A provincial power grid power supply planning method based on LHS and NSGA-II under a carbon peak vision

Abstract

The application discloses a provincial power grid power source planning method based on LHS and NSGA-II under a carbon peak vision, which comprises the following steps: a coal consumption and CO2 emission calculation model of a coal-fired unit is established; a provincial power grid power source planning multi-objective optimization model is established by taking into account the power carbon peak constraint and by giving consideration to the economy and cleanness of the power source planning scheme; relevant parameters required by a power source planning objective function and constraint conditions are set, and the Pareto optimal solution of the power source planning scheme is obtained by solving based on the LHS and NSGA-II algorithms. The method can plan the power source of the provincial power grid, and the result verifies that the method can obtain the power source planning scheme for realizing the carbon peak target within a specified period, and the economy and cleanness of the planning scheme are improved under the premise of meeting the supply and demand balance.

Description

Technical Field

[0001] This invention relates to the field of energy and power development planning technology, and in particular to a provincial power grid power planning method based on LHS and NSGA-II under the vision of carbon peaking. Background Technology

[0002] In recent years, extreme weather events and natural disasters related to global climate change have occurred frequently, bringing a series of irreversible negative impacts on the human living environment. Accelerating the clean and low-carbon energy transition and actively addressing climate change have gradually become a consensus in the international community.

[0003] In my country, energy activities are the main source of CO2 emissions, with the power sector being a significant carbon emitter. As the largest source of CO2 emissions and a key industry supporting the development of end-use electrification, the low-carbon transformation of the power sector is crucial to achieving carbon peaking targets, and power planning plays a vital role in this process.

[0004] Traditional power planning primarily aims to determine, based on predicted load demand during the planning period and under certain reliability requirements, when to construct (or add) what type and scale of power plants (or units) to minimize total investment and ensure safe power system operation. However, under the constraint of carbon peaking, power planning must further consider factors such as CO2 emissions beyond traditional models. Currently, research on carbon peaking power planning is still in its early stages. Existing methods mainly rely on the determined CO2 emission trajectory during the planning period, using a reverse-engineering approach to determine the development scale of various power generation technologies over time. However, such methods have certain shortcomings in practical applications. First, existing methods do not consider the varying standard coal consumption rates of coal-fired power units in different capacity ranges, leading to significant errors in CO2 emission calculations and affecting the establishment of reasonable power planning schemes. Second, provinces still face difficulties in determining the annual CO2 emissions of the power industry during the planning period, making it difficult to set constraints for existing methods in practical applications. Summary of the Invention

[0005] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0006] In view of the problems existing in the above and / or existing grid power planning methods under the carbon peaking vision, the present invention is proposed.

[0007] Therefore, the problem to be solved by the present application is to provide a provincial power grid power source planning method based on LHS and NSGA-II under the carbon peak vision, so as to solve the problems of large calculation error and difficult planning of the existing power grid power source planning method.

[0008] To solve the above technical problems, the present application provides the following technical scheme, a provincial power grid power source planning method based on LHS and NSGA-II under the carbon peak vision, which comprises: establishing a coal consumption and CO2 emission calculation model of coal-fired generating units; considering the power carbon peak constraint, establishing a provincial power grid power source planning multi-objective optimization model considering the economy and cleanliness of the power source planning scheme; setting relevant parameters required by the power source planning objective function and constraint conditions, and solving and obtaining the Pareto optimal solution of the power source planning scheme based on LHS and NSGA-II algorithm.

[0009] As a preferred scheme of the provincial power grid power source planning method based on LHS and NSGA-II under the carbon peak vision, wherein: the coal consumption and CO2 emission calculation model of coal-fired generating units specifically comprises,

[0010] Under the condition of considering the difference between the coal consumption per unit of electricity generation and the annual utilization hours of different capacity coal-fired generating units, the annual total coal consumption calculation model of the coal-fired generating units is as follows:

[0011]

[0012] In the formula, C represents the annual total coal consumption of all coal-fired generating units in the power source planning scheme; m represents a capacity partition of the coal-fired generating unit; M total represents the total number of capacity partitions; k m represents the average coal consumption per unit of electricity generation of the coal-fired generating unit in the capacity interval m; S m represents the total capacity of the coal-fired generating unit in the capacity interval m; T m,min is the minimum annual utilization hours of the coal-fired generating unit in the capacity interval m; ΔQ m is the minimum power generation of the coal-fired generating unit in the capacity interval m, that is, S m ×T m,min on the basis, according to the principle that large-capacity units are preferentially generated and do not exceed their maximum annual utilization hours, the remaining power is allocated;

[0013] Under the condition of considering the difference between the coal consumption per unit of electricity generation and the annual utilization hours of different capacity coal-fired generating units, the annual total CO2 emission calculation model of the coal-fired generating units is as follows:

[0014] E=F0C

[0015] In the formula, E is the total annual CO2 emission of all coal power units; F0 represents the carbon emission factor of unit mass standard coal; and C represents the total annual coal consumption of all coal power units in the power source planning scheme.

[0016] As a preferred scheme of the provincial power grid power source planning method based on LHS and NSGA-II under the carbon peak vision of the present application, wherein: the power carbon peak constraint is considered, and a provincial power grid power source planning multi-objective optimization model considering the economy and cleanliness of the power source planning scheme is established, specifically including,

[0017] The objective function of the power source planning optimization model under the carbon peak vision is set;

[0018] The constraint condition of the power source planning optimization model under the carbon peak vision is set;

[0019] The objective function includes an economy target and a cleanliness target;

[0020] The constraint condition includes a carbon peak constraint, a power and electricity balance constraint, a peak regulation balance constraint, a power source annual installed capacity upper limit constraint, a non-fossil energy power generation installed capacity proportion constraint, and a non-fossil energy power generation proportion constraint.

[0021] As a preferred scheme of the provincial power grid power source planning method based on LHS and NSGA-II under the carbon peak vision of the present application, wherein: the economy target, i.e., the comprehensive cost of the power source planning scheme in the planning period is the lowest, and the specific expression is:

[0022]

[0023] In the formula, f1 represents the comprehensive cost of the power source planning scheme in the planning period; t is the year; T0 and T max represent the planning start and end years, respectively; and represent the annual equivalent value of the investment cost, the annual fixed operation and maintenance cost, and the cost of the annual consumed power generation raw materials of the newly added unit relative to T0 year, respectively, and the specific calculation method of the three is as shown below:

[0024]

[0025]

[0026]

[0027] In the formula, Θ NU represents a set of newly added power plants and substations, including coal-fired power plants, gas-fired power plants, nuclear power plants, hydropower plants, wind power plants, photovoltaic power stations, biomass power plants, and energy storage power stations; i is a certain type of plant and substation in Θ NU ; I represents a discount rate; ΔS i,tis the cumulative newly added installed capacity of the i-type power station by the year t in the planning period; k i,t is the unit comprehensive cost of the newly added installed capacity of the i-type power station by the year t; N i is the service life of the newly added unit of the i-type power station; p i,t is the unit capacity operation and maintenance cost of the unit of the i-type power station by the year t; q MA represents the raw material type; F j,t and U j are respectively the total consumption of the jth fuel by all newly added units by the year t in the planning period and the unit price thereof.

[0028] As a preferred scheme of the provincial power grid power source planning method based on LHS and NSGA-II under the carbon peak vision described in the application, wherein the cleanness target, i.e., the total CO2 emission amount in the planning period, is specifically expressed as:

[0029]

[0030] In the formula, f2 is the total CO2 emission amount in the planning period; q FU is a fossil energy consumption power plant; E i,t is the CO2 emission amount of the ith type of power plant by the year t.

[0031] As a preferred scheme of the provincial power grid power source planning method based on LHS and NSGA-II under the carbon peak vision described in the application, wherein the carbon peak constraint is specifically represented as:

[0032]

[0033] In the formula, T is the carbon peak year; E i,T is the total CO2 emission amount of the ith type of fossil energy power plant by the year T; is the set of expected CO2 emission amounts of the peak year; set of years is related to the value of T, i.e., when q T2 is the set of years other than T0 in the planning period;

[0034] The power balance constraint is specifically represented as:

[0035]

[0036] In the formula, q U is the set of all original and newly added power plants; P i,t is the installed capacity of the i-type power station by the year t; b i represents the output resistance coefficient of the i-type power station; AP t is the net received capacity on the planning area tie line; Dt,max denotes the maximum generation load of the whole society in year t; R m denotes the effective reserve rate of system load;

[0037] The power balance constraint is specifically represented as:

[0038]

[0039] In the formula, H i,t is the annual utilization hours of the i-type power plant in year t; ΔQ t is the net received power on the planning regional tie line; Q t,max is the power consumption of the whole society in year t;

[0040] The peak regulation balance constraint is specifically represented as:

[0041]

[0042] In the formula, t is the year; q is the quarter; γ i,q is the negative reserve capacity coefficient provided by the i-type unit in q quarter; D t,q,min denotes the minimum generation load in q quarter of year t;

[0043] The upper limit constraint of the annual installed capacity of the power source is specifically represented as:

[0044]

[0045] In the formula, P i,t,lim denotes the maximum capacity of the i-type power source station in year t due to construction capacity;

[0046] The non-fossil energy generation installed capacity ratio constraint is specifically represented as:

[0047]

[0048] In the formula, Θ NFU denotes the set of non-fossil energy stations; P t,min denotes the lower limit of the installed capacity of non-fossil energy in year t;

[0049] The non-fossil energy generation capacity ratio constraint is specifically represented as:

[0050]

[0051] In the formula, Q i,t is the power generation of the i-type station in year t considering the tie line; δ t,min denotes the lower limit of the non-fossil energy generation capacity ratio in year t.

[0052] As a preferred embodiment of the provincial power grid power planning method based on LHS and NSGA-II under the carbon peaking vision described in this invention, the method of solving and obtaining the Pareto optimal solution of the power planning scheme based on LHS and NSGA-II algorithms includes the following steps:

[0053] Construct a fitness function based on the objective function and constraints;

[0054] Use LHS to obtain the annual lower limit of installed capacity for various types of power supplies;

[0055] The Pareto optimal solution for the power planning scheme is obtained using NSGA-II.

[0056] As a preferred embodiment of the provincial power grid power planning method based on LHS and NSGA-II under the carbon peaking vision described in this invention, wherein: the construction of the adaptive value function based on the objective function and constraints includes,

[0057] Each constraint is incorporated into the objective function as a penalty, and the fitness function is constructed as follows:

[0058]

[0059] In the formula, W is the penalty value. If all constraints are satisfied, W = 0; if there is a constraint that is not satisfied, W is taken as a sufficiently large positive value.

[0060] As a preferred embodiment of the provincial power grid power planning method based on LHS and NSGA-II under the carbon peaking vision described in this invention, wherein: obtaining the annual installed capacity lower limit of various types of power sources using LHS includes,

[0061] The annual increase in the commissioning capacity of various types of power sources is shown as the range. The random variable follows a uniform distribution, where The upper limit of the production capacity of power source of type i in year t is represented by an M×N dimensional sampling matrix S generated using the LHS method. M×N Where M represents the total number of random variables, N represents the number of Latin hypercube samplings, and its m rows and n columns have s elements. mn This represents a single sample value of a random variable;

[0062] Using the sampling matrix S respectively M×N Each column serves as the lower limit of the annual installed capacity for each type of power supply. Combined with the set upper limit of the annual installed capacity for each type of power supply, a population P of size G is randomly generated, consisting of {P1 P2 ... P}. t …P G}, where P t Let t be the t-th individual in the population, which contains a set of specific annual installed capacity information for each type of power source;

[0063] Calculate the fitness value of each individual P t The sum of the corresponding two-dimensional fitness values F t = F 1,t + F 2,t , and then calculate the mean value of all individual Ft values At this time, if the jth column of S M×N is taken as the lower limit of the annual installed capacity of each type of power supply, and the value corresponding to each lower limit value in the column is the minimum compared to other columns, then the jth column of S M×N is finally taken as the lower limit of the annual installed capacity of each type of power supply.

[0064] As a preferred solution of the provincial power grid power supply planning method based on LHS and NSGA-II under the carbon peak vision described in the present application, wherein: the Pareto optimal solution of the power supply planning scheme solved by NSGA-II includes,

[0065] According to the upper limit of the annual incremental installed capacity of each type of power supply and the calculated lower limit of the annual installed capacity of each type of power supply, the value range of the variable is determined, and an initial population P = {P1 P2…P t …P G} of size G is randomly generated, and the fitness value of each individual P t is calculated, and then the offspring population Q n is generated through selection, crossover and mutation;

[0066] From the second generation, the parent and child populations are combined into a population R n of size 2G, and then the individuals in R n are quickly non-dominated sorted to form a series of non-dominated layers, and the crowding degree of the individuals in each non-dominated layer is calculated and sorted, and then the first G individuals are selected to form a new parent population;

[0067] The new offspring population is generated through the basic operation of genetic algorithm, and the above operation is repeated using the current latest parent and child populations until the program loop ends and the Pareto optimal solution is output.

[0068] The application has the beneficial effects that: considering the difference between the coal consumption of generating electricity and the annual utilization hours of different capacity coal power units, the proposed CO2 emission calculation method based on the capacity segmentation of coal power units improves the calculation accuracy of the CO2 emission of the power supply scheme; taking into account the economy and cleanliness of the power supply planning, a multi-objective optimization model of the provincial power grid power supply planning scheme considering the carbon peak constraint is proposed, and the model can obtain the power supply planning scheme with the optimal comprehensive cost and total CO2 emission under the premise of achieving the carbon peak of electricity within the specified period, which eliminates the trouble of setting the annual CO2 emission of the power supply planning of the power grid enterprise; according to the proposed multi-objective optimization model of the power supply planning scheme, the LHS and NSGA-II joint solving algorithm is further applied to effectively obtain the Pareto optimal solution of the power supply planning scheme with respect to the planning comprehensive cost and the total CO2 emission, and the overall convergence of the algorithm is strong. BRIEF DESCRIPTION OF DRAWINGS

[0069] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor. Among them:

[0070] Figure 1 The flowchart of the provincial power grid power supply planning method based on LHS and NSGA-II under the carbon peak vision.

[0071] Figure 2 is the installed capacity of each type of power supply in the planning starting year in the specific embodiment of the present application.

[0072] Figure 3 is the upper limit value of the annual incremental installed capacity of each type of power supply in the specific embodiment of the present application.

[0073] Figure 4 is the statistical result of the coal consumption of generating electricity of coal power units in the specific embodiment of the present application.

[0074] Figure 5 is the statistical result of the annual utilization hours of coal power units in the specific embodiment of the present application.

[0075] Figure 6 is the two-dimensional fitness value distribution of the solution under different iteration numbers in the specific embodiment of the present application.

[0076] Figure 7 is the CO2 annual emission of the Pareto optimal solution in the specific embodiment of the present application.

[0077] Figure 8 is the annual incremental installed capacity of each type of power supply of scheme 1 in the specific embodiment of the present application.

[0078] Figure 9 is the annual incremental installed capacity of each type of power supply in Scheme 2 in the specific embodiment of the present application.

[0079] Figure 10 is the incremental power supply capacity of Scheme 2 relative to Scheme 1 in the specific embodiment of the present application.

[0080] Figure 11 is the annual power generation proportion of fossil energy in Scheme 1 and 2 in the specific embodiment of the present application. DETAILED DESCRIPTION

[0081] In order to make the above objectives, characteristics and advantages of the present application more apparent and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0082] In the following description, a large number of specific details are set forth in order to facilitate a thorough understanding of the present application, but the present application can also be implemented in other ways different from those described herein, and those skilled in the art can make similar generalizations without departing from the connotation of the present application, therefore the present application is not limited to the specific embodiments disclosed below.

[0083] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. "In one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an independent or alternative embodiment that excludes other embodiments.

[0084] Embodiment 1

[0085] Reference Figure 1 For the first embodiment of the present application, the embodiment provides a provincial power grid power supply planning method based on LHS (Latin Hypercube Sampling) and NSGA-II (nondominated sorting genetic algorithm II with elitist strategy) under the carbon peak vision, which includes the following steps:

[0086] S1: considering the difference between the coal consumption per kilowatt-hour and the annual utilization hours of coal-fired units of different capacities, a coal consumption and CO2 emission calculation model of coal-fired units is established;

[0087] In this step, the following steps are further included:

[0088] S1.1: under the condition of considering the difference between the coal consumption per kilowatt-hour and the annual utilization hours of coal-fired units of different capacities, the annual total coal consumption calculation model of coal-fired units is established as:

[0089]

[0090] In the formula, C represents the total annual coal consumption of all coal-fired units in the power source planning scheme; m represents a certain capacity partition of coal-fired units, which can be divided into three intervals of 0-200 MW, 200-600 MW and 600-1000 MW according to the dominant single machine capacity level of coal-fired power plants in China; E is the total annual CO2 emission of all coal-fired units; M total = 3 represents the total number of capacity partitions; k m represents the average standard coal consumption per unit of electricity of coal-fired units in the capacity interval m; S m represents the total capacity of coal-fired units in the capacity interval m; T m,min is the minimum annual utilization hours of coal-fired units in the capacity interval m; ΔQ m is the remaining power allocated to the capacity interval m on the basis of the fixed minimum power generation (S m × T m,min ) under the principle that large-capacity units are given priority and do not exceed their maximum annual utilization hours.

[0091] S1.2: Under the condition of considering the difference between the coal consumption per unit of electricity of coal-fired units of different capacities and the annual utilization hours, a calculation model of the total annual CO2 emission of coal-fired units is established as follows:

[0092] E = F0C (2)

[0093] In the formula, F0 = 2.66 tCO2 / t standard coal represents the carbon emission factor per unit mass of standard coal; C represents the total annual coal consumption of all coal-fired units in the power source planning scheme in step S1.1.

[0094] S2: A multi-objective optimization model of power source planning is established considering the constraint of carbon peak, which takes into account the economy and cleanliness of the power source planning scheme, wherein the objective function includes the dual objectives of minimizing the system comprehensive investment cost and the total CO2 emission in the planning period, and the constraint conditions include the carbon peak constraint, the power balance constraint, the peak regulation balance constraint, the annual installed capacity constraint of power source, and the installed capacity and power generation constraint of non-fossil energy power generation;

[0095] In this step, the following steps are further included:

[0096] S2.1: The objective function of the power source planning optimization model under the carbon peak vision is set as follows:

[0097] 1) Economic objective: The comprehensive cost of the power source planning scheme in the planning period is the lowest. The specific expression is:

[0098]

[0099] wherein f1 represents the comprehensive cost of the power supply planning scheme in the planning period; t is the year; T0 and T max represent the starting and ending years of the planning, respectively; and represent the equal annual value of the investment cost, the annual fixed operation and maintenance cost, and the cost of the annual consumption of power generation raw materials of the newly added units relative to T0, respectively, and the specific calculation methods of the three are as follows:

[0100]

[0101]

[0102]

[0103] wherein Θ NU represents the set of newly added power plants and substations, including coal-fired power plants, gas-fired power plants, nuclear power plants, hydropower plants, wind farms, photovoltaic power stations, biomass power plants, and energy storage power stations (including new energy storage power stations and pumped storage power stations); i is a certain type of plant and substation in Θ NU ; I represents the discount rate; ΔS i,t is the cumulative newly added installed capacity of the i-type plant and substation as of the year t in the planning period; k i,t is the unit comprehensive cost of the newly added installed capacity of the i-type plant and substation in the year t; N i is the service life of the newly added units of the i-type plant and substation; ρ i,t is the unit capacity operation and maintenance cost of the i-type plant and substation in the year t; Θ MA represents the type of raw materials (including coal, natural gas, natural uranium, and biomass particles); F j,t and U j are the total consumption of the jth fuel by all newly added units as of the year t in the planning period and the unit price thereof, respectively, wherein the coal consumption of coal-fired units is calculated according to the method in step S1.1.

[0104] 2) Cleanliness target: the total amount of CO2 emissions in the planning period is minimum. The specific expression is:

[0105]

[0106] wherein f2 is the total amount of CO2 emissions in the planning period; Θ FU is a power plant consuming fossil energy; E i,t is the CO2 emission amount of the i-type power plant in the year t, wherein the annual CO2 emission amount of coal-fired units is calculated according to the method in step S2.2.

[0107] S2.2: Set the constraint conditions of the power supply planning optimization model under the carbon peak vision as:

[0108] 1) Carbon peak constraint:

[0109]

[0110] where T is the carbon peak year; E i,T is the total amount of CO2 emissions from i-type fossil energy power plants in the carbon peak year; is the set of expected CO2 emissions peak years (no later than 2030); T is related to the value of T, that is, when , is the set of years other than T0 in the planning period.

[0111] 2) Power balance constraints:

[0112]

[0113] where Θ U is the set of all existing and new power plants; P i,t is the installed capacity of i-type plants in year t; β i represents the output resistance coefficient of i-type plants; ΔP t is the net received capacity on the planning area tie line; D t,max represents the maximum power generation load of society in year t; R m represents the effective reserve rate of system load.

[0114] 3) Power balance constraints:

[0115]

[0116] where H i,t is the annual utilization hours of i-type plants in year t; ΔQ t is the net received power on the planning area tie line; Q t,max is the total power consumption of society in year t.

[0117] 4) Peak balancing constraints:

[0118]

[0119] where t is the year; q is the quarter; γ i,q is the i-type unit q quarter negative reserve capacity coefficient; D t,q,min represents the minimum power generation load in t year q quarter.

[0120] 5) Upper limit constraint on annual installed capacity of power sources:

[0121]

[0122] where P i,t,limPit represents the maximum production capacity of the i-type power plant in the t-year due to the construction capacity.

[0123] 6) Non-fossil energy installed capacity proportion constraint:

[0124]

[0125] In the formula, Θ NFU Pit represents the i-type power plant set; P t,min Pit represents the lower limit of non-fossil energy installed capacity in the t-year.

[0126] 7) Non-fossil energy power generation proportion constraint:

[0127]

[0128] In the formula, Q i,t Pit represents the i-type plant power generation in the t-year considering the tie line; δ t,min Pit represents the lower limit of non-fossil energy power generation proportion in the t-year.

[0129] S3: Set the relevant parameters required for the power supply planning objective function and constraint conditions, solve and obtain the Pareto optimal solution of the power supply planning scheme based on LHS and NSGA-II algorithm.

[0130] This step further includes the following steps:

[0131] S3.1: Set the relevant parameters required for the power supply planning objective function and constraint conditions

[0132] 1) Load forecasting, determine the annual power demand in the system planning period;

[0133] 2) Statistics of coal-fired unit coal consumption and utilization hour historical data, determine the average degree of coal consumption of coal-fired units in different capacity intervals and the value interval of annual utilization hours;

[0134] 3) Determine the parameters required in the process of calculating the comprehensive cost of each power source, the interval to which the carbon peak realization year belongs, the upper limit of the annual production capacity of each type of power source in the planning period, the proportion of non-fossil energy installed capacity and power generation, and other related parameters required for the establishment of objective function and constraint conditions.

[0135] S3.2: Construct the fitness function according to the objective function and constraint conditions

[0136] Each constraint condition shown in step S2.2 is counted in the form of penalty value in each objective function, and the fitness function is constructed as:

[0137]

[0138] where f1 and f2 are as shown in 1) and 2) in step S2.1, respectively, and W is a penalty value, W = 0 if all the constraints in step S2.2 are satisfied; otherwise, W is a sufficiently large positive value.

[0139] S3.3: Obtain the lower limit of the annual installed capacity of each type of power source using LHS

[0140] 1) Let X i,t denote the random variable of the installed capacity of type i power source in the tth year, and let X i,t be a random variable uniformly distributed in the interval , where denotes the upper limit of the installed capacity of type i power source in the tth year.

[0141] 2) Let M denote the total number of random variables X i,t , and form a random vector X = [X1X2…X m …X M ] for representing the lower limit of the annual installed capacity of each power source, where X m is any random variable, and the cumulative distribution function of X m is Y m = F m (X m );

[0142] 3) Generate an M x N matrix L M×N , where N denotes the number of Latin hypercube sampling times. Each row of the matrix is a random sequence of integers in the interval (1, N), and a mn is the m x n element of the matrix;

[0143] 4) Generate an M x N matrix U M×N , where each element of the matrix is uniformly distributed in [0, 1], and u mn is the m x n element of the matrix;

[0144] 5) Calculate the M x N sampling matrix S M×N , where the m x n element of the matrix is

[0145]

[0146] where m = 1, 2, …, M; n = 1, 2, …, N. It can be seen that s mn is actually a sampling value of the random variable X m ;

[0147] 6) Respectively, use the sampling matrix S M×Neach column of S as the lower limit of the annual installed capacity of each type of power supply, and combining the upper limit of the annual installed capacity of each type of power supply set in step S3.1, a population P of size G is randomly generated P = {P1, P2, …, PG} t …P G} where Pt is the tth individual in the population, which contains a set of determined annual installed capacity information of each type of power supply. t

[0148] 7) According to step S2.2, the sum of the two-dimensional fitness values of each individual P t in the population P is calculated F t = F 1,t + F 2,t , and the average value of the F t values of all individuals is calculated At this time, if the jth column of S M×N is taken as the lower limit of the annual installed capacity of each type of power supply, and the corresponding to each lower limit value in the column takes the minimum value compared to other columns, then the jth column of S M×N is finally taken as the lower limit of the annual installed capacity of each type of power supply.

[0149] This step S3 solves the problem that the probability of individuals in the population being located in the feasible region is low, which leads to the difficulty of convergence of the NSGA-II algorithm, in the case of a large number of optimization model variables, a wide range of values, and complex constraint conditions.

[0150] S3.4: Using NSGA-II to solve the Pareto optimal solution of the power supply planning scheme

[0151] 1) According to the upper limit of the annual installed capacity of each type of power supply objectively existing and the lower limit of the annual installed capacity of each type of power supply calculated in step S3.3, the value range of the variable is determined, thereby a population P of size F is randomly generated P = {P1, P2, …, PF} t …P G} and the fitness value corresponding to each individual P t is calculated, and then the offspring population Q n is generated through selection, crossover, and mutation;

[0152] 2) From the second generation, the parent and child populations are combined into a population R of size 2G n , then the individuals in R n are quickly non-dominated sorted to form a series of non-dominated layers, and the crowding degree of the individuals in each non-dominated layer is calculated and sorted by crowding degree, and then the first G individuals are selected to form a new parent population;

[0153] ​3) New child population is generated by the basic operation of genetic algorithm, and the above operation is repeated by using the latest parent and child population, until the program loop ends and the Pareto optimal solution is output.

[0154] In this embodiment, referring to the coal consumption and CO2 emission of coal-fired units in a certain province of China from 2017 to 2021, the proposed coal consumption and CO2 emission calculation model of coal-fired units is derived and verified, which serves as the basis for power source planning.

[0155] Embodiment 2

[0156] Reference Figures 2-11 As a second embodiment of the present application, in order to better verify and illustrate the technical effects adopted in the method of the present application, this embodiment selects to compare the test results by scientific demonstration means to verify the real effect of the method; the present application carries out power source planning under the constraint of carbon peak for a certain provincial power system in China, and analyzes the obtained results.

[0157] 1. Power source planning basic data

[0158] The planning period of this example is selected as 2022-2035, and the total social electricity consumption and maximum power generation load characteristics of the system to be planned (hereinafter referred to as system A) in the planning period are shown in Table 1. The net annual import electricity quantity and power of the province outside the planning period are 40 billion kWh and 9.5 million kW respectively, and the net import coal-fired power scale is maintained at 28 billion kWh, and the effective standby rate of the maximum power generation load is taken as 15%. The power source types owned by system A at the beginning of the planning period include conventional hydropower, coal-fired power, nuclear power, wind power, photovoltaic power, pumped storage power and biomass power, and the specific installed capacity of each type of power source is shown in Table 2. The upper limit value of the annual installed capacity of each type of unit in the planning period is shown in Table 3. Figure 2 Figure 3

[0159] Table 1: Electricity demand prediction of system A

[0160] Year Total electricity consumption / 100 million kWh Maximum power generation load / 10,000 kW 2022 2671 3987 2023 2805 4218 2024 2945 4462 2025 3092 4721 2030 3817 5983 2035 4599 7359

[0161] The statistical data of the coal consumption per kilowatt-hour of coal-fired units in different capacity intervals of system A are shown in the box plot as shown in Table 4. The coal consumption per kilowatt-hour is taken as the position of the mean value of the box plot, and the coal consumption of coal-fired units in the intervals of 0-200 MW, 200-600 MW and 600-1000 MW is 310, 285 and 274 g / kWh respectively. Figure 4

[0162] The annual utilization hours of coal-fired units in different capacity intervals of system A are shown in Table 5. Figure 5 ​​​As shown, the upper and lower 25% quantiles of each box plot are taken as the upper and lower limits of the annual utilization hours of each capacity interval coal-fired unit, respectively. The value intervals of the annual utilization hours of coal-fired units in the three intervals of 0-200 MW, 200-600 MW and 600-1000 MW are 2060-3913 h, 3290-4321 h and 3840-4326 h, respectively. According to the "three public" dispatching mechanism, after determining the total power generation of coal-fired units according to power balance, the fixed power generation is calculated according to the total capacity of the unit and the minimum annual utilization hours, and the remaining power is allocated according to the maximum annual utilization hours and the principle of priority power generation of large-capacity units.

[0163] 2. Results of power supply planning under carbon peak constraint

[0164] The population size is set to 100, the maximum number of evolution generations is 20000, and the crossover rate is 0.9. The expected carbon peak year interval is set to 2025-2030.

[0165] According to the flow shown in Figure 1 , the two-dimensional fitness value (F1, F2) distribution of the population solution in the feasible region at different iteration numbers n is shown in Figure 6 . It can be observed that Figure 6 , at the beginning of iteration, the fitness values of the randomly distributed initial population solutions have an aggregation effect in space. As the number of evolution generations increases, the solutions gradually move to the lower left of the space, and the mutual non-dominance between individuals gradually increases. The set of fitness values of the population solutions at the 10000th and 20000th generations almost coincide in space, so it can be considered that when the number of evolution generations exceeds about 10000, genetic evolution has converged, and the solution set obtained by further iteration to the end (n = 20000) can be regarded as the Pareto optimal solution.

[0166] For all solutions corresponding to the Pareto optimal front in Figure 6 , the curve of CO2 emission versus year is plotted, and the obtained curve cluster is shown in Figure 7 . It can be seen from Figure 7 that the Pareto optimal power supply planning schemes of system A all achieve carbon peak in 2029.

[0167] Generally, the objectives of a multi-objective optimization model need to have certain contradictions. To further analyze the reasons for the contradiction between the economy and cleanliness of the power supply planning scheme, two groups of power supply planning schemes are randomly selected from the Pareto optimal solutions as shown in Figure 8 and 9 , and the objective function values corresponding to them are shown in Table 2.

[0168] Table 2: Objective function values of selected power supply planning schemes

[0169] planning comprehensive cost f1 / billion yuan CO2 total emissions f2 / billion tons Scheme 1 5363.26 16.36 Scheme 2 5656.55 15.54

[0170] Figure 10 The capacity growth of each type of power supply in scheme 2 relative to scheme 1 in each year is given. Figure 11 The change in the proportion of fossil energy power generation in each year is given.

[0171] As shown in Table 2 and Figures 8-10 It can be seen that the planning comprehensive cost of scheme 2 is higher than that of scheme 1, because the installed capacity of each type of power supply in scheme 2 is higher than that in scheme 1 as a whole. As shown in Figure 10 and 11 As shown in Table 3 and Figure 10 and 11 As shown in Table 3 and

[0172] Therefore, from the above analysis, it can be seen that there is a contradiction between the economy and the cleanliness of the power supply planning scheme, and it is reasonable to establish a multi-objective optimization model of power supply planning with the economy and the cleanliness as the objective functions.

[0173] Importantly, it should be noted that the constructions and arrangements of the present application shown in the various example embodiments are merely illustrative. Although only a few embodiments have been described in detail in this disclosure, those skilled in the art who review this disclosure will readily appreciate that many modifications are possible (e.g., variations in sizes, dimensions, structures, shapes and proportions of the various elements, values of parameters, mounting arrangements, use of materials, colors, orientations, etc.) without materially departing from the novel teachings and advantages of the subject matter described in the application. For example, elements shown as integrally formed can be constructed of multiple parts or elements, the position of elements can be reversed or otherwise varied, and the nature or number of elements or positions can be modified or changed. Accordingly, all such modifications are intended to be included within the scope of the application. The order or sequence of any process or method steps can be changed, or reordered, according to alternative embodiments. Any "means plus function" clauses are intended to cover the structures described herein as performing the recited functions and not only structural equivalents but also equivalent structures. Other substitutions, modifications, changes, and omissions can be made in the design, operating conditions, and arrangement of the example embodiments without departing from the scope of the application as expressed in the appended claims. Accordingly, the application is not limited to the particular embodiments described and shown herein, but extends to all structures that fall within the scope of the claims.

[0174] Furthermore, in the interest of providing a concise description of illustrative embodiments, not all features of an actual implementation can be described (that is, not all

[0175] It will be appreciated that in the development of any actual embodiment, as in any engineering or design project, numerous implementation-specific decisions can be made. Such development efforts might be complex and time-consuming, but would nevertheless be a routine undertaking for those of ordinary skill in the art having the benefit of this disclosure.

[0176] It should be noted that the above-mentioned embodiments are only used to illustrate the technical scheme of the present application, but not limit the present application. Although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical scheme of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical scheme of the present application, and they should be covered in the scope of claims of the present application.

Claims

1. A method for provincial power grid power source planning based on LHS and NSGA-II under the carbon peak vision, characterized in that: Comprise, a coal consumption and CO2 emission calculation model of coal power units is established; Specifically comprising, Under the condition of considering the difference between the coal consumption per kilowatt-hour and the annual utilization hours of coal power units with different capacities, the annual total coal consumption calculation model of coal power units is established as follows: In the formula, C represents the total annual coal consumption of all coal-fired units in the power supply planning scheme; m represents a certain capacity partition of coal-fired units; M total represents the total number of capacity partitions; k m represents the average standard coal consumption per kilowatt-hour of coal-fired units in the capacity interval m; S m represents the total capacity of coal-fired units in the capacity interval m; T m,min is the minimum annual utilization hours of coal-fired units in the capacity interval m; ΔQ m is the minimum power generation of coal-fired units in the capacity interval m, i.e. m ×T m,min , on the basis of which the remaining power is allocated according to the principle that large-capacity units are given priority in power generation and do not exceed their maximum annual utilization hours. Under the condition of considering the difference between the coal consumption per kilowatt-hour and the annual utilization hours of coal power units with different capacities, the annual total CO2 emission calculation model of coal power units is established as follows: E=F0C In the formula, E is the annual total CO2 emission of all coal power units; F0 represents the carbon emission factor of unit mass standard coal; C represents the annual total coal consumption of all coal power units in the power source planning scheme; A multi-objective optimization model of provincial power grid power source planning is established, which takes into account the economy and cleanliness of the power source planning scheme under the constraint of carbon peak; Specifically comprising, setting the objective function of the power source planning optimization model under the carbon peak vision; setting the constraint conditions of the power source planning optimization model under the carbon peak vision; The objective function includes an economy target and a cleanliness target; The constraint conditions include a carbon peak constraint, a power balance constraint, a peak regulation balance constraint, a power source annual installed capacity upper limit constraint, a non-fossil energy power generation installed capacity proportion constraint, and a non-fossil energy power generation proportion constraint; The economy target is that the comprehensive cost of the power source planning scheme in the planning period is the lowest, and the specific expression is as follows: In the formula, f1 represents the comprehensive cost of the power supply planning scheme in the planning period; t is the year; T0 and T max respectively represent the starting and ending years of the planning; and respectively represent the equal annual value of the investment cost of the newly added unit relative to T0 year, the annual fixed operation and maintenance cost, and the cost of the annual consumption of power raw materials, and the specific calculation methods of the three are shown as follows: wherein Θ NU represents the newly added power plant set, including coal-fired power plants, gas-fired power plants, nuclear power plants, hydropower plants, wind farms, photovoltaic power stations, biomass power plants and energy storage power stations; i is a certain plant type in Θ NU . I represents the discount rate; ΔS i,t is the cumulative incremental installed capacity of the i-type power station by the year t in the planning period; k i,t is the unit comprehensive cost of the incremental installed capacity of the i-type power station by the year t. N i The service life of the newly added unit for the class i station; i,t The operation and maintenance cost per unit capacity of the unit of the class i station in the t year; Θ MA denotes the type of raw material; F j,t and U j are the total consumption of the jth fuel by all new units and its unit price, respectively, up to year t within the planning period. The cleanliness target is that the total CO2 emission in the planning period is the smallest, and the specific expression is as follows: In the formula, f2 is the total CO2 emission in the planning period; Θ FU for fossil energy consuming power plants;E i,t CO2 emissions of the ith power plant in year t; The carbon peak constraint is specifically represented as: where T is the carbon peak year; E i,T is the total amount of CO2 emitted by the i-th fossil energy power plant in the carbon peak year; is the set of expected CO2 emission peak years; year set is related to the value of T, i.e. when , is the set of years in the planning period except T0. The power balance constraint is specifically represented as: wherein Θ U is the set of all existing and new power plants; P i,t is the installed capacity of i-type plants in year t; β i is the outflow resistance coefficient of i-type plants; ΔP t is the net received capacity on the tie line of the planning area; D t,max represents the maximum power generation load of the whole society in year t; R m represents the effective reserve rate of system load; The power balance constraint is specifically represented as: In the formula, H i,t is the annual utilization hours of the i-type power plant in the t year; AQ t is the net received power on the planning regional tie line; Q t,max is the total social power consumption in the t year; The peak regulation balance constraint is specifically represented as: where t is the year; q is the quarter; γ i,q is the negative reserve capability coefficient for i-type units in q quarter; D t,q,min represents the minimum power generation load in t year q quarter; The power source annual installed capacity upper limit constraint is specifically represented as: where P i,t,lim represents the maximum production capacity of the i-type power plant in the t-year due to the construction capacity; The non-fossil energy power generation installed capacity proportion constraint is specifically represented as: In the formula, Θ NFU denotes a non-fossil energy plant station set; P t,min denotes the lower limit of the installed capacity of non-fossil energy in the t year The non-fossil energy power generation proportion constraint is specifically represented as: In the formula, Q i,t is the i-type plant t-year power generation of the interconnection line; δ t,min represents the lower limit of the proportion of non-fossil energy power generation in t-year Related parameters required for setting the power source planning objective function and constraint conditions are obtained based on the LHS and NSGA-II algorithms to solve and obtain the Pareto optimal solution of the power source planning scheme; Comprise the following steps: An adaptive value function is constructed according to the objective function and the constraint conditions; The LHS is used to obtain the lower limit of the annual installed capacity of each type of power source; The NSGA-II is used to solve the Pareto optimal solution of the power source planning scheme; The adaptive value function is constructed by adding each constraint condition in the form of a penalty value to each objective function as follows: In the formula, W is a penalty value, W=0 if all constraint conditions are satisfied, and W takes a sufficiently large positive value if there is a constraint condition that is not satisfied; The LHS is used to obtain the lower limit of the annual installed capacity of each type of power source; The NSGA-II is used to solve the Pareto optimal solution of the power source planning scheme; The annual incremental capacity of each type of power source is shown in the interval The internal consumption is a random variable from a uniform distribution, where The upper limit of the capacity of type i power source in the t year is represented, and a MxN dimensional sampling matrix S is generated by LHS method M×N , where M represents the total number of random variables, N represents the number of Latin hypercube sampling times, and the m row n column element s mn is a random variable; Using the sampling matrix S respectively M×N Each column serves as the lower limit of the annual installed capacity for each type of power supply, and combined with the set upper limit of the annual installed capacity for each type of power supply, a population P = {P1 P2 ... P} of size G is randomly generated. t …P G }, where P t Let t be the t-th individual in the population, which contains a set of specific annual installed capacity information for each type of power source; Calculate the fitness value of each individual P in the population P t The sum of the corresponding two-dimensional fitness values F t = F 1,t + F 2,t Calculate the average of the Ft values of all individuals At this time, if the jth column of S M×N is taken as the lower limit of the annual installed capacity of each type of power supply, and the corresponding value in the column is the minimum compared to other columns, then the jth column of S M×N is finally taken as the lower limit of the annual installed capacity of each type of power supply.

2. The carbon peaking vision based LHS and NSGA-II based provincial power grid power source planning method of claim 1, wherein: The basic operations of the genetic algorithm are used to generate a new child population, and the above operations are repeated using the latest parent and child populations until the program loop ends and the Pareto optimal solution is output. According to the upper limit of the annual newly installed capacity of each type of power supply and the lower limit of the annual installed capacity of each type of power supply calculated, the value range of the variable is determined, so as to randomly generate an initial population P of size G, P = {P1, P2, …, P t … G}, and calculate the fitness value of each individual P t , and then generate a child population Q through selection, crossover and mutation n ; From the second generation, the parent and child populations are combined into a population R of size 2G n The individuals in R n are then fast non-dominated sorted to form a series of non-dominated layers and the crowding distance of the individuals in each non-dominated layer is calculated and sorted by crowding distance, and the first G individuals are selected to form the new parent population; ​

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