiment of the application, the renewable energy reserve rate r _RE in the model is set to 0, so that the random unit combination model can be converted into a deterministic unit combination model.

S204: and solving the deterministic unit combination model day by day to obtain the annual market clearing result and running cost under the virtual scene of no uncertainty of new energy.

For a given power system, a deterministic unit combination model which does not consider the uncertainty of the output of new energy is established for 365 days in the whole year, and the model can be a mixed integer linear programming model and is solved by adopting a commercial solver CPLEX or Gurobi and the like. After the optimization model is solved, the embodiment of the application obtains the annual market clearing result and the annual running cost C ₂ of the system.

S205: and converting the new energy unit into a conventional unit according to an equivalent rule, and establishing a deterministic unit combination model under a virtual scene of the new energy with controllability similar to that of the conventional unit.

When the new energy and the conventional controllable unit generate certain electric quantity, the output curve of the conventional controllable unit has controllability, and the output curve of the new energy unit is mainly determined by meteorological conditions, so that the controllable electric energy generating system has lower controllability. In order to quantify the extra consumption cost caused by uncontrollable new energy, the embodiment of the application needs to set a virtual scene which reasonably and equivalently uses the new energy as a conventional controllable unit.

The equivalent conventional unit parameter determination rules are as follows:

a. equivalent minimum/maximum force

By solving the nonlinear programming problem as follows:

The objective function is:

The constraint conditions are as follows:

B. Equivalent single period maximum up/down amount

Calculated by the following formula.

C. Equivalent minimum on-time and minimum off-time

Constraints of the optimization model include:

a. system power balance constraint:

b. Power transmission network constraints:

c. Conventional unit operation constraints:

d. Renewable energy equivalent conventional unit output constraint:

f. system capacity reserve constraints:

For constants in the optimization model, SU _i represents the start-up cost of the ith unit, voLL represents the cut load unit penalty cost, B _l represents the first line susceptance, F _l ^Max represents the upper limit of the first line capacity, P _i ^min/P_i ^max represents the i-th unit minimum/maximum output,Refers to the nth row of the admittance matrix in the meaning of the direct current flow of the power system,Representing s scene, t period, maximum Xu Keqie load of nth node, D _t,n is load of period t node n,The maximum power up/down adjustment amount in a period of time of the unit i is respectively, T _i ^on/T_i ^off is the minimum start-up/stop time of the unit i,The method is characterized in that the method is s scenes, t time periods, namely the maximum available output of an ith unit belonging to renewable energy, r _Load is load reserve rate, r _RE is renewable energy reserve rate, and VoLL is unit cut load penalty coefficient.Minimum/maximum output of a conventional controllable unit equivalent to the new energy unit; The maximum power up-regulating/down-regulating quantity of the conventional controllable unit i equivalent to the new energy unit in a period of time;

the conventional controllable unit i equivalent to the new energy unit has the minimum startup/shutdown time.

S206: and solving the deterministic unit combination model day by day to obtain the annual market clearing result and running cost under the virtual scene of the controllability of the new energy.

For a given power system, a deterministic unit combination model in which new energy is equivalent to a conventional controllable unit combination virtual scene is established in 365 days of the whole year, wherein the model can be a mixed integer linear programming model, and is solved by adopting a commercial solver CPLEX or Gurobi and the like. After the optimization model is solved, the embodiment of the application obtains the annual market clearing result and the annual running cost C ₃ of the system.

S207: and comparing the system running costs of the actual scene and the two virtual scenes, and calculating to obtain the consumption cost caused by the uncertainty and uncontrollability of the new energy.

S₁＝C₁-C₂。

S₁＝C₃-C₂。

S＝C₃-C₁。

According to the new energy consumption cost calculation method for the electric power system in the market environment, which is provided by the embodiment of the application, the new energy consumption cost of the electric power system is firstly decomposed into the additional operation cost of the electric power system caused by the uncertainty and uncontrollability of the new energy, then the market clear of the new energy electric power system and the calculation of the operation cost are simulated by adopting a random programming technology, the annual market clear of the electric power system is simulated day by day, and the annual new energy consumption cost of the electric power system is finally obtained through case comparison, so that the quantitative evaluation of the new energy consumption cost is realized, the consumption cost caused by the uncertainty and uncontrollability can be intuitively compared, and the demand of a user on the calculation of the new energy consumption cost of the electric power system can be more met. Therefore, the problems that the related technology focuses on calculating the change of the total operation cost of the new energy installation of the electric power system after the new energy installation is increased, but the system is not used for coping with the consumption cost caused by the uncertainty and uncontrollability of the new energy output, the consumption cost caused by the uncertainty and uncontrollability of the new energy output is difficult to directly quantify by the electric power system and the like are solved.

Next, a new energy consumption cost calculation device for an electric power system in a market environment according to an embodiment of the present application will be described with reference to the accompanying drawings.

Fig. 3 is a block diagram of a new energy consumption cost calculation device of an electric power system in a market environment according to an embodiment of the present application.

As shown in fig. 3, the power system new energy consumption cost calculation device 10 in the market environment includes: the first acquisition module 100, the second acquisition module 200, the third acquisition module 300, and the calculation module 400.

Specifically, the first obtaining module 100 is configured to obtain a first annual market clearing result and an operation cost of the electric power system by using a preset random set combination model, where the random set combination model is established by an actual scenario of uncertainty of new energy and uncontrollable output curve.

The second obtaining module 200 is configured to obtain a second annual market clearing result and an operation cost of the electric power system by using a preset first deterministic aggregate model, where the first deterministic aggregate model is established by a virtual scenario where there is no uncertainty in new energy.

The third obtaining module 300 is configured to convert a new energy unit in the electric power system into a target controllable unit, and obtain a third annual market clearing result and an operation cost of the target controllable unit by using a preset second deterministic unit combination model, where the second deterministic unit combination model is established by a virtual scene where there is no uncertainty in the controllable unit.

The calculation module 400 is configured to compare the first annual market clearing result and the operation cost, the second annual market clearing result and the operation cost, and the third annual market clearing result and the operation cost, and calculate a new energy consumption cost of the power system based on the comparison result.

Optionally, in one embodiment of the present application, the electric power system new energy consumption cost calculating apparatus 10 in the market environment further includes: the scene presetting module and the modeling module.

The scene presetting module is used for setting a plurality of renewable energy source output scenes serving as actual scenes in a preset sampling mode based on the uncertainty of the output of the renewable energy source unit.

The modeling module is used for estimating the occurrence probability of each renewable energy output scene and establishing a random set combination model considering multiple scenes based on the programming problem considering uncertainty.

Optionally, in one embodiment of the present application, the new energy consumption cost calculation device 10 for electric power system in market environment further includes; a prediction module and a conversion module.

The prediction module is used for obtaining a new energy output single-point prediction scene serving as a virtual scene according to the new energy output probability distribution characteristics.

And the conversion module is used for converting the random unit combination model into a first deterministic unit combination model based on the new energy output single-point prediction scene.

Optionally, in an embodiment of the present application, the third obtaining module 300 is further configured to: and for each day in the whole year, calculating conventional unit parameters obtained by equivalent of all renewable energy units in the power system one by one according to a preset description rule, wherein the conventional unit parameters comprise minimum output/maximum output, maximum up-regulation/down-regulation quantity in a single period, minimum start-up time and minimum stop time.

It should be noted that, the explanation of the foregoing embodiment of the method for calculating the new energy consumption cost of the electric power system in the market environment is also applicable to the device for calculating the new energy consumption cost of the electric power system in the market environment of the embodiment, and will not be repeated here.

According to the new energy consumption cost calculation device for the electric power system in the market environment, which is provided by the embodiment of the application, the new energy consumption cost of the electric power system is firstly decomposed into the additional operation cost of the electric power system caused by the uncertainty and uncontrollability of the new energy, then the market clear of the new energy electric power system and the calculation of the operation cost are simulated by adopting a random programming technology, the annual market clear of the electric power system is simulated day by day, and the annual new energy consumption cost of the electric power system is finally obtained through case comparison, so that the quantitative evaluation of the new energy consumption cost is realized, the consumption cost caused by the uncertainty and uncontrollability can be intuitively compared, and the demand of a user on the calculation of the new energy consumption cost of the electric power system can be more met. Therefore, the problems that the related technology focuses on calculating the change of the total operation cost of the new energy installation of the electric power system after the new energy installation is increased, but the system is not used for coping with the consumption cost caused by the uncertainty and uncontrollability of the new energy output, the consumption cost caused by the uncertainty and uncontrollability of the new energy output is difficult to directly quantify by the electric power system and the like are solved.

Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application. The electronic device may include:

Memory 401, processor 402, and a computer program stored on memory 401 and executable on processor 402.

The processor 402 executes the program to implement the new energy consumption cost calculation method of the electric power system in the market environment provided in the above embodiment.

Further, the electronic device further includes:

A communication interface 403 for communication between the memory 401 and the processor 402.

A memory 401 for storing a computer program executable on the processor 402.

Memory 401 may comprise high-speed RAM memory or may also include non-volatile memory (non-volatile memory), such as at least one disk memory.

If the memory 401, the processor 402, and the communication interface 403 are implemented independently, the communication interface 403, the memory 401, and the processor 402 may be connected to each other by a bus and perform communication with each other. The bus may be an industry standard architecture (Industry Standard Architecture, abbreviated ISA) bus, an external device interconnect (PERIPHERAL COMPONENT, abbreviated PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, abbreviated EISA) bus, among others. The buses may be divided into address buses, data buses, control buses, etc. For ease of illustration, only one thick line is shown in fig. 4, but not only one bus or one type of bus.

Alternatively, in a specific implementation, if the memory 401, the processor 402, and the communication interface 403 are integrated on a chip, the memory 401, the processor 402, and the communication interface 403 may perform communication with each other through internal interfaces.

Processor 402 may be a central processing unit (Central Processing Unit, abbreviated as CPU), or an Application SPECIFIC INTEGRATED Circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the application.

The present embodiment also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the power system new energy consumption cost calculation method in the market environment as above.

In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or N embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.

Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, "N" means at least two, for example, two, three, etc., unless specifically defined otherwise.

Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more N executable instructions for implementing specific logical functions or steps of the process, and further implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.

Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or N wires, a portable computer cartridge (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.

It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the N steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As with the other embodiments, if implemented in hardware, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.

Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.

In addition, each functional unit in the embodiments of the present application may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.

The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like. While embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.

Claims

1. The new energy consumption cost calculation method for the electric power system in the market environment is characterized by comprising the following steps of:

Based on the uncertainty of the output of the renewable energy unit, setting a plurality of renewable energy output scenes serving as actual scenes in a preset sampling mode;

Estimating the occurrence probability of each renewable energy output scene, and establishing a random unit combination model considering multiple scenes based on a planning problem considering uncertainty;

Obtaining a first annual market clearing result and running cost of the power system by utilizing the random unit combination model, wherein the random unit combination model is established by an actual scene of new energy uncertainty and uncontrollable output curve;

wherein, the random set combination objective function is:

；

Constraints of the optimization model include:

a. system power balance constraint:

，

；

b. Power transmission network constraints:

，

；

c. Conventional unit operation constraints:

，

；

d. renewable energy source output constraint:

；

e. system capacity reserve constraints:

；

Wherein, for the upper mark and the lower mark, Represent the firstThe number of the scenes in which the video is displayed,Represent the firstA time period, i represents the ith unit,Represent the firstA number of bus nodes are arranged on the bus,Represent the firstA plurality of power transmission lines; s represents a scene set, T represents a period set, I represents a unit set, N represents all node sets,Representing a set of conventional units,Represents a set of renewable energy units,To be located at the nodeIs a set of units;

for the constants in the optimization model, The starting-up cost of the ith unit is represented,Finger numberThe probability of the occurrence of an individual scene,The penalty cost of the load unit is indicated,Finger numberThe susceptance of the strip line,Finger numberThe upper limit of the capacity of the line,/Refers to the minimum/maximum output of the ith machine set,Refers to the nth row of the admittance matrix in the meaning of the direct current flow of the power system,Representation ofThe scene is set up in such a way that,Time period of firstThe maximum allowable cut load amount for each node,For a period of timeNodeIs used for the load of the (a),/The maximum power up/down amounts in a period of time of the unit i,/For the unit i to minimize the start-up/shut-down time,Is thatThe scene is set up in such a way that,The time period belongs to the maximum available output of the ith machine set of renewable energy sources,In order to achieve a load reserve rate,In order to achieve a renewable energy source reserve rate,Penalty coefficients for unit cut load;

as to the variables in the optimization model, Is thatThe scene is set up in such a way that,Column vectors consisting of phase angles of all nodes of the time interval power system,Is thatThe scene is set up in such a way that,The output of the ith machine set in the period,/For the period t, the start-up/stop action indicating variable of the ith unit,Is thatThe time period, whether the ith machine set indicates a variable online,Representation ofThe scene is set up in such a way that,Time period of firstThe individual nodes cut the amount of load,Is thatThe scene is set up in such a way that,The time period during which the first time period,The power of the line is supplied to the power line,Is thatThe scene is set up in such a way that,The time period, the total cut load of the system,A functional relation of the generating cost of the unit i with respect to the output of the unit;

Obtaining a new energy output single-point prediction scene serving as a virtual scene according to the new energy output probability distribution characteristics;

based on the new energy output single-point prediction scene, converting the random unit combination model to obtain a first deterministic unit combination model;

Acquiring a second annual market clearing result and operation cost of the power system by using the first deterministic unit combination model, wherein the first deterministic unit combination model is established by a virtual scene without uncertainty of new energy;

Wherein the renewable energy reserve rate in the random unit combination model is calculated Setting to 0 to convert the random crew combination model to the first deterministic crew combination model;

converting a new energy unit in the electric power system into a target controllable unit, and acquiring a third annual market clearing result and running cost of the target controllable unit by using a preset second deterministic unit combination model, wherein the second deterministic unit combination model is established by a virtual scene without uncertainty of the controllable unit;

The method for converting the new energy unit in the electric power system into a target controllable unit, and obtaining a third annual market clearing result and running cost of the target controllable unit by using a preset second deterministic unit combination model comprises the following steps: for each day in the whole year, calculating conventional unit parameters obtained by equivalent of all renewable energy units in the power system one by one according to a preset description rule, wherein the conventional unit parameters comprise minimum output/maximum output, maximum up-regulation/down-regulation quantity in a single period, minimum start-up time and minimum stop time;

For any appointed date, the maximum available output of each period in the random appointed date is respectively ；

The equivalent conventional unit parameter determination rules are as follows:

a. equivalent minimum/maximum force ：

By solving the nonlinear programming problem as follows:

The objective function is:

，

The constraint conditions are as follows:

，

；

wherein sgn (·) represents a sign function, the function value is 1 when the argument is non-negative, otherwise the function value is 0; For a given penalty factor; Is the ratio of the minimum equivalent capacity to the maximum equivalent capacity; The capacity of the new energy unit i;

b. Equivalent single period maximum up/down amount ：

Calculated by the following formula:

，

；

Wherein, AndThe relative up-regulation/down-regulation coefficients of the equivalent conventional units are respectively;

c. Equivalent minimum on-time and minimum off-time ：

Setting equivalent minimum startup time and minimum shutdown time as main force conventional unit parameters of the electric power system;

，

Constraints of the optimization model include:

a. system power balance constraint:

，

；

b. Power transmission network constraints:

，

；

c. Conventional unit operation constraints:

，

；

d. Renewable energy equivalent conventional unit output constraint:

，

；

f. system capacity reserve constraints:

；

Wherein, for the upper mark and the lower mark, Represent the firstA time period, i represents the ith unit,Represent the firstA number of bus nodes are arranged on the bus,Represent the firstA plurality of power transmission lines; s represents a scene set, T represents a period set, I represents a unit set, N represents all node sets,Representing a set of conventional units,Represents a set of renewable energy units,Is a set of units located at a node n;

for the constants in the optimization model, The starting-up cost of the ith unit is represented,The penalty cost of the load unit is indicated,Finger numberThe susceptance of the strip line,Finger numberThe upper limit of the capacity of the line,/Refers to the minimum/maximum output of the ith machine set,Refers to the nth row of the admittance matrix in the meaning of the direct current flow of the power system,The s-scene is represented as such,The period, the nth node maximum allowable cut load,For the load of the period t node n,/The maximum power up/down amounts in a period of time of the unit i,/For the unit i to minimize the start-up/shut-down time,For an s-scene,The time period belongs to the maximum available output of the ith machine set of renewable energy sources,In order to achieve a load reserve rate,In order to achieve a renewable energy source reserve rate,Penalty coefficients for unit cut load; Minimum/maximum output of a conventional controllable unit equivalent to the new energy unit; The maximum power up-regulating/down-regulating quantity of the conventional controllable unit i equivalent to the new energy unit in a period of time; Minimum start-up/stop time of the conventional controllable unit i equivalent to the new energy unit;

as to the variables in the optimization model, Is thatColumn vectors consisting of phase angles of all nodes of the time interval power system,Is thatThe output of the ith machine set in the period,/Is thatThe time period, the start-up/stop action indicating variable of the ith unit,Is thatThe time period, whether the ith machine set indicates a variable online,Representation ofThe nth node cuts the load amount during the period,Is thatThe time period during which the first time period,The power of the line is supplied to the power line,Is thatThe time period, the total cut load of the power system,A functional relation of the generating cost of the unit i with respect to the output of the unit; and

And comparing the first annual market clearing result with the operation cost, the second annual market clearing result with the operation cost and the third annual market clearing result with the operation cost, and calculating new energy consumption cost of the power system based on the comparison result.

2. The method of claim 1, wherein the new energy consumption cost comprises a consumption cost due to new energy output uncertainty, a consumption cost due to new energy output uncontrollability, and/or a total cost of new energy consumption.

3. A new energy consumption cost calculation device for an electric power system in a market environment, comprising:

the scene presetting module is used for setting a plurality of renewable energy source output scenes serving as actual scenes in a preset sampling mode based on the uncertainty of the output of the renewable energy source unit;

The modeling module is used for estimating the occurrence probability of each renewable energy output scene and establishing a random unit combination model considering multiple scenes based on a planning problem considering uncertainty;

the first acquisition module is used for acquiring a first annual market clearing result and operation cost of the power system by utilizing the random unit combination model, wherein the random unit combination model is established by an actual scene of new energy uncertainty and uncontrollable output curve;

wherein, the random set combination objective function is:

；

Constraints of the optimization model include:

a. system power balance constraint:

，

；

b. Power transmission network constraints:

，

；

c. Conventional unit operation constraints:

，

；

d. renewable energy source output constraint:

；

e. system capacity reserve constraints:

；

The prediction module is used for obtaining a new energy output single-point prediction scene serving as a virtual scene according to the new energy output probability distribution characteristics;

The conversion module is used for converting the random unit combination model into a first deterministic unit combination model based on the new energy output single-point prediction scene;

the second acquisition module is used for acquiring a second annual market clearing result and operation cost of the electric power system by utilizing the first deterministic unit combination model, wherein the first deterministic unit combination model is established by a virtual scene without uncertainty of new energy;

The third acquisition module is used for converting a new energy unit in the electric power system into a target controllable unit and acquiring a third annual market clearing result and operation cost of the target controllable unit by utilizing a preset second deterministic unit combination model, wherein the second deterministic unit combination model is established by a virtual scene without uncertainty of the controllable unit;

The third obtaining module is further configured to: for each day in the whole year, calculating conventional unit parameters obtained by equivalent of all renewable energy units in the power system one by one according to a preset description rule, wherein the conventional unit parameters comprise minimum output/maximum output, maximum up-regulation/down-regulation quantity in a single period, minimum start-up time and minimum stop time;

The equivalent conventional unit parameter determination rules are as follows:

a. equivalent minimum/maximum force ：

By solving the nonlinear programming problem as follows:

The objective function is:

，

The constraint conditions are as follows:

，

；

b. Equivalent single period maximum up/down amount ：

Calculated by the following formula:

，

；

c. Equivalent minimum on-time and minimum off-time ：

，

Constraints of the optimization model include:

a. system power balance constraint:

，

；

b. Power transmission network constraints:

，

；

c. Conventional unit operation constraints:

，

；

d. Renewable energy equivalent conventional unit output constraint:

，

；

f. system capacity reserve constraints:

；

And the calculation module is used for comparing the first annual market clearing result with the operation cost, the second annual market clearing result with the third annual market clearing result with the operation cost, and calculating the new energy consumption cost of the power system based on the comparison result.

4. A device according to claim 3, wherein the new energy consumption costs comprise consumption costs due to uncertainty in new energy output, consumption costs due to uncontrollability of new energy output, and/or total cost of new energy consumption.

5. An electronic device, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the method for calculating new energy consumption costs of an electric power system in a market environment according to claim 1 or 2.

6. A computer-readable storage medium having stored thereon a computer program, characterized in that the program is executed by a processor for realizing the new energy consumption cost calculation method of an electric power system in a market environment according to claim 1 or 2.